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DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization
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[
"Gang Li",
"Ming Lin",
"Tomer Galanti",
"Zhengzhong Tu",
"Tianbao Yang"
] |
The recent success and openness of DeepSeek-R1 have brought widespread attention to Group Relative Policy Optimization (GRPO) as a reinforcement learning method for large reasoning models (LRMs). In this work, we analyze the GRPO objective under a binary reward setting and reveal an inherent limitation of question-level difficulty bias arising from its group relative advantage function. We also identify a connection between GRPO and traditional discriminative methods in supervised learning. Motivated by these insights, we introduce a new **Discriminative Constrained Optimization (DisCO)** framework for reinforcing LRMs, grounded in the principle of discriminative learning: increasing the scores of positive answers while decreasing those of negative ones. The main differences between DisCO and GRPO and its recent variants are: (1) it replaces the group relative objective with a discriminative objective defined by a scoring function; (2) it abandons clipping-based surrogates in favor of non-clipping RL surrogate objectives used as scoring functions; (3) it employs a simple yet effective constrained optimization approach to enforce the KL divergence constraint. As a result, DisCO offers notable advantages over GRPO and its variants: (i) it completely eliminates difficulty bias by adopting discriminative objectives; (ii) it addresses the entropy instability in GRPO and its variants through the use of non-clipping scoring functions and a constrained optimization approach, yielding long and stable training dynamics; (iii) it allows the incorporation of advanced discriminative learning techniques to address data imbalance, where a significant number of questions have more negative than positive generated answers during training. Our experiments on enhancing the mathematical reasoning capabilities of SFT-finetuned models show that DisCO significantly outperforms GRPO and its improved variants such as DAPO, achieving average gains of 7\% over GRPO and 6\% over DAPO across six benchmark tasks for an 1.5B model.
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https://openreview.net/forum?id=zzUXS4f91r
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Main
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Poster
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zzUXS4f91r
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Private Zeroth-Order Optimization with Public Data
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[
"Xuchen Gong",
"Tian Li"
] |
One of the major bottlenecks for deploying popular first-order differentially private (DP) machine learning algorithms (e.g., DP-SGD) lies in their high computation and memory cost, despite the existence of optimized implementations. Zeroth-order methods have promise in mitigating the overhead, as they leverage function evaluations to approximate the gradients, hence significantly easier to privatize. While recent works have explored zeroth-order approaches in both private and non-private settings, they still suffer from relatively low utilities compared with DP-SGD, and have only been evaluated in limited application domains.
In this work, we propose to leverage public information to guide and improve gradient approximation of private zeroth-order algorithms. We explore a suite of \underline{p}ublic-data-\underline{a}ssisted \underline{z}eroth-\underline{o}rder optimizers (PAZO) with minimal overhead. We provide theoretical analyses of the PAZO framework under an assumption of the similarity between public and private data. Empirically, we demonstrate that PAZO achieves superior privacy/utility tradeoffs across vision and text tasks in both pre-training and fine-tuning settings, outperforming the best first-order baselines (with public data) especially in highly private regimes, while offering up to $16\times$ runtime speedup.
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https://openreview.net/forum?id=zytITzY4IW
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Main
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Poster
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zytITzY4IW
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GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
|
[
"Mengbo Wang",
"Shourya Verma",
"Aditya Malusare",
"Luopin Wang",
"Yiyang Lu",
"Vaneet Aggarwal",
"Mario Sola",
"Ananth Grama",
"Nadia Atallah Lanman"
] |
Spatial transcriptomics technologies can be used to align transcriptomes with histopathological morphology, presenting exciting new opportunities for biomolecular discovery. Using spatial transcriptomic gene expression and corresponding histology data, we construct a novel framework, GeneFlow, to map single- and multi-cell gene expression onto paired cellular images. By combining an attention-based RNA encoder with a conditional UNet guided by rectified flow, we generate high-resolution images with different staining methods (e.g., H\&E, DAPI) to highlight various cellular/ tissue structures. Rectified flow with high-order ODE solvers creates a continuous, bijective mapping between expression and image manifolds, addressing the many-to-one relationship inherent in this problem. Our method enables the generation of realistic cellular morphology features and spatially resolved intercellular interactions under genetic or chemical perturbations. This enables minimally invasive disease diagnosis by revealing dysregulated patterns in imaging phenotypes. Our rectified flow based method outperforms diffusion methods and baselines in all experiments. Code is available at https://github.com/wangmengbo/GeneFlow.
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https://openreview.net/forum?id=zyopvwZbSj
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Main
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Poster
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zyopvwZbSj
|
MultiNet: Adaptive Multi-Viewed Subgraph Convolutional Networks for Graph Classification
|
[
"Xinya Qin",
"Lu Bai",
"Lixin Cui",
"Ming Li",
"Hangyuan Du",
"Edwin Hancock"
] |
The problem of over-smoothing has emerged as a fundamental issue for Graph Convolutional Networks (GCNs). While existing efforts primarily focus on enhancing the discriminability of node representations for node classification, they tend to overlook the over-smoothing at the graph level, significantly influencing the performance of graph classification. In this paper, we provide an explanation of the graph-level over-smoothing phenomenon, and propose a novel Adaptive Multi-Viewed Subgraph Convolutional Network (MultiNet) to address this challenge. Specifically, the MultiNet introduces a local subgraph convolution module that adaptively divides each input graph into multiple subgraph views. Then a number of subgraph-based view-specific convolution operations are applied to constrain the extent of node information propagation over the original global graph structure, not only mitigating the over-smoothing issue but also generating more discriminative local node representations. Moreover, we develop an alignment-based readout that establishes correspondences between nodes over different graphs, thereby effectively preserving the local node-level structure information and improving the discriminative ability of the resulting graph-level representations. Theoretical analysis and empirical studies show that the MultiNet mitigates the graph-level over-smoothing and achieves excellent performance for graph classification.
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https://openreview.net/forum?id=zxfwVts5it
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Main
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Poster
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zxfwVts5it
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EPA: Boosting Event-based Video Frame Interpolation with Perceptually Aligned Learning
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[
"Yuhan Liu",
"LingHui Fu",
"Zhen Yang",
"Hao Chen",
"Youfu Li",
"Yongjian Deng"
] |
Event cameras, with their capacity to provide high temporal resolution information between frames, are increasingly utilized for video frame interpolation (VFI) in challenging scenarios characterized by high-speed motion and significant occlusion. However, prevalent issues of blur and distortion within the keyframes and ground truth data used for training and inference in these demanding conditions are frequently overlooked. This oversight impedes the perceptual realism and multi-scene generalization capabilities of existing event-based VFI (E-VFI) methods when generating interpolated frames. Motivated by the observation that semantic-perceptual discrepancies between degraded and pristine images are considerably smaller than their image-level differences, we introduce EPA. This novel E-VFI framework diverges from approaches reliant on direct image-level supervision by constructing multilevel, degradation-insensitive semantic perceptual supervisory signals to enhance the perceptual realism and multi-scene generalization of the model's predictions. Specifically, EPA operates in two phases: it first employs a DINO-based perceptual extractor, a customized style adapter, and a reconstruction generator to derive multi-layered, degradation-insensitive semantic-perceptual features ($\mathcal{S}$). Second, a novel Bidirectional Event-Guided Alignment (BEGA) module utilizes deformable convolutions to align perceptual features from keyframes to ground truth with inter-frame temporal guidance extracted from event signals. By decoupling the learning process from direct image-level supervision, EPA enhances model robustness against degraded keyframes and unreliable ground truth information. Extensive experiments demonstrate that this approach yields interpolated frames more consistent with human perceptual preferences. *The code will be released upon acceptance.*
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https://openreview.net/forum?id=zxZPpVoCNO
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Main
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Poster
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zxZPpVoCNO
|
Novel View Synthesis from A Few Glimpses via Test-Time Natural Video Completion
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[
"Yan Xu",
"Yixing Wang",
"Stella X. Yu"
] |
Given just a few glimpses of a scene, can you imagine the movie playing out as the camera glides through it? That’s the lens we take on sparse-input novel view synthesis, not only as filling spatial gaps between widely spaced views, but also as completing a natural video unfolding through space. We recast the task as test-time natural video completion, using powerful priors from pretrained video diffusion models to hallucinate plausible in-between views. Our zero-shot, generation-guided framework produces pseudo views at novel camera
poses, modulated by an uncertainty-aware mechanism for spatial coherence. These synthesized frames densify supervision for 3D Gaussian Splatting (3D-GS) for scene reconstruction, especially in under-observed regions. An iterative feedback loop lets 3D geometry and 2D view synthesis inform each other, improving both the scene reconstruction and the generated views. The result is coherent, high-fidelity renderings from sparse inputs without any scene-specific training or fine-tuning. On LLFF, DTU, DL3DV, and MipNeRF-360, our method significantly outperforms strong 3D-GS baselines under extreme sparsity. Our project page is at https://decayale.github.io/project/SV2CGS.
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https://openreview.net/forum?id=zwmq0MsIMG
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Main
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Poster
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zwmq0MsIMG
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SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing
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[
"Mingfei Chen",
"Zijun Cui",
"Xiulong Liu",
"Jinlin Xiang",
"Caleb Zheng",
"Jingyuan Li",
"Eli Shlizerman"
] |
3D spatial reasoning in dynamic, audio-visual environments is a cornerstone of human cognition yet remains largely unexplored by existing Audio-Visual Large Language Models (AV-LLMs) and benchmarks, which predominantly focus on static or 2D scenes. We introduce SAVVY-Bench, the first benchmark for 3D spatial reasoning in dynamic scenes with synchronized spatial audio. SAVVY-Bench is comprised of thousands of carefully curated question–answer pairs probing both directional and distance relationships involving static and moving objects, and requires fine-grained temporal grounding, consistent 3D localization, and multi-modal annotation. To tackle this challenge, we propose SAVVY, a novel training-free reasoning pipeline that consists of two stages: (i) Egocentric Spatial Tracks Estimation, which leverages AV-LLMs as well as other audio-visual methods to track the trajectories of key objects related to the query using both visual and spatial audio cues, and (ii) Dynamic Global Map Construction, which aggregates multi-modal queried object trajectories and converts them into a unified global dynamic map. Using the constructed map, a final QA answer is obtained through a coordinate transformation that aligns the global map with the queried viewpoint. Empirical evaluation demonstrates that SAVVY substantially enhances performance of state-of-the-art AV-LLMs, setting a new standard and stage for approaching dynamic 3D spatial reasoning in AV-LLMs.
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https://openreview.net/forum?id=zwCb9cKHpd
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Main
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Oral
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zwCb9cKHpd
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Training the Untrainable: Introducing Inductive Bias via Representational Alignment
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[
"Vighnesh Subramaniam",
"David Mayo",
"Colin Conwell",
"Tomaso Poggio",
"Boris Katz",
"Brian Cheung",
"Andrei Barbu"
] |
We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. We call a network untrainable when it overfits, underfits, or converges to poor results even when tuning their hyperparameters. For example, fully connected networks overfit on object recognition while deep convolutional networks without residual connections underfit. The traditional answer is to change the architecture to impose some inductive bias, although the nature of that bias is unknown. We introduce guidance, where a guide network steers a target network using a neural distance function. The target minimizes its task loss plus a layerwise representational similarity against the frozen guide. If the guide is trained, this transfers over the architectural prior and knowledge of the guide to the target. If the guide is untrained, this transfers over only part of the architectural prior of the guide. We show that guidance prevents FCN overfitting on ImageNet, narrows the vanilla RNN–Transformer gap, boosts plain CNNs toward ResNet accuracy, and aids Transformers on RNN-favored tasks. We further identify that guidance-driven initialization alone can mitigate FCN overfitting. Our method provides a mathematical tool to investigate priors and architectures, and in the long term, could automate architecture design.
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https://openreview.net/forum?id=zvYxXhlQHM
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Main
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Poster
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zvYxXhlQHM
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Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations
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[
"Yuhao Yang",
"Zhi Ji",
"Zhaopeng Li",
"YI LI",
"Zhonglin Mo",
"Yue Ding",
"Kai Chen",
"Zijian Zhang",
"Jie Li",
"shuanglong li",
"LIU LIN"
] |
Generative models have recently gained attention in recommendation systems by directly predicting item identifiers from user interaction sequences. However, existing methods suffer from significant information loss due to the separation of stages such as quantization and sequence modeling, hindering their ability to achieve the modeling precision and accuracy of sequential dense retrieval techniques. Integrating generative and dense retrieval methods remains a critical challenge. To address this, we introduce the Cascaded Organized Bi-Represented generAtive retrieval (COBRA) framework, which innovatively integrates sparse semantic IDs and dense vectors through a cascading process. Our method alternates between generating these representations by first generating sparse IDs, which serve as conditions to aid in the generation of dense vectors. End-to-end training enables dynamic refinement of dense representations, capturing both semantic insights and collaborative signals from user-item interactions. During inference, COBRA employs a coarse-to-fine strategy, starting with sparse ID generation and refining them into dense vectors via the generative model. We further propose BeamFusion, an innovative approach combining beam search with nearest neighbor scores to enhance inference flexibility and recommendation diversity. Extensive experiments on public datasets and offline tests validate our method's robustness. Online A/B tests on a real-world advertising platform with over 200 million daily users demonstrate substantial improvements in key metrics, highlighting COBRA's practical advantages.
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https://openreview.net/forum?id=zugMif2nm6
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Main
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Poster
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zugMif2nm6
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Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks
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[
"Gavin McCracken",
"Gabriela Moisescu-Pareja",
"Vincent Létourneau",
"Doina Precup",
"Jonathan Love"
] |
We propose a testable universality hypothesis, asserting that seemingly disparate neural network solutions observed in the simple task of modular addition actually reflect a common abstract algorithm. While prior work interpreted variations in neuron-level representations as evidence for distinct algorithms, we demonstrate---through multi-level analyses spanning neurons, neuron clusters, and entire networks---that multilayer perceptrons and transformers universally implement the abstract algorithm we call the approximate Chinese Remainder Theorem. Crucially, we introduce approximate cosets and show that neurons activate exclusively on them. Furthermore, our theory works for deep neural networks (DNNs). It predicts that universally learned solutions in DNNs with trainable embeddings or more than one hidden layer require only $\mathcal{O}(\log n)$ features, a result we empirically confirm. This work thus provides the first theory‑backed interpretation of \textit{multilayer} networks solving modular addition. It advances generalizable interpretability and opens a testable universality hypothesis for group multiplication beyond modular addition.
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https://openreview.net/forum?id=zuHs6RHQwT
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Main
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Poster
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zuHs6RHQwT
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Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
|
[
"Szymon Plotka",
"Gizem Mert",
"Maciej Chrabaszcz",
"Ewa Szczurek",
"Arkadiusz Sitek"
] |
In recent years, artificial intelligence has significantly advanced medical image segmentation. Nonetheless, challenges remain, including efficient 3D medical image processing across diverse modalities and handling data variability. In this work, we introduce Hierarchical Soft Mixture-of-Experts (HoME), a two-level token-routing layer for efficient long-context modeling, specifically designed for 3D medical image segmentation. Built on the Mamba Selective State Space Model (SSM) backbone, HoME enhances sequential modeling through adaptive expert routing. In the first level, a Soft Mixture-of-Experts (SMoE) layer partitions input sequences into local groups, routing tokens to specialized per-group experts for localized feature extraction. The second level aggregates these outputs through a global SMoE layer, enabling cross-group information fusion and global context refinement. This hierarchical design, combining local expert routing with global expert refinement, enhances generalizability and segmentation performance, surpassing state-of-the-art results across datasets from the three most widely used 3D medical imaging modalities and varying data qualities. The code is publicly available at https://github.com/gmum/MambaHoME.
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https://openreview.net/forum?id=ztgYn0Uk94
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Main
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Poster
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ztgYn0Uk94
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HAIF-GS: Hierarchical and Induced Flow-Guided Gaussian Splatting for Dynamic Scene
|
[
"Jianing Chen",
"Zehao Li",
"Yujun Cai",
"Hao Jiang",
"Chengxuan Qian",
"Juyuan Kang",
"Shuqin Gao",
"Honglong Zhao",
"Tianlu Mao",
"Yucheng Zhang"
] |
Reconstructing dynamic 3D scenes from monocular videos remains a fundamental challenge in 3D vision. While 3D Gaussian Splatting (3DGS) achieves real-time rendering in static settings, extending it to dynamic scenes is challenging due to the difficulty of learning structured and temporally consistent motion representations. This challenge often manifests as three limitations in existing methods: redundant Gaussian updates, insufficient motion supervision, and weak modeling of complex non-rigid deformations. These issues collectively hinder coherent and efficient dynamic reconstruction. To address these limitations, we propose HAIF-GS, a unified framework that enables structured and consistent dynamic modeling through sparse anchor-driven deformation. It first identifies motion-relevant regions via an Anchor Filter to suppress redundant updates in static areas. A self-supervised Induced Flow-Guided Deformation module induces anchor motion using multi-frame feature aggregation, eliminating the need for explicit flow labels. To further handle fine-grained deformations, a Hierarchical Anchor Propagation mechanism increases anchor resolution based on motion complexity and propagates multi-level transformations. Extensive experiments on synthetic and real-world benchmarks validate that HAIF-GS significantly outperforms prior dynamic 3DGS methods in rendering quality, temporal coherence, and reconstruction efficiency.
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https://openreview.net/forum?id=ztVk8XNffY
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Main
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Poster
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ztVk8XNffY
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Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
|
[
"Vinam Arora",
"Divyansha Lachi",
"Ian Jarratt Knight",
"Mehdi Azabou",
"Blake Aaron Richards",
"Cole Lincoln Hurwitz",
"Josh Siegle",
"Eva L Dyer"
] |
Identifying the functional identity of individual neurons is essential for interpreting circuit dynamics, yet it remains a major challenge in large-scale _in vivo_ recordings where anatomical and molecular labels are often unavailable. Here we introduce NuCLR, a self-supervised framework that learns context-aware representations of neuron identity by modeling each neuron's role within the broader population.
NuCLR employs a spatio-temporal transformer that captures both within-neuron dynamics and across-neuron interactions. It is trained with a sample-wise contrastive objective that encourages temporally-stable and discriminative embeddings. Across multiple open-access datasets, NuCLR outperforms prior methods in both cell type and brain region classification. Critically, it exhibits strong zero-shot generalization to entirely new populations, without any retraining or access to stimulus labels. Furthermore, we demonstrate that our framework scales effectively with data size. Overall, our results demonstrate that modeling population context is crucial for understanding neuron identity and that rich signal for cell-typing and neuron localization is present in neural activity alone.
Code available at: https://github.com/nerdslab/nuclr.
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https://openreview.net/forum?id=zt3RKc6VBp
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Main
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Poster
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zt3RKc6VBp
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RAST: Reasoning Activation in LLMs via Small-model Transfer
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[
"Siru Ouyang",
"Xinyu Zhu",
"Zilin Xiao",
"Minhao Jiang",
"Yu Meng",
"Jiawei Han"
] |
Reinforcement learning (RL) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs), as evidenced by recent successes such as OpenAI's o1 and Deepseek-R1. However, applying RL at scale remains intimidatingly resource-intensive, requiring multiple model copies and extensive GPU workloads. On the other hand, while being powerful, recent studies suggest that RL does not fundamentally endow models with new knowledge; rather, it primarily reshapes the model's output distribution to activate reasoning capabilities latent in the base model. Building on this insight, we hypothesize that the changes in output probabilities induced by RL are largely model-size invariant, opening the door to a more efficient paradigm: training a small model with RL and transferring its induced probability shifts to larger base models. To verify our hypothesis, we conduct a token-level analysis of decoding trajectories and find high alignment in RL-induced output distributions across model scales, validating our hypothesis. Motivated by this, we propose RAST, a simple yet effective method that transfers reasoning behaviors by injecting RL-induced probability adjustments from a small RL-trained model into larger models. Experiments across multiple mathematical reasoning benchmarks show that RAST substantially and consistently enhances the reasoning capabilities of base models while requiring significantly lower GPU memory than direct RL training, sometimes even yielding better performance than the RL-trained counterparts. Our findings offer new insights into the nature of RL-driven reasoning and practical strategies for scaling its benefits without incurring its full computational cost. The project page of RAST is available at https://ozyyshr.github.io/RAST/.
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https://openreview.net/forum?id=zswylB4Wnt
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Main
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Poster
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zswylB4Wnt
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Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
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[
"Piotr Gaiński",
"Oussama Boussif",
"Andrei Rekesh",
"Dmytro Shevchuk",
"Ali Parviz",
"Mike Tyers",
"Robert A. Batey",
"Michał Koziarski"
] |
Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose **Recursive Cost Guidance**, a backward policy framework that employs auxiliary machine learning models to approximate synthesis cost and viability. This guidance steers generation toward low-cost synthesis pathways, significantly enhancing cost-efficiency, molecular diversity, and quality, especially when paired with an **Exploitation Penalty** that balances the trade-off between exploration and exploitation. To enhance performance in smaller building block libraries, we develop a **Dynamic Library** mechanism that reuses intermediate high-reward states to construct full synthesis trees. Our approach establishes state-of-the-art results in template-based molecular generation.
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https://openreview.net/forum?id=zssWxiiJZ1
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Main
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Poster
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zssWxiiJZ1
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Accelerating Feature Conformal Prediction via Taylor Approximation
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[
"Zihao Tang",
"Boyuan Wang",
"Chuan Wen",
"Jiaye Teng"
] |
Conformal prediction is widely adopted in uncertainty quantification, due to its post-hoc, distribution-free, and model-agnostic properties.
In the realm of modern deep learning, researchers have proposed Feature Conformal Prediction (FCP), which deploys conformal prediction in a feature space, yielding reduced band lengths.
However, the practical utility of FCP is limited due to the time-consuming non-linear operations required to transform confidence bands from feature space to output space.
In this paper, we present Fast Feature Conformal Prediction (FFCP), a method that accelerates FCP by leveraging a first-order Taylor expansion to approximate these non-linear operations.
The proposed FFCP introduces a novel non-conformity score that is both effective and efficient for real-world applications.
Empirical validations showcase that FFCP performs comparably with FCP (both outperforming the vanilla version) while achieving a significant reduction in computational time by approximately 50x in both regression and classification tasks.
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https://openreview.net/forum?id=zsUOQRUFOy
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Main
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Poster
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zsUOQRUFOy
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PhySwin: An Efficient and Physically-Informed Foundation Model for Multispectral Earth Observation
|
[
"Chong Tang",
"Joseph Powell",
"Dirk Koch",
"Robert D. Mullins",
"Alex S. Weddell",
"Jagmohan Chauhan"
] |
Recent progress on Remote Sensing Foundation Models (RSFMs) aims toward universal representations for Earth observation imagery. However, current efforts often scale up in size significantly without addressing efficiency constraints critical for real-world applications (e.g., onboard processing, rapid disaster response) or treat multispectral (MS) data as generic imagery, overlooking valuable physical priors. We introduce PhySwin, a foundation model for MS data that integrates physical priors with computational efficiency. PhySwin combines three innovations: (i) physics-informed pretraining objectives leveraging radiometric constraints to enhance feature learning; (ii) an efficient MixMAE formulation tailored to SwinV2 for low-FLOP, scalable pretraining; and (iii) token-efficient spectral embedding to retain spectral detail without increasing token counts. Pretrained on over 1M Sentinel-2 tiles, PhySwin achieves SOTA results (+1.32\% mIoU segmentation, +0.80\% F1 change detection) while reducing inference latency by up to 14.4$\times$ and computational complexity by up to 43.6$\times$ compared to ViT-based RSFMs.
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https://openreview.net/forum?id=zrBucj9BwG
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Main
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Poster
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zrBucj9BwG
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CURE: Concept Unlearning via Orthogonal Representation Editing in Diffusion Models
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[
"Shristi Das Biswas",
"Arani Roy",
"Kaushik Roy"
] |
As Text-to-Image models continue to evolve, so does the risk of generating unsafe, copyrighted, or privacy-violating content. Existing safety interventions - ranging from training data curation and model fine-tuning to inference-time filtering and guidance - often suffer from incomplete concept removal, susceptibility to jail-breaking, computational inefficiency, or collateral damage to unrelated capabilities. In this paper, we introduce CURE, a training-free concept unlearning framework that operates directly in the weight space of pre-trained diffusion models, enabling fast, interpretable, and highly specific suppression of undesired concepts. At the core of our method is the Spectral Eraser, a closed-form, orthogonal projection module that identifies discriminative subspaces using Singular Value Decomposition over token embeddings associated with the concepts to forget and retain. Intuitively, the Spectral Eraser identifies and isolates features unique to the undesired concept while preserving safe attributes. This operator is then applied in a single step update to yield an edited model in which the target concept is effectively unlearned - without retraining, supervision, or iterative optimization. To balance the trade-off between filtering toxicity and preserving unrelated concepts, we further introduce an Expansion Mechanism for spectral regularization which selectively modulates singular vectors based on their relative significance to control the strength of forgetting. All the processes above are in closed-form, guaranteeing extremely efficient erasure in only $2$ seconds. Benchmarking against prior approaches, CURE achieves a more efficient and thorough removal for targeted artistic styles, objects, identities, or explicit content, with minor damage to original generation ability and demonstrates enhanced robustness against red-teaming. Project Page at \url{https://sites.google.com/view/cure-unlearning/home}.
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https://openreview.net/forum?id=zprMrpiLgT
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Main
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Spotlight
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zprMrpiLgT
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Implicit-ARAP: Efficient Handle-Guided Neural Field Deformation via Local Patch Meshing
|
[
"Daniele Baieri",
"Filippo Maggioli",
"Emanuele Rodolà",
"Simone Melzi",
"Zorah Lähner"
] |
Neural fields have emerged as a powerful representation for 3D geometry, enabling compact and continuous modeling of complex shapes. Despite their expressive power, manipulating neural fields in a controlled and accurate manner -- particularly under spatial constraints -- remains an open challenge, as existing approaches struggle to balance surface quality, robustness, and efficiency. We address this by introducing a novel method for handle-guided neural field deformation, which leverages discrete local surface representations to optimize the As-Rigid-As-Possible deformation energy. To this end, we propose the local patch mesh representation, which discretizes level sets of a neural signed distance field by projecting and deforming flat mesh patches guided solely by the SDF and its gradient. We conduct a comprehensive evaluation showing that our method consistently outperforms baselines in deformation quality, robustness, and computational efficiency. We also present experiments that motivate our choice of discretization over marching cubes. By bridging classical geometry processing and neural representations through local patch meshing, our work enables scalable, high-quality deformation of neural fields and paves the way for extending other geometric tasks to neural domains.
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https://openreview.net/forum?id=zp7W2QmxHS
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Main
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Poster
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zp7W2QmxHS
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Robust Integrated Learning and Pauli Noise Mitigation for Parametrized Quantum Circuits
|
[
"Md Mobasshir Arshed Naved",
"Wenbo Xie",
"Wojciech Szpankowski",
"Ananth Grama"
] |
We propose a novel gradient-based framework for learning parameterized quantum circuits (PQCs) in the presence of Pauli noise in gate operation. The key innovation in our framework is the simultaneous optimization of model parameters and learning of an inverse noise channel, specifically designed to mitigate Pauli noise. Our parametrized inverse noise model utilizes the Pauli-Lindblad equation and relies on the principle underlying the Probabilistic Error Cancellation (PEC) protocol to learn an effective and scalable mechanism for noise mitigation. In contrast to conventional approaches that apply predetermined inverse noise models during execution, our method systematically mitigates Pauli noise by dynamically updating the inverse noise parameters in conjunction with the model parameters, facilitating task-specific noise adaptation throughout the learning process. We employ proximal stochastic gradient descent (proximal SGD) to ensure that updates are bounded within a feasible range to ensure stability. This approach allows the model to converge efficiently to a stationary point, balancing the trade-off between noise mitigation and computational overhead, resulting in a highly adaptable quantum model that performs robustly in noisy quantum environments. Our framework is well-suited to near-term quantum devices in the noisy intermediate-scale quantum (NISQ) era, where noise is a significant challenge.
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https://openreview.net/forum?id=zoNpnBlJWh
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Main
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Poster
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zoNpnBlJWh
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Analog Foundation Models
|
[
"Julian Büchel",
"Iason Chalas",
"Giovanni Acampa",
"An Chen",
"Omobayode Fagbohungbe",
"Hsinyu Tsai",
"Kaoutar El Maghraoui",
"Manuel Le Gallo",
"Abbas Rahimi",
"Abu Sebastian"
] |
Analog in-memory computing (AIMC) is a promising compute paradigm to improve speed and power efficiency of neural network inference beyond the limits of conventional von Neumann-based architectures. However, AIMC introduces fundamental challenges such as noisy computations and strict constraints on input and output quantization. Because of these constraints and imprecisions, off-the-shelf LLMs are not able to achieve 4-bit-level performance when deployed on AIMC-based hardware. While researchers previously investigated recovering this accuracy gap on small, mostly vision-based models, a generic method applicable to LLMs pre-trained on trillions of tokens does not yet exist. In this work, we introduce a general and scalable method to robustly adapt LLMs for execution on noisy, low-precision analog hardware. Our approach enables state-of-the-art models — including Phi-3-mini-4k-instruct and Llama-3.2-1B-Instruct — to retain performance comparable to 4-bit weight, 8-bit activation baselines, despite the presence of analog noise and quantization constraints. Additionally, we show that as a byproduct of our training methodology, analog foundation models can be quantized for inference on low-precision digital hardware. Finally, we show that our models also benefit from test-time compute scaling, showing better scaling behavior than models trained with 4-bit weight and 8-bit static input quantization. Our work bridges the gap between high-capacity LLMs and efficient analog hardware, offering a path toward energy-efficient foundation models. Code is available at [github.com/IBM/analog-foundation-models](https://github.com/IBM/analog-foundation-models).
|
https://openreview.net/forum?id=zo4zYTR8vn
|
Main
|
Poster
|
zo4zYTR8vn
|
From Synapses to Dynamics: Obtaining Function from Structure in a Connectome Constrained Model of the Head Direction Circuit
|
[
"Sunny Duan",
"Ling Liang Dong",
"Ila R Fiete"
] |
How precisely does circuit wiring specify function? This fundamental question is particularly relevant for modern neuroscience, as large-scale electron microscopy now enables the reconstruction of neural circuits at single-synapse resolution across many organisms. To interpret circuit function from such datasets, we must understand the extent to which [measured] structure constrains dynamics. We investigate this question in the drosophila head direction (HD) circuit, which maintains an internal heading estimate through attractor dynamics that integrate self-motion velocity cues. This circuit serves as a sensitive assay for functional specification: continuous attractor networks are theoretically known to require finely tuned wiring, whereas connectomes reveal that biological wiring can be variable and omit key cellular parameters such as synaptic gains, neuronal thresholds, and time constants.
We introduce a method that combines self-supervised and unsupervised learning objectives to estimate unknown parameters at the level of cell types, rather than individual neurons and synapses. Given the raw connectivity matrix, our approach recovers a network that robustly exhibits continuous attractor dynamics and accurately integrates a range of velocity inputs, despite minimal parameter tuning on a connectome which notably departs from the symmetric regularity of an idealized ring attractor. We characterize how deviations from the original connectome shape the space of viable solutions. We also perform in-silico ablation experiments to probe the distinct functional roles of specific cell types in the circuit, demonstrating how connectome-derived structure, when augmented with minimal, biologically grounded tuning, can replicate known physiology and elucidate circuit function.
|
https://openreview.net/forum?id=zn4F6os6cq
|
Main
|
Poster
|
zn4F6os6cq
|
Plug-and-play Feature Causality Decomposition for Multimodal Representation Learning
|
[
"Ye Liu",
"Zihan Ji",
"Hongmin Cai"
] |
Multimodal representation learning is critical for a wide range of applications, such as multimodal sentiment analysis. Current multimodal representation learning methods mainly focus on the multimodal alignment or fusion strategies, such that the complementary and consistent information among heterogeneous modalities can be fully explored. However, they mistakenly treat the uncertainty noise within each modality as the complementary information, failing to simultaneously leverage both consistent and complementary information while eliminating the aleatoric uncertainty within each modality. To address this issue, we propose a plug-and-play feature causality decomposition method for multimodal representation learning from causality perspective, which can be integrated into existing models with no affects on the original model structures. Specifically, to deal with the heterogeneity and consistency, according to whether it can be aligned with other modalities, the unimodal feature is first disentangled into two parts: modality-invariant (the synergistic information shared by all heterogeneous modalities) and modality-specific part. To deal with complementarity and uncertainty, the modality-specific part is further decomposed into unique and redundant features, where the redundant feature is removed and the unique feature is reserved based on the backdoor-adjustment. The effectiveness of noise removal is supported by causality theory. Finally, the task-related information, including both synergistic and unique components, is further fed to the original fusion module to obtain the final multimodal representations. Extensive experiments show the effectiveness of our proposed strategies.
|
https://openreview.net/forum?id=zmCBCbr2Wj
|
Main
|
Poster
|
zmCBCbr2Wj
|
Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions
|
[
"Aobo Li",
"Jinjian Wu",
"Yongxu Liu",
"Leida Li",
"Weisheng Dong"
] |
Blind Image Quality Assessment (BIQA) has advanced significantly through deep learning, but the scarcity of large-scale labeled datasets remains a challenge. While synthetic data offers a promising solution, models trained on existing synthetic datasets often show limited generalization ability. In this work, we make a key observation that representations learned from synthetic datasets often exhibit a discrete and clustered pattern that hinders regression performance: features of high-quality images cluster around reference images, while those of low-quality images cluster based on distortion types. Our analysis reveals that this issue stems from the distribution of synthetic data rather than model architecture. Consequently, we introduce a novel framework SynDR-IQA, which reshapes synthetic data distribution to enhance BIQA generalization. Based on theoretical derivations of sample diversity and redundancy's impact on generalization error, SynDR-IQA employs two strategies: distribution-aware diverse content upsampling, which enhances visual diversity while preserving content distribution, and density-aware redundant cluster downsampling, which balances samples by reducing the density of densely clustered areas. Extensive experiments across three cross-dataset settings (synthetic-to-authentic, synthetic-to-algorithmic, and synthetic-to-synthetic) demonstrate the effectiveness of our method. The code is available at https://github.com/Li-aobo/SynDR-IQA.
|
https://openreview.net/forum?id=zlRvBwWFII
|
Main
|
Poster
|
zlRvBwWFII
|
Causality Meets the Table: Debiasing LLMs for Faithful TableQA via Front-Door Intervention
|
[
"Zhen Yang",
"Ziwei Du",
"Minghan Zhang",
"Wei Du",
"Jie Chen",
"Fulan Qian",
"Shu Zhao"
] |
Table Question Answering (TableQA) combines natural language understanding and structured data reasoning, posing challenges in semantic interpretation and logical inference. Recent advances in Large Language Models (LLMs) have improved TableQA performance through Direct Prompting and Agent paradigms. However, these models often rely on spurious correlations, as they tend to overfit to token co-occurrence patterns in pretraining corpora, rather than perform genuine reasoning. To address this issue, we propose Causal Intervention TableQA (CIT), which is based on a structural causal graph and applies front-door adjustment to eliminate bias caused by token co-occurrence. CIT formalizes TableQA as a causal graph and identifies token co-occurrence patterns as confounders. By applying front-door adjustment, CIT guides question variant generation and reasoning to reduce confounding effects. Experiments on multiple benchmarks show that CIT achieves state-of-the-art performance, demonstrating its effectiveness in mitigating bias. Consistent gains across various LLMs further confirm its generalizability.
|
https://openreview.net/forum?id=zlMupLoKRf
|
Main
|
Poster
|
zlMupLoKRf
|
Learning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Imaging Inverse Problems
|
[
"Deliang Wei",
"Peng Chen",
"Haobo Xu",
"Jiale Yao",
"Fang Li",
"Tieyong Zeng"
] |
Plug-and-play (PnP) methods with deep denoisers have shown impressive results in imaging problems. They typically require strong convexity or smoothness of the fidelity term and a (residual) non-expansive denoiser for convergence. These assumptions, however, are violated in Poisson inverse problems, and non-expansiveness can hinder denoising performance. To address these challenges, we propose a cocoercive conservative (CoCo) denoiser, which may be (residual) expansive, leading to improved denoising performance. By leveraging the generalized Helmholtz decomposition, we introduce a novel training strategy that combines Hamiltonian regularization to promote conservativeness and spectral regularization to ensure cocoerciveness. We prove that CoCo denoiser is a proximal operator of a weakly convex function, enabling a restoration model with an implicit weakly convex prior. The global convergence of PnP methods to a stationary point of this restoration model is established. Extensive experimental results demonstrate that our approach outperforms closely related methods in both visual quality and quantitative metrics.
|
https://openreview.net/forum?id=zl4FR39Ibh
|
Main
|
Poster
|
zl4FR39Ibh
|
TAMI: Taming Heterogeneity in Temporal Interactions for Temporal Graph Link Prediction
|
[
"Zhongyi Yu",
"Jianqiu Wu",
"Zhenghao Wu",
"Shuhan Zhong",
"Weifeng Su",
"Chul-Ho Lee",
"Weipeng Zhuo"
] |
Temporal graph link prediction aims to predict future interactions between nodes in a graph based on their historical interactions, which are encoded in node embeddings. We observe that heterogeneity naturally appears in temporal interactions, e.g., a few node pairs can make most interaction events, and interaction events happen at varying intervals. This leads to the problems of ineffective temporal information encoding and forgetting of past interactions for a pair of nodes that interact intermittently for their link prediction. Existing methods, however, do not consider such heterogeneity in their learning process, and thus their learned temporal node embeddings are less effective, especially when predicting the links for infrequently interacting node pairs. To cope with the heterogeneity, we propose a novel framework called TAMI, which contains two effective components, namely log time encoding function (LTE) and link history aggregation (LHA). LTE better encodes the temporal information through transforming interaction intervals into more balanced ones, and LHA prevents the historical interactions for each target node pair from being forgotten. State-of-the-art temporal graph neural networks can be seamlessly and readily integrated into TAMI to improve their effectiveness. Experiment results on 13 classic datasets and three newest temporal graph benchmark (TGB) datasets show that TAMI consistently improves the link prediction performance of the underlying models in both transductive and inductive settings. Our code is available at https://github.com/Alleinx/TAMI_temporal_graph.
|
https://openreview.net/forum?id=zjQLUiguRz
|
Main
|
Poster
|
zjQLUiguRz
|
Activity Pruning for Efficient Spiking Neural Networks
|
[
"Tong Bu",
"Xinyu Shi",
"Zhaofei Yu"
] |
While sparse coding plays an important role in promoting the efficiency of biological neural systems, it has not been fully utilized by artificial models as the activation sparsity is not well suited to the current structure of deep networks. Spiking Neural Networks (SNNs), with their event-driven characteristics, offer a more natural platform for leveraging activation sparsity. In this work, we specifically target the reduction of neuronal activity, which directly leads to lower computational cost and facilitates efficient SNN deployment on Neuromorphic hardware. We begin by analyzing the limitations of existing activity regularization methods and identifying critical challenges in training sparse SNNs. To address these issues, we propose a modified neuron model, AT-LIF, coupled with a threshold adaptation technique that stabilizes training and effectively suppresses spike activity. Through extensive experiments on multiple datasets, we demonstrate that our approach achieves significant reductions in average firing rates and synaptic operations without sacrificing much accuracy. Furthermore, we show that our method complements weight-based pruning techniques and successfully trains an SNN with only 0.06 average firing rate and 2.22M parameters on ImageNet, highlighting its potential for building highly efficient and scalable SNN models. Code is available at https://github.com/putshua/Activity-Pruning-SNN.
|
https://openreview.net/forum?id=zjOXZEXQKZ
|
Main
|
Poster
|
zjOXZEXQKZ
|
Private Hyperparameter Tuning with Ex-Post Guarantee
|
[
"Badih Ghazi",
"Pritish Kamath",
"Alexander Knop",
"Ravi Kumar",
"Pasin Manurangsi",
"Chiyuan Zhang"
] |
The conventional approach in differential privacy (DP) literature formulates the privacy-utility tradeoff with a "privacy-first" perspective:
for a predetermined level of privacy, a certain utility is achievable.
However, practitioners often operate under a "utility-first" paradigm, prioritizing a desired level of utility and then determining
the corresponding privacy cost.
Wu et al. [2019] initiated a formal study of this ``utility-first'' perspective by introducing ex-post DP.
They demonstrated that by adding correlated Laplace noise and progressively reducing it on demand, a sequence of increasingly
accurate estimates of a private parameter can be generated, with the privacy cost attributed only to the least noisy iterate released.
This led to a Laplace mechanism variant that achieves a specified utility with minimal privacy loss.
However, their work, and similar findings by Whitehouse et al. [2023], are primarily limited to simple mechanisms based
on Laplace or Gaussian noise.
In this paper, we significantly generalize these results. In particular, we extend the findings of Wu et al. [2019] and Liu and Talwar [2019]
to support any sequence of private estimators, incurring at most a doubling of the original privacy budget.
Furthermore, we demonstrate that hyperparameter tuning for these estimators, including the selection of an optimal privacy budget,
can be performed without additional privacy cost.
Finally, we extend our results to ex-post R\'{e}nyi DP, further broadening the applicability of utility-first privacy mechanisms.
|
https://openreview.net/forum?id=zjMd3yfyWv
|
Main
|
Spotlight
|
zjMd3yfyWv
|
Scalable In-context Ranking with Generative Models
|
[
"Nilesh Gupta",
"Chong You",
"Srinadh Bhojanapalli",
"Sanjiv Kumar",
"Inderjit S Dhillon",
"Felix X. Yu"
] |
In-context Ranking (ICR) is an emerging paradigm for Information Retrieval (IR), which leverages contextual understanding of LLMs by directly incorporating the task description, candidate documents, and the query into the model's input prompt and tasking the LLM to identify relevant document(s). While it is effective, efficiency is a significant challenge in this paradigm, especially as the candidate list grows due to quadratic/super-linear scaling of attention operation with context length. To this end, this paper first identifies inherent and exploitable structures in the attention of LLMs finetuned for ICR: (1) inter-document block sparsity: attention is dense within each document block but sparse across different documents in the context; and (2) query-document block relevance: the attention scores from certain query tokens to a document block in middle layers strongly correlate with that document's actual relevance. Motivated by these observations, we introduce BlockRank (Blockwise In-context Ranking), a novel method that adapts the attention operation in an LLM by (a) architecturally enforcing the observed inter-document block sparsity, reducing attention complexity from quadratic to linear without loss in performance, and (b) optimizing query-document block relevance for true relevant documents during fine-tuning using an auxiliary contrastive training objective, improving retrieval in attention. Experiments on BEIR, MSMarco and NQ with Mistral-7B demonstrate that BlockRank Mistral matches or outperforms existing SOTA listwise rankers and controlled fine-tuned baseline while being significantly more efficient at inference (4.7x for 100 MSMarco documents in context) and scaling gracefully to long-context shortlists, around 500 documents in-context (approximately 100K context length) within a second, presenting a scalable and effective solution for ICR.
|
https://openreview.net/forum?id=zj45hoQhjD
|
Main
|
Poster
|
zj45hoQhjD
|
Optimal Adjustment Sets for Nonparametric Estimation of Weighted Controlled Direct Effect
|
[
"Ruiyang Lin",
"Yongyi Guo",
"Kyra Gan"
] |
The weighted controlled direct effect (WCDE) generalizes the standard controlled direct effect (CDE) by averaging over the mediator distribution, providing a robust estimate when treatment effects vary across mediator levels. This makes the WCDE especially relevant in fairness analysis, where it isolates the direct effect of an exposure on an outcome, independent of mediating pathways. In this work, we first establish necessary and sufficient conditions for the unique identifiability of the WCDE, clarifying when it diverges from the CDE. Next, we derive the efficient influence function for the WCDE and consider the class of regular and asymptotically linear estimators. We characterize the optimal covariate adjustment set that minimizes asymptotic variance, demonstrating how mediator-confounder interactions introduce distinct requirements compared to average treatment effect estimation. Our results offer a principled framework for efficient estimation of direct effects in complex causal systems, with practical applications in fairness and mediation analysis.
|
https://openreview.net/forum?id=zho5kN8jTn
|
Main
|
Poster
|
zho5kN8jTn
|
Constrained Optimization From a Control Perspective via Feedback Linearization
|
[
"Runyu Zhang",
"Arvind Raghunathan",
"Jeff S Shamma",
"Na Li"
] |
Tools from control and dynamical systems have proven valuable for analyzing and developing optimization methods. In this paper, we establish rigorous theoretical foundations for using feedback linearization—a well-established nonlinear control technique—to solve constrained optimization problems. For equality-constrained optimization, we establish global convergence rates to first-order Karush-Kuhn-Tucker (KKT) points and uncover the close connection between the FL method and the Sequential Quadratic Programming (SQP) algorithm. Building on this relationship, we extend the FL approach to handle inequality-constrained problems. Furthermore, we introduce a momentum-accelerated feedback linearization algorithm and provide a rigorous convergence guarantee.
|
https://openreview.net/forum?id=zhgfM0dJ3F
|
Main
|
Poster
|
zhgfM0dJ3F
|
Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners
|
[
"Michal Nauman",
"Marek Cygan",
"Carmelo Sferrazza",
"Aviral Kumar",
"Pieter Abbeel"
] |
Recent advances in language modeling and vision stem from training large models on diverse, multi‑task data. This paradigm has had limited impact in value-based reinforcement learning (RL), where improvements are often driven by small models trained in a single-task context. This is because in multi-task RL sparse rewards and gradient conflicts make optimization of temporal difference brittle. Practical workflows for generalist policies therefore avoid online training, instead cloning expert trajectories or distilling collections of single‑task policies into one agent. In this work, we show that the use of high-capacity value models trained via cross-entropy and conditioned on learnable task embeddings addresses the problem of task interference in online RL, allowing for robust and scalable multi‑task training. We test our approach on 7 multi-task benchmarks with over 280 unique tasks, spanning high degree-of-freedom humanoid control and discrete vision-based RL. We find that, despite its simplicity, the proposed approach leads to state-of-the-art single and multi-task performance, as well as sample-efficient transfer to new tasks.
|
https://openreview.net/forum?id=zhOUfuOIzA
|
Main
|
Poster
|
zhOUfuOIzA
|
LoRA-EnVar: Parameter-Efficient Hybrid Ensemble Variational Assimilation for Weather Forecasting
|
[
"Yi Xiao",
"Hang Fan",
"Kun Chen",
"Ye Cao",
"Ben Fei",
"Wei Xue",
"LEI BAI"
] |
Accurate estimation of background error (i.e., forecast error) distribution is critical for effective data assimilation (DA) in numerical weather prediction (NWP). In state-of-the-art operational DA systems, it is common to account for the temporal evolution of background errors by employing hybrid methods, which blend a static climatological covariance with a flow-dependent ensemble-derived component. While effective to some extent, these methods typically assume Gaussian-distributed errors and rely heavily on hand-crafted covariance structures and domain expertise, limiting their ability to capture the complex, non-Gaussian nature of atmospheric dynamics. In this work, we propose LoRA-EnVar, a novel hybrid ensemble variational DA algorithm that integrates low-rank adaptation (LoRA) into a deep generative modeling framework. We first learn a climatological background error distribution using a variational autoencoder (VAE) trained on historical data. To incorporate flow-dependent uncertainty, we introduce LoRA modules that efficiently adapt the learned distribution in response to flow-dependent ensemble perturbations. Our approach supports online finetuning, enabling dynamic updates of the background error distribution without catastrophic forgetting. We validate LoRA-EnVar in high-resolution assimilation settings using the FengWu forecast model and simulated observations from ERA5 reanalysis. Experimental results show that LoRA-EnVar significantly improves assimilation accuracy over models assuming static background error distribution and achieves comparable or better performance than full finetuning while reducing the number of trainable parameters by three orders of magnitude. This demonstrates the potential of parameter-efficient adaptation for scalable, non-Gaussian DA in operational meteorology.
|
https://openreview.net/forum?id=zhMl4Smau7
|
Main
|
Poster
|
zhMl4Smau7
|
Co-PatcheR: Collaborative Software Patching with Component-specific Small Reasoning Models
|
[
"Yuheng Tang",
"Hongwei Li",
"Kaijie Zhu",
"Michael Yang",
"Yangruibo Ding",
"Wenbo Guo"
] |
Motivated by the success of general‑purpose large language models (LLMs) in software patching, recent works started to train specialized patching models. Most works trained one model to handle the end‑to‑end patching pipeline (including issue localization, patch generation, and patch validation). However, it is hard for a small model to handle all tasks, as different sub-tasks have different workflows and require different expertise. As such, by using a 70 billion model, SOTA methods can only reach up to 41% resolved rate on SWE-bench-Verified. Motivated by the collaborative nature, we propose Co-PatcheR, the first collaborative patching system with small and specialized reasoning models for individual components. Our key technique novelties are the specific task designs and training recipes. First, we train a model for localization and patch generation. Our localization pinpoints the suspicious lines through a two-step procedure, and our generation combines patch generation and critique. We then propose a hybrid patch validation that includes two models for crafting issue-reproducing test cases with and without assertions and judging patch correctness, followed by a majority vote-based patch selection. Through extensive evaluation, we show that Co-PatcheR achieves 46% resolved rate on SWE-bench-Verified with only 3 x 14B models. This makes Co-PatcheR the best patcher with specialized models, requiring the least training resources and the smallest models. We conduct a comprehensive ablation study to validate our recipes, as well as our choice of training data number, model size, and testing-phase scaling strategy.
|
https://openreview.net/forum?id=zhFEO67s5w
|
Main
|
Poster
|
zhFEO67s5w
|
Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via $\textit{In-the-wild}$ Cascading Flow Optimization
|
[
"Yixiao Chen",
"Shikun Sun",
"Jianshu Li",
"Ruoyu Li",
"Zhe Li",
"Junliang Xing"
] |
Adversarial attacks are widely used to evaluate model robustness, and in black-box scenarios, the transferability of these attacks becomes crucial. Existing generator-based attacks have excellent generalization and transferability due to their instance-agnostic nature. However, when training generators for multi-target tasks, the success rate of transfer attacks is relatively low due to the limitations of the model's capacity. To address these challenges, we propose a novel Dual-Flow framework for multi-target instance-agnostic adversarial attacks, utilizing Cascading Distribution Shift Training to develop an adversarial velocity function. Extensive experiments demonstrate that Dual-Flow significantly improves transferability over previous multi-target generative attacks. For example, it increases the success rate from Inception-v3 to ResNet-152 by 34.58%. Furthermore, our attack method shows substantially stronger robustness against defense mechanisms, such as adversarially trained models.
|
https://openreview.net/forum?id=zhCv5uZ8bh
|
Main
|
Poster
|
zhCv5uZ8bh
|
Structure-Aware Cooperative Ensemble Evolutionary Optimization on Combinatorial Problems with Multimodal Large Language Models
|
[
"Jie Zhao",
"Kang Hao Cheong"
] |
Evolutionary algorithms (EAs) have proven effective in exploring the vast solution spaces typical of graph-structured combinatorial problems. However, traditional encoding schemes, such as binary or numerical representations, often fail to straightforwardly capture the intricate structural properties of networks. Through employing the image-based encoding to preserve topological context, this study utilizes multimodal large language models (MLLMs) as evolutionary operators to facilitate structure-aware optimization over graph data. To address the visual clutter inherent in large-scale network visualizations, we leverage graph sparsification techniques to simplify structures while maintaining essential structural features. To further improve robustness and mitigate bias from different sparsification views, we propose a cooperative evolutionary optimization framework that facilitates cross-domain knowledge transfer and unifies multiple sparsified variants of diverse structures. Additionally, recognizing the sensitivity of MLLMs to network layout, we introduce an ensemble strategy that aggregates outputs from various layout configurations through consensus voting. Finally, experiments on real-world networks through various tasks demonstrate that our approach improves both the quality and reliability of solutions in MLLM-driven evolutionary optimization.
|
https://openreview.net/forum?id=zftxlb1AOo
|
Main
|
Poster
|
zftxlb1AOo
|
Noisy Multi-Label Learning through Co-Occurrence-Aware Diffusion
|
[
"Senyu Hou",
"Yuru Ren",
"Gaoxia Jiang",
"Wenjian Wang"
] |
Noisy labels often compel models to overfit, especially in multi-label classification tasks. Existing methods for noisy multi-label learning (NML) primarily follow a discriminative paradigm, which relies on noise transition matrix estimation or small-loss strategies to correct noisy labels. However, they remain substantial optimization difficulties compared to noisy single-label learning. In this paper, we propose a Co-Occurrence-Aware Diffusion (CAD) model, which reformulates NML from a generative perspective. We treat features as conditions and multi-labels as diffusion targets, optimizing the diffusion model for multi-label learning with theoretical guarantees. Benefiting from the diffusion model's strength in capturing multi-object semantics and structured label matrix representation, we can effectively learn the posterior mapping from features to true multi-labels. To mitigate the interference of noisy labels in the forward process, we guide generation using pseudo-clean labels reconstructed from the latent neighborhood space, replacing original point-wise estimates with neighborhood-based proxies. In the reverse process, we further incorporate label co-occurrence constraints to enhance the model's awareness of incorrect generation directions, thereby promoting robust optimization. Extensive experiments on both synthetic (Pascal-VOC, MS-COCO) and real-world (NUS-WIDE) noisy datasets demonstrate that our approach outperforms state-of-the-art methods.
|
https://openreview.net/forum?id=zft0zTOFkN
|
Main
|
Poster
|
zft0zTOFkN
|
RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
|
[
"Yilang Zhang",
"Bingcong Li",
"Georgios B. Giannakis"
] |
Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to inconsistent and imbalanced weight updates induced by its nonunique low-rank factorizations. To overcome these limitations, this article identifies the optimal low-rank factorization per step that minimizes an upper bound on the loss. The resultant refactored low-rank adaptation (RefLoRA) method promotes a flatter loss landscape, along with consistent and balanced weight updates, thus speeding up stable convergence. Extensive experiments evaluate RefLoRA on natural language understanding, and commonsense reasoning tasks with popular large language models including DeBERTaV3, LLaMA-7B, LLaMA2-7B and LLaMA3-8B. The numerical tests corroborate that RefLoRA converges faster, outperforms various benchmarks, and enjoys negligible computational overhead compared to state-of-the-art LoRA variants.
|
https://openreview.net/forum?id=zefDc9oi5T
|
Main
|
Poster
|
zefDc9oi5T
|
Architectural and Inferential Inductive Biases for Exchangeable Sequence Modeling
|
[
"Daksh Mittal",
"Ang Li",
"Thomson Yen",
"C. Daniel Guetta",
"Hongseok Namkoong"
] |
Autoregressive models have emerged as a powerful framework for modeling exchangeable sequences---i.i.d. observations when conditioned on some latent factor---enabling direct modeling of uncertainty from missing data (rather than a latent). Motivated by the critical role posterior inference plays as a subroutine in decision-making (e.g., active learning, bandits), we study the inferential and architectural inductive biases that are most effective for exchangeable sequence modeling. For the inference stage, we highlight a fundamental limitation of the prevalent single-step generation approach: its inability to distinguish between epistemic and aleatoric uncertainty. Instead, a long line of works in Bayesian statistics advocates for multi-step autoregressive generation; we demonstrate this "correct approach" enables superior uncertainty quantification that translates into better performance on downstream decision-making tasks. This naturally leads to the next question: which architectures are best suited for multi-step inference? We identify a subtle yet important gap between recently proposed Transformer architectures for exchangeable sequences (Müller et al., 2022; Nguyen & Grover, 2022; Ye & Namkoong, 2024), and prove that they in fact cannot guarantee exchangeability despite introducing significant computational overhead. Through empirical evaluation, we find that these custom architectures can significantly underperform compared to standard causal masking, highlighting the need for new architectural innovations in Transformer-based modeling of exchangeable sequences.
|
https://openreview.net/forum?id=zdRW39Tc3C
|
Main
|
Poster
|
zdRW39Tc3C
|
ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
|
[
"Weijie Wang",
"Donny Y. Chen",
"Zeyu Zhang",
"Duochao Shi",
"Akide Liu",
"Bohan Zhuang"
] |
Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene 3DGS optimization. However, their scalability is fundamentally constrained by the limited capacity of their encoders, leading to degraded performance or excessive memory consumption as the number of input views increases. In this work, we analyze feed-forward 3DGS frameworks through the lens of the Information Bottleneck principle and introduce ZPressor, a lightweight architecture-agnostic module that enables efficient compression of multi-view inputs into a compact latent state $Z$ that retains essential scene information while discarding redundancy. Concretely, ZPressor enables existing feed-forward 3DGS models to scale to over 100 input views at 480P resolution on an 80GB GPU, by partitioning the views into anchor and support sets and using cross attention to compress the information from the support views into anchor views, forming the compressed latent state $Z$. We show that integrating ZPressor into several state-of-the-art feed-forward 3DGS models consistently improves performance under moderate input views and enhances robustness under dense view settings on two large-scale benchmarks DL3DV-10K and RealEstate10K.
|
https://openreview.net/forum?id=zbucdbZ0fU
|
Main
|
Poster
|
zbucdbZ0fU
|
scSplit: Bringing Severity Cognizance to Image Decomposition in Fluorescence Microscopy
|
[
"Ashesh",
"Florian Jug"
] |
Fluorescence microscopy, while being a key driver for progress in the life sciences, is also subject to technical limitations. To overcome them, computational multiplexing techniques have recently been proposed, which allow multiple cellular structures to be captured in a single image and later be unmixed. Existing image decomposition methods are trained on a set of superimposed input images and the respective unmixed target images. It is critical to note that the relative strength (mixing ratio) of the superimposed images for a given input is a priori unknown. However, existing methods are trained on a fixed intensity ratio of superimposed inputs, making them not cognizant of the range of relative intensities that can occur in fluorescence microscopy. In this work, we propose a novel method called scSplit that is cognizant of the severity of the above-mentioned mixing ratio. Our idea is based on InDI, a popular iterative method for image restoration, and an ideal starting point to embrace the unknown mixing ratio in any given input. We introduce (i) a suitably trained regressor network that predicts the degradation level (mixing asymmetry) of a given input image and (ii) a degradation-specific normalization module, enabling degradation-aware inference across all mixing ratios. We show that this method solves two relevant tasks in fluorescence microscopy, namely image splitting and bleedthrough removal, and empirically demonstrate the applicability of scSplit on 5 public datasets. The source code with pre-trained models is hosted at https://github.com/juglab/scSplit/.
|
https://openreview.net/forum?id=zb16xZ1NGB
|
Main
|
Poster
|
zb16xZ1NGB
|
UniGTE: Unified Graph–Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains
|
[
"Duo Wang",
"Yuan Zuo",
"Guangyue Lu",
"Junjie Wu"
] |
Generalizing to unseen graph tasks without task-specific supervision is challenging: conventional graph neural networks are typically tied to a fixed label space, while large language models (LLMs) struggle to capture graph structure. We introduce UniGTE, an instruction-tuned encoder–decoder framework that unifies structural and semantic reasoning. The encoder augments a pretrained autoregressive LLM with learnable alignment tokens and a structure-aware graph–text attention mechanism, enabling it to attend jointly to a tokenized graph and a natural-language task prompt while remaining permutation-invariant to node order. This yields compact, task-aware graph representations. Conditioned solely on these representations, a frozen LLM decoder predicts and reconstructs: it outputs the task answer and simultaneously paraphrases the input graph in natural language. The reconstruction objective regularizes the encoder to preserve structural cues. UniGTE is instruction-tuned on five datasets spanning node-, edge-, and graph-level tasks across diverse domains, yet requires no fine-tuning at inference. It achieves new state-of-the-art zero-shot results on node classification, link prediction, graph classification and graph regression under cross-task and cross-domain settings, demonstrating that tight integration of graph structure with LLM semantics enables robust, transferable graph reasoning.
|
https://openreview.net/forum?id=zaV9s8iM2T
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Main
|
Poster
|
zaV9s8iM2T
|
Datasets, Documents, and Repetitions: The Practicalities of Unequal Data Quality
|
[
"Alex Fang",
"Hadi Pouransari",
"Matt Jordan",
"Alexander T Toshev",
"Vaishaal Shankar",
"Ludwig Schmidt",
"Tom Gunter"
] |
Data filtering has become a powerful tool for improving model performance while reducing computational cost. However, as large language model compute budgets continue to grow, the limited data volume provided by heavily filtered and deduplicated datasets will become a practical constraint. In efforts to better understand how to proceed, we study model performance at various compute budgets and across multiple pre-training datasets created through data filtering and deduplication. We find that, given appropriate modifications to the training recipe, repeating existing aggressively filtered datasets for up to ten epochs can outperform training on the ten times larger superset for a single epoch across multiple compute budget orders of magnitude. While this finding relies on repeating the dataset for many epochs, we also investigate repeats within these datasets at the document level. We find that not all documents within a dataset are equal, and we can create better datasets relative to a token budget by explicitly manipulating the counts of individual documents. We conclude by arguing that even as large language models scale, data filtering remains an important direction of research.
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https://openreview.net/forum?id=zZecO3RZ7Z
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Main
|
Poster
|
zZecO3RZ7Z
|
UltraLED: Learning to See Everything in Ultra-High Dynamic Range Scenes
|
[
"Yuang Meng",
"Xin Jin",
"Lina Lei",
"Chun-Le Guo",
"Chongyi Li"
] |
Ultra-high dynamic range (UHDR) scenes exhibit pronounced exposure disparities between bright and dark regions. Such conditions are Ultra-high dynamic range (UHDR) scenes exhibit significant exposure disparities between bright and dark regions. Such conditions are commonly encountered in nighttime scenes with light sources. Even with standard exposure settings, a bimodal intensity distribution with boundary peaks often emerges, making it difficult to preserve both highlight and shadow details simultaneously. RGB-based bracketing methods can capture details at both ends using short-long exposure pairs, but are susceptible to misalignment and ghosting artifacts. We found that a short-exposure image already retains sufficient highlight detail. The main challenge of UHDR reconstruction lies in denoising and recovering information in dark regions. In comparison to the RGB images, RAW images, thanks to their higher bit depth and more predictable noise characteristics, offer greater potential for addressing this challenge. This raises a key question: can we learn to see everything in UHDR scenes using only a single short-exposure RAW image? In this study, we rely solely on a single short-exposure frame, which inherently avoids ghosting and motion blur, making it particularly robust in dynamic scenes. To achieve that, we introduce UltraLED, a two-stage framework that performs exposure correction via a ratio map to balance dynamic range, followed by a brightness-aware RAW denoiser to enhance detail recovery in dark regions. To support this setting, we design a 9-stop bracketing pipeline to synthesize realistic UHDR images and contribute a corresponding dataset based on diverse scenes, using only the shortest exposure as input for reconstruction. Extensive experiments show that UltraLED significantly outperforms existing single-frame approaches. Our code and dataset are made publicly available at https://srameo.github.io/projects/ultraled.
|
https://openreview.net/forum?id=zZLfHw4Erp
|
Main
|
Poster
|
zZLfHw4Erp
|
Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems
|
[
"Shangbin Feng",
"Zifeng Wang",
"Palash Goyal",
"Yike Wang",
"Weijia Shi",
"Huang Xia",
"Hamid Palangi",
"Luke Zettlemoyer",
"Yulia Tsvetkov",
"Chen-Yu Lee",
"Tomas Pfister"
] |
We propose Heterogeneous Swarms, an algorithm to design multi-LLM systems by jointly optimizing model roles and weights. We represent multi-LLM systems as directed acyclic graphs (DAGs) of LLMs with topological message passing for collaborative generation. Given a pool of LLM experts and a utility function, Heterogeneous Swarms employs two iterative steps: role-step and weight-step. For role-step, we interpret model roles as learning a DAG that specifies the flow of inputs and outputs between LLMs. Starting from a swarm of random continuous adjacency matrices, we decode them into discrete DAGs, call the LLMs in topological order, evaluate on the utility function (e.g. accuracy on a task), and optimize the adjacency matrices with particle swarm optimization based on the utility score. For weight-step, we assess the contribution of individual LLMs in the multi-LLM systems and optimize model weights with swarm intelligence. We propose JFK-score to quantify the individual contribution of each LLM in the best-found DAG of the role-step, then optimize model weights with particle swarm optimization based on the JFK-score. Experiments demonstrate that Heterogeneous Swarms outperforms 17 role- and/or weight-based baselines by 18.5% on average across 12 tasks. Further analysis reveals that Heterogeneous Swarms discovers multi-LLM systems with heterogeneous model roles and substantial collaborative gains, and benefits from the diversity of language models.
|
https://openreview.net/forum?id=zYEZ5KqtDO
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Main
|
Poster
|
zYEZ5KqtDO
|
RDD: Retrieval-Based Demonstration Decomposer for Planner Alignment in Long-Horizon Tasks
|
[
"Mingxuan Yan",
"Yuping Wang",
"Zechun Liu",
"Jiachen Li"
] |
To tackle long-horizon tasks, recent hierarchical vision-language-action (VLAs) frameworks employ vision-language model (VLM)-based planners to decompose complex manipulation tasks into simpler sub-tasks that low-level visuomotor policies can easily handle. Typically, the VLM planner is finetuned to learn to decompose a target task. This finetuning requires target task demonstrations segmented into sub-tasks by either human annotation or heuristic rules. However, the heuristic subtasks can deviate significantly from the training data of the visuomotor policy, which degrades task performance. To address these issues, we propose a Retrieval-based Demonstration Decomposer (RDD) that automatically decomposes demonstrations into sub-tasks by aligning the visual features of the decomposed sub-task intervals with those from the training data of the low-level visuomotor policies. Our method outperforms the state-of-the-art sub-task decomposer on both simulation and real-world tasks, demonstrating robustness across diverse settings. Code and more results are available at rdd-neurips.github.io
|
https://openreview.net/forum?id=zY5J1vp7tZ
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Main
|
Poster
|
zY5J1vp7tZ
|
Imagined Autocurricula
|
[
"Ahmet H. Güzel",
"Matthew Thomas Jackson",
"Jarek Luca Liesen",
"Tim Rocktäschel",
"Jakob Nicolaus Foerster",
"Ilija Bogunovic",
"Jack Parker-Holder"
] |
Training agents to act in embodied environments typically requires vast training data or access to accurate simulation, neither of which exists for many cases in the real world. Instead, world models are emerging as an alternative–leveraging offline, passively collected data, they make it possible to generate diverse worlds for training agents in simulation. In this work, we harness world models to generate “imagined” environments to train robust agents capable of generalizing to novel task variations. One of the challenges in doing this is ensuring the agent trains on useful generated data. We thus propose a novel approach IMAC (Imagined Autocurricula) leveraging Unsupervised Environment Design (UED), induces an automatic curriculum over generated worlds. In a series of challenging, procedurally generated environments, we show it is possible to achieve strong transfer performance on held-out environments having trained only inside a world model learned from a narrower dataset. We believe this opens the path to utilizing larger-scale, foundation world models for generally capable agents.
|
https://openreview.net/forum?id=zXlB9A5xya
|
Main
|
Poster
|
zXlB9A5xya
|
Mozart: Modularized and Efficient MoE Training on 3.5D Wafer-Scale Chiplet Architectures
|
[
"Shuqing Luo",
"Ye Han",
"Pingzhi Li",
"Jiayin Qin",
"Jie Peng",
"Yang Katie Zhao",
"Yu Cao",
"Tianlong Chen"
] |
Mixture-of-Experts (MoE) architecture offers enhanced efficiency for Large Language Models (LLMs) with modularized computation, yet its inherent sparsity poses significant hardware deployment challenges, including memory locality issues, communication overhead, and inefficient computing resource utilization. Inspired by the modular organization of the human brain, we propose $\texttt{Mozart}$, a novel algorithm-hardware co-design framework tailored for efficient training of MoE-based LLMs on 3.5D wafer-scale chiplet architectures. On the algorithm side, $\texttt{Mozart}$ exploits the inherent modularity of chiplets and introduces:
($1$) an expert allocation strategy that enables efficient on-package all-to-all communication, and ($2$) a fine-grained scheduling mechanism that improves communication-computation overlap through streaming tokens and experts. On the architecture side, $\texttt{Mozart}$ adaptively co-locates heterogeneous modules on specialized chiplets with a 2.5D NoP-Tree topology and hierarchical memory structure.
Evaluation across three popular MoE models demonstrates significant efficiency gains, enabling more effective parallelization and resource utilization for large-scale modularized MoE-LLMs.
|
https://openreview.net/forum?id=zWHKKspghT
|
Main
|
Spotlight
|
zWHKKspghT
|
Towards Generalizable Retina Vessel Segmentation with Deformable Graph Priors
|
[
"Ke Liu",
"Shangde Gao",
"Yichao Fu",
"Shangqi Gao"
] |
Retinal vessel segmentation is critical for medical diagnosis, yet existing models often struggle to generalize across domains due to appearance variability, limited annotations, and complex vascular morphology. We propose GraphSeg, a variational Bayesian framework that integrates anatomical graph priors with structure-aware image decomposition to enhance cross-domain segmentation. GraphSeg factorizes retinal images into structure-preserved and structure-degraded components, enabling domain-invariant representation. A deformable graph prior, derived from a statistical retinal atlas, is incorporated via a differentiable alignment and guided by an unsupervised energy function. Experiments on three public benchmarks (CHASE, DRIVE, HRF) show that GraphSeg consistently outperforms existing methods under domain shifts. These results highlight the importance of jointly modeling anatomical topology and image structure for robust generalizable vessel segmentation.
|
https://openreview.net/forum?id=zVkbsGlKn9
|
Main
|
Poster
|
zVkbsGlKn9
|
On the Expressive Power of Mixture-of-Experts for Structured Complex Tasks
|
[
"Mingze Wang",
"Weinan E"
] |
Mixture-of-experts networks (MoEs) have demonstrated remarkable efficiency in modern deep learning. Despite their empirical success, the theoretical foundations underlying their ability to model complex tasks remain poorly understood.
In this work, we conduct a systematic study of the expressive power of MoEs in modeling complex tasks with two common structural priors: low-dimensionality and sparsity.
For shallow MoEs, we prove that they can efficiently approximate functions supported on low-dimensional manifolds, overcoming the curse of dimensionality.
For deep MoEs, we show that $\mathcal{O}(L)$-layer MoEs with $E$ experts per layer can approximate piecewise functions comprising $E^L$ pieces with compositional sparsity, i.e., they can exhibit an exponential number of structured tasks.
Our analysis reveals the roles of critical architectural components and hyperparameters in MoEs, including the gating mechanism, expert networks, the number of experts, and the number of layers, and offers natural suggestions for MoE variants.
|
https://openreview.net/forum?id=zSrb8rtH9M
|
Main
|
Spotlight
|
zSrb8rtH9M
|
Depth-Supervised Fusion Network for Seamless-Free Image Stitching
|
[
"Zhiying Jiang",
"Ruhao Yan",
"Zengxi Zhang",
"Bowei Zhang",
"Jinyuan Liu"
] |
Image stitching synthesizes images captured from multiple perspectives into a single image with a broader field of view. The significant variations in object depth often lead to large parallax, resulting in ghosting and misalignment in the stitched results. To address this, we propose a depth-consistency-constrained seamless-free image stitching method. First, to tackle the multi-view alignment difficulties caused by parallax, a multi-stage mechanism combined with global depth regularization constraints is developed to enhance the alignment accuracy of the same apparent target across different depth ranges. Second, during the multi-view image fusion process, an optimal stitching seam is determined through graph-based low-cost computation, and a soft-seam region is diffused to precisely locate transition areas, thereby effectively mitigating alignment errors induced by parallax and achieving natural and seamless stitching results. Furthermore, considering the computational overhead in the shift regression process, a reparameterization strategy is incorporated to optimize the structural design, significantly improving algorithm efficiency while maintaining optimal performance. Extensive experiments demonstrate the superior performance of the proposed method against the existing methods. Code is available at https://github.com/DLUT-YRH/DSFN.
|
https://openreview.net/forum?id=zQqDqfja4Y
|
Main
|
Poster
|
zQqDqfja4Y
|
OpenHype: Hyperbolic Embeddings for Hierarchical Open-Vocabulary Radiance Fields
|
[
"Lisa Weijler",
"Sebastian Koch",
"Fabio Poiesi",
"Timo Ropinski",
"Pedro Hermosilla"
] |
Modeling the inherent hierarchical structure of 3D objects and 3D scenes is highly desirable, as it enables a more holistic understanding of environments for autonomous agents. Accomplishing this with implicit representations, such as Neural Radiance Fields, remains an unexplored challenge. Existing methods that explicitly model hierarchical structures often face significant limitations: they either require multiple rendering passes to capture embeddings at different levels of granularity, significantly increasing inference time, or rely on predefined, closed-set discrete hierarchies that generalize poorly to the diverse and nuanced structures encountered by agents in the real world. To address these challenges, we propose OpenHype, a novel approach that represents scene hierarchies using a continuous hyperbolic latent space. By leveraging the properties of hyperbolic geometry, OpenHype naturally encodes multi-scale relationships and enables smooth traversal of hierarchies through geodesic paths in latent space. Our method outperforms state-of-the-art approaches on standard benchmarks, demonstrating superior efficiency and adaptability in 3D scene understanding.
|
https://openreview.net/forum?id=zQmXDUbZ5D
|
Main
|
Poster
|
zQmXDUbZ5D
|
Dynamic Masking and Auxiliary Hash Learning for Enhanced Cross-Modal Retrieval
|
[
"Shuang Zhang",
"Yue Wu",
"Lei Shi",
"Yingxue Zhang",
"Feifei Kou",
"Huilong Jin",
"Pengfei Zhang",
"Meiyu Liang",
"Mingying Xu"
] |
The demand for multimodal data processing drives the development of information technology. Cross-modal hash retrieval has attracted much attention because it can overcome modal differences and achieve efficient retrieval, and has shown great application potential in many practical scenarios. Existing cross-modal hashing methods have difficulties in fully capturing the semantic information of different modal data, which leads to a significant semantic gap between modalities. Moreover, these methods often ignore the importance differences of channels, and due to the limitation of a single goal, the matching effect between hash codes is also affected to a certain extent, thus facing many challenges. To address these issues, we propose a Dynamic Masking and Auxiliary Hash Learning (AHLR) method for enhanced cross-modal retrieval. By jointly leveraging the dynamic masking and auxiliary hash learning mechanisms, our approach effectively resolves the problems of channel information imbalance and insufficient key information capture, thereby significantly improving the retrieval accuracy. Specifically, we introduce a dynamic masking mechanism that automatically screens and weights the key information in images and texts during the training process, enhancing the accuracy of feature matching. We further construct an auxiliary hash layer to adaptively balance the weights of features across each channel, compensating for the deficiencies of traditional methods in key information capture and channel processing. In addition, we design a contrastive loss function to optimize the generation of hash codes and enhance their discriminative power, further improving the performance of cross-modal retrieval. Comprehensive experimental results on NUS-WIDE, MIRFlickr-25K and MS-COCO benchmark datasets show that the proposed AHLR algorithm outperforms several existing algorithms.
|
https://openreview.net/forum?id=zQK6IluJi3
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Main
|
Poster
|
zQK6IluJi3
|
Delving into Cascaded Instability: A Lipschitz Continuity View on Image Restoration and Object Detection Synergy
|
[
"Qing Zhao",
"Weijian Deng",
"Pengxu Wei",
"ZiYi Dong",
"Hannan Lu",
"Xiangyang Ji",
"Liang Lin"
] |
To improve detection robustness in adverse conditions (e.g., haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the detector. However, the functional mismatch between restoration and detection networks can introduce instability and hinder effective integration---an issue that remains underexplored. We revisit this limitation through the lens of Lipschitz continuity, analyzing the functional differences between restoration and detection networks in both the input space and the parameter space. Our analysis shows that restoration networks perform smooth, continuous transformations, while object detectors operate with discontinuous decision boundaries, making them highly sensitive to minor perturbations. This mismatch introduces instability in traditional cascade frameworks, where even imperceptible noise from restoration is amplified during detection, disrupting gradient flow and hindering optimization. To address this, we propose Lipschitz-regularized object detection (LROD), a simple yet effective framework that integrates image restoration directly into the detector’s feature learning, harmonizing the Lipschitz continuity of both tasks during training. We implement this framework as Lipschitz-regularized YOLO (LR-YOLO), extending seamlessly to existing YOLO detectors. Extensive experiments on haze and low-light benchmarks demonstrate that LR-YOLO consistently improves detection stability, optimization smoothness, and overall accuracy.
|
https://openreview.net/forum?id=zPgPDHupcE
|
Main
|
Poster
|
zPgPDHupcE
|
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
|
[
"Sang Keun Choe",
"Hwijeen Ahn",
"Juhan Bae",
"Kewen Zhao",
"Youngseog Chung",
"Adithya Pratapa",
"Willie Neiswanger",
"Emma Strubell",
"Teruko Mitamura",
"Jeff Schneider",
"Eduard Hovy",
"Roger Baker Grosse",
"Eric P. Xing"
] |
Large language models (LLMs) are trained on a vast amount of human-written data, but data providers often remain uncredited. In response to this issue, data valuation (or data attribution), which quantifies the contribution or value of each data to the model output, has been discussed as a potential solution. Nevertheless, applying existing data valuation methods to recent LLMs and their vast training datasets has been largely limited by prohibitive compute and memory costs. In this work, we focus on influence functions, a popular gradient-based data valuation method, and significantly improve its scalability with an efficient gradient projection strategy called LoGra that leverages the gradient structure in backpropagation. We then provide a theoretical motivation of gradient projection approaches to influence functions to promote trust in the data valuation process. Lastly, we lower the barrier to implementing data valuation systems by introducing LogIX, a software package that can transform existing training code into data valuation code with minimal effort. In our data valuation experiments, LoGra achieves competitive accuracy against more expensive baselines while showing up to 6,500x improvement in throughput and 5x reduction in GPU memory usage when applied to Llama3-8B-Instruct and the 1B-token dataset.
|
https://openreview.net/forum?id=zPKeJAEo27
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Main
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Poster
|
zPKeJAEo27
|
Online Portfolio Selection with ML Predictions
|
[
"Ziliang Zhang",
"Tianming Zhao",
"Albert Zomaya"
] |
Online portfolio selection seeks to determine a sequence of allocations to maximize capital growth. Classical universal strategies asymptotically match the best constant-rebalanced portfolio but ignore potential forecasts, whereas heuristic methods often collapse when belief fails. We formalize this tension in a learning-augmented setting in which an investor observes (possibly erroneous) predictions prior to each decision moment, and we introduce the Rebalanced Arithmetic Mean portfolio with predictions (RAM). Under arbitrary return sequences, we prove that RAM captures at least a constant fraction of the hindsight-optimal wealth when forecasts are perfect while still exceeding the geometric mean of the sequence even when the predictions are adversarial. Comprehensive experiments on large-scale equity data strengthen our theory, spanning both synthetic prediction streams and production-grade machine-learning models. RAM advantages over universal-portfolio variants equipped with side information across various regimes. These results demonstrate that modest predictive power can be reliably converted into tangible gains without sacrificing worst-case guarantees.
|
https://openreview.net/forum?id=zOFxp98km2
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Main
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Poster
|
zOFxp98km2
|
Private Evolution Converges
|
[
"Tomás González",
"Giulia Fanti",
"Aaditya Ramdas"
] |
Private Evolution (PE) is a promising training-free method for differentially private (DP) synthetic data generation. While it achieves strong performance in some domains (e.g., images and text), its behavior in others (e.g., tabular data) is less consistent. To date, the only theoretical analysis of the convergence of PE depends on unrealistic assumptions about both the algorithm’s behavior and the structure of the sensitive dataset. In this work, we develop a new theoretical framework to understand PE’s practical behavior and identify sufficient conditions for its convergence. For $d$-dimensional sensitive datasets with $n$ data points from a convex and compact domain, we prove that under the right hyperparameter settings and given access to the Gaussian variation API proposed in \cite{PE23}, PE
produces an $(\varepsilon, \delta)$-DP synthetic dataset with expected 1-Wasserstein distance $\tilde{O}(d(n\varepsilon)^{-1/d})$ from the original; this establishes worst-case convergence of the algorithm as $n \to \infty$. Our analysis extends to general Banach spaces as well. We also connect PE to the Private Signed Measure Mechanism, a method for DP synthetic data generation that has thus far not seen much practical adoption. We demonstrate the practical relevance of our theoretical findings in experiments.
|
https://openreview.net/forum?id=zOCENGh1Jg
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Main
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Poster
|
zOCENGh1Jg
|
Bootstrap Off-policy with World Model
|
[
"Guojian Zhan",
"Likun Wang",
"Xiangteng Zhang",
"Jiaxin Gao",
"Masayoshi Tomizuka",
"Shengbo Eben Li"
] |
Online planning has proven effective in reinforcement learning (RL) for improving sample efficiency and final performance. However, using planning for environment interaction inevitably introduces a divergence between the collected data and the policy's actual behaviors, degrading both model learning and policy improvement. To address this, we propose BOOM (Bootstrap Off-policy with WOrld Model), a framework that tightly integrates planning and off-policy learning through a bootstrap loop: the policy initializes the planner, and the planner refines actions to bootstrap the policy through behavior alignment. This loop is supported by a jointly learned world model, which enables the planner to simulate future trajectories and provides value targets to facilitate policy improvement. The core of BOOM is a likelihood-free alignment loss that bootstraps the policy using the planner’s non-parametric action distribution, combined with a soft value-weighted mechanism that prioritizes high-return behaviors and mitigates variability in the planner’s action quality within the replay buffer. Experiments on the high-dimensional DeepMind Control Suite and Humanoid-Bench show that BOOM achieves state-of-the-art results in both training stability and final performance. The code is accessible at \url{https://github.com/molumitu/BOOM_MBRL}.
|
https://openreview.net/forum?id=zNqDCSokDR
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Main
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Poster
|
zNqDCSokDR
|
AdaSPEC: Selective Knowledge Distillation for Efficient Speculative Decoders
|
[
"Yuezhou Hu",
"Jiaxin Guo",
"Xinyu Feng",
"Tuo Zhao"
] |
Speculative Decoding (SD) accelerates large language model inference by employing a small draft model to generate predictions, which are then verified by a larger target model. The effectiveness of SD hinges on the alignment between these models, which is typically enhanced by Knowledge Distillation (KD). However, conventional KD methods aim to minimize the KL divergence between the draft and target models across all tokens, a goal that is misaligned with the true objective of SD, which is to maximize token acceptance rate. Therefore, draft models often struggle to fully assimilate the target model's knowledge due to capacity constraints, leading to suboptimal performance. To address this challenge, we propose AdaSPEC, a novel method that incorporates selective token filtering into the KD process. AdaSPEC utilizes a reference model to identify and filter out difficult-to-fit tokens, enabling the distillation of a draft model that better aligns with the target model on simpler tokens. This approach improves the overall token acceptance rate without compromising generation quality. We evaluate AdaSPEC across diverse tasks, including arithmetic reasoning, instruction-following, coding, and summarization, using model configurations of 31M/1.4B and 350M/2.7B parameters. Our results demonstrate that AdaSPEC consistently outperforms the state-of-the-art DistillSpec method, achieving higher acceptance rates across all tasks (up to 15\%). The code is publicly available at \url{https://github.com/yuezhouhu/adaspec}.
|
https://openreview.net/forum?id=zNLlglSOwD
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Main
|
Spotlight
|
zNLlglSOwD
|
LLMs Encode Harmfulness and Refusal Separately
|
[
"Jiachen Zhao",
"Jing Huang",
"Zhengxuan Wu",
"David Bau",
"Weiyan Shi"
] |
LLMs are trained to refuse harmful instructions, but do they truly understand
harmfulness beyond just refusing? Prior work has shown that LLMs’ refusal
behaviors can be mediated by a one-dimensional subspace, i.e., a refusal direction.
In this work, we identify a new dimension to analyze safety mechanisms in LLMs,
i.e., harmfulness, which is encoded internally as a separate concept from refusal.
And there exists a harmfulness direction that is distinct from the refusal direction.
As causal evidence, steering along the harmfulness direction can lead LLMs to
interpret harmless instructions as harmful, but steering along the refusal direction
tends to elicit refusal responses directly without reversing the model’s judgment on
harmfulness. Furthermore, using our identified harmfulness concept, we find that
certain jailbreak methods work by reducing the refusal signals without suppressing
the model’s internal belief of harmfulness. We also find that adversarially fine-
tuning models to accept harmful instructions has minimal impact on the model’s
internal belief of harmfulness. These insights lead to a practical safety application:
The model’s latent harmfulness representation can serve as an intrinsic safeguard
(Latent Guard) for detecting unsafe inputs and reducing over-refusals that is
robust to finetuning attacks. For instance, our Latent Guard achieves performance
comparable to or better than Llama Guard 3 8B, a dedicated finetuned safeguard
model, across different jailbreak methods. Our findings suggest that LLMs’
internal understanding of harmfulness is more robust than their refusal decision
to diverse input instructions, offering a new perspective to study AI safety.
|
https://openreview.net/forum?id=zLkpt30ngy
|
Main
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Poster
|
zLkpt30ngy
|
Learnable Burst-Encodable Time-of-Flight Imaging for High-Fidelity Long-Distance Depth Sensing
|
[
"Manchao Bao",
"Shengjiang Fang",
"Tao Yue",
"Xuemei Hu"
] |
Long-distance depth imaging holds great promise for applications such as autonomous driving and robotics. Direct time-of-flight (dToF) imaging offers high-precision, long-distance depth sensing, yet demands ultra-short pulse light sources and high-resolution time-to-digital converters. In contrast, indirect time-of-flight (iToF) imaging often suffers from phase wrapping and low signal-to-noise ratio (SNR) as the sensing distance increases. In this paper, we introduce a novel ToF imaging paradigm, termed Burst-Encodable Time-of-Flight (BE-ToF), which facilitates high-fidelity, long-distance depth imaging. Specifically, the BE-ToF system emits light pulses in burst mode and estimates the phase delay of the reflected signal over the entire burst period, thereby effectively avoiding the phase wrapping inherent to conventional iToF systems. Moreover, to address the low SNR caused by light attenuation over increasing distances, we propose an end-to-end learnable framework that jointly optimizes the coding functions and the depth reconstruction network. A specialized double well function and first-order difference term are incorporated into the framework to ensure the hardware implementability of the coding functions. The proposed approach is rigorously validated through comprehensive simulations and real-world prototype experiments, demonstrating its effectiveness and practical applicability. The code is available at: https://github.com/ComputationalPerceptionLab/BE-ToF.
|
https://openreview.net/forum?id=zL4ifL17bU
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Main
|
Spotlight
|
zL4ifL17bU
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Consistency of Physics-Informed Neural Networks for Second-Order Elliptic Equations
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[
"Yuqian Cheng",
"Zhuo Chen",
"Qian Lin"
] |
The physics-informed neural networks (PINNs) are widely applied in solving differential equations. However, few studies have discussed their consistency. In this paper, we consider the consistency of PINNs when applied to second-order elliptic equations with Dirichlet boundary conditions. We first provide the necessary and sufficient condition for the consistency of the physics-informed kernel gradient flow algorithm, and then as a direct corollary, when the neural network is sufficiently wide, we obtain a necessary and sufficient condition for the consistency of PINNs based on the neural tangent kernel theory. We also estimate the non-asymptotic loss bounds of physics-informed kernel gradient flow and PINN under suitable stronger assumptions. Finally, these results inspires us to construct a notable pathological example where the PINN method is inconsistent.
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https://openreview.net/forum?id=zL4JRfBr7R
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Main
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Poster
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zL4JRfBr7R
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Don't Just Chase “Highlighted Tokens” in MLLMs: Revisiting Visual Holistic Context Retention
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[
"Xin Zou",
"Di Lu",
"Yizhou Wang",
"Yibo Yan",
"Yuanhuiyi Lyu",
"Xu Zheng",
"Linfeng Zhang",
"Xuming Hu"
] |
Despite their powerful capabilities, multimodal large language models (MLLMs) suffer from considerable computational overhead due to their reliance on massive visual tokens. Recent studies have explored token pruning to alleviate this problem, which typically uses text-vision cross-attention or [CLS] attention to assess and discard redundant visual tokens. In this work, we identify a critical limitation of such attention-first pruning approaches, i.e., they tend to preserve semantically similar tokens, resulting in pronounced performance drops under high pruning rates. To this end, we propose HoloV, a simple yet effective, plug-and-play visual token pruning framework for efficient inference. Distinct from previous attention-first schemes, HoloV rethinks token retention from a holistic perspective. By adaptively distributing the pruning budget across different spatial crops, HoloV ensures that the retained tokens capture the global visual context rather than isolated salient features. This strategy minimizes representational collapse and maintains task-relevant information even under aggressive pruning. Experimental results demonstrate that our HoloV achieves superior performance across various tasks, MLLM architectures, and pruning ratios compared to SOTA methods. For instance, LLaVA1.5 equipped with HoloV preserves 95.8% of the original performance after pruning 88.9% of visual tokens, achieving superior efficiency-accuracy trade-offs.
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https://openreview.net/forum?id=zKoeRtye8o
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Main
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Poster
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zKoeRtye8o
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BeyondMix: Leveraging Structural Priors and Long-Range Dependencies for Domain-Invariant LiDAR Segmentation
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[
"Yujia Chen",
"Rui Sun",
"Wangkai Li",
"Huayu Mai",
"Si Chen",
"Zhuoyuan Li",
"Zhixin Cheng",
"Tianzhu Zhang"
] |
Domain adaptation for LiDAR semantic segmentation remains challenging due to the complex structural properties of point cloud data. While mix-based paradigms have shown promise, they often fail to fully leverage the rich structural priors inherent in 3D LiDAR point clouds. In this paper, we identify three critical yet underexploited structural priors: permutation invariance, local consistency, and geometric consistency. We introduce BeyondMix, a novel framework that harnesses the capabilities of State Space Models (specifically Mamba) to construct and exploit these structural priors while modeling long-range dependencies that transcend the limited receptive fields of conventional voxel-based approaches. By employing space-filling curves to impose sequential ordering on point cloud data and implementing strategic spatial partitioning schemes, BeyondMix effectively captures domain-invariant representations. Extensive experiments on challenging LiDAR semantic segmentation benchmarks demonstrate that our approach consistently outperforms existing state-of-the-art methods, establishing a new paradigm for unsupervised domain adaptation in 3D point cloud understanding.
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https://openreview.net/forum?id=zKV3CN40tE
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Main
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Poster
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zKV3CN40tE
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LittleBit: Ultra Low-Bit Quantization via Latent Factorization
|
[
"Banseok Lee",
"Dongkyu Kim",
"Youngcheon you",
"Young-Min Kim"
] |
Deploying large language models (LLMs) often faces challenges from substantial memory and computational costs. Quantization offers a solution, yet performance degradation in the sub-1-bit regime remains particularly difficult. This paper introduces LittleBit, a novel method for extreme LLM compression. It targets levels like 0.1 bits per weight (BPW), achieving nearly 31$\times$ memory reduction, e.g., Llama2-13B to under 0.9 GB. LittleBit represents weights in a low-rank form using latent matrix factorization, subsequently binarizing these factors. To counteract information loss from this extreme precision, it integrates a multi-scale compensation mechanism. This includes row, column, and an additional latent dimension that learns per-rank importance. Two key contributions enable effective training: Dual Sign-Value-Independent Decomposition (Dual-SVID) for quantization-aware training (QAT) initialization, and integrated Residual Compensation to mitigate errors. Extensive experiments confirm LittleBit's superiority in sub-1-bit quantization: e.g., its 0.1 BPW performance on Llama2-7B surpasses the leading method's 0.7 BPW. LittleBit establishes a new, viable size-performance trade-off—unlocking a potential 11.6$\times$ speedup over FP16 at the kernel level—and makes powerful LLMs practical for resource-constrained environments.
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https://openreview.net/forum?id=zJzu9evD5K
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Main
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Poster
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zJzu9evD5K
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Discovering Opinion Intervals from Conflicts in Signed Graphs
|
[
"Peter Blohm",
"Florian Chen",
"Aristides Gionis",
"Stefan Neumann"
] |
Online social media provide a platform for people to discuss current events and exchange opinions with their peers. While interactions are predominantly positive, in recent years, there has been a lot of research to understand the conflicts in social networks and how they are based on different views and opinions. In this paper, we ask whether the conflicts in a network reveal a small and interpretable set of prevalent opinion ranges that explain the users' interactions. More precisely, we consider signed graphs, where the edge signs indicate positive and negative interactions of node pairs, and our goal is to infer opinion intervals that are consistent with the edge signs. We introduce an optimization problem that models this question, and we give strong hardness results and a polynomial-time approximation scheme by utilizing connections to interval graphs and the Correlation Clustering problem. We further provide scalable heuristics and show that in experiments they yield more expressive solutions than Correlation Clustering baselines. We also present a case study on a novel real-world dataset from the German parliament, showing that our algorithms can recover the political leaning of German parties based on co-voting behavior.
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https://openreview.net/forum?id=zJdutIT6vT
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Main
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Oral
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zJdutIT6vT
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SALS: Sparse Attention in Latent Space for KV Cache Compression
|
[
"Junlin Mu",
"Hantao Huang",
"Jihang Zhang",
"Minghui Yu",
"Tao Wang",
"Yidong Li"
] |
Large Language Models (LLMs) capable of handling extended contexts are in high demand, yet their inference remains challenging due to substantial Key-Value (KV) cache size and high memory bandwidth requirements. Previous research has demonstrated that KV cache exhibits low-rank characteristics within the hidden dimension, suggesting the potential for effective compression. However, due to the widely adopted Rotary Position Embedding (RoPE) mechanism in modern LLMs, naive low‑-rank compression suffers severe accuracy degradation or creates a new speed bottleneck, as the low-rank cache must first be reconstructed in order to apply RoPE. In this paper, we introduce two key insights: first, the application of RoPE to the key vectors increases their variance, which in turn results in a higher rank; second, after the key vectors are transformed into the latent space, they largely maintain their representation across most layers. Based on these insights, we propose the Sparse Attention in Latent Space (SALS) framework. SALS projects the KV cache into a compact latent space via low-rank projection, and performs sparse token selection using RoPE-free query--key interactions in this space. By reconstructing only a small subset of important tokens, it avoids the overhead of full KV cache reconstruction. We comprehensively evaluate SALS on various tasks using two large-scale models: LLaMA2-7b-chat and Mistral-7b, and additionally verify its scalability on the RULER-128k benchmark with LLaMA3.1-8B-Instruct. Experimental results demonstrate that SALS achieves SOTA performance by maintaining competitive accuracy. Under different settings, SALS achieves 6.4-fold KV cache compression and 5.7-fold speed-up in the attention operator compared to FlashAttention2 on the 4K sequence. For the end-to-end throughput performance, we achieves 1.4-fold and 4.5-fold improvement compared to GPT-fast on 4k and 32K sequences, respectively. The source code will be publicly available in the future.
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https://openreview.net/forum?id=zJSZupQ889
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Main
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Poster
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zJSZupQ889
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PhySense: Sensor Placement Optimization for Accurate Physics Sensing
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[
"Yuezhou Ma",
"Haixu Wu",
"Hang Zhou",
"Huikun Weng",
"Jianmin Wang",
"Mingsheng Long"
] |
Physics sensing plays a central role in many scientific and engineering domains, which inherently involves two coupled tasks: reconstructing dense physical fields from sparse observations and optimizing scattered sensor placements to observe maximum information. While deep learning has made rapid advances in sparse-data reconstruction, existing methods generally omit optimization of sensor placements, leaving the mutual enhancement between reconstruction and placement on the shelf. To change this suboptimal practice, we propose PhySense, a synergistic two-stage framework that learns to jointly reconstruct physical fields and to optimize sensor placements, both aiming for accurate physics sensing. The first stage involves a flow-based generative model enhanced by cross-attention to adaptively fuse sparse observations. Leveraging the reconstruction feedback, the second stage performs sensor placement via projected gradient descent to satisfy spatial constraints. We further prove that the learning objectives of the two stages are consistent with classical variance-minimization principles, providing theoretical guarantees. Extensive experiments across three challenging benchmarks, especially a 3D geometry dataset, indicate PhySense achieves state-of-the-art physics sensing accuracy and discovers informative sensor placements previously unconsidered. Code is available at this repository: https://github.com/thuml/PhySense.
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https://openreview.net/forum?id=zIzZxDsNNP
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Main
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Oral
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zIzZxDsNNP
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A Reinforcement Learning-based Bidding Strategy for Data Consumers in Auction-based Federated Learning
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[
"Xiaoli Tang",
"Han Yu",
"Xiaoxiao Li"
] |
Auction-based Federated Learning (AFL) fosters collaboration among self-interested data consumers (DCs) and data owners (DOs). A major challenge in AFL pertains to how DCs select and bid for DOs. Existing methods are generally static, making them ill-suited for dynamic AFL markets. To address this issue, we propose the R}einforcement Learning-based Bidding Strategy for DCs in Auction-based Federated Learning (RLB-AFL). We incorporate historical states into a Deep Q-Network to capture sequential information critical for bidding decisions. To mitigate state space sparsity, where specific states rarely reoccur for each DC during auctions, we incorporate the Gaussian Mixture Model into RLB-AFL. This facilitates soft clustering on sequential states, reducing the state space dimensionality and easing exploration and action-value function approximation. In addition, we enhance the $\epsilon$-greedy policy to help the RLB-AFL agent balance exploitation and exploration, enabling it to be more adaptable in the AFL decision-making process. Extensive experiments under 6 widely used benchmark datasets demonstrate that RLB-AFL achieves superior performance compared to 8 state-of-the-art approaches. It outperforms the best baseline by 10.56% and 3.15% in terms of average total utility
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https://openreview.net/forum?id=zIbNGkaYij
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Main
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Poster
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zIbNGkaYij
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Semi-off-Policy Reinforcement Learning for Vision-Language Slow-Thinking Reasoning
|
[
"Junhao Shen",
"Haiteng Zhao",
"Yuzhe Gu",
"Songyang Gao",
"Kuikun Liu",
"Haian Huang",
"Jianfei Gao",
"Dahua Lin",
"Wenwei Zhang",
"Kai Chen"
] |
Enhancing large vision-language models (LVLMs) with visual slow-thinking reasoning is crucial for solving complex multimodal tasks. However, since LVLMs are mainly trained with vision-language alignment, it is difficult to adopt on-policy reinforcement learning (RL) to develop the slow thinking ability because the rollout space is restricted by its initial abilities. Off-policy RL offers a way to go beyond the current policy, but directly distilling trajectories from external models may cause visual hallucinations due to mismatched visual perception abilities across models. To address these issues, this paper proposes **SOPHIA**, a simple and scalable **S**emi-**O**ff-**P**olicy RL for vision-language slow-t**HI**nking re**A**soning. SOPHIA builds a semi-off-policy behavior model by combining on-policy visual understanding from a trainable LVLM with off-policy slow-thinking reasoning from a language model, assigns outcome-based rewards to reasoning, and propagates visual rewards backward. Then LVLM learns slow-thinking reasoning ability from the obtained reasoning trajectories using propagated rewards via off-policy RL algorithms. Extensive experiments with InternVL2.5 and InternVL3.0 with 8B and 38B sizes show the effectiveness of SOPHIA. Notably, SOPHIA improves InternVL3.0-38B by 8.50\% in average, reaching state-of-the-art performance among open-source LVLMs on multiple multimodal reasoning benchmarks, and even outperforms some closed-source models (e.g., GPT-4.1) on the challenging MathVision and OlympiadBench, achieving 49.08\% and 49.95\% pass@1 accuracy, respectively. Analysis shows SOPHIA outperforms supervised fine-tuning and direct on-policy RL methods, offering a better policy initialization for further on-policy training.
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https://openreview.net/forum?id=zIFuLxUAu9
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Main
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Poster
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zIFuLxUAu9
|
Test-Time Adaptation by Causal Trimming
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[
"Yingnan Liu",
"Rui Qiao",
"Mong-Li Lee",
"Wynne Hsu"
] |
Test-time adaptation aims to improve model robustness under distribution shifts by adapting models with access to unlabeled target samples. A primary cause of performance degradation under such shifts is the model’s reliance on features that lack a direct causal relationship with the prediction target. We introduce Test-time Adaptation by Causal Trimming (TACT), a method that identifies and removes non-causal components from representations for test distributions. TACT applies data augmentations that preserve causal features while varying non-causal ones. By analyzing the changes in the representations using Principal Component Analysis, TACT identifies the highest variance directions associated with non-causal features. It trims the representations by removing their projections on the identified directions, and uses the trimmed representations for the predictions. During adaptation, TACT continuously tracks and refines these directions to get a better estimate of non-causal features. We theoretically analyze the effectiveness of this approach and empirically validate TACT on real-world out-of-distribution benchmarks. TACT consistently outperforms state-of-the-art methods by a significant margin.
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https://openreview.net/forum?id=zFGdHL9pcD
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Main
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Poster
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zFGdHL9pcD
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ReplaceMe: Network Simplification via Depth Pruning and Transformer Block Linearization
|
[
"Dmitriy Shopkhoev",
"Ammar Ali",
"Magauiya Zhussip",
"Valentin Malykh",
"Stamatios Lefkimmiatis",
"Nikos Komodakis",
"Sergey Zagoruyko"
] |
We introduce ReplaceMe, a generalized training-free depth pruning method that effectively replaces transformer blocks with a linear operation, while maintaining high performance for low compression ratios. In contrast to conventional pruning approaches that require additional training or fine-tuning, our approach requires only a small calibration dataset that is used to estimate a linear transformation, which approximates the pruned blocks. The estimated linear mapping can be seam- lessly merged with the remaining transformer blocks, eliminating the need for any additional network parameters. Our experiments show that ReplaceMe consistently outperforms other training-free approaches and remains highly competitive with state-of-the-art pruning methods that involve extensive retraining/fine-tuning and architectural modifications. Applied to several large language models (LLMs), ReplaceMe achieves up to 25% pruning while retaining approximately 90% of the original model’s performance on open benchmarks—without any training or healing steps, resulting in minimal computational overhead. We provide an open- source library implementing ReplaceMe alongside several state-of-the-art depth pruning techniques, available at https://github.com/mts-ai/ReplaceMe.
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https://openreview.net/forum?id=zEj1FSYCRn
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Main
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Poster
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zEj1FSYCRn
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Curriculum Design for Trajectory-Constrained Agent: Compressing Chain-of-Thought Tokens in LLMs
|
[
"Georgios Tzannetos",
"Parameswaran Kamalaruban",
"Adish Singla"
] |
Training agents to operate under strict constraints during deployment, such as limited resource budgets or stringent safety requirements, presents significant challenges, especially when these constraints render the task complex. In this work, we propose a curriculum learning strategy that gradually tightens constraints during training, enabling the agent to incrementally master the deployment requirements. Inspired by self-paced learning techniques in unconstrained reinforcement learning (RL), our approach facilitates a smoother transition to challenging environments by initially training on simplified versions of the constraints and progressively introducing the full deployment conditions. We provide a theoretical analysis using an RL agent in a binary-tree Markov Decision Process (MDP) to demonstrate that our curriculum strategy can accelerate training relative to a baseline approach that imposes the trajectory constraints from the outset. Moreover, we empirically validate the effectiveness and generality of our method across both RL and large language model (LLM) agents in diverse settings, including a binary-tree MDP, a multi-task navigation domain, and a math reasoning task with two benchmarks. These results highlight the potential of curriculum design in enhancing the efficiency and performance of agents operating under complex trajectory constraints during deployment. Moreover, when applied to LLMs, our strategy enables compression of output chain-of-thought tokens, achieving a substantial inference speedup on consumer hardware, demonstrating its effectiveness for resource-constrained deployment.
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https://openreview.net/forum?id=zDU5sfYK1Z
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Main
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Poster
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zDU5sfYK1Z
|
Accelerated Evolving Set Processes for Local PageRank Computation
|
[
"BinbinHuang",
"Luo Luo",
"Yanghua Xiao",
"Deqing Yang",
"Baojian Zhou"
] |
This work proposes a novel framework based on nested evolving set processes to accelerate Personalized PageRank (PPR) computation. At each stage of the process, we employ a localized inexact proximal point iteration to solve a simplified linear system. We show that the time complexity of such localized methods is upper bounded by $\min\{\tilde{\mathcal{O}}(R^2/\epsilon^2), \tilde{\mathcal{O}}(m)\}$ to obtain an $\epsilon$-approximation of the PPR vector, where $m$ denotes the number of edges in the graph and $R$ is a constant defined via nested evolving set processes. Furthermore, the algorithms induced by our framework require solving only $\tilde{\mathcal{O}}(1/\sqrt{\alpha})$ such linear systems, where $\alpha$ is the damping factor. When $1/\epsilon^2\ll m$, this implies the existence of an algorithm that computes an $\epsilon$-approximation of the PPR vector with an overall time complexity of $\tilde{\mathcal{O}}(R^2 / (\sqrt{\alpha}\epsilon^2))$, independent of the underlying graph size. Our result resolves an open conjecture from existing literature. Experimental results on real-world graphs validate the efficiency of our methods, demonstrating significant convergence in the early stages.
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https://openreview.net/forum?id=zDOo34mbpl
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Main
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Poster
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zDOo34mbpl
|
Continual Model Merging without Data: Dual Projections for Balancing Stability and Plasticity
|
[
"Enneng Yang",
"Anke Tang",
"Li Shen",
"Guibing Guo",
"Xingwei Wang",
"Xiaochun Cao",
"Jie Zhang"
] |
Model merging integrates multiple expert models with diverse capabilities into a unified framework, facilitating collaborative learning. However, most existing methods assume simultaneous access to all models, which is often impractical in real-world scenarios where models are received sequentially. While some studies have investigated continual model merging (CMM)--which involves sequentially merging multiple models--the challenge of balancing prior knowledge (stability) and incorporating new tasks (plasticity) remains unresolved. This paper, for the first time, formally defines the stability and plasticity of CMM from the perspective of orthogonal projection. Subsequently, we analyze the relationships among the spaces spanned by task data, historical gradients, and accumulated gradients. Building on this, we propose a data-free \textbf{D}ual \textbf{O}rthogonal \textbf{P}rojection (DOP) method, which eliminates data dependence and mitigates interference between the merged model and models for old and new tasks by projecting their parameter differences onto their respective approximate data spaces. Finally, to solve potential conflicts between stability and plasticity, we reformulate DOP as a multi-objective optimization problem and employ a multi-gradient descent algorithm to obtain a Pareto-optimal solution. Extensive experiments across multiple architectures and task configurations validate that our approach significantly outperforms state-of-the-art CMM methods.
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https://openreview.net/forum?id=zD5cUX67b9
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Main
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Poster
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zD5cUX67b9
|
Robust Egocentric Referring Video Object Segmentation via Dual-Modal Causal Intervention
|
[
"Haijing Liu",
"Zhiyuan Song",
"Hefeng Wu",
"Tao Pu",
"Keze Wang",
"Liang Lin"
] |
Egocentric Referring Video Object Segmentation (Ego-RVOS) aims to segment the specific object actively involved in a human action, as described by a language query, within first-person videos. This task is critical for understanding egocentric human behavior. However, achieving such segmentation robustly is challenging due to ambiguities inherent in egocentric videos and biases present in training data. Consequently, existing methods often struggle, learning spurious correlations from skewed object-action pairings in datasets and fundamental visual confounding factors of the egocentric perspective, such as rapid motion and frequent occlusions. To address these limitations, we introduce Causal Ego-REferring Segmentation (CERES), a plug-in causal framework that adapts strong, pre-trained RVOS backbones to the egocentric domain. CERES implements dual-modal causal intervention: applying backdoor adjustment principles to counteract language representation biases learned from dataset statistics, and leveraging front-door adjustment concepts to address visual confounding by intelligently integrating semantic visual features with geometric depth information guided by causal principles, creating representations more robust to egocentric distortions. Extensive experiments demonstrate that CERES achieves state-of-the-art performance on Ego-RVOS benchmarks, highlighting the potential of applying causal reasoning to build more reliable models for broader egocentric video understanding.
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https://openreview.net/forum?id=z9xyREqxzq
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Main
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Poster
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z9xyREqxzq
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ARM: Adaptive Reasoning Model
|
[
"Siye Wu",
"Jian Xie",
"Yikai Zhang",
"Aili Chen",
"Kai Zhang",
"Yu Su",
"Yanghua Xiao"
] |
While large reasoning models demonstrate strong performance on complex tasks, they lack the ability to adjust reasoning token usage based on task difficulty. This often leads to the "overthinking" problem—excessive and unnecessary reasoning—which, although potentially mitigated by human intervention to control the token budget, still fundamentally contradicts the goal of achieving fully autonomous AI. In this work, we propose Adaptive Reasoning Model (ARM), a reasoning model capable of adaptively selecting appropriate reasoning formats based on the task at hand. These formats include three efficient ones—Direct Answer, Short CoT, and Code—as well as a more elaborate format, Long CoT. To train ARM, we introduce Ada-GRPO, an adaptation of Group Relative Policy Optimization (GRPO), which addresses the format collapse issue in traditional GRPO. Ada-GRPO enables ARM to achieve high token efficiency, reducing tokens by an average of $\sim$30%, and up to $\sim$70%, while maintaining performance comparable to the model that relies solely on Long CoT. Furthermore, not only does it improve inference efficiency through reduced token generation, but it also brings a $\sim$2$\times$ speedup in training. In addition to the default Adaptive Mode, ARM supports two additional reasoning modes: 1) Instruction-Guided Mode, which allows users to explicitly specify the reasoning format via special tokens—ideal when the appropriate format is known for a batch of tasks. 2) Consensus-Guided Mode, which aggregates the outputs of the three efficient formats and resorts to Long CoT in case of disagreement, prioritizing performance with higher token usage. All the resources will be released.
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https://openreview.net/forum?id=z9oeQrcNh9
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Main
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Spotlight
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z9oeQrcNh9
|
Spatially-aware Weights Tokenization for NeRF-Language Models
|
[
"Andrea Amaduzzi",
"Pierluigi Zama Ramirez",
"Giuseppe Lisanti",
"Samuele Salti",
"Luigi Di Stefano"
] |
Neural Radiance Fields (NeRFs) are neural networks -- typically multilayer perceptrons (MLPs) -- that represent the geometry and appearance of objects, with applications in vision, graphics, and robotics. Recent works propose understanding NeRFs with natural language using Multimodal Large Language Models (MLLMs) that directly process the weights of a NeRF's MLP. However, these approaches rely on a global representation of the input object, making them unsuitable for spatial reasoning and fine-grained understanding. In contrast, we propose **weights2space**, a self-supervised framework featuring a novel meta-encoder that can compute a sequence of spatial tokens directly from the weights of a NeRF. Leveraging this representation, we build **Spatial LLaNA**, a novel MLLM for NeRFs, capable of understanding details and spatial relationships in objects represented as NeRFs. We evaluate Spatial LLaNA on NeRF captioning and NeRF Q&A tasks, using both existing benchmarks and our novel **Spatial ObjaNeRF** dataset consisting of $100$ manually-curated language annotations for NeRFs. This dataset features 3D models and descriptions that challenge the spatial reasoning capability of MLLMs. Spatial LLaNA outperforms existing approaches across all tasks.
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https://openreview.net/forum?id=z9MxyboJ7R
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Main
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Poster
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z9MxyboJ7R
|
A compressive-expressive communication framework for compositional representations
|
[
"Rafael Elberg",
"Felipe del Rio",
"Mircea Petrache",
"Denis Parra"
] |
Compositionality in knowledge and language—the ability to represent complex concepts as a combination of simpler ones—is a hallmark of human cognition and communication. Despite recent advances, deep neural networks still struggle to acquire this property reliably. Neural models for emergent communication look to endow artificial agents with compositional language by simulating the pressures that form human language. In this work, we introduce CELEBI (Compressive-Expressive Language Emergence through a discrete Bottleneck and Iterated learning), a novel self-supervised framework for inducing compositional representations through a reconstruction-based communication game between a sender and a receiver. Building on theories of language emergence and the iterated learning framework, we integrate three mechanisms that jointly promote compressibility, expressivity, and efficiency in the emergent language. First, Progressive Decoding incentivizes intermediate reasoning by requiring the receiver to produce partial reconstructions after each symbol. Second, Final-State Imitation trains successive generations of agents to imitate reconstructions rather than messages, enforcing a tighter communication bottleneck. Third, Pairwise Distance Maximization regularizes message diversity by encouraging high distances between messages, with formal links to entropy maximization. Our method significantly improves both the efficiency and compositionality of the learned messages on the Shapes3D and MPI3D datasets, surpassing prior discrete communication frameworks in both reconstruction accuracy and topographic similarity. This work provides new theoretical and empirical evidence for the emergence of structured, generalizable communication protocols from simplicity-based inductive biases.
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https://openreview.net/forum?id=z6mwI6VcHA
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Main
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Poster
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z6mwI6VcHA
|
Faithful Group Shapley Value
|
[
"Kiljae Lee",
"Ziqi Liu",
"Weijing Tang",
"Yuan Zhang"
] |
Data Shapley is an important tool for data valuation, which quantifies the contribution of individual data points to machine learning models.
In practice, group-level data valuation is desirable when data providers contribute data in batch. However, we identify that existing group-level extensions of Data Shapley are vulnerable to \emph{shell company attacks}, where strategic group splitting can unfairly inflate valuations. We propose Faithful Group Shapley Value (FGSV) that uniquely defends against such attacks. Building on original mathematical insights, we develop a provably fast and accurate approximation algorithm for computing FGSV. Empirical experiments demonstrate that our algorithm significantly outperforms state-of-the-art methods in computational efficiency and approximation accuracy, while ensuring faithful group-level valuation.
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https://openreview.net/forum?id=z6d5MRMDNf
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Main
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Poster
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z6d5MRMDNf
|
Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games
|
[
"Runyu Lu",
"Peng Zhang",
"Ruochuan Shi",
"Yuanheng Zhu",
"Dongbin Zhao",
"Yang Liu",
"Dong Wang",
"Cesare Alippi"
] |
Equilibrium learning in adversarial games is an important topic widely examined in the fields of game theory and reinforcement learning (RL). Pursuit-evasion game (PEG), as an important class of real-world games from the fields of robotics and security, requires exponential time to be accurately solved. When the underlying graph structure varies, even the state-of-the-art RL methods require recomputation or at least fine-tuning, which can be time-consuming and impair real-time applicability. This paper proposes an Equilibrium Policy Generalization (EPG) framework to effectively learn a generalized policy with robust cross-graph zero-shot performance. In the context of PEGs, our framework is generally applicable to both pursuer and evader sides in both no-exit and multi-exit scenarios. These two generalizability properties, to our knowledge, are the first to appear in this domain. The core idea of the EPG framework is to train an RL policy across different graph structures against the equilibrium policy for each single graph. To construct an equilibrium oracle for single-graph policies, we present a dynamic programming (DP) algorithm that provably generates pure-strategy Nash equilibrium with near-optimal time complexity. To guarantee scalability with respect to pursuer number, we further extend DP and RL by designing a grouping mechanism and a sequence model for joint policy decomposition, respectively. Experimental results show that, using equilibrium guidance and a distance feature proposed for cross-graph PEG training, the EPG framework guarantees desirable zero-shot performance in various unseen real-world graphs. Besides, when trained under an equilibrium heuristic proposed for the graphs with exits, our generalized pursuer policy can even match the performance of the fine-tuned policies from the state-of-the-art PEG methods.
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https://openreview.net/forum?id=z67on2D0j1
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Main
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Poster
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z67on2D0j1
|
NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables
|
[
"Lanrui Wang",
"Mingyu Zheng",
"Hongyin Tang",
"Zheng Lin",
"Yanan Cao",
"Jingang Wang",
"Xunliang Cai",
"Weiping Wang"
] |
Processing structured tabular data, particularly large and lengthy tables, constitutes a fundamental yet challenging task for large language models (LLMs). However, existing long-context benchmarks like Needle-in-a-Haystack primarily focus on unstructured text, neglecting the challenge of diverse structured tables. Meanwhile, previous tabular benchmarks mainly consider downstream tasks that require high-level reasoning abilities, and overlook models' underlying fine-grained perception of individual table cells, which is crucial for practical and robust LLM-based table applications. To address this gap, we introduce \textsc{NeedleInATable} (NIAT), a new long-context tabular benchmark that treats each table cell as a ``needle'' and requires models to extract the target cell based on cell locations or lookup questions. Our comprehensive evaluation of various LLMs and multimodal LLMs reveals a substantial performance gap between popular downstream tabular tasks and the simpler NIAT task, suggesting that they may rely on dataset-specific correlations or shortcuts to obtain better benchmark results but lack truly robust long-context understanding towards structured tables. Furthermore, we demonstrate that using synthesized NIAT training data can effectively improve performance on both NIAT task and downstream tabular tasks, which validates the importance of NIAT capability for LLMs' genuine table understanding ability. Our data, code and models will be released to facilitate future research.
|
https://openreview.net/forum?id=z5vZDI2r6J
|
Main
|
Poster
|
z5vZDI2r6J
|
From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review
|
[
"Yaohui Zhang",
"Haijing ZHANG",
"Wenlong Ji",
"Tianyu Hua",
"Nick Haber",
"Hancheng Cao",
"Weixin Liang"
] |
The advent of large language models (LLMs) offers unprecedented opportunities to reimagine peer review beyond the constraints of traditional workflows.
Despite these opportunities, prior efforts have largely focused on replicating traditional review workflows with LLMs serving as direct substitutes for human reviewers, while limited attention has been given to exploring new paradigms that fundamentally rethink how LLMs can participate in the academic review process.
In this paper, we introduce and explore a novel mechanism that employs LLM agents to perform pairwise comparisons among manuscripts instead of individual scoring. By aggregating outcomes from substantial pairwise evaluations, this approach enables a more accurate and robust measure of relative manuscript quality.
Our experiments demonstrate that this comparative approach significantly outperforms traditional rating-based methods in identifying high-impact papers. However, our analysis also reveals emergent biases in the selection process, notably a reduced novelty in research topics and an increased institutional imbalance. These findings highlight both the transformative potential of rethinking peer review with LLMs and critical challenges that future systems must address to ensure equity and diversity.
|
https://openreview.net/forum?id=z5KTxW5sJd
|
Main
|
Poster
|
z5KTxW5sJd
|
Composing Linear Layers from Irreducibles
|
[
"Travis Pence",
"Daisuke Yamada",
"Vikas Singh"
] |
Contemporary large models often exhibit behaviors suggesting the presence of low-level primitives that compose into modules with richer functionality, but these fundamental building blocks remain poorly understood.
We investigate this compositional structure in linear layers by asking: \textit{can we identify/synthesize linear transformations from a minimal set of geometric primitives?}
Using Clifford algebra, we show that linear layers can be expressed as compositions of bivectors---geometric objects encoding oriented planes---and introduce a differentiable algorithm that decomposes them into products of rotors.
This construction uses only $\mathcal{O}(\log^2 d)$ parameters, versus $\mathcal{O}(d^2)$ required by dense matrices. Applied to the key, query, and value projections in LLM attention layers, our rotor-based layers match the performance of strong baselines such as block-Hadamard and low-rank approximations. Our findings provide an algebraic perspective on how these geometric primitives can compose into higher-level functions within deep models.
|
https://openreview.net/forum?id=z5FGi0vyCr
|
Main
|
Poster
|
z5FGi0vyCr
|
LogicTree: Improving Complex Reasoning of LLMs via Instantiated Multi-step Synthetic Logical Data
|
[
"Zehao Wang",
"Lin Yang",
"Jie Wang",
"Kehan Wang",
"Hanzhu Chen",
"Bin Wang",
"Jianye HAO",
"Defu Lian",
"Bin Li",
"Enhong Chen"
] |
Despite their remarkable performance on various tasks, Large Language Models (LLMs) still struggle with logical reasoning, particularly in complex and multi-step reasoning processes.
Among various efforts to enhance LLMs' reasoning capabilities, synthesizing large-scale, high-quality logical reasoning datasets has emerged as a promising direction.
However, existing methods often rely on predefined templates for logical reasoning data generation, limiting their adaptability to real-world scenarios.
To address the limitation, we propose **LogicTree**, a novel framework for efficiently synthesizing multi-step logical reasoning dataset that excels in both complexity and instantiation.
By iteratively searching for applicable logic rules based on structural pattern matching to perform backward deduction, **LogicTree** constructs multi-step logic trees that capture complex reasoning patterns.
Furthermore, we employ a two-stage LLM-based approach to instantiate various real-world scenarios for each logic tree, generating consistent real-world reasoning processes that carry contextual significance. This helps LLMs develop generalizable logical reasoning abilities across diverse scenarios rather than merely memorizing templates.
Experiments on multiple benchmarks demonstrate that our approach achieves an average improvement of 9.4\% in accuracy on complex logical reasoning tasks.
|
https://openreview.net/forum?id=z4AMrCOetn
|
Main
|
Spotlight
|
z4AMrCOetn
|
GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals
|
[
"Jiachen Lu",
"Hailan Shanbhag",
"Haitham Al Hassanieh"
] |
GeRaF is the first method to use neural implicit learning for near-range 3D geometry reconstruction from radio frequency (RF) signals. Unlike RGB or LiDAR-based methods, RF sensing can see through occlusion but suffers from low resolution and noise due to its lens-less imaging nature. While lenses in RGB imaging constrain sampling to 1D rays, RF signals propagate through the entire space, introducing significant noise and leading to cubic complexity in volumetric rendering. Moreover, RF signals interact with surfaces via specular reflections requiring fundamentally different modeling. To address these challenges, GeRaF (1) introduces filter-based rendering to suppress irrelevant signals, (2) implements a physics-based RF volumetric rendering pipeline, and (3) proposes a novel lens-less sampling and lens-less alpha blending strategy that makes full-space sampling feasible during training. By learning signed distance functions, reflectiveness, and signal power through MLPs and trainable parameters, GeRaF takes the first step towards reconstructing millimeter-level geometry from RF signals in real-world settings.
|
https://openreview.net/forum?id=z3PMVmzoya
|
Main
|
Spotlight
|
z3PMVmzoya
|
Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding
|
[
"Sunwoo Kim",
"Hyunjin Hwang",
"Kijung Shin"
] |
The performance of a deep learning model on a specific task and dataset depends heavily on its neural architecture, motivating considerable efforts to rapidly and accurately identify architectures suited to the target task and dataset. To achieve this, researchers use machine learning models—typically neural architecture encoders—to predict the performance of a neural architecture. Many state-of-the-art encoders aim to capture information flow within a neural architecture, which reflects how information moves through the forward pass and backpropagation, via a specialized model structure. However, due to their complicated structures, these flow-based encoders are significantly slower to process neural architectures compared to simpler encoders, presenting a notable practical challenge. To address this, we propose FGP, a novel pre-training method for neural architecture encoding that trains an encoder to capture the information flow without requiring specialized model structures. FGP trains an encoder to reconstruct a flow surrogate, our proposed representation of the neural architecture's information flow. Our experiments show that FGP boosts encoder performance by up to 106\% in Precision@1\%, compared to the same encoder trained solely with supervised learning.
|
https://openreview.net/forum?id=z2vJpjopJk
|
Main
|
Poster
|
z2vJpjopJk
|
SGCD: Stain-Guided CycleDiffusion for Unsupervised Domain Adaptation of Histopathology Image Classification
|
[
"Hsi-Ling Chen",
"Chun-Shien Lu",
"Pau-Choo Chung"
] |
The effectiveness of domain translation in addressing image-based problems of Unsupervised Domain Adaptation (UDA) depends on the quality of the translated images and the preservation of crucial discriminative features. However, achieving high-quality and stable translations typically requires paired data, which poses a challenge in scenarios with limited annotations in the target domain. To address this issue, this paper proposes a novel method termed Stain-Guided Cycle Diffusion (SGCD), employing a dual diffusion model with bidirectional generative constraints to synthesize highly realistic data for downstream task fine-tuning. The bidirectional generative constraints ensure that the translated images retain the features critical to the downstream model in properly controlling the generation process. Additionally, a stain-guided consistency loss is introduced to enhance the denoising capability of the dual diffusion model, thereby improving the quality of images translated between different domains using latents from one domain and a diffusion model trained on another. Experiments conducted on four public datasets demonstrate that SGCD can effectively enhance the performance of downstream task models on the target domain.
|
https://openreview.net/forum?id=z2SGaPIhLT
|
Main
|
Spotlight
|
z2SGaPIhLT
|
COALA: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation
|
[
"Uliana Parkina",
"Maxim Rakhuba"
] |
Recent studies suggest that context-aware low-rank approximation is a useful tool for compression and fine-tuning of modern large-scale neural networks.
In this type of approximation, a norm is weighted by a matrix of input activations, significantly improving metrics over the unweighted case.
Nevertheless, existing methods for neural networks suffer from numerical instabilities due to their reliance on classical formulas involving explicit Gram matrix computation and their subsequent inversion.
We demonstrate that this can degrade the approximation quality or cause numerically singular matrices.
To address these limitations, we propose a novel _inversion-free regularized framework_ that is based entirely on stable decompositions and overcomes the numerical pitfalls of prior art.
Our method can handle all possible challenging scenarios: (1) when calibration matrices exceed GPU memory capacity, (2) when input activation matrices are nearly singular, and even (3) when insufficient data prevents unique approximation.
For the latter, we prove that our solution converges to a desired approximation and derive explicit error bounds.
|
https://openreview.net/forum?id=z1wIUZtBmK
|
Main
|
Poster
|
z1wIUZtBmK
|
Sim-LLM: Optimizing LLM Inference at the Edge through Inter-Task KV Reuse
|
[
"Ruikun Luo",
"Changwei Gu",
"Qiang He",
"Feifei Chen",
"Song Wu",
"Hai Jin",
"Yun Yang"
] |
KV cache technology, by storing key-value pairs, helps reduce the computational overhead incurred by *large language models* (LLMs). It facilitates their deployment on resource-constrained edge computing nodes like edge servers. However, as the complexity and size of tasks increase, KV cache usage leads to substantial GPU memory consumption. Existing research has focused on mitigating KV cache memory usage through sequence length reduction, task-specific compression, and dynamic eviction policies. However, these methods are computationally expensive for resource-constrained edge computing nodes. To tackle this challenge, this paper presents Sim-LLM, a novel inference optimization mechanism that leverages task similarity to reduce KV cache memory consumption for LLMs. By caching KVs from processed tasks and reusing them for subsequent similar tasks during inference, Sim-LLM significantly reduces memory consumption while boosting system throughput and increasing maximum batch size, all with minimal accuracy degradation. Evaluated on both A40 and A100 GPUs, Sim-LLM achieves a system throughput improvement of up to 39.40\% and a memory reduction of up to 34.65%, compared to state-of-the-art approaches. Our source code is available at https://github.com/CGCL-codes/SimLLM.
|
https://openreview.net/forum?id=z1Cvcovlms
|
Main
|
Poster
|
z1Cvcovlms
|
Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents
|
[
"Han Lin",
"Jaemin Cho",
"Amir Zadeh",
"Chuan Li",
"Mohit Bansal"
] |
There is growing interest in integrating high-fidelity visual synthesis capabilities into large language models (LLMs) without compromising their strong reasoning capabilities. Existing methods that directly train LLMs or bridge LLMs and diffusion models usually suffer from costly training since the backbone LLMs have not seen image representations during pretraining. We present Bifrost-1, a unified framework that bridges pretrained multimodal LLMs (MLLMs) and diffusion models using patch-level CLIP image embeddings as latent variables, which are natively aligned with the MLLM's CLIP visual encoder. These patch-level image embeddings are integrated into the diffusion model with a lightweight adaptation of its ControlNet. To retain the original multimodal reasoning capabilities of MLLMs, we equip the MLLM with a visual generation branch initialized from the original MLLM parameters when predicting the patch-level image embeddings. By seamlessly integrating pretrained MLLMs and diffusion models with patch-level CLIP latents, our framework enables high-fidelity controllable image generation with significant training efficiency. Our experiments demonstrate that Bifrost-1 achieves comparable or better performance than previous methods in terms of visual fidelity and multimodal understanding, with substantially lower compute during training. We also provide comprehensive ablation studies showing the effectiveness of our design choices. Project page: https://bifrost-1.github.io.
|
https://openreview.net/forum?id=z0WhTwZscg
|
Main
|
Poster
|
z0WhTwZscg
|
VETA-DiT: Variance-Equalized and Temporally Adaptive Quantization for Efficient 4-bit Diffusion Transformers
|
[
"QinkaiXu",
"yijin liu",
"YangChen",
"Lin Yang",
"Li Li",
"Yuxiang Fu"
] |
Diffusion Transformers (DiTs) have recently demonstrated remarkable performance in visual generation tasks, surpassing traditional U-Net-based diffusion models by significantly improving image and video generation quality and scalability. However, the large model size and iterative denoising process introduce substantial computational and memory overhead, limiting their deployment in real-world applications. Post-training quantization (PTQ) is a promising solution that compresses models and accelerates inference by converting weights and activations to low-bit representations. Despite its potential, PTQ faces significant challenges when applied to DiTs, often resulting in severe degradation of generative quality. To address these issues, we propose VETA-DiT (**V**ariance-**E**qualized and **T**emporal **A**daptation for **Di**ffusion **T**ransformers), a dedicated quantization framework for DiTs. Our method first analyzes the sources of quantization error from the perspective of inter-channel variance and introduces a Karhunen–Loève Transform enhanced alignment to equalize variance across channels, facilitating effective quantization under low bit-widths. Furthermore, to handle the temporal variation of activation distributions inherent in the iterative denoising steps of DiTs, we design an incoherence-aware adaptive method that identifies and properly calibrates timesteps with high quantization difficulty. We validate VETA-DiT on extensive image and video generation tasks, preserving acceptable visual quality under the more aggressive W4A4 configuration. Specifically, VETA-DiT reduces FID by 33.65 on the DiT-XL/2 model and by 45.76 on the PixArt-$\Sigma$ model compared to the baseline under W4A4, demonstrating its strong quantization capability and generative performance. Code is available at: https://github.com/xululi0223/VETA-DiT.
|
https://openreview.net/forum?id=z0BgfL1FRV
|
Main
|
Poster
|
z0BgfL1FRV
|
Optimistic Query Routing in Clustering-based Approximate Maximum Inner Product Search
|
[
"Sebastian Bruch",
"Aditya Krishnan",
"Franco Maria Nardini"
] |
Clustering-based nearest neighbor search algorithms partition points into shards to form an index, and search only a subset of shards to process a query. Even though search efficacy is heavily influenced by the algorithm that identifies the shards to probe, it has received little attention in the literature. We study routing in clustering-based maximum inner product search, which includes cosine similarity search. We unpack existing routers and notice the surprising role of optimism. We then take a page from the sequential decision making literature and formalize that insight following the principle of ``optimism in the face of uncertainty.'' In particular, we present a framework that incorporates the moments of the distribution of inner products within each shard to estimate the maximum inner product. We then develop a practical instance of our algorithm that uses only the first two moments to reach the same accuracy as state-of-the-art routers by probing up to $50\%$ fewer points on benchmark datasets without compromising efficiency. Our algorithm is also space-efficient: we design a sketch of the second moment whose size is independent of the number of points and requires $\mathcal{O}(1)$ vectors per shard.
|
https://openreview.net/forum?id=yzvpEHNL70
|
Main
|
Poster
|
yzvpEHNL70
|
Variational Task Vector Composition
|
[
"Boyuan Zhang",
"Yingjun Du",
"Xiantong Zhen",
"Ling Shao"
] |
Task vectors capture how a model changes during fine-tuning by recording the difference between pre-trained and task-specific weights. The composition of task vectors, a key operator in task arithmetic, enables models to integrate knowledge from multiple tasks without incurring significant additional inference costs. In this paper, we propose variational task vector composition (VTVC), where composition coefficients are taken as latent variables and estimated in a Bayesian inference framework. Unlike previous methods that operate at the task level, our framework focuses on sample-specific composition. Motivated by the observation of structural redundancy in task vectors, we introduce a Spike-and-Slab prior that promotes sparsity and aims to preserve the most informative components. To further address the high variance and sampling inefficiency in sparse, high-dimensional spaces, we develop a gated sampling mechanism that constructs a controllable posterior by filtering the composition coefficients based on both uncertainty and importance. This yields a more stable and interpretable variational framework by deterministically selecting reliable task components, reducing sampling variance while improving transparency and generalization. Experimental results demonstrate that our method achieves state-of-the-art average performance across a diverse range of benchmarks, including image classification and natural language understanding. These findings highlight the practical value of our approach, offering a new, efficient, and effective framework for task vector composition.
|
https://openreview.net/forum?id=yzv6kysYbw
|
Main
|
Poster
|
yzv6kysYbw
|
Semantic Representation Attack against Aligned Large Language Models
|
[
"Jiawei Lian",
"Jianhong Pan",
"Lefan Wang",
"Yi Wang",
"Shaohui Mei",
"Lap-Pui Chau"
] |
Large Language Models (LLMs) increasingly employ alignment techniques to prevent harmful outputs. Despite these safeguards, attackers can circumvent them by crafting prompts that induce LLMs to generate harmful content. Current methods typically target exact affirmative responses, suffering from limited convergence, unnatural prompts, and high computational costs. We introduce semantic representation attacks, a novel paradigm that fundamentally reconceptualizes adversarial objectives against aligned LLMs. Rather than targeting exact textual patterns, our approach exploits the semantic representation space that can elicit diverse responses that share equivalent harmful meanings. This innovation resolves the inherent trade-off between attack effectiveness and prompt naturalness that plagues existing methods. Our Semantic Representation Heuristic Search (SRHS) algorithm efficiently generates semantically coherent adversarial prompts by maintaining interpretability during incremental search. We establish rigorous theoretical guarantees for semantic convergence and demonstrate that SRHS achieves unprecedented attack success rates (89.4% averaged across 18 LLMs, including 100% on 11 models) while significantly reducing computational requirements. Extensive experiments show that our method consistently outperforms existing approaches.
|
https://openreview.net/forum?id=yzl5tL0Z2M
|
Main
|
Poster
|
yzl5tL0Z2M
|
Visual Instruction Bottleneck Tuning
|
[
"Changdae Oh",
"Jiatong Li",
"Shawn Im",
"Sharon Li"
] |
Despite widespread adoption, multimodal large language models (MLLMs) suffer performance degradation when encountering unfamiliar queries under distribution shifts. Existing methods to improve MLLM generalization typically require either more instruction data or larger advanced model architectures, both of which incur non-trivial human labor or computational costs. In this work, we take an alternative approach to enhance the generalization and robustness of MLLMs under distribution shifts, from a representation learning perspective. Inspired by information bottleneck (IB) principle, we derive a variational lower bound of the IB for MLLMs and devise a practical implementation, Visual Instruction Bottleneck Tuning (Vittle). We then provide a theoretical justification of Vittle by revealing its connection to an information-theoretic robustness metric of MLLM. Empirical validation of multiple MLLMs on open-ended and closed-form question answering and object hallucination detection tasks over 45 datasets, including 30 shift scenarios, demonstrates that Vittle consistently improves the MLLM's robustness under shifts by pursuing the learning of a minimal sufficient representation.
|
https://openreview.net/forum?id=yzHiEmLSk8
|
Main
|
Poster
|
yzHiEmLSk8
|
Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing
|
[
"Junfei Wu",
"Jian Guan",
"Kaituo Feng",
"Qiang Liu",
"Shu Wu",
"Liang Wang",
"Wei Wu",
"Tieniu Tan"
] |
As textual reasoning with large language models (LLMs) has advanced significant, there has been growing interest in enhancing the multimodal reasoning capabilities of large vision-language models (LVLMs). However, existing methods primarily approach multimodal reasoning in a straightforward, text-centric manner, where both reasoning and answer derivation are conducted purely through text, with the only difference being the presence of multimodal input. As a result, these methods often encounter fundamental limitations in spatial reasoning tasks that demand precise geometric understanding and continuous spatial tracking\textemdash capabilities that humans achieve through mental visualization and manipulation. To address the limitations, we propose drawing to reason in space, a novel paradigm that enables LVLMs to reason through elementary drawing operations in the visual space. By equipping models with basic drawing operations including annotating bounding boxes and drawing auxiliary lines, we empower them to express and analyze spatial relationships through direct visual manipulation, meanwhile avoiding the performance ceiling imposed by specialized perception tools in previous tool-integrated reasoning approaches. To cultivate this capability, we develop a three-stage training framework: cold-start training with synthetic data to establish basic drawing abilities, reflective rejection sampling to enhance self-reflection behaviors, and reinforcement learning to directly optimize for target rewards. Extensive experiments demonstrate that our model, named \textsc{Spark}, consistently outperforms existing methods across diverse spatial reasoning benchmarks involving maze navigation, static spatial reasoning, video-based reasoning and multi-view-based reasoning tasks, with an average improvement of 11.5\%. Ablation studies reveal the critical role of each training stage, with reflective rejection sampling particularly enhancing the model's self-correction capabilities and reasoning potential.
|
https://openreview.net/forum?id=yyWeSAsOhs
|
Main
|
Poster
|
yyWeSAsOhs
|
Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling
|
[
"Dehao Zhang",
"Malu Zhang",
"Shuai Wang",
"Jingya Wang",
"Wenjie Wei",
"Zeyu Ma",
"Guoqing Wang",
"Yang Yang",
"Haizhou Li"
] |
The explosive growth in sequence length has intensified the demand for effective and efficient long sequence modeling. Benefiting from intrinsic oscillatory membrane dynamics, Resonate-and-Fire (RF) neurons can efficiently extract frequency components from input signals and encode them into spatiotemporal spike trains, making them well-suited for long sequence modeling. However, RF neurons exhibit limited effective memory capacity and a trade-off between energy efficiency and training speed on complex temporal tasks. Inspired by the dendritic structure of biological neurons, we propose a Dendritic Resonate-and-Fire (D-RF) model, which explicitly incorporates a multi-dendritic and soma architecture. Each dendritic branch encodes specific frequency bands by utilizing the intrinsic oscillatory dynamics of RF neurons, thereby collectively achieving comprehensive frequency representation. Furthermore, we introduce an adaptive threshold mechanism into the soma structure. his mechanism adjusts the firing threshold according to historical spiking activity, thereby reducing redundant spikes while maintaining training efficiency in long-sequence tasks. Extensive experiments demonstrate that our method maintains competitive accuracy while substantially ensuring sparse spikes without compromising computational efficiency during training. These results underscore its potential as an effective and efficient solution for long sequence modeling on edge platforms.
|
https://openreview.net/forum?id=ywzGKDStrm
|
Main
|
Poster
|
ywzGKDStrm
|
Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization
|
[
"Arie Soeteman",
"Balder ten Cate"
] |
We propose and study Hierarchical Ego Graph Neural Networks (HE-GNNs), an expressive extension of graph neural networks (GNNs) with hierarchical node individualization, inspired by the Individualization-Refinement paradigm for isomorphism testing. HE-GNNs generalize subgraph-GNNs and form a hierarchy of increasingly expressive models that, in the limit, distinguish graphs up to isomorphism. We show that, over graphs of bounded degree, the separating power of HE-GNN node classifiers equals that of graded hybrid logic. This characterization enables us to relate the separating power of HE-GNNs to that of higher-order GNNs, GNNs enriched with local homomorphism count features, and color refinement algorithms based on Individualization-Refinement. Our experimental results confirm the practical feasibility of HE-GNNs and show benefits in comparison with traditional GNN architectures, both with and without local homomorphism count features.
|
https://openreview.net/forum?id=yvGnOqy0Zf
|
Main
|
Poster
|
yvGnOqy0Zf
|
End of preview. Expand
in Data Studio
NeurIPS 2025 Papers Dataset
This dataset contains all accepted papers from NeurIPS 2025, scraped from OpenReview.
Dataset Statistics
Overview
- Total Papers: 5772
- Unique Paper IDs: 5772
- ✅ No duplicate IDs
Track Distribution
- Main Track: 5,275 papers (91.4%)
- Datasets and Benchmarks Track: 497 papers (8.6%)
Award Distribution
- Poster: 4,949 papers (85.7%)
- Oral: 84 papers (1.5%)
- Spotlight: 739 papers (12.8%)
Track × Award Combinations
- Main - Poster: 4,515 papers (78.2%)
- Main - Spotlight: 683 papers (11.8%)
- Datasets and Benchmarks - Poster: 434 papers (7.5%)
- Main - Oral: 77 papers (1.3%)
- Datasets and Benchmarks - Spotlight: 56 papers (1.0%)
- Datasets and Benchmarks - Oral: 7 papers (0.1%)
Author Statistics
- Total Authors (across all papers): 33,878 if stats else 'N/A'
- Unique Authors: 23,704 if stats else 'N/A'
- Average Authors per Paper: 5.87 if stats else 'N/A'
- Authors per Paper Range: Min: 1 if stats else 'N/A', Max: 95 if stats else 'N/A', Avg: 5.87 if stats else 'N/A'
- Papers with Authors: 5,772 (100%) if stats else 'N/A'
Abstract Statistics
- Papers with Abstracts: 5,772 (100%) if stats else 'N/A'
- Average Abstract Length: 1376 characters if stats else 'N/A'
- Total Abstract Text: 7,939,587 characters if stats else 'N/A'
Dataset Structure
Each paper contains the following fields:
paper: Title of the paperauthors: List of author namesabstract: Abstract textlink: Direct link to OpenReviewtrack: Track name (Main or Datasets and Benchmarks)award: Award type (Oral, Spotlight, or Poster)paper_id: Unique OpenReview paper ID
Usage
from datasets import load_dataset
dataset = load_dataset("neurips-2025-papers", split="train")
print(dataset[0])
Citation
If you use this dataset, please cite the original NeurIPS 2025 conference and OpenReview.
License
This dataset is provided for research purposes. Please refer to OpenReview's terms of service.
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