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QiMeng-SALV: Signal-Aware Learning for Verilog Code Generation
|
[
"Yang Zhang",
"Rui Zhang",
"Jiaming Guo",
"Huang Lei",
"Di Huang",
"Yunpu Zhao",
"Shuyao Cheng",
"Pengwei Jin",
"Chongxiao Li",
"Zidong Du",
"Xing Hu",
"Qi Guo",
"Yunji Chen"
] |
The remarkable progress of Large Language Models (LLMs) presents promising opportunities for Verilog code generation which is significantly important for automated circuit design. The lacking of meaningful functional rewards hinders the preference optimization based on Reinforcement Learning (RL) for producing functionally correct Verilog code. In this paper, we propose Signal-Aware Learning for Verilog code generation (QiMeng-SALV) by leveraging code segments of functionally correct output signal to optimize RL training. Considering Verilog code specifies the structural interconnection of hardware gates and wires so that different output signals are independent, the key insight of QiMeng-SALV is to extract verified signal-aware implementations in partially incorrect modules, so as to enhance the extraction of meaningful functional rewards. Roughly, we verify the functional correctness of signals in generated module by comparing with that of reference module in the training data. Then abstract syntax tree (AST) is employed to identify signal-aware code segments which can provide meaningful functional rewards from erroneous modules. Finally, we introduce signal-aware DPO which is optimized on the correct signal-level code segments, thereby preventing noise and interference from incorrect signals.
The proposed QiMeng-SALV underscores the paradigm shift from conventional module-level to fine-grained signal-level optimization in Verilog code generation, addressing the issue of insufficient functional rewards.
Experiments demonstrate that our method achieves state-of-the-art performance on VerilogEval and RTLLM, with a 7B parameter model matching the performance of the DeepSeek v3 671B model and significantly outperforming the leading open-source model CodeV trained on the same dataset.
|
https://openreview.net/forum?id=vaosMuNvOt
|
Main
|
Poster
|
vaosMuNvOt
|
Let Brain Rhythm Shape Machine Intelligence for Connecting Dots on Graphs
|
[
"Jiaqi Ding",
"Tingting Dan",
"Zhixuan Zhou",
"Guorong Wu"
] |
In both neuroscience and artificial intelligence (AI), it is well-established that neural “coupling” gives rise to dynamically distributed systems. These systems exhibit self-organized spatiotemporal patterns of synchronized neural oscillations, enabling the representation of abstract concepts. By capitalizing on the unprecedented amount of human neuroimaging data, we propose that advancing the theoretical understanding of rhythmic coordination in neural circuits can offer powerful design principles for the next generation of machine learning models with improved efficiency and robustness. To this end, we introduce a physics-informed deep learning framework for \underline{B}rain \underline{R}hythm \underline{I}dentification by \underline{K}uramoto and \underline{C}ontrol (coined \modelname{}) to characterize the synchronization of neural oscillations that shapes the dynamics of evolving cognitive states. Recognizing that brain networks are structurally connected yet behaviorally dynamic, we further conceptualize rhythmic neural activity as an artificial dynamical system of coupled oscillators, offering a shared mechanistic bridge to brain-inspired machine intelligence. By treating each node as an oscillator interacting with its neighbors, this approach moves beyond the conventional paradigm of graph heat diffusion and establishes a new regime of representation compression through oscillatory synchronization. Empirical evaluations demonstrate that this synchronization-driven mechanism not only mitigates over-smoothing in deep GNNs but also enhances the model’s capacity for reasoning and solving complex graph-based problems.
|
https://openreview.net/forum?id=vZfqDwF09z
|
Main
|
Poster
|
vZfqDwF09z
|
FSEO: Few-Shot Evolutionary Optimization via Meta-Learning for Expensive Multi-Objective Optimization
|
[
"Xunzhao Yu"
] |
Meta-learning has been demonstrated to be useful to improve the sampling efficiency of Bayesian optimization (BO) and surrogate-assisted evolutionary algorithms (SAEAs) when solving expensive optimization problems (EOPs).
Existing studies mainly focus on either combinations of existing meta-learning modeling methods with optimization algorithms, or the development of meta-learning acquisition functions for specific meta BO. However, the meta-learning models used in the literature are not designed for optimization purpose, and the generalization ability of meta-learning acquisition functions is limited.
In this work, we develop a novel architecture of meta-learning model for optimization purpose and propose a generalized few-shot evolutionary optimization (FSEO) framework to solve EOPs.
We focus on the scenario of expensive multi-objective EOPs (EMOPs) in the context of few-shot optimization as there are few studies on it and its high requirement on surrogate modeling performance.
The surrogates in FSEO framework combines neural network with Gaussian Processes (GPs), their network parameters and some parameters of GPs represent task-independent experience and are meta-learned across related optimization tasks, the remaining GPs parameters are task-specific parameters that represent unique features of the target task.
We demonstrate that our FSEO framework is able to improve the sampling efficiency of existing SAEAs on EMOPs.
|
https://openreview.net/forum?id=vYgkWtq6F1
|
Main
|
Poster
|
vYgkWtq6F1
|
Constrained Feedback Learning for Non-Stationary Multi-Armed Bandits
|
[
"Shaoang Li",
"Jian Li"
] |
Non-stationary multi-armed bandits (nsMAB) enable agents to adapt to changing environments by incorporating mechanisms to detect and respond to shifts in reward distributions, making them well-suited for dynamic settings. However, existing approaches typically assume that reward feedback is available at every round—an assumption that overlooks many real-world scenarios where feedback is limited. In this paper, we take a significant step forward by introducing a new model of *constrained feedback in non-stationary multi-armed bandits* (ConFee-nsMAB), where the availability of reward feedback is restricted. We propose the first prior-free algorithm—that is, one that does not require prior knowledge of the degree of non-stationarity—that achieves near-optimal dynamic regret in this setting. Specifically, our algorithm attains a dynamic regret of $\tilde {\mathcal{O}}({K^{1/3} V_T^{1/3} T }/{ B^{1/3}})$, where $T$ is the number of rounds, $K$ is the number of arms, $B$ is the query budget, and $V_T$ is the variation budget capturing the degree of non-stationarity.
|
https://openreview.net/forum?id=vXGySQIPyL
|
Main
|
Poster
|
vXGySQIPyL
|
Data-Dependent Regret Bounds for Constrained MABs
|
[
"Gianmarco Genalti",
"Francesco Emanuele Stradi",
"Matteo Castiglioni",
"Alberto Marchesi",
"Nicola Gatti"
] |
This paper initiates the study of data-dependent regret bounds in constrained MAB settings. These are bounds that depend on the sequence of losses that characterize the problem instance. Thus, in principle they can be much smaller than classical $\widetilde{\mathcal{O}}(\sqrt{T})$ regret bounds, while being equivalent to them in the worst case. Despite this, data-dependent regret bounds have been completely overlooked in constrained MABs. The goal of this paper is to answer the question: Can data-dependent regret bounds be derived in the presence of constraints? We provide an affirmative answer in constrained MABs with adversarial losses and stochastic constraints. Specifically, our main focus is on the most challenging and natural settings with hard constraints, where the learner must ensure that the constraints are always satisfied with high probability. We design an algorithm with a regret bound consisting of two data-dependent terms. The first one captures the difficulty of satisfying the constraints, while the second one encodes the complexity of learning independently of their presence. We also prove a lower bound showing that these two terms are not artifacts of our specific approach and analysis, but rather the fundamental components that inherently characterize the problem complexity. Finally, in designing our algorithm, we also derive some novel results in the related (and easier) soft constraints settings, which may be of independent interest.
|
https://openreview.net/forum?id=vWeyFCtYxx
|
Main
|
Poster
|
vWeyFCtYxx
|
Scaling Laws for Optimal Data Mixtures
|
[
"Mustafa Shukor",
"Louis Béthune",
"Dan Busbridge",
"David Grangier",
"Enrico Fini",
"Alaaeldin El-Nouby",
"Pierre Ablin"
] |
Large foundation models are typically trained on data from multiple domains, with the data mixture--the proportion of each domain used--playing a critical role in model performance. The standard approach to selecting this mixture relies on trial and error, which becomes impractical for large-scale pretraining. We propose a systematic method to determine the optimal data mixture for any target domain using scaling laws. Our approach accurately predicts the loss of a model of size $N$ trained with $D$ tokens and a specific domain weight vector $h$. We validate the universality of these scaling laws by demonstrating their predictive power in three distinct and large-scale settings: large language model (LLM), native multimodal model (NMM), and large vision models (LVM) pretraining. We further show that these scaling laws can extrapolate to new data mixtures and across scales: their parameters can be accurately estimated using a few small-scale training runs, and used to estimate the performance at larger scales and unseen domain weights. The scaling laws allow to derive the optimal domain weights for any target domain under a given training budget ($N$,$D$), providing a principled alternative to costly trial-and-error methods.
|
https://openreview.net/forum?id=vVU1KTOsju
|
Main
|
Poster
|
vVU1KTOsju
|
DataRater: Meta-Learned Dataset Curation
|
[
"Dan A. Calian",
"Gregory Farquhar",
"Iurii Kemaev",
"Luisa Zintgraf",
"Matteo Hessel",
"Jeremy Shar",
"Junhyuk Oh",
"András György",
"Tom Schaul",
"Jeff Dean",
"Hado van Hasselt",
"David Silver"
] |
The quality of foundation models depends heavily on their training data.
Consequently, great efforts have been put into dataset curation.
Yet most approaches rely on manual tuning of coarse-grained mixtures of large buckets of data, or filtering by hand-crafted heuristics.
An approach that is ultimately more scalable (let alone more satisfying) is to \emph{learn} which data is actually valuable for training.
This type of meta-learning could allow more sophisticated, fine-grained, and effective curation.
Our proposed \emph{DataRater} is an instance of this idea. It estimates the value of training on any particular data point. This is done by meta-learning using `meta-gradients', with the objective of improving training efficiency on held out data.
In extensive experiments across a range of model scales and datasets, we find that using our DataRater to filter data is highly effective, resulting in significantly improved compute efficiency.
|
https://openreview.net/forum?id=vUtQFnlDyv
|
Main
|
Poster
|
vUtQFnlDyv
|
Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning
|
[
"Jiayu Wang",
"Yifei Ming",
"Zixuan Ke",
"Caiming Xiong",
"Shafiq Joty",
"Aws Albarghouthi",
"Frederic Sala"
] |
Reinforcement learning (RL) has become the dominant paradigm for improving the performance of language models on complex reasoning tasks. Despite the substantial empirical gains demonstrated by RL-based training methods like GRPO, a granular understanding of why and how RL enhances performance is still lacking. To bridge this gap, we introduce SPARKLE, a fine-grained analytic framework to dissect the effects of RL across three key dimensions: (1) plan following and execution, (2) knowledge integration, and (3) chain of subproblems. Using this framework, we gain insights beyond mere accuracy. For instance, providing models with explicit human-crafted, step-by-step plans can surprisingly degrade performance on the most challenging benchmarks, yet RL-tuned models exhibit greater robustness, experiencing markedly smaller performance drops than base or SFT models. This suggests that RL may not primarily enhance the execution of external plans but rather empower models to formulate and follow internal strategies better suited to their reasoning processes. Conversely, we observe that RL enhances models' ability to integrate provided knowledge into their reasoning process, yielding consistent gains across diverse tasks. Finally, we study whether difficult problems---those yielding no RL signals and mixed-quality reasoning traces---can still be effectively used for training. We introduce SparkleRL-PSS, a multi-stage RL pipeline that reuses hard problems with partial step scaffolding, guiding exploration effectively without additional data generation. Together, our findings provide a principled foundation for understanding how RL shapes model behavior, offering practical insights for building more adaptive, data-efficient, and interpretable RL pipelines for reasoning tasks. Our code, data, and checkpoints are available at: https://sparkle-reasoning.github.io/.
|
https://openreview.net/forum?id=vTWNVYuvuF
|
Main
|
Poster
|
vTWNVYuvuF
|
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
|
[
"Xinghao Wu",
"Xuefeng Liu",
"Jianwei Niu",
"Guogang Zhu",
"Mingjia Shi",
"Shaojie Tang",
"Jing Yuan"
] |
Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model’s performance across diverse client distributions. Personalized Federated Learning (PFL) addresses this by enabling each client to process an individual model adapted to its local distribution. Many existing methods assume that certain global model parameters are difficult to train effectively in a collaborative manner under heterogeneous data. Consequently, they localize or fine-tune these parameters to obtain personalized models. In this paper, we reveal that both the feature extractor and classifier of the global model are inherently strong, and the primary cause of its suboptimal performance is the mismatch between local features and the global classifier. Although existing methods alleviate this mismatch to some extent and improve performance, we find that they either (1) fail to fully resolve the mismatch while degrading the feature extractor, or (2) address the mismatch only post-training, allowing it to persist during training. This increases inter-client gradient divergence, hinders model aggregation, and ultimately leaves the feature extractor suboptimal for client data. To address this issue, we propose FedPFT, a novel framework that resolves the mismatch during training using personalized prompts. These prompts, along with local features, are processed by a shared self-attention-based transformation module, ensuring alignment with the global classifier. Additionally, this prompt-driven approach offers strong flexibility, enabling task-specific prompts to incorporate additional training objectives (\eg, contrastive learning) to further enhance the feature extractor. Extensive experiments show that FedPFT outperforms state-of-the-art methods by up to 5.07%, with further gains of up to 7.08% when collaborative contrastive learning is incorporated.
|
https://openreview.net/forum?id=vTJFQu5YXz
|
Main
|
Poster
|
vTJFQu5YXz
|
OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
|
[
"Junting Chen",
"Haotian Liang",
"Lingxiao Du",
"Weiyun Wang",
"Mengkang Hu",
"Yao Mu",
"Wenhai Wang",
"Jifeng Dai",
"Ping Luo",
"Wenqi Shao",
"Lin Shao"
] |
The rapid progress of navigation, manipulation, and vision models has made mobile manipulators capable in many specialized tasks.
However, the open-world mobile manipulation (OWMM) task remains a challenge due to the need for generalization to open-ended instructions and environments, as well as the systematic complexity to integrate high-level decision making with low-level robot control based on both global scene understanding and current agent state. To address this complexity, we propose a novel multi-modal agent architecture that maintains multi-view scene frames and agent states for decision-making and controls the robot by function calling.
A second challenge is the hallucination from domain shift. To enhance the agent performance, we further introduce an agentic data synthesis pipeline for the OWMM task to adapt the VLM model to our task domain with instruction fine-tuning. We highlight our fine-tuned OWMM-VLM as the first dedicated foundation model for mobile manipulators with global scene understanding, robot state tracking, and multi-modal action generation in a unified model. Through experiments, we demonstrate that our model achieves SOTA performance compared to other foundation models including GPT-4o and strong zero-shot generalization in real world.
The project page is at https://hhyhrhy.github.io/owmm-agent-project.
|
https://openreview.net/forum?id=vSLzoUoJt6
|
Main
|
Poster
|
vSLzoUoJt6
|
MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement
|
[
"Jaehyun Nam",
"Jinsung Yoon",
"Jiefeng Chen",
"Jinwoo Shin",
"Sercan O Arik",
"Tomas Pfister"
] |
Agents based on large language models (LLMs) for machine learning engineering (MLE) can automatically implement ML models via code generation. However, existing approaches to build such agents often rely heavily on inherent LLM knowledge and employ coarse exploration strategies that modify the entire code structure at once. This limits their ability to select effective task-specific models and perform deep exploration within specific components, such as experimenting extensively with feature engineering options. To overcome these, we propose MLE-STAR, a novel approach to build MLE agents. MLE-STAR first leverages external knowledge by using a search engine to retrieve effective models from the web, forming an initial solution, then iteratively refines it by exploring various strategies targeting specific ML components. This exploration is guided by ablation studies analyzing the impact of individual code blocks. Furthermore, we introduce a novel ensembling method using an effective strategy suggested by MLE-STAR. Our experimental results show that MLE-STAR achieves medals in 64% of the Kaggle competitions on the MLE-bench, significantly outperforming the best alternative.
|
https://openreview.net/forum?id=vS1M06Px6u
|
Main
|
Poster
|
vS1M06Px6u
|
Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models
|
[
"Yulei Qin",
"Gang Li",
"Zongyi Li",
"Zihan Xu",
"Yuchen Shi",
"Zhekai Lin",
"Xiao Cui",
"Ke Li",
"Xing Sun"
] |
Existing large language models (LLMs) face challenges of following complex instructions, especially when multiple constraints are present and organized in paralleling, chaining, and branching structures. One intuitive solution, namely chain-of-thought (CoT), is expected to universally improve capabilities of LLMs. However, we find that the vanilla CoT exerts a negative impact on performance due to its superficial reasoning pattern of simply paraphrasing the instructions. It fails to peel back the compositions of constraints for identifying their relationship across hierarchies of types and dimensions. To this end, we propose RAIF, a systematic method to boost LLMs in dealing with complex instructions via incentivizing reasoning for test-time compute scaling. First, we stem from the decomposition of complex instructions under existing taxonomies and propose a reproducible data acquisition method. Second, we exploit reinforcement learning (RL) with verifiable rule-centric reward signals to cultivate reasoning specifically for instruction following. We address the shallow, non-essential nature of reasoning under complex instructions via sample-wise contrast for superior CoT enforcement. We also exploit behavior cloning of experts to facilitate steady distribution shift from fast-thinking LLMs to skillful reasoners. Extensive evaluations on seven comprehensive benchmarks confirm the validity of the proposed method, where a 1.5B LLM achieves 11.74% gains with performance comparable to a 8B LLM. Evaluation on OOD constraints also confirms the generalizability of our RAIF.
|
https://openreview.net/forum?id=vRVfgcoeIl
|
Main
|
Poster
|
vRVfgcoeIl
|
Subgraph Federated Learning via Spectral Methods
|
[
"Javad Aliakbari",
"Johan Östman",
"Ashkan Panahi",
"Alexandre Graell i Amat"
] |
We consider the problem of federated learning (FL) with graph-structured data distributed across multiple clients. In particular, we address the common scenario of interconnected subgraphs, where interconnections between clients significantly influence the learning process. Existing approaches suffer from critical limitations, either requiring the exchange of sensitive node embeddings, thereby posing privacy risks, or relying on computationally-intensive steps, which hinders scalability.
To tackle these challenges, we propose FedLap, a novel framework that leverages global structure information via Laplacian smoothing in the spectral domain to effectively capture inter-node dependencies while ensuring privacy and scalability. We provide a formal analysis of the privacy of FedLap, demonstrating that it preserves privacy. Notably, FedLap is the first subgraph FL scheme with strong privacy guarantees. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves competitive or superior utility compared to existing techniques.
|
https://openreview.net/forum?id=vOijARaWym
|
Main
|
Poster
|
vOijARaWym
|
Backdoor Mitigation via Invertible Pruning Masks
|
[
"Kealan Dunnett",
"Reza Arablouei",
"Volkan Dedeoglu",
"Dimity Miller",
"Raja Jurdak"
] |
Model pruning has gained traction as a promising defense strategy against backdoor attacks in deep learning. However, existing pruning-based approaches often fall short in accurately identifying and removing the specific parameters responsible for inducing backdoor behaviors. Despite the dominance of fine-tuning-based defenses in recent literature, largely due to their superior performance, pruning remains a compelling alternative, offering greater interpretability and improved robustness in low-data regimes. In this paper, we propose a novel pruning approach featuring a learned \emph{selection} mechanism to identify parameters critical to both main and backdoor tasks, along with an \emph{invertible} pruning mask designed to simultaneously achieve two complementary goals: eliminating the backdoor task while preserving it through the inverse mask. We formulate this as a bi-level optimization problem that jointly learns selection variables, a sparse invertible mask, and sample-specific backdoor perturbations derived from clean data. The inner problem synthesizes candidate triggers using the inverse mask, while the outer problem refines the mask to suppress backdoor behavior without impairing clean-task accuracy. Extensive experiments demonstrate that our approach outperforms existing pruning-based backdoor mitigation approaches, maintains strong performance under limited data conditions, and achieves competitive results compared to state-of-the-art fine-tuning approaches. Notably, the proposed approach is particularly effective in restoring correct predictions for compromised samples after successful backdoor mitigation.
|
https://openreview.net/forum?id=vOAtjgCAAO
|
Main
|
Poster
|
vOAtjgCAAO
|
Learning to Reason under Off-Policy Guidance
|
[
"Jianhao Yan",
"Yafu Li",
"Zican Hu",
"Zhi Wang",
"Ganqu Cui",
"Xiaoye Qu",
"Yu Cheng",
"Yue Zhang"
] |
Recent advances in large reasoning models (LRMs) demonstrate that sophisticated behaviors such as multi-step reasoning and self-reflection can emerge via reinforcement learning with verifiable rewards~(RLVR).
However, existing RLVR approaches are inherently ``on-policy'', limiting learning to a model's own outputs and failing to acquire reasoning abilities beyond its initial capabilities.
To address this issue, we introduce LUFFY (Learning to reason Under oFF-policY guidance), a framework that augments RLVR with off-policy reasoning traces.
LUFFY dynamically balances imitation and exploration by combining off-policy demonstrations with on-policy rollouts during training.
Specifically, LUFFY combines the Mixed-Policy GRPO framework, which has a theoretically guaranteed convergence rate, alongside policy shaping via regularized importance sampling to avoid superficial and rigid imitation during mixed-policy training.
Compared with previous RLVR methods, LUFFY achieves an over +6.4 average gain across six math benchmarks and an advantage of over +6.2 points in out-of-distribution tasks.
Most significantly, we show that LUFFY successfully trains weak models in scenarios where on-policy RLVR completely fails. These results provide compelling evidence that LUFFY transcends the fundamental limitations of on-policy RLVR and demonstrates the great potential of utilizing off-policy guidance in RLVR.
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https://openreview.net/forum?id=vO8LLoNWWk
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Main
|
Poster
|
vO8LLoNWWk
|
Aux-Think: Exploring Reasoning Strategies for Data-Efficient Vision-Language Navigation
|
[
"Shuo Wang",
"Yongcai Wang",
"Wanting Li",
"Xudong Cai",
"Yucheng Wang",
"Maiyue Chen",
"kaihui.wang",
"Zhizhong Su",
"Deying Li",
"Zhaoxin Fan"
] |
Vision-Language Navigation is a critical task for developing embodied agents that can follow natural language instructions to navigate in complex real-world environments. Recent advances by finetuning large pretrained models have significantly improved generalization and instruction grounding compared to traditional approaches. However, the role of reasoning strategies in navigation—an action-centric, long-horizon task—remains underexplored, despite Chain-of-Thought reasoning's demonstrated success in static tasks like question answering and visual reasoning. To address this gap, we conduct the first systematic evaluation of reasoning strategies for VLN, including No-Think (direct action prediction), Pre-Think (reason before action), and Post-Think (reason after action). Surprisingly, our findings reveal the Inference-time Reasoning Collaps issue, where inference-time reasoning degrades navigation accuracy, highlighting the challenges of integrating reasoning into VLN. Based on this insight, we propose Aux-Think, a framework that trains models to internalize structured reasoning patterns through CoT supervision during training, while preserving No-Think inference for efficient action prediction. To support this framework, we release R2R-CoT-320k, a large-scale Chain-of-Thought annotated dataset. Empirically, Aux-Think significantly reduces training effort without compromising performance.
|
https://openreview.net/forum?id=vNmWbINtwH
|
Main
|
Poster
|
vNmWbINtwH
|
Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach
|
[
"Yuting Huang",
"Ziquan Fang",
"Zhihao Zeng",
"Lu Chen",
"Yunjun Gao"
] |
Spatio-temporal prediction plays a crucial role in intelligent transportation, weather forecasting, and urban planning. While integrating multi-modal data has shown potential for enhancing prediction accuracy, key challenges persist: (i) inadequate fusion of multi-modal information, (ii) confounding factors that obscure causal relations, and (iii) high computational complexity of prediction models. To address these challenges, we propose E$^2$-CSTP, an Effective and Efficient Causal multi-modal Spatio-Temporal Prediction framework. E$^2$-CSTP leverages cross-modal attention and gating mechanisms to effectively integrate multi-modal data. Building on this, we design a dual-branch causal inference approach: the primary branch focuses on spatio-temporal prediction, while the auxiliary branch mitigates bias by modeling additional modalities and applying causal interventions to uncover true causal dependencies. To improve model efficiency, we integrate GCN with the Mamba architecture for accelerated spatio-temporal encoding. Extensive experiments on 4 real-world datasets show that E$^2$-CSTP significantly outperforms 9 state-of-the-art methods, achieving up to 9.66% improvements in accuracy as well as 17.37%-56.11% reductions in computational overhead.
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https://openreview.net/forum?id=vN3ZRS7L3I
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Main
|
Poster
|
vN3ZRS7L3I
|
MINT-CoT: Enabling Interleaved Visual Tokens in Mathematical Chain-of-Thought Reasoning
|
[
"Xinyan Chen",
"Renrui Zhang",
"Dongzhi Jiang",
"Aojun Zhou",
"Shilin Yan",
"Weifeng Lin",
"Hongsheng Li"
] |
Chain-of-Thought (CoT) has widely enhanced mathematical reasoning in Large Language Models (LLMs), but it still remains challenging for extending it to multimodal domains. Existing works either adopt a similar textual reasoning for image input, or seek to interleave visual signals into mathematical CoT. However, they face three key limitations for math problem-solving: *reliance on coarse-grained box-shaped image regions, limited perception of vision encoders on math content, and dependence on external capabilities for visual modification*. In this paper, we propose **MINT-CoT**, introducing **M**athematical **IN**terleaved **T**okens for **C**hain-**o**f-**T**hought visual reasoning. MINT-CoT adaptively interleaves relevant visual tokens into textual reasoning steps via an Interleave Token, which dynamically selects visual regions of any shapes within math figures. To empower this capability, we construct the MINT-CoT dataset, containing 54K mathematical problems aligning each reasoning step with visual regions at the token level, accompanied by a rigorous data generation pipeline. We further present a three-stage MINT-CoT training strategy, progressively combining text-only CoT SFT, interleaved CoT SFT, and interleaved CoT RL, which derives our MINT-CoT-7B model. Extensive experiments demonstrate the effectiveness of our method for effective visual interleaved reasoning in mathematical domains, where MINT-CoT-7B outperforms the baseline model by +34.08% on MathVista and +28.78% on GeoQA, respectively.
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https://openreview.net/forum?id=vMpvtSmtXY
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Main
|
Poster
|
vMpvtSmtXY
|
How Does Sequence Modeling Architecture Influence Base Capabilities of Pre-trained Language Models? Exploring Key Architecture Design Principles to Avoid Base Capabilities Degradation
|
[
"Xin Lu",
"Yanyan Zhao",
"Si Wei",
"Shijin Wang",
"Bing Qin",
"Ting Liu"
] |
Pre-trained language models represented by the Transformer have been proven to possess strong base capabilities, and the representative self-attention mechanism in the Transformer has become a classic in sequence modeling architectures. Different from the work of proposing sequence modeling architecture to improve the efficiency of attention mechanism, this work focuses on the impact of sequence modeling architectures on base capabilities. Specifically, our concern is: How exactly do sequence modeling architectures affect the base capabilities of pre-trained language models? In this work, we first point out that the mixed domain pre-training setting commonly adopted in existing architecture design works fails to adequately reveal the differences in base capabilities among various architectures. To address this, we propose a limited domain pre-training setting with out-of-distribution testing, which successfully uncovers significant differences in base capabilities among architectures at an early stage. Next, we analyze the base capabilities of stateful sequence modeling architectures, and find that they exhibit significant degradation in base capabilities compared to the Transformer. Then, through a series of architecture component analysis, we summarize a key architecture design principle: A sequence modeling architecture need possess full-sequence arbitrary selection capability to avoid degradation in base capabilities. Finally, we empirically validate this principle using an extremely simple Top-1 element selection architecture and further generalize it to a more practical Top-1 chunk selection architecture. Experimental results demonstrate our proposed sequence modeling architecture design principle and suggest that our work can serve as a valuable reference for future architecture improvements and novel designs.
|
https://openreview.net/forum?id=vMkJWaa02n
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Main
|
Poster
|
vMkJWaa02n
|
Anomaly Detection by an Ensemble of Random Pairs of Hyperspheres
|
[
"Walid Durani",
"Collin Leiber",
"Khalid Durani",
"Claudia Plant",
"Christian Böhm"
] |
Anomaly detection is a crucial task in data mining, focusing on identifying data points that deviate significantly from the main patterns in the data. This paper introduces Anomaly Detection by an Ensemble of Random Pairs of Hyperspheres (ADERH), a new isolation-based technique leveraging two key observations: (i) anomalies are comparatively rare, and (ii) they typically deviate more strongly from general patterns than normal data points. Drawing on a delta-separation argument, ADERH constructs an ensemble of multi-scale hyperspheres built upon randomly paired data points to identify anomalies. To address inevitable overlaps between anomalous and normal regions in the feature space, ADERH integrates two complementary concepts: Pitch, which highlights points near hypersphere boundaries, and NDensity, which down-weights hyperspheres centered on sparse (and often anomalous) regions. By averaging these local, density-adjusted ``isolation'' indicators across many random subsets, ADERH yields robust anomaly scores that clearly separate normal from abnormal samples. Extensive experiments on diverse real-world datasets show that ADERH consistently outperforms state-of-the-art methods while maintaining linear runtime scalability and stable performance across varying hyperparameter settings.
|
https://openreview.net/forum?id=vM4PIjsJDG
|
Main
|
Poster
|
vM4PIjsJDG
|
Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel
|
[
"Yilan Chen",
"Zhichao Wang",
"Wei Huang",
"Andi Han",
"Taiji Suzuki",
"Arya Mazumdar"
] |
Gradient-based optimization methods have shown remarkable empirical success, yet their theoretical generalization properties remain only partially understood. In this paper, we establish a generalization bound for gradient flow that aligns with the classical Rademacher complexity bounds for kernel methods—specifically those based on the RKHS norm and kernel trace—through a data-dependent kernel called the loss path kernel (LPK). Unlike static kernels such as NTK, the LPK captures the entire training trajectory, adapting to both data and optimization dynamics, leading to tighter and more informative generalization guarantees. Moreover, the bound highlights how the norm of the training loss gradients along the optimization trajectory influences the final generalization performance. The key technical ingredients in our proof combine stability analysis of gradient flow with uniform convergence via Rademacher complexity. Our bound recovers existing kernel regression bounds for overparameterized neural networks and shows the feature learning capability of neural networks compared to kernel methods. Numerical experiments on real-world datasets validate that our bounds correlate well with the true generalization gap.
|
https://openreview.net/forum?id=vLYpKbZBkD
|
Main
|
Poster
|
vLYpKbZBkD
|
Optimism Without Regularization: Constant Regret in Zero-Sum Games
|
[
"John Lazarsfeld",
"Georgios Piliouras",
"Ryann Sim",
"Stratis Skoulakis"
] |
This paper studies the *optimistic* variant of Fictitious Play for learning in two-player zero-sum games. While it is known that Optimistic FTRL -- a *regularized* algorithm with a bounded stepsize parameter -- obtains constant regret in this setting, we show for the first time that similar, optimal rates are also achievable *without* regularization: we prove for two-strategy games that Optimistic Fictitious Play (using *any* tiebreaking rule) obtains only *constant regret*, providing surprising new evidence on the ability of *non*-no-regret algorithms for fast learning in games. Our proof technique leverages a geometric view of Optimistic Fictitious Play in the dual space of payoff vectors, where we show a certain energy function of the iterates remains bounded over time. Additionally, we also prove a regret *lower bound* of $\Omega(\sqrt{T})$ for *Alternating* Fictitious Play. In the unregularized regime, this separates the ability of optimism and alternation in achieving $o(\sqrt{T})$ regret.
|
https://openreview.net/forum?id=vLUW0OZGWD
|
Main
|
Poster
|
vLUW0OZGWD
|
Enhancing Zero-Shot Black-Box Optimization via Pretrained Models with Efficient Population Modeling, Interaction, and Stable Gradient Approximation
|
[
"Muqi Han",
"Xiaobin Li",
"Kai Wu",
"Xiaoyu Zhang",
"Handing Wang"
] |
Zero-shot optimization aims to achieve both generalization and performance gains on solving previously unseen black-box optimization problems over SOTA methods without task-specific tuning. Pre-trained optimization models (POMs) address this challenge by learning a general mapping from task features to optimization strategies, enabling direct deployment on new tasks.
In this paper, we identify three essential components that determine the effectiveness of POMs: (1) task feature modeling, which captures structural properties of optimization problems; (2) optimization strategy representation, which defines how new candidate solutions are generated; and (3) the feature-to-strategy mapping mechanism learned during pre-training. However, existing POMs often suffer from weak feature representations, rigid strategy modeling, and unstable training.
To address these limitations, we propose EPOM, an enhanced framework for pre-trained optimization. EPOM enriches task representations using a cross-attention-based tokenizer, improves strategy diversity through deformable attention, and stabilizes training by replacing non-differentiable operations with a differentiable crossover mechanism. Together, these enhancements yield better generalization, faster convergence, and more reliable performance in zero-shot black-box optimization.
|
https://openreview.net/forum?id=vLLYlSK6qJ
|
Main
|
Poster
|
vLLYlSK6qJ
|
AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
|
[
"Samuel Bright-Thonney",
"Christina Reissel",
"Gaia Grosso",
"Nathaniel S. Woodward",
"Katya Govorkova",
"Andrzej Novak",
"Sang Eon Park",
"Eric A. Moreno",
"Philip Harris"
] |
Novelty detection in large scientific datasets faces two key challenges: the noisy and high-dimensional nature of experimental data,
and the necessity of making *statistically robust* statements about any observed outliers. While there is a wealth of literature on anomaly detection via dimensionality reduction, most methods do not produce outputs compatible with quantifiable claims of scientific discovery. In this work we directly address these challenges, presenting the first step towards a unified pipeline for novelty detection adapted for the rigorous statistical demands of science. We introduce AutoSciDACT (Automated Scientific Discovery with Anomalous Contrastive Testing), a general-purpose pipeline for detecting novelty in scientific data. AutoSciDACT begins by creating expressive low-dimensional data representations using a contrastive pre-training, leveraging the abundance of high-quality simulated data in many scientific domains alongside expertise that can guide principled data augmentation strategies. These compact embeddings then enable an extremely sensitive machine learning-based two-sample test using the New Physics Learning Machine (NPLM) framework, which identifies and statistically quantifies deviations in observed data relative to a reference distribution (null hypothesis). We perform experiments across a range of astronomical, physical, biological, image, and synthetic datasets, demonstrating strong sensitivity to small injections of anomalous data across all domains.
|
https://openreview.net/forum?id=vKyiv67VWa
|
Main
|
Poster
|
vKyiv67VWa
|
A Single-Loop Gradient Algorithm for Pessimistic Bilevel Optimization via Smooth Approximation
|
[
"Qichao Cao",
"Shangzhi Zeng",
"Jin Zhang"
] |
Bilevel optimization has garnered significant attention in the machine learning community recently, particularly regarding the development of efficient numerical methods.
While substantial progress has been made in developing efficient algorithms for optimistic bilevel optimization, the study of methods for solving Pessimistic Bilevel Optimization (PBO) remains relatively less explored,
especially the design of fully first-order, single-loop gradient-based algorithms. This paper aims to bridge this research gap. We first propose a novel smooth approximation to the PBO problem, using penalization and regularization techniques. Building upon this approximation, we then propose SiPBA (Single-loop Pessimistic Bilevel Algorithm), a new gradient-based method specifically designed for PBO which avoids second-order derivative information or inner-loop iterations for subproblem solving. We provide theoretical validation for the proposed smooth approximation scheme and establish theoretical convergence for the algorithm SiPBA. Numerical experiments on synthetic examples and practical applications demonstrate the effectiveness and efficiency of SiPBA.
|
https://openreview.net/forum?id=vKmWKHlQBe
|
Main
|
Poster
|
vKmWKHlQBe
|
Dimensional Collapse in VQVAEs: Evidence and Remedies
|
[
"Jiayou Zhang",
"Yifan Shen",
"Guangyi Chen",
"Le Song",
"Eric P. Xing"
] |
Vector-Quantized Variational Autoencoders (VQVAEs) have enabled strong performance in generative modeling by mapping continuous data to learnable codes.
In this work, we identify a surprising yet consistent phenomenon that we term \emph{dimensional collapse}: despite using high-dimensional embeddings, VQVAEs tend to compress their representations into a much smaller subspace, typically only 4 to 10 dimensions.
We provide an in-depth analysis of this phenomenon and reveal its relation to model performance and learning dynamics.
Interestingly, VQVAEs naturally gravitate toward this low-dimensional regime, and enforcing higher-dimensional usage (e.g., via rank regularization) could lead to degraded performance.
To overcome this low-dimensionality limitation, we propose \textbf{Divide-and-Conquer VQ (DCVQ)}, which partitions the latent space into multiple low-dimensional subspaces, each quantized independently.
By design, each subspace respects the model’s preference for low dimensionality, while their combination expands the overall capacity.
Our results show that DCVQ overcomes the inherent dimensional bottleneck and achieves improved reconstruction quality across image datasets.
|
https://openreview.net/forum?id=vJtnJfS2mQ
|
Main
|
Poster
|
vJtnJfS2mQ
|
SAFEPATH: Preventing Harmful Reasoning in Chain-of-Thought via Early Alignment
|
[
"Wonje Jeung",
"Sangyeon Yoon",
"Minsuk Kahng",
"Albert No"
] |
Large Reasoning Models (LRMs) have become powerful tools for complex problem solving, but their structured reasoning pathways can lead to unsafe outputs when exposed to harmful prompts. Existing safety alignment methods reduce harmful outputs but can degrade reasoning depth, leading to significant trade-offs in complex, multi-step tasks, and remain vulnerable to sophisticated jailbreak attacks. To address this, we introduce SAFEPATH, a lightweight alignment method that fine-tunes LRMs to emit a short, 8-token Safety Primer at the start of their reasoning, in response to harmful prompts, while leaving the rest of the reasoning process unsupervised. Empirical results across multiple benchmarks indicate that SAFEPATH effectively reduces harmful outputs while maintaining reasoning performance. Specifically, SAFEPATH reduces harmful responses by up to 90.0\% and blocks 83.3\% of jailbreak attempts in the DeepSeek-R1-Distill-Llama-8B model, while requiring 295.9x less compute than Direct Refusal and 314.1x less than SafeChain. We further introduce a zero-shot variant that requires no fine-tuning. In addition, we provide a comprehensive analysis of how existing methods in LLMs generalize, or fail, when applied to reasoning-centric models, revealing critical gaps and new directions for safer AI.
|
https://openreview.net/forum?id=vIaNnnQxcl
|
Main
|
Poster
|
vIaNnnQxcl
|
Zero-shot World Models via Search in Memory
|
[
"Federico Malato",
"Ville Hautamaki"
] |
World Models have vastly permeated the field of Reinforcement Learning. Their ability to model the transition dynamics of an environment have led to tremendous improvements in sample efficiency for online RL. Among them, the most notorious example is Dreamer, a model that learns to act in a diverse set of image-based environments. In this paper, we leverage similarity search and stochastic representations to approximate a world model without a training procedure. We establish a comparison with PlaNet, a well-established world model of the Dreamer family. We evaluate the models on the quality of latent reconstruction and on the perceived similarity of the reconstructed image, on both next-step and long horizon dynamics prediction. The results of our study demonstrate that a search-based world model is comparable to a training based one in both cases. Notably, our model shows stronger performance in long-horizon prediction with respect to the baseline on a range of visually different environments.
|
https://openreview.net/forum?id=vHaShO76T8
|
Main
|
Poster
|
vHaShO76T8
|
Efficient Bayesian Experiment Design with Equivariant Networks
|
[
"Conor Igoe",
"Tejus Gupta",
"Jeff Schneider"
] |
Recent work in Bayesian Experiment Design (BED) has shown the value of using Deep Learning (DL) to obtain highly efficient adaptive experiment designs. In this paper, we argue that a central bottleneck of DL training for BED is belief explosion. Specifically, as an agent progresses deeper into an experiment, the effective number of realisable beliefs grows enormously, placing significant sampling burdens on offline training schemes in an effort to gather experience from all regions of belief space. We argue that choosing an appropriate inductive bias for actor/critic networks is a critical component in mitigating the effects of belief explosion and has so far been overlooked in the BED literature. We show how Graph Neural Networks are particularly well-suited for BED DL training due to their domain permutation equivariance properties, resulting in multiple orders of magnitude improvement to sample efficiency compared to naive parameterizations.
|
https://openreview.net/forum?id=vHTkg57tPW
|
Main
|
Poster
|
vHTkg57tPW
|
Boundary-to-Region Supervision for Offline Safe Reinforcement Learning
|
[
"Huikang Su",
"Dengyun Peng",
"Zifeng Zhuang",
"YuHan Liu",
"Qiguang Chen",
"Donglin Wang",
"Qinghe Liu"
] |
Offline safe reinforcement learning aims to learn policies that satisfy predefined safety constraints from static datasets. Existing sequence-model-based methods condition action generation on symmetric input tokens for return-to-go and cost-to-go, neglecting their intrinsic asymmetry: RTG serves as a flexible performance target, while CTG should represent a rigid safety boundary. This symmetric conditioning leads to unreliable constraint satisfaction, especially when encountering out-of-distribution cost trajectories. To address this, we propose Boundary-to-Region (B2R), a framework that enables asymmetric conditioning through cost signal realignment . B2R redefines CTG as a boundary constraint under a fixed safety budget, unifying the cost distribution of all feasible trajectories while preserving reward structures. Combined with rotary positional embeddings , it enhances exploration within the safe region. Experimental results show that B2R satisfies safety constraints in 35 out of 38 safety-critical tasks while achieving superior reward performance over baseline methods. This work highlights the limitations of symmetric token conditioning and establishes a new theoretical and practical approach for applying sequence models to safe RL.
|
https://openreview.net/forum?id=vFLrQgI6MW
|
Main
|
Poster
|
vFLrQgI6MW
|
Rig3R: Rig-Aware Conditioning and Discovery for 3D Reconstruction
|
[
"Samuel Li",
"Pujith Kachana",
"Prajwal Chidananda",
"Saurabh Nair",
"Yasutaka Furukawa",
"Matthew Brown"
] |
Estimating agent pose and 3D scene structure from multi-camera rigs is a central task in embodied AI applications such as autonomous driving. Recent learned approaches such as DUSt3R have shown impressive performance in multiview settings. However, these models treat images as unstructured collections, limiting effectiveness in scenarios where frames are captured from synchronized rigs with known or inferable structure. To this end, we introduce Rig3R, a generalization of prior multiview reconstruction models that incorporates rig structure when available, and learns to infer it when not. Rig3R conditions on optional rig metadata including camera ID, time, and rig poses to develop a rig-aware latent space that remains robust to missing information. It jointly predicts pointmaps and two types of raymaps: a pose raymap relative to a global frame, and a rig raymap relative to a rig-centric frame consistent across time. Rig raymaps allow the model to infer rig structure directly from input images when metadata is missing. The global pose raymaps allow the model to reason about the agent’s ego-motion, while the rig raymaps allow the model to infer rig structure directly from input images when metadata is
missing. Rig3R achieves state-of-the-art performance in 3D reconstruction, camera pose estimation, and rig discovery -- outperforming both traditional and learned methods by 17-45% mAA across diverse real-world rig datasets, all in a single forward pass without post-processing or iterative refinement.
|
https://openreview.net/forum?id=vEFPm6gw2s
|
Main
|
Spotlight
|
vEFPm6gw2s
|
Tight High-Probability Bounds for Nonconvex Heavy-Tailed Scenario under Weaker Assumptions
|
[
"Weixin An",
"Yuanyuan Liu",
"Fanhua Shang",
"Han Yu",
"Junkang Liu",
"Hongying Liu"
] |
Gradient clipping is increasingly important in centralized learning (CL) and federated learning (FL). Many works focus on its optimization properties under strong assumptions involving Gaussian noise and standard smoothness. However, practical machine learning tasks often only satisfy weaker conditions, such as heavy-tailed noise and $(L_0, L_1)$-smoothness. To bridge this gap, we propose a high-probability analysis for clipped Stochastic Gradient Descent (SGD) under these weaker assumptions. Our findings show a better convergence rate than existing ones can be achieved, and our high-probability analysis does not rely on the bounded gradient assumption. Moreover, we extend our analysis to FL, where a gap remains between expected and high-probability convergence, which the naive clipped SGD cannot bridge. Thus, we design a new \underline{Fed}erated \underline{C}lipped \underline{B}atched \underline{G}radient (FedCBG) algorithm, and prove the convergence and generalization bounds with high probability for the first time. Our analysis reveals the trade-offs between the optimization and generalization performance. Extensive experiments demonstrate that \methodname{} can generalize better to unseen client distributions than state-of-the-art baselines.
|
https://openreview.net/forum?id=vEFAR8KH1l
|
Main
|
Poster
|
vEFAR8KH1l
|
Intermediate Domain Alignment and Morphology Analogy for Patent-Product Image Retrieval
|
[
"Haifan Gong",
"Xuanye Zhang",
"Ruifei Zhang",
"Yun Su",
"Zhuo Li",
"Yuhao Du",
"Anningzhe Gao",
"Xiang Wan",
"Haofeng Li"
] |
Recent advances in artificial intelligence have significantly impacted image retrieval tasks, yet Patent-Product Image Retrieval (PPIR) has received limited attention. PPIR, which retrieves patent images based on product images to identify potential infringements, presents unique challenges: (1) both product and patent images often contain numerous categories of artificial objects, but models pre-trained on standard datasets exhibit limited discriminative power to recognize some of those unseen objects; and (2) the significant domain gap between binary patent line drawings and colorful RGB product images further complicates similarity comparisons for product-patent pairs. To address these challenges, we formulate it as an open-set image retrieval task and introduce a comprehensive Patent-Product Image Retrieval Dataset (PPIRD) including a test set with 439 product-patent pairs, a retrieval pool of 727,921 patents, and an unlabeled pre-training set of 3,799,695 images. We further propose a novel Intermediate Domain Alignment and Morphology Analogy (IDAMA) strategy. IDAMA maps both image types to an intermediate sketch domain using edge detection to minimize the domain discrepancy, and employs a Morphology Analogy Filter to select discriminative patent images based on visual features via analogical reasoning. Extensive experiments on PPIRD demonstrate that IDAMA significantly outperforms baseline methods (+7.58 mAR) and offers valuable insights into domain mapping and representation learning for PPIR. (The PPIRD dataset is available at: \href{https://loslorien.github.io/idama-project/}{https://loslorien.github.io/idama-project/})
|
https://openreview.net/forum?id=vE98S8BmzP
|
Main
|
Poster
|
vE98S8BmzP
|
TrackingWorld: World-centric Monocular 3D Tracking of Almost All Pixels
|
[
"Jiahao Lu",
"Weitao Xiong",
"Jiacheng Deng",
"Peng Li",
"Tianyu Huang",
"Zhiyang Dou",
"Cheng Lin",
"Sai-Kit Yeung",
"Yuan Liu"
] |
Monocular 3D tracking aims to capture the long-term motion of pixels in 3D space from a single monocular video and has witnessed rapid progress in recent years. However, we argue that the existing monocular 3D tracking methods still fall short in separating the camera motion from foreground dynamic motion and cannot densely track newly emerging dynamic subjects in the videos. To address these two limitations, we propose TrackingWorld, a novel pipeline for dense 3D tracking of almost all pixels within a world-centric 3D coordinate system. First, we introduce a tracking upsampler that efficiently lifts the arbitrary sparse 2D tracks into dense 2D tracks. Then, to generalize the current tracking methods to newly emerging objects, we apply the upsampler to all frames and reduce the redundancy of 2D tracks by eliminating the tracks in overlapped regions. Finally, we present an efficient optimization-based framework to back-project dense 2D tracks into world-centric 3D trajectories by estimating the camera poses and the 3D coordinates of these 2D tracks. Extensive evaluations on both synthetic and real-world datasets demonstrate that our system achieves accurate and dense 3D tracking in a world-centric coordinate frame.
|
https://openreview.net/forum?id=vDV912fa3t
|
Main
|
Poster
|
vDV912fa3t
|
Calibrating Translation Decoding with Quality Estimation on LLMs
|
[
"Di Wu",
"Yibin Lei",
"Christof Monz"
] |
Neural machine translation (NMT) systems typically employ maximum *a posteriori* (MAP) decoding to select the highest-scoring translation from the distribution. However, recent evidence highlights the inadequacy of MAP decoding, often resulting in low-quality or even pathological hypotheses as the decoding objective is only weakly aligned with real-world translation quality. This paper proposes to directly calibrate hypothesis likelihood with translation quality from a distributional view by directly optimizing their Pearson correlation, thereby enhancing decoding effectiveness. With our method, translation with large language models (LLMs) improves substantially after limited training (2K instances per direction). This improvement is orthogonal to those achieved through supervised fine-tuning, leading to substantial gains across a broad range of metrics and human evaluations. This holds even when applied to top-performing translation-specialized LLMs fine-tuned on high-quality translation data, such as Tower, or when compared to recent preference optimization methods, like CPO. Moreover, the calibrated translation likelihood can directly serve as a strong proxy for translation quality, closely approximating or even surpassing some state-of-the-art translation quality estimation models, like CometKiwi.
Lastly, our in-depth analysis demonstrates that calibration enhances the effectiveness of MAP decoding, thereby enabling greater efficiency in real-world deployment. The resulting state-of-the-art translation model, which covers 10 languages, along with the accompanying code and human evaluation data, has been released: https://github.com/moore3930/calibrating-llm-mt.
|
https://openreview.net/forum?id=vCTnwpAcma
|
Main
|
Poster
|
vCTnwpAcma
|
Distributional Adversarial Attacks and Training in Deep Hedging
|
[
"Guangyi He",
"Tobias Sutter",
"Lukas Gonon"
] |
In this paper, we study the robustness of classical deep hedging strategies under distributional shifts by leveraging the concept of adversarial attacks. We first demonstrate that standard deep hedging models are highly vulnerable to small perturbations in the input distribution, resulting in significant performance degradation. Motivated by this, we propose an adversarial training framework tailored to increase the robustness of deep hedging strategies. Our approach extends pointwise adversarial attacks to the distributional setting and introduces a computationally tractable reformulation of the adversarial optimization problem over a Wasserstein ball. This enables the efficient training of hedging strategies that are resilient to distributional perturbations. Through extensive numerical experiments, we show that adversarially trained deep hedging strategies consistently outperform their classical counterparts in terms of out-of-sample performance and resilience to model misspecification. Additional results indicate that the robust strategies maintain reliable performance on real market data and remain effective during periods of market change. Our findings establish a practical and effective framework for robust deep hedging under realistic market uncertainties.
|
https://openreview.net/forum?id=vBtfIafffU
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Main
|
Poster
|
vBtfIafffU
|
AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding
|
[
"Ahmed Masry",
"Juan A. Rodriguez",
"Tianyu Zhang",
"Suyuchen Wang",
"Chao Wang",
"Aarash Feizi",
"Akshay Kalkunte Suresh",
"Abhay Puri",
"Xiangru Jian",
"Pierre-Andre Noel",
"Sathwik Tejaswi Madhusudhan",
"Marco Pedersoli",
"Bang Liu",
"Nicolas Chapados",
"Yoshua Bengio",
"Enamul Hoque",
"Christopher Pal",
"Issam H. Laradji",
"David Vazquez",
"Perouz Taslakian",
"Spandana Gella",
"Sai Rajeswar"
] |
Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), lack inductive bias to constrain visual features within the linguistic structure of the LLM’s embedding space, making them data-hungry and prone to cross-modal misalignment. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where visual and textual modalities are highly correlated. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods, with larger gains on document understanding and under low-resource setups. We provide further analysis demonstrating its efficiency and robustness to noise.
|
https://openreview.net/forum?id=vAxGuGmshO
|
Main
|
Poster
|
vAxGuGmshO
|
Robust Graph Condensation via Classification Complexity Mitigation
|
[
"Jiayi Luo",
"Qingyun Sun",
"Beining Yang",
"Haonan Yuan",
"Xingcheng Fu",
"Yanbiao Ma",
"Jianxin Li",
"Philip S. Yu"
] |
Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC in scenarios where the original graph is corrupted. In such cases, we observe that the performance of GC deteriorates significantly, while existing robust graph learning technologies offer only limited effectiveness. Through both empirical investigation and theoretical analysis, we reveal that GC is inherently an intrinsic-dimension-reducing process, synthesizing a condensed graph with lower classification complexity. Although this property is critical for effective GC performance, it remains highly vulnerable to adversarial perturbations.
To tackle this vulnerability and improve GC robustness, we adopt the geometry perspective of graph data manifold and propose a novel **M**anifold-constrained **R**obust **G**raph **C**ondensation framework named **MRGC**. Specifically, we introduce three graph data manifold learning modules that guide the condensed graph to lie within a smooth, low-dimensional manifold with minimal class ambiguity, thereby preserving the classification complexity reduction capability of GC and ensuring robust performance under universal adversarial attacks. Extensive experiments demonstrate the robustness of MRGC across diverse attack scenarios.
|
https://openreview.net/forum?id=vATe64ktAo
|
Main
|
Spotlight
|
vATe64ktAo
|
MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
|
[
"Zeyu Zhang",
"Quanyu Dai",
"Luyu Chen",
"Zeren Jiang",
"Rui Li",
"Jieming Zhu",
"Xu Chen",
"Yi Xie",
"Zhenhua Dong",
"Ji-Rong Wen"
] |
LLM-based agents have been widely applied as personal assistants, capable of memorizing information from user messages and responding to personal queries. However, there still lacks an objective and automatic evaluation on their memory capability, largely due to the challenges in constructing reliable questions and answers (QAs) according to user messages. In this paper, we propose MemSim, a Bayesian simulator designed to automatically construct reliable QAs from generated user messages, simultaneously keeping their diversity and scalability. Specifically, we introduce the Bayesian Relation Network (BRNet) and a causal generation mechanism to mitigate the impact of LLM hallucinations on factual information, facilitating the automatic creation of an evaluation dataset. Based on MemSim, we generate a dataset in the daily-life scenario, named MemDaily, and conduct extensive experiments to assess the effectiveness of our approach. We also provide a benchmark for evaluating different memory mechanisms in LLM-based agents with the MemDaily dataset.
|
https://openreview.net/forum?id=vAT2xlaWJY
|
Main
|
Poster
|
vAT2xlaWJY
|
Learning to Clean: Reinforcement Learning for Noisy Label Correction
|
[
"Marzi Heidari",
"Hanping Zhang",
"Yuhong Guo"
] |
The challenge of learning with noisy labels is significant in machine learning, as it can severely degrade the performance of prediction models if not addressed properly. This paper introduces a novel framework that conceptualizes noisy label correction as a reinforcement learning (RL) problem. The proposed approach, Reinforcement Learning for Noisy Label Correction (RLNLC), defines a comprehensive state space representing data and their associated labels, an action space that indicates possible label corrections, and a reward mechanism that evaluates the efficacy of label corrections. RLNLC learns a deep feature representation based policy network to perform label correction through reinforcement learning, utilizing an actor-critic method. The learned policy is subsequently deployed to iteratively correct noisy training labels and facilitate the training of the prediction model. The effectiveness of RLNLC is demonstrated through extensive experiments on multiple benchmark datasets, where it consistently outperforms existing state-of-the-art techniques for learning with noisy labels.
|
https://openreview.net/forum?id=v8InI8hobW
|
Main
|
Poster
|
v8InI8hobW
|
TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
|
[
"Andreas Auer",
"Patrick Podest",
"Daniel Klotz",
"Sebastian Böck",
"Günter Klambauer",
"Sepp Hochreiter"
] |
In-context learning, the ability of large language models to perform tasks using only examples provided in the prompt, has recently been adapted for time series forecasting.
This paradigm enables zero-shot prediction, where past values serve as context for forecasting future values,
making powerful forecasting tools accessible to non-experts and increasing the performance when training data are scarce.
Most existing zero-shot forecasting approaches rely on transformer architectures, which, despite their success in language, often fall short of expectations in time series forecasting, where recurrent models like LSTMs frequently have the edge. Conversely, while LSTMs are well-suited for time series modeling due to their state-tracking capabilities, they lack strong in-context learning abilities.
We introduce *TiRex* that closes this gap by leveraging xLSTM, an enhanced LSTM with competitive in-context learning skills. Unlike transformers, state-space models, or parallelizable RNNs such as RWKV, TiRex retains state tracking, a critical property for long-horizon forecasting. To further facilitate its state-tracking ability, we propose a training-time masking strategy called CPM.
TiRex sets a new state of the art in zero-shot time series forecasting on the Hugging Face benchmarks *GiftEval* and *Chronos-ZS*, outperforming significantly larger models including *TabPFN-TS* (Prior Labs), *Chronos Bolt* (Amazon), *TimesFM* (Google), and *Moirai* (Salesforce) across both short- and long-term forecasts.
|
https://openreview.net/forum?id=v7UqniC9pF
|
Main
|
Poster
|
v7UqniC9pF
|
Last-Iterate Convergence of Smooth Regret Matching$^+$ Variants in Learning Nash Equilibria
|
[
"Linjian Meng",
"Youzhi Zhang",
"Zhenxing Ge",
"Tianyu Ding",
"Shangdong Yang",
"Zheng Xu",
"Wenbin Li",
"Yang Gao"
] |
Regret Matching$^+$ (RM$^+$) variants are widely used to build superhuman Poker AIs, yet few studies investigate their last-iterate convergence in learning a Nash equilibrium (NE). Although their last-iterate convergence is established for games satisfying the Minty Variational Inequality (MVI), no studies have demonstrated that these algorithms achieve such convergence in the broader class of games satisfying the weak MVI. A key challenge in proving last-iterate convergence for RM$^+$ variants in games satisfying the weak MVI is that even if the game's loss gradient satisfies the weak MVI, RM$^+$ variants operate on a transformed loss feedback which does not satisfy the weak MVI. To provide last-iterate convergence for RM$^+$ variants, we introduce a concise yet novel proof paradigm that involves: (i) transforming an RM$^+$ variant into an Online Mirror Descent (OMD) instance that updates within the original strategy space of the game to recover the weak MVI, and (ii) showing last-iterate convergence by proving the distance between accumulated regrets converges to zero via the recovered weak MVI of the feedback. Inspired by our proof paradigm, we propose Smooth Optimistic Gradient Based RM$^+$ (SOGRM$^+$) and show that it achieves last-iterate and finite-time best-iterate convergence in learning an NE of games satisfying the weak MVI, the weakest condition among all known RM$^+$ variants. Experiments show that SOGRM$^+$ significantly outperforms other algorithms. Our code is available at https://github.com/menglinjian/NeurIPS-2025-SOGRM.
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https://openreview.net/forum?id=v771lscnlS
|
Main
|
Poster
|
v771lscnlS
|
Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Training
|
[
"Reza Shirkavand",
"Peiran Yu",
"Qi He",
"Heng Huang"
] |
Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks using First-Order (FO) optimizers presents significant computational challenges. Parameter-Efficient Fine-Tuning~(PEFT) methods have been proposed to address these challenges by freezing most model parameters and training only a small subset. While PEFT is efficient, it may not outperform full fine-tuning when high task-specific performance is required.
Zeroth-Order (ZO) methods offer an alternative for fine-tuning the entire pre-trained model by approximating gradients using only the forward pass, thus eliminating the computational burden of back-propagation,
% in first-order methods,
but they converge painfully slowly and are very sensitive to the choice of task prompts.
We bridge these worlds with Bilevel‑ZOFO, a penalty‑based bilevel formulation that treats adapter parameters as a lower‑level learner coupled to an upper‑level ZO optimizer of the full backbone. This double-loop optimization strategy only requires the gradient of the PEFT model and the forward pass of the base model. We provide theoretical convergence guarantees for Bilevel ZOFO. Empirically, we demonstrate that Bilevel-ZOFO significantly outperforms existing ZO methods, achieves 2–4$\times$ faster training, and reduces sensitivity to prompts. Bilevel-ZOFO also outperforms FO PEFT methods while maintaining similar memory efficiency. Additionally, we show its strong potential for meta learning.
|
https://openreview.net/forum?id=v6vBK4t8vB
|
Main
|
Poster
|
v6vBK4t8vB
|
Flex-Judge: Text-Only Reasoning Unleashes Zero-Shot Multimodal Evaluators
|
[
"Jongwoo Ko",
"Sungnyun Kim",
"Sungwoo Cho",
"Se-Young Yun"
] |
Human-generated reward signals are critical for aligning generative models with human preferences, guiding both training and inference-time evaluations. While large language models (LLMs) employed as proxy evaluators, i.e., LLM-as-a-Judge, significantly reduce the costs associated with manual annotations, they typically require extensive modality-specific training data and fail to generalize well across diverse multimodal tasks. In this paper, we propose **Flex-Judge**, a reasoning-guided multimodal judge model that leverages minimal textual reasoning data to robustly generalize across multiple modalities and evaluation formats. Our core intuition is that structured textual reasoning explanations inherently encode generalizable decision-making patterns, enabling an effective transfer to multimodal judgments, e.g., with images or videos. Empirical results demonstrate that Flex-Judge, despite being trained on significantly fewer text data, achieves competitive or superior performance compared to state-of-the-art commercial APIs and extensively trained multimodal evaluators. Notably, Flex-Judge presents broad impact in modalities like molecule, where comprehensive evaluation benchmarks are scarce, underscoring its practical value in resource-constrained domains. Our framework highlights reasoning-based text supervision as a powerful, cost-effective alternative to traditional annotation-intensive approaches, substantially advancing scalable multimodal model-as-a-judge.
|
https://openreview.net/forum?id=v6kyF3S7dM
|
Main
|
Poster
|
v6kyF3S7dM
|
LangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation
|
[
"Yang Miao",
"Jan-Nico Zaech",
"Xi Wang",
"Fabien Despinoy",
"Danda Pani Paudel",
"Luc Van Gool"
] |
We propose LangHOPS, the first Multimodal Large Language Model (MLLM)-based framework for open-vocabulary object–part instance segmentation. Given an image, LangHOPS can jointly detect and segment hierarchical object and part instances from open-vocabulary candidate categories. Unlike prior approaches that rely on heuristic or learnable visual grouping, our approach grounds object–part hierarchies in language space. It integrates the MLLM into the object-part parsing pipeline to leverage rich knowledge and reasoning capabilities, and link multi-granularity concepts within the hierarchies. We evaluate LangHOPS across multiple challenging scenarios, including in-domain and cross-dataset object-part instance segmentation, and zero-shot semantic segmentation. LangHOPS achieves state-of-the-art results, surpassing previous methods by 5.5% Average Precision(AP) (in-domain) and 4.8% (cross-dataset) on the PartImageNet dataset and by 2.5% mIOU on unseen object parts in ADE20K (zero-shot). Ablation studies further validate the effectiveness of the language-grounded hierarchy and MLLM-driven part query refinement strategy. Our results establish LangHOPS as a strong foundation for advancing open-vocabulary fine-grained visual understanding applicable in multiple scenarios.
|
https://openreview.net/forum?id=v6Oo0zO2oA
|
Main
|
Poster
|
v6Oo0zO2oA
|
Generalization Guarantees for Learning Score-Based Branch-and-Cut Policies in Integer Programming
|
[
"Hongyu Cheng",
"Amitabh Basu"
] |
Mixed-integer programming (MIP) provides a powerful framework for optimization problems, with Branch-and-Cut (B&C) being the predominant algorithm in state-of-the-art solvers. The efficiency of B&C critically depends on heuristic policies for making sequential decisions, including node selection, cut selection, and branching variable selection. While traditional solvers often employ heuristics with manually tuned parameters, recent approaches increasingly leverage machine learning, especially neural networks, to learn these policies directly from data. A key challenge is to understand the theoretical underpinnings of these learned policies, particularly their generalization performance from finite data. This paper establishes rigorous sample complexity bounds for learning B&C policies where the scoring functions guiding each decision step (node, cut, branch) have a certain piecewise polynomial structure. This structure generalizes the linear models that form the most commonly deployed policies in practice and investigated recently in a foundational series of theoretical works by Balcan et al. Such piecewise polynomial policies also cover the neural network architectures (e.g., using ReLU activations) that have been the focal point of contemporary practical studies. Consequently, our theoretical framework closely reflects the models utilized by practitioners investigating machine learning within B&C, offering a unifying perspective relevant to both established theory and modern empirical research in this area. Furthermore, our theory applies to quite general sequential decision making problems beyond B&C.
|
https://openreview.net/forum?id=v5ru9MGjsW
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Main
|
Poster
|
v5ru9MGjsW
|
CG-SSL: Concept-Guided Self-Supervised Learning
|
[
"Sara Atito",
"Josef Kittler",
"Imran Razzak",
"Muhammad Awais"
] |
Humans understand visual scenes by first capturing a global impression and then refining this understanding into distinct, object-like components. Inspired by this process, we introduce \textbf{C}oncept-\textbf{G}uided \textbf{S}elf-\textbf{S}upervised \textbf{L}earning (CG-SSL), a novel framework that brings structure and interpretability to representation learning through a curriculum of three training phases: (1) global scene encoding, (2) discovery of visual concepts via tokenised cross-attention, and (3) alignment of these concepts across views.
Unlike traditional SSL methods, which simply enforce similarity between multiple augmented views of the same image, CG-SSL accounts for the fact that these views may highlight different parts of an object or scene. To address this, our method establishes explicit correspondences between views and aligns the representations of meaningful image regions. At its core, CG-SSL augments standard SSL with a lightweight decoder that learns and refines concept tokens via cross-attention with patch features. The concept tokens are trained using masked concept distillation and a feature-space reconstruction objective. A final alignment stage enforces view consistency by geometrically matching concept regions under heavy augmentation, enabling more compact, robust, and disentangled representations of scene regions.
Across multiple backbone sizes, CG-SSL achieves state-of-the-art results on image segmentation benchmarks using $k$-NN and linear probes, substantially outperforming prior methods and approaching, or even surpassing, the performance of leading SSL models trained on over $100\times$ more data. Code and pretrained models will be released.
|
https://openreview.net/forum?id=v4e5Fb3mQL
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Main
|
Poster
|
v4e5Fb3mQL
|
SimulMEGA: MoE Routers are Advanced Policy Makers for Simultaneous Speech Translation
|
[
"Chenyang Le",
"Bing Han",
"Jinshun Li",
"Chen Songyong",
"Yanmin Qian"
] |
Simultaneous Speech Translation (SimulST) enables real-time cross-lingual communication by jointly optimizing speech recognition and machine translation under strict latency constraints. Existing systems struggle to balance translation quality, latency, and semantic coherence, particularly in multilingual many-to-many scenarios where divergent read/write policies hinder unified strategy learning. In this paper, we present SimulMEGA(Simultaneous Generation by Mixture-of-Experts GAting), an unsupervised policy learning framework that combines prefix-based training with a Mixture-of-Experts refiner to learn effective read/write decisions in an implicit manner, without adding inference-time overhead. Our design requires only minimal modifications to standard transformer architectures and generalizes across both speech-to-text and text-to-speech streaming tasks. Through comprehensive evaluation on six language pairs, our 500 M-parameter speech-to-text model outperforms the Seamless baseline, achieving under 7% BLEU degradation at 1.5 s average lag and under 3% at 3 s. We further demonstrate SimulMEGA’s versatility by extending it to streaming TTS via a unidirectional backbone, yielding superior latency–quality trade-offs.
|
https://openreview.net/forum?id=v4AT18kysa
|
Main
|
Poster
|
v4AT18kysa
|
Size-adaptive Hypothesis Testing for Fairness
|
[
"Antonio Ferrara",
"Francesco Cozzi",
"Alan Perotti",
"André Panisson",
"Francesco Bonchi"
] |
Determining whether an algorithmic decision-making system discriminates against a specific demographic typically involves comparing a single point estimate of a fairness metric against a predefined threshold. This practice is statistically brittle: it ignores sampling error and treats small demographic subgroups the same as large ones. The problem intensifies in intersectional analyses, where multiple sensitive attributes are considered jointly, giving rise to a larger number of smaller groups. As these groups become more granular, the data representing them becomes too sparse for reliable estimation, and fairness metrics yield excessively wide confidence intervals, precluding meaningful conclusions about potential unfair treatments.
In this paper, we introduce a unified, size-adaptive, hypothesis‑testing framework that turns fairness assessment into an evidence‑based statistical decision.
Our contribution is twofold. (i) For sufficiently large subgroups, we prove a Central‑Limit result for the statistical parity difference, leading to analytic confidence intervals and a Wald test whose type‑I (false positive) error is guaranteed at level $\alpha$. (ii) For the long tail of small intersectional groups, we derive a fully Bayesian Dirichlet–multinomial estimator; Monte-Carlo credible intervals are calibrated for any sample size and naturally converge to Wald intervals as more data becomes available.
We validate our approach empirically on benchmark datasets, demonstrating how our tests provide interpretable, statistically rigorous decisions under varying degrees of data availability and intersectionality.
|
https://openreview.net/forum?id=v34WBRPSon
|
Main
|
Poster
|
v34WBRPSon
|
Towards Irreversible Attack: Fooling Scene Text Recognition via Multi-Population Coevolution Search
|
[
"Jingyu Li",
"Pengwen Dai",
"Mingqing Zhu",
"Chengwei Wang",
"Haolong Liu",
"Xiaochun Cao"
] |
Recent work has shown that scene text recognition (STR) models are vulnerable to adversarial examples.
Different from non-sequential vision tasks, the output sequence of STR models contains rich information.
However, existing adversarial attacks against STR models can only lead to a few incorrect characters in the predicted text.
These attack results still carry partial information about the original prediction and could be easily corrected by an external dictionary or a language model.
Therefore, we propose the Multi-Population Coevolution Search (MPCS) method to attack each character in the image.
We first decompose the global optimization objective into sub-objectives to solve the attack pixel concentration problem existing in previous attack methods.
While this distributed optimization paradigm brings a new joint perturbation shift problem, we propose a novel coevolution energy function to solve it.
Experiments on recent STR models show the superiority of our method.
The code is available at \url{https://github.com/Lee-Jingyu/MPCS}.
|
https://openreview.net/forum?id=v31jzDdDts
|
Main
|
Poster
|
v31jzDdDts
|
The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics
|
[
"Marco Sälzer",
"Przemysław Andrzej Wałęga",
"Martin Lange"
] |
In recent years, the expressive power of various neural architectures---including graph neural networks (GNNs), transformers, and recurrent neural networks---has been characterised using tools from logic and formal language theory. As the capabilities of basic architectures are becoming well understood, increasing attention is turning to models that combine multiple architectural paradigms. Among them particularly important, and challenging to analyse, are temporal extensions of GNNs, which integrate both spatial (graph-structure) and temporal (evolution over time) dimensions. In this paper, we initiate the study of logical characterisation of temporal GNNs by connecting them to two-dimensional product logics. We show that the expressive power of temporal GNNs depends on how graph and temporal components are combined. In particular, temporal GNNs that apply static GNNs recursively over time can capture all properties definable in the product logic of (past) propositional temporal logic PTL and the modal logic K. In contrast, architectures such as graph-and-time TGNNs and global TGNNs can only express restricted fragments of this logic, where the interaction between temporal and spatial operators is syntactically constrained. These provide us with the first results on the logical expressiveness of temporal GNNs.
|
https://openreview.net/forum?id=v13yQBxhut
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Main
|
Poster
|
v13yQBxhut
|
On the Relation between Rectified Flows and Optimal Transport
|
[
"Johannes Hertrich",
"Antonin Chambolle",
"Julie Delon"
] |
This paper investigates the connections between rectified flows, flow matching, and optimal transport. Flow matching is a recent approach to learning generative models by estimating velocity fields that guide transformations from a source to a target distribution. Rectified flow matching aims to straighten the learned transport paths, yielding more direct flows between distributions. Our first contribution is a set of invariance properties of rectified flows and explicit velocity fields. In addition, we also provide explicit constructions and analysis in the Gaussian (not necessarily independent) and Gaussian mixture settings and study the relation to optimal transport. Our second contribution addresses recent claims suggesting that rectified flows, when constrained such that the learned velocity field is a gradient, can yield (asymptotically) solutions to optimal transport problems. We study the existence of solutions for this problem and demonstrate that they only relate to optimal transport under assumptions that are significantly stronger than those previously acknowledged. In particular, we present several counterexamples that invalidate earlier equivalence results in the literature, and we argue that enforcing a gradient constraint on rectified flows is, in general, not a reliable method for computing optimal transport maps.
|
https://openreview.net/forum?id=v04csnvCfd
|
Main
|
Poster
|
v04csnvCfd
|
A Plug-and-Play Query Synthesis Active Learning Framework for Neural PDE Solvers
|
[
"Zhiyuan Wang",
"Jinwoo Go",
"Byung-Jun Yoon",
"Nathan Urban",
"Xiaoning Qian"
] |
In recent developments in scientific machine learning (SciML), neural surrogate solvers for partial differential equations (PDEs) have become powerful tools for accelerating scientific computation for various science and engineering applications. However, training neural PDE solvers often demands a large amount of high-fidelity PDE simulation data, which are expensive to generate. Active learning (AL) offers a promising solution by adaptively selecting training data from the PDE settings--including parameters, initial and boundary conditions--that are expected to be most informative to help reduce this data burden. In this work, we introduce PaPQS, a Plug-and-Play Query Synthesis AL framework that synthesizes informative PDE settings directly in the continuous design space. PaPQS optimizes the Expected Information Gain (EIG) while encouraging batch diversity, enabling model-aware exploration of the design space via backpropagation through the neural PDE solution trajectories. The framework is applicable to general PDE systems and surrogate architectures, and can be seamlessly integrated with existing AL strategies. Extensive experiments across different PDE systems demonstrate that our AL framework, PaPQS, consistently improves sample efficiency over existing AL baselines.
|
https://openreview.net/forum?id=uyJcF4cwMc
|
Main
|
Poster
|
uyJcF4cwMc
|
Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay
|
[
"Yifan Sun",
"Jingyan Shen",
"Yibin Wang",
"Tianyu Chen",
"Zhendong Wang",
"Mingyuan Zhou",
"Huan Zhang"
] |
Reinforcement learning (RL) has become an effective approach for fine-tuning large language models (LLMs), particularly to enhance their reasoning capabilities. However, RL fine-tuning remains highly resource-intensive, and existing work has largely overlooked the problem of data efficiency. In this paper, we propose two techniques to improve data efficiency in LLM RL fine-tuning: difficulty-targeted online data selection and rollout replay. We introduce the notion of adaptive difficulty to guide online data selection, prioritizing questions of moderate difficulty that are more likely to yield informative learning signals. To estimate adaptive difficulty efficiently, we develop an attention-based framework that requires rollouts for only a small reference set of questions. The adaptive difficulty of the remaining questions is then estimated based on their similarity to this set. To further reduce rollout cost, we introduce a rollout replay mechanism inspired by experience replay in traditional RL. This technique reuses recent rollouts, lowering per-step computation while maintaining stable updates. Experiments across 6 LLM-dataset combinations show that our method reduces RL fine-tuning time by 23% to 62% while reaching the same level of performance as the original GRPO algorithm.
Our code repository is available at https://github.com/ASTRAL-Group/data-efficient-llm-rl/.
|
https://openreview.net/forum?id=uwUkETPIJN
|
Main
|
Poster
|
uwUkETPIJN
|
SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications
|
[
"Gabriele Oliaro",
"Zhihao Jia",
"Daniel F Campos",
"Aurick Qiao"
] |
Speculative decoding is widely adopted to reduce latency in large language model (LLM) inference by leveraging smaller draft models capable of handling diverse user tasks. However, emerging AI applications, such as LLM-based agents, present unique workload characteristics: instead of diverse independent requests, agentic frameworks typically submit repetitive inference requests, such as multi-agent pipelines performing similar subtasks or self-refinement loops iteratively enhancing outputs. These workloads result in long and highly predictable sequences, which current speculative decoding methods do not effectively exploit. To address this gap, we introduce \emph{SuffixDecoding}, a novel method that utilizes efficient suffix trees to cache long token sequences from prompts and previous outputs. By adaptively speculating more tokens when acceptance likelihood is high and fewer when it is low, SuffixDecoding effectively exploits opportunities for longer speculations while conserving computation when those opportunities are limited. Evaluations on agentic benchmarks, including SWE-Bench and Text-to-SQL, demonstrate that SuffixDecoding achieves speedups of up to 3.9$\times$, outperforming state-of-the-art methods -- 2.2$\times$ faster than model-based approaches like EAGLE-2/3 and 1.6$\times$ faster than model-free approaches such as Token Recycling. SuffixDecoding is open-sourced.
|
https://openreview.net/forum?id=uwL0vbeEVn
|
Main
|
Spotlight
|
uwL0vbeEVn
|
A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings
|
[
"Xiaoang Xu",
"Shuo Wang",
"Xu Han",
"Zhenghao Liu",
"Huijia Wu",
"Pei Pei Li",
"Zhiyuan Liu",
"Maosong Sun",
"Zhaofeng He"
] |
Large Reasoning Models (LRMs) achieve superior performance by extending the thought length. However, a lengthy thinking trajectory leads to reduced efficiency. Most of the existing methods are stuck in the assumption of overthinking and attempt to reason efficiently by compressing the Chain-of-Thought, but this often leads to performance degradation. To address this problem, we introduce A*-Thought, an efficient tree search-based unified framework designed to identify and isolate the most essential thoughts from the extensive reasoning chains produced by these models. It formulates the reasoning process of LRMs as a search tree, where each node represents a reasoning span in the giant reasoning space. By combining the A* search algorithm with a cost function specific to the reasoning path, it can efficiently compress the chain of thought and determine a reasoning path with high information density and low cost. In addition, we also propose a bidirectional importance estimation mechanism, which further refines this search process and enhances its efficiency beyond uniform sampling. Extensive experiments on several advanced math tasks show that A*-Thought effectively balances performance and efficiency over a huge search space. Specifically, A*-Thought can improve the performance of QwQ-32B by 2.39$\times$ with low-budget and reduce the length of the output token by nearly 50\% with high-budget. The proposed method is also compatible with several other LRMs, demonstrating its generalization capability.
|
https://openreview.net/forum?id=uvyr9bYwL6
|
Main
|
Poster
|
uvyr9bYwL6
|
Extracting task-relevant preserved dynamics from contrastive aligned neural recordings
|
[
"Yiqi Jiang",
"Kaiwen Sheng",
"Yujia Gao",
"E. Kelly Buchanan",
"Yu Shikano",
"Seung Je Woo",
"Yixiu Zhao",
"Tony Hyun Kim",
"Fatih Dinc",
"Scott Linderman",
"Mark Schnitzer"
] |
Recent work indicates that low-dimensional dynamics of neural and behavioral data are often preserved across days and subjects. However, extracting these preserved dynamics remains challenging: high-dimensional neural population activity and the recorded neuron populations vary across recording sessions. While existing modeling tools can improve alignment between neural and behavioral data, they often operate on a per-subject basis or discretize behavior into categories, disrupting its natural continuity and failing to capture the underlying dynamics. We introduce $\underline{\text{C}}$ontrastive $\underline{\text{A}}$ligned $\underline{\text{N}}$eural $\underline{\text{D}}$$\underline{\text{Y}}$namics (CANDY), an end‑to‑end framework that aligns neural and behavioral data using rank-based contrastive learning, adapted for continuous behavioral variables, to project neural activity from different sessions onto a shared low-dimensional embedding space. CANDY fits a shared linear dynamical system to the aligned embeddings, enabling an interpretable model of the conserved temporal structure in the latent space. We validate CANDY on synthetic and real-world datasets spanning multiple species, behaviors, and recording modalities. Our results show that CANDY is able to learn aligned latent embeddings and preserved dynamics across neural recording sessions and subjects, and it achieves improved cross-session behavior decoding performance. We further show that the latent linear dynamical system generalizes to new sessions and subjects, achieving comparable or even superior behavior decoding performance to models trained from scratch. These advances enable robust cross‑session behavioral decoding and offer a path towards identifying shared neural dynamics that underlie behavior across individuals and recording conditions. The code and two-photon imaging data of striatal neural activity that we acquired here are available at https://github.com/schnitzer-lab/CANDY-public.git.
|
https://openreview.net/forum?id=uvTea5Rfek
|
Main
|
Spotlight
|
uvTea5Rfek
|
Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs
|
[
"Zhixin Xie",
"Xurui Song",
"Jun Luo"
] |
Despite substantial efforts in safety alignment, recent research indicates that Large
Language Models (LLMs) remain highly susceptible to jailbreak attacks. Among
these attacks, finetuning-based ones that compromise LLMs’ safety alignment via
fine-tuning stand out due to its stable jailbreak performance. In particular, a recent
study indicates that fine-tuning with as few as 10 harmful question-answer (QA)
pairs can lead to successful jailbreaking across various harmful questions. However,
such malicious fine-tuning attacks are readily detectable and hence thwarted by
moderation models. In this paper, we demonstrate that LLMs can be jailbroken
by fine-tuning with only 10 benign QA pairs; our attack exploits the increased
sensitivity of LLMs to fine-tuning data after being overfitted. Specifically, our
fine-tuning process starts with overfitting an LLM via fine-tuning with benign QA
pairs involving identical refusal answers. Further fine-tuning is then performed
with standard benign answers, causing the overfitted LLM to forget the refusal
attitude and thus provide compliant answers regardless of the harmfulness of a
question. We implement our attack on the ten LLMs and compare it with five
existing baselines. Experiments demonstrate that our method achieves significant
advantages in both attack effectiveness and attack stealth. Our findings expose
previously unreported security vulnerabilities in current LLMs and provide a new
perspective on understanding how LLMs’ security is compromised, even with
benign fine-tuning. Our code is available at https://github.com/ZHIXINXIE/ten_benign.git.
|
https://openreview.net/forum?id=utvu4PJ0Ct
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Main
|
Poster
|
utvu4PJ0Ct
|
A Generalist Intracortical Motor Decoder
|
[
"Joel Ye",
"Fabio Rizzoglio",
"Xuan Ma",
"Adam Smoulder",
"Hongwei Mao",
"Gary H Blumenthal",
"William Hockeimer",
"Nicolas Guazzelli Kunigk",
"Dalton D. Moore",
"Patrick J. Marino",
"Raeed H. Chowdhury",
"J. Patrick Mayo",
"Aaron Batista",
"Steven Chase",
"Michael L Boninger",
"Charles M. Greenspon",
"Andrew B. Schwartz",
"Nicholas G. Hatsopoulos",
"Lee E. Miller",
"Kristofer Bouchard",
"Jennifer L Collinger",
"Leila Wehbe",
"Robert Gaunt"
] |
Mapping the relationship between neural activity and motor behavior is a central aim of sensorimotor neuroscience and neurotechnology. While most progress to this end has relied on restricting complexity, the advent of foundation models instead proposes integrating a breadth of data as an alternate avenue for broadly advancing downstream modeling. We quantify this premise for motor decoding from intracortical microelectrode data, pretraining an autoregressive Transformer on 2000 hours of neural population spiking activity paired with diverse motor covariates from over 30 monkeys and humans. The resulting model is broadly useful, benefiting decoding on 8 downstream decoding tasks and generalizing to a variety of neural distribution shifts. However, we also highlight that scaling autoregressive Transformers seems unlikely to resolve limitations stemming from sensor variability and output stereotypy in neural datasets.
|
https://openreview.net/forum?id=utXSSdD9mt
|
Main
|
Poster
|
utXSSdD9mt
|
InstructRestore: Region-Customized Image Restoration with Human Instructions
|
[
"Shuaizheng Liu",
"Jianqi Ma",
"Lingchen Sun",
"Xiangtao Kong",
"Lei Zhang"
] |
Despite the significant progress in diffusion prior-based image restoration for real-world scenarios, most existing methods apply uniform processing to the entire image, lacking the capability to perform region-customized image restoration according to user preferences. In this work, we propose a new framework, namely InstructRestore, to perform region-adjustable image restoration following human instructions. To achieve this, we first develop a data generation engine to produce training triplets, each consisting of a high-quality image, the target region description, and the corresponding region mask. With this engine and careful data screening, we construct a comprehensive dataset comprising 536,945 triplets to support the training and evaluation of this task. We then examine how to integrate the low-quality image features under the ControlNet architecture to adjust the degree of image details enhancement. Consequently, we develop a ControlNet-like model to identify the target region and allocate different integration scales to the target and surrounding regions, enabling region-customized image restoration that aligns with user instructions. Experimental results demonstrate that our proposed InstructRestore approach enables effective human-instructed image restoration, including restoration with controllable bokeh blur effects and region-specific restoration with continuous intensity control. Our work advances the investigation of interactive image restoration and enhancement techniques. Data, code, and models are publicly available at https://github.com/shuaizhengliu/InstructRestore.git.
|
https://openreview.net/forum?id=utA0BT3BKF
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Main
|
Poster
|
utA0BT3BKF
|
Feedback-Aware MCTS for Goal-Oriented Information Seeking
|
[
"Harshita Chopra",
"Chirag Shah"
] |
Effective decision-making and problem-solving in conversational systems require the ability to identify and acquire missing information through targeted questioning. A key challenge lies in efficiently narrowing down a large space of possible outcomes by posing questions that minimize uncertainty. To address this, we introduce a novel framework that leverages Large Language Models (LLMs) to generate information-seeking questions, with Monte Carlo Tree Search (MCTS) to strategically select questions that maximize information gain, as a part of inference-time planning. Our primary contribution includes a hierarchical feedback mechanism that exploits past interaction patterns to guide future strategy. Specifically, each new problem is mapped to a cluster based on semantic similarity, and our UCT (Upper Confidence bound for Trees) formulation employs a cluster-specific bonus reward to prioritize successful question trajectories that have proven effective for similar problems in the past. Extensive empirical evaluation across medical diagnosis and technical troubleshooting domains shows that our method achieves an average of 12\% improvement in success rates and about 10x reduction in the number of LLM calls made for planning per conversation, compared to the state of the art. An additional 8\% gain in success rate is observed on average when we start with a constrained set of possibilities. Our results underscore the efficacy of feedback-aware MCTS in enhancing information-seeking in goal-oriented dialogues.
|
https://openreview.net/forum?id=ustF8MMZDJ
|
Main
|
Spotlight
|
ustF8MMZDJ
|
UFT: Unifying Supervised and Reinforcement Fine-Tuning
|
[
"Mingyang Liu",
"Gabriele Farina",
"Asuman E. Ozdaglar"
] |
Post-training has demonstrated its importance in enhancing the reasoning capabilities of large language models (LLMs). The primary post-training methods can be categorized into supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). SFT is efficient and well-suited for small language models, but it may lead to overfitting and limit the reasoning abilities of larger models. In contrast, RFT generally yields better generalization but depends heavily on the strength of the base model. To address the limitations of SFT and RFT, we propose Unified Fine-Tuning (UFT), a novel post-training paradigm that unifies SFT and RFT into a single, integrated process. UFT enables the model to effectively explore solutions while incorporating informative supervision signals, bridging the gap between memorizing and thinking underlying existing methods. Notably, UFT outperforms both SFT and RFT in general, regardless of model sizes. Furthermore, we theoretically prove that UFT breaks RFT's inherent exponential sample complexity bottleneck, showing for the first time that unified training can exponentially accelerate convergence on long-horizon reasoning tasks.
|
https://openreview.net/forum?id=usOkGv1S7M
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Main
|
Poster
|
usOkGv1S7M
|
A faster training algorithm for regression trees with linear leaves, and an analysis of its complexity
|
[
"Kuat Gazizov",
"Miguel Á. Carreira-Perpiñán"
] |
We consider the Tree Alternating Optimization (TAO) algorithm to train regression trees with linear predictors in the leaves. Unlike the traditional, greedy recursive partitioning algorithms such as CART, TAO guarantees a monotonic decrease of the objective function and results in smaller trees of much better accuracy. We modify the TAO algorithm so that it produces exactly the same result but is much faster, particularly for high input dimensionality or deep trees. The idea is based on the fact that, at each iteration of TAO, each leaf receives only a subset of the training instances. Thus, the optimization of the leaf model can be done exactly but faster by using the Sherman-Morrison-Woodbury formula. This has the unexpected advantage that, once a tree exceeds a critical depth, then making it deeper makes it faster to train, even though the tree is larger and has more parameters. Indeed, this can make learning a nonlinear model (the tree) asymptotically faster than a regular linear regression model. We analyze the corresponding computational complexity and verify the speedups experimentally in various datasets. The argument can be applied to other types of trees, whenever the optimization of a node can be computed in superlinear time of the number of instances.
|
https://openreview.net/forum?id=urDdBuhbLx
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Main
|
Poster
|
urDdBuhbLx
|
ReMA: Learning to Meta-Think for LLMs with Multi-agent Reinforcement Learning
|
[
"Ziyu Wan",
"Yunxiang LI",
"Xiaoyu Wen",
"Yan Song",
"Hanjing Wang",
"Linyi Yang",
"Mark Schmidt",
"Jun Wang",
"Weinan Zhang",
"Shuyue Hu",
"Ying Wen"
] |
Recent research on Reasoning of Large Language Models (LLMs) has sought to further enhance their performance by integrating meta-thinking—enabling models to monitor, evaluate, and control their reasoning processes for more adaptive and effective problem-solving.
However, current single-agent work lacks a specialized design for acquiring meta-thinking, resulting in low efficacy.
To address this challenge, we introduce Reinforced Meta-thinking Agents (ReMA), a novel framework that leverages Multi-Agent Reinforcement Learning (MARL) to elicit meta-thinking behaviors, encouraging LLMs to think about thinking.
ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions.
Through iterative reinforcement learning with aligned objectives, these agents explore and learn collaboration, leading to improved generalization and robustness.
Empirical results from single-turn experiments demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks, including competitive-level mathematical benchmarks and LLM-as-a-Judge benchmarks.
Additionally, we further extend ReMA to multi-turn interaction settings, leveraging turn-level ratio and parameter sharing to improve efficiency.
Comprehensive ablation studies further illustrate the evolving dynamics of each distinct agent, providing valuable insights into how the meta-thinking reasoning process enhances the reasoning capabilities of LLMs.
|
https://openreview.net/forum?id=ur295YVtmt
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Main
|
Poster
|
ur295YVtmt
|
Support Vector Generation: Kernelizing Large Language Models for Efficient Zero‑Shot NLP
|
[
"Shohei Ohsawa"
] |
We introduce Support Vector Generation (SVG), a kernel-based framework that converts a frozen language model into an interpretable, training-free classifier for zero- and few-shot learning. SVG operates by combining Metropolis–Hastings sampling with support vector machine optimization in the reproducing kernel Hilbert space (RKHS) induced by the language model's embedding. Each classification decision is based on a weighted combination of at most 32 natural-language sentences, which serve as explicit support vectors and provide faithful rationales. Our theoretical analysis proves that SVG minimizes the empirical hinge loss over the span of the supports and admits a generalization bound independent of the language model size. Experiments on the GLUE benchmark show that SVG matches or surpasses prompting-based zero-shot baselines in accuracy across multiple tasks—without any fine-tuning or GPU acceleration. Notably, our CPU-only implementation completes training in under three minutes per task, and maintains competitive inference speed. These results suggest that SVG offers a viable path toward efficient, interpretable NLP systems under compute constraints.
|
https://openreview.net/forum?id=upU88pUpzX
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Main
|
Poster
|
upU88pUpzX
|
OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates
|
[
"Jinpei Guo",
"Yifei Ji",
"Zheng Chen",
"Kai Liu",
"Min Liu",
"Wang Rao",
"Wenbo Li",
"Yong Guo",
"Yulun Zhang"
] |
Pretrained latent diffusion models have shown strong potential for lossy image compression, owing to their powerful generative priors. Most existing diffusion-based methods reconstruct images by iteratively denoising from random noise, guided by compressed latent representations. While these approaches have achieved high reconstruction quality, their multi-step sampling process incurs substantial computational overhead. Moreover, they typically require training separate models for different compression bit-rates, leading to significant training and storage costs. To address these challenges, we propose a one-step diffusion codec across multiple bit-rates. termed OSCAR. Specifically, our method views compressed latents as noisy variants of the original latents, where the level of distortion depends on the bit-rate. This perspective allows them to be modeled as intermediate states along a diffusion trajectory. By establishing a mapping from the compression bit-rate to a pseudo diffusion timestep, we condition a single generative model to support reconstructions at multiple bit-rates. Meanwhile, we argue that the compressed latents retain rich structural information, thereby making one-step denoising feasible. Thus, OSCAR replaces iterative sampling with a single denoising pass, significantly improving inference efficiency. Extensive experiments demonstrate that OSCAR achieves superior performance in both quantitative and visual quality metrics. The code and models are available at https://github.com/jp-guo/OSCAR/.
|
https://openreview.net/forum?id=uodE9CAXaF
|
Main
|
Poster
|
uodE9CAXaF
|
FineRS: Fine-grained Reasoning and Segmentation of Small Objects with Reinforcement Learning
|
[
"Lu Zhang",
"Jiazuo Yu",
"Haomiao Xiong",
"Ping Hu",
"Yunzhi Zhuge",
"Huchuan Lu",
"You He"
] |
Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities across a wide range of vision-language tasks. However, due to the restricted input resolutions, MLLMs face significant challenges in precisely understanding and localizing visual details in high-resolution images---particularly when dealing with extra-small objects embedded in cluttered contexts.
To address this issue, we propose FineRS, a two-stage MLLM-based reinforcement learning framework for jointly reasoning and segmenting extremely small objects within high-resolution scenes. FineRS adopts a coarse-to-fine pipeline comprising Global Semantic Exploration (GSE) and Localized Perceptual Refinement (LPR). Specifically, GSE performs instruction-guided reasoning to generate a textural response and a coarse target region, while LPR refines this region to produce an accurate bounding box and segmentation mask. To couple the two stages, we introduce a locate-informed retrospective reward, where LPR's outputs are used to optimize GSE for more robust coarse region exploration. Additionally, we present FineRS-4k, a new dataset for evaluating MLLMs on attribute-level reasoning and pixel-level segmentation on subtle, small-scale targets in complex high-resolution scenes. Experimental results on FineRS-4k and public datasets demonstrate that our method consistently outperforms state-of-the-art MLLM-based approaches on both instruction-guided segmentation and visual reasoning tasks.
|
https://openreview.net/forum?id=unMwI4JLrP
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Main
|
Poster
|
unMwI4JLrP
|
Thinking vs. Doing: Improving Agent Reasoning by Scaling Test-Time Interaction
|
[
"Junhong Shen",
"Hao Bai",
"Lunjun Zhang",
"Yifei Zhou",
"Amrith Setlur",
"Shengbang Tong",
"Diego Caples",
"Nan Jiang",
"Tong Zhang",
"Ameet Talwalkar",
"Aviral Kumar"
] |
Test-time scaling in agentic tasks often relies on generating long reasoning traces ("think" more) before acting, but this does not allow agents to acquire new information from the environment or adapt behavior over time. In this work, we propose scaling test-time interaction, an untapped dimension for test-time scaling that increases the agent's interaction horizon to enable rich behaviors such as exploration, backtracking, and dynamic re-planning within a single rollout. To demonstrate the promise of this scaling dimension, we situate our study in the domain of web agents. We first show that even prompting-based interaction scaling can improve task success on web benchmarks non-trivially. Building on this, we introduce TTI, a curriculum-based online reinforcement learning (RL) approach that trains agents by adaptively adjusting their interaction lengths during rollout. Using a Gemma 3 12B model, TTI sets a new state-of-the-art among open-source agents trained on public data on WebVoyager and WebArena. Case studies further reveal that TTI enables agents to balance exploration and exploitation adaptively. Our results establish interaction scaling as a powerful, complementary axis to scaling per-action compute, offering new avenues for training robust and adaptive agents.
|
https://openreview.net/forum?id=un1TRwNgiv
|
Main
|
Poster
|
un1TRwNgiv
|
Better Estimation of the Kullback--Leibler Divergence Between Language Models
|
[
"Afra Amini",
"Tim Vieira",
"Ryan Cotterell"
] |
Estimating the Kullback--Leibler (KL) divergence between language models has many applications, e.g., reinforcement learning from human feedback (RLHF), interpretability, and knowledge distillation. However, computing the exact KL divergence between two arbitrary language models is intractable. Thus, practitioners often resort to sampling-based estimators. While it is easy to fashion a simple Monte Carlo (MC) estimator that provides an unbiased estimate of the KL divergence between language models, this estimator notoriously suffers from high variance and can even result in a negative estimate of the KL divergence, a non-negative quantity. In this paper, we introduce a Rao--Blackwellized estimator that is unbiased and provably has variance less than or equal to that of the standard Monte Carlo estimator.
In an empirical study on sentiment-controlled fine-tuning, we show that our estimator provides more stable KL estimates and reduces variance substantially. Additionally, we derive an analogous Rao--Blackwellized estimator of the gradient of the KL divergence, which leads to more stable training and produces models that more frequently appear on the Pareto frontier of reward vs. KL compared to the ones trained with the MC estimator of the gradient.
|
https://openreview.net/forum?id=um9kHMof0c
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Main
|
Poster
|
um9kHMof0c
|
Mellow: a small audio language model for reasoning
|
[
"Soham Deshmukh",
"Satvik Dixit",
"Rita Singh",
"Bhiksha Raj"
] |
Multimodal Audio-Language Models (ALMs) can understand and reason over both audio and text. Typically, reasoning performance correlates with model size, with the best results achieved by models exceeding 8 billion parameters. However, no prior work has explored enabling small audio-language models to perform reasoning tasks, despite the potential applications for edge devices. To address this gap, we introduce Mellow, a small Audio-Language Model specifically designed for reasoning. Mellow achieves state-of-the-art performance among existing small audio-language models and surpasses several larger models in reasoning capabilities. For instance, Mellow scores 52.11 on MMAU, comparable to SoTA Qwen2 Audio (which scores 52.5) while using 50 times fewer parameters and being trained on 60 times less data (audio hrs). To train Mellow, we introduce ReasonAQA, a dataset designed to enhance audio-grounded reasoning in models. It consists of a mixture of existing datasets (30\% of the data) and synthetically generated data (70\%). The synthetic dataset is derived from audio captioning datasets, where Large Language Models (LLMs) generate detailed and multiple-choice questions focusing on audio events, objects, acoustic scenes, signal properties, semantics, and listener emotions. To evaluate Mellow’s reasoning ability, we benchmark it on a diverse set of tasks, assessing on both in-distribution and out-of-distribution data, including audio understanding, deductive reasoning, and comparative reasoning. Finally, we conduct extensive ablation studies to explore the impact of projection layer choices, synthetic data generation methods, and language model pretraining on reasoning performance. Our training dataset, findings, and baseline pave the way for developing small ALMs capable of reasoning.
|
https://openreview.net/forum?id=um4aiicz3L
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Main
|
Poster
|
um4aiicz3L
|
OOD Detection with Relative Angles
|
[
"Berker Demirel",
"Marco Fumero",
"Francesco Locatello"
] |
Deep learning systems deployed in real-world applications often encounter data that is different from their in-distribution (ID). A reliable model should ideally abstain from making decisions in this out-of-distribution (OOD) setting. Existing state-of-the-art methods primarily focus on feature distances, such as k-th nearest neighbors and distances to decision boundaries, either overlooking or ineffectively using in-distribution statistics. In this work, we propose a novel angle-based metric for OOD detection that is computed relative to the in-distribution structure. We demonstrate that the angles between feature representations and decision boundaries, viewed from the mean of in-distribution features, serve as an effective discriminative factor between ID and OOD data. We evaluate our method on nine ImageNet-pretrained models. Our approach achieves the lowest FPR in 5 out of 9 ImageNet models, obtains the best average FPR overall, and consistently ranking among the top 3 across all evaluated models. Furthermore, we highlight the benefits of contrastive representations by showing strong performance with ResNet SCL and CLIP architectures. Finally, we demonstrate that the scale-invariant nature of our score enables an ensemble strategy via simple score summation.
|
https://openreview.net/forum?id=ul5mlXrLZb
|
Main
|
Poster
|
ul5mlXrLZb
|
Synthetic-powered predictive inference
|
[
"Meshi Bashari",
"Roy Maor Lotan",
"Yonghoon Lee",
"Edgar Dobriban",
"Yaniv Romano"
] |
Conformal prediction is a framework for predictive inference with a distribution-free, finite-sample guarantee. However, it tends to provide uninformative prediction sets when calibration data are scarce. This paper introduces Synthetic-powered predictive inference (SPI), a novel framework that incorporates synthetic data---e.g., from a generative model---to improve sample efficiency. At the core of our method is a score transporter: an empirical quantile mapping that aligns nonconformity scores from trusted, real data with those from synthetic data. By carefully integrating the score transporter into the calibration process, SPI provably achieves finite-sample coverage guarantees without making any assumptions about the real and synthetic data distributions. When the score distributions are well aligned, SPI yields substantially tighter and more informative prediction sets than standard conformal prediction. Experiments on image classification---augmenting data with synthetic diffusion-model generated images---and on tabular regression demonstrate notable improvements in predictive efficiency in data-scarce settings.
|
https://openreview.net/forum?id=ujg9XKwqWT
|
Main
|
Poster
|
ujg9XKwqWT
|
Part-Aware Bottom-Up Group Reasoning for Fine-Grained Social Interaction Detection
|
[
"Dongkeun Kim",
"Minsu Cho",
"Suha Kwak"
] |
Social interactions often emerge from subtle, fine-grained cues such as facial expressions, gaze, and gestures. However, existing methods for social interaction detection overlook such nuanced cues and primarily rely on holistic representations of individuals. Moreover, they directly detect social groups without explicitly modeling the underlying interactions between individuals. These drawbacks limit their ability to capture localized social signals and introduce ambiguity when group configurations should be inferred from social interactions grounded in nuanced cues. In this work, we propose a part-aware bottom-up group reasoning framework for fine-grained social interaction detection. The proposed method infers social groups and their interactions using body part cues and their interpersonal relations. Our model first detects individuals and enhances their features using part-aware features, and then infers group configuration by associating individuals via similarity-based reasoning, which considers not only spatial relations but also subtle social cues that signal interactions, leading to more accurate group inference. Experiments on the NVI dataset demonstrate that our method outperforms prior methods, achieving the new state of the art.
|
https://openreview.net/forum?id=uiuA0ixKLd
|
Main
|
Poster
|
uiuA0ixKLd
|
Improved Regret Bounds for Linear Bandits with Heavy-Tailed Rewards
|
[
"Artin Tajdini",
"Jonathan Scarlett",
"Kevin Jamieson"
] |
We study stochastic linear bandits with heavy-tailed rewards, where the rewards have a finite $(1+\epsilon)$-absolute central moment bounded by $\upsilon$ for some $\epsilon \in (0,1]$. We improve both upper and lower bounds on the minimax regret compared to prior work. When $\upsilon = \mathcal{O}(1)$, the best prior known regret upper bound is $\tilde{O}(d T^{\frac{1}{1+\epsilon}})$. While a lower with the same scaling has been given, it relies on a construction using $\upsilon = d$, and adapting the construction to the bounded-moment regime with $\upsilon = \mathcal{O}(1)$ yields only a $\Omega(d^{\frac{\epsilon}{1+\epsilon}} T^{\frac{1}{1+\epsilon}})$ lower bound. This matches the known rate for multi-armed bandits and is generally loose for linear bandits, in particular being $\sqrt{d}$ below the optimal rate in the finite-variance case ($\epsilon = 1$).
We propose a new elimination-based algorithm guided by experimental design, which achieves regret $\tilde{\mathcal{O}}(d^{\frac{1+3\epsilon}{2(1+\epsilon)}} T^{\frac{1}{1+\epsilon}})$, thus improving the dependence on $d$ for all $\epsilon \in (0,1)$ and recovering a known optimal result for $\epsilon = 1$. We also establish a lower bound of $\Omega(d^{\frac{2\epsilon}{1+\epsilon}} T^{\frac{1}{1+\epsilon}})$, which strictly improves upon the multi-armed bandit rate and highlights the hardness of heavy-tailed linear bandit problems. For finite action sets of size $n$, we derive upper and lower bounds of
$\tilde{\mathcal{O}}(\sqrt d (\log n)^{\frac{\epsilon}{1+\epsilon}}T^{\frac{1}{1+\epsilon}})$ and
$\tilde\Omega(d^{\frac{\epsilon}{1+\epsilon}}(\log n)^{\frac{\epsilon}{1+\epsilon}} T^{\frac{1}{1+\epsilon}})$, respectively.
Finally, we provide action-set-dependent regret upper bounds and show that for some geometries, such as $l_p$-norm balls for $p \le 1 + \epsilon$, we can further reduce the dependence on $d$.
|
https://openreview.net/forum?id=uih8cWS3JF
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Main
|
Poster
|
uih8cWS3JF
|
Less Is More, but Where? Dynamic Token Compression via LLM-Guided Keyframe Prior
|
[
"Yulin Li",
"Haokun GUI",
"Ziyang Fan",
"Junjie Wang",
"Bin Kang",
"BIN CHEN",
"Zhuotao Tian"
] |
Recent advances in Video Large Language Models (VLLMs) have achieved remarkable video understanding capabilities, yet face critical efficiency bottlenecks due to quadratic computational growth with lengthy visual token sequences of long videos. While existing keyframe sampling methods can improve temporal modeling efficiency, additional computational cost is introduced before feature encoding, and the binary frame selection paradigm is found suboptimal. Therefore, in this work, we propose **Dy**namic **To**ken compression via LLM-guided **K**eyframe prior (**DyToK**), a training-free paradigm that enables dynamic token compression by harnessing VLLMs' inherent attention mechanisms. Our analysis reveals that VLLM attention layers naturally encoding query-conditioned keyframe priors, by which DyToK dynamically adjusts per-frame token retention ratios, prioritizing semantically rich frames while suppressing redundancies. Extensive experiments demonstrate that DyToK achieves state-of-the-art efficiency-accuracy tradeoffs. DyToK shows plug-and-play compatibility with existing compression methods, such as VisionZip and FastV, attaining 2.5x faster inference while preserving accuracy across multiple VLLMs, such as LLaVA-OneVision and Qwen2.5-VL. Code and models will be made publicly available.
|
https://openreview.net/forum?id=uhFx1RGD1g
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Main
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Poster
|
uhFx1RGD1g
|
Influence Guided Context Selection for Effective Retrieval-Augmented Generation
|
[
"Jiale Deng",
"Yanyan Shen",
"Ziyuan Pei",
"Youmin Chen",
"Linpeng Huang"
] |
Retrieval-Augmented Generation (RAG) addresses large language model (LLM) hallucinations by grounding responses in external knowledge, but its effectiveness is compromised by poor-quality retrieved contexts containing irrelevant or noisy information. While existing approaches attempt to improve performance through context selection based on predefined context quality assessment metrics, they show limited gains over standard RAG. We attribute this limitation to their failure in holistically utilizing available information (query, context list, and generator) for comprehensive quality assessment. Inspired by recent advances in data selection, we reconceptualize context quality assessment as an inference-time data valuation problem and introduce the Contextual Influence Value (CI value). This novel metric quantifies context quality by measuring the performance degradation when removing each context from the list, effectively integrating query-aware relevance, list-aware uniqueness, and generator-aware alignment. Moreover, CI value eliminates complex selection hyperparameter tuning by simply retaining contexts with positive CI values. To address practical challenges of label dependency and computational overhead, we develop a parameterized surrogate model for CI value prediction during inference. The model employs a hierarchical architecture that captures both local query-context relevance and global inter-context interactions, trained through oracle CI value supervision and end-to-end generator feedback. Extensive experiments across 8 NLP tasks and multiple LLMs demonstrate that our context selection method significantly outperforms state-of-the-art baselines, effectively filtering poor-quality contexts while preserving critical information. Code is available at https://github.com/SJTU-DMTai/RAG-CSM.
|
https://openreview.net/forum?id=ugaepulZyA
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Main
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Poster
|
ugaepulZyA
|
Towards Prospective Medical Image Reconstruction via Knowledge-Informed Dynamic Optimal Transport
|
[
"Taoran Zheng",
"Yan Yang",
"Xing Li",
"Xiang Gu",
"Jian Sun",
"Zongben Xu"
] |
Medical image reconstruction from measurement data is a vital but challenging inverse problem. Deep learning approaches have achieved promising results, but often requires paired measurement and high-quality images, which is typically simulated through a forward model, i.e., retrospective reconstruction. However, training on simulated pairs commonly leads to performance degradation on real prospective data due to the retrospective-to-prospective gap caused by incomplete imaging knowledge in simulation. To address this challenge, this paper introduces imaging Knowledge-Informed Dynamic Optimal Transport (KIDOT), a novel dynamic optimal transport framework with optimality in the sense of preserving consistency with imaging physics in transport, that conceptualizes reconstruction as finding a dynamic transport path. KIDOT learns from unpaired data by modeling reconstruction as a continuous evolution path from measurements to images, guided by an imaging knowledge-informed cost function and transport equation. This dynamic and knowledge-aware approach enhances robustness and better leverages unpaired data while respecting acquisition physics. Theoretically, we demonstrate that KIDOT naturally generalizes dynamic optimal transport, ensuring its mathematical rationale and solution existence. Extensive experiments on MRI and CT reconstruction demonstrate KIDOT's superior performance. Code is available at https://github.com/TaoranZheng717/KIDOT.
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https://openreview.net/forum?id=ugOn7Pohxv
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Main
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Poster
|
ugOn7Pohxv
|
Fast MRI for All: Bridging Access Gaps by Training without Raw Data
|
[
"Yasar Utku Alcalar",
"Merve Gulle",
"Mehmet Akcakaya"
] |
Physics-driven deep learning (PD-DL) approaches have become popular for improved reconstruction of fast magnetic resonance imaging (MRI) scans. Though PD-DL offers higher acceleration rates than existing clinical fast MRI techniques, their use has been limited outside specialized MRI centers. A key challenge is generalization to rare pathologies or different populations, noted in multiple studies, with fine-tuning on target populations suggested for improvement. However, current approaches for PD-DL training require access to raw k-space measurements, which is typically only available at specialized MRI centers that have research agreements for such data access. This is especially an issue for rural and under-resourced areas, where commercial MRI scanners only provide access to a final reconstructed image. To tackle these challenges, we propose Compressibility-inspired Unsupervised Learning via Parallel Imaging Fidelity (CUPID) for high-quality PD-DL training using only routine clinical reconstructed images exported from an MRI scanner. CUPID evaluates output quality with a compressibility-based approach while ensuring that the output stays consistent with the clinical parallel imaging reconstruction through well-designed perturbations. Our results show CUPID achieves similar quality to established PD-DL training that requires k-space data while outperforming compressed sensing (CS) and diffusion-based generative methods. We further demonstrate its effectiveness in a zero-shot training setup for retrospectively and prospectively sub-sampled acquisitions, attesting to its minimal training burden. As an approach that radically deviates from existing strategies, CUPID presents an opportunity to provide broader access to fast MRI for remote and rural populations in an attempt to reduce the obstacles associated with this expensive imaging modality. Code is available at https://github.com/ualcalar17/CUPID.
|
https://openreview.net/forum?id=ugBmWX3H1R
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Main
|
Spotlight
|
ugBmWX3H1R
|
Generalizable Domain Adaptation for Sim-and-Real Policy Co-Training
|
[
"Shuo Cheng",
"Liqian Ma",
"Zhenyang Chen",
"Ajay Mandlekar",
"Caelan Reed Garrett",
"Danfei Xu"
] |
Behavior cloning has shown promise for robot manipulation, but real-world demonstrations are costly to acquire at scale. While simulated data offers a scalable alternative, particularly with advances in automated demonstration generation, transferring policies to the real world is hampered by various simulation and real domain gaps. In this work, we propose a unified sim-and-real co-training framework for learning generalizable manipulation policies that primarily leverages simulation and only requires a few real-world demonstrations. Central to our approach is learning a domain-invariant, task-relevant feature space. Our key insight is that aligning the joint distributions of observations and their corresponding actions across domains provides a richer signal than aligning observations (marginals) alone. We achieve this by embedding an Optimal Transport (OT)-inspired loss within the co-training framework, and extend this to an Unbalanced OT framework to handle the imbalance between abundant simulation data and limited real-world examples. We validate our method on challenging manipulation tasks, showing it can leverage abundant simulation data to achieve up to a 30\% improvement in the real-world success rate and even generalize to scenarios seen only in simulation.
|
https://openreview.net/forum?id=ufKaXYJt1F
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Main
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Poster
|
ufKaXYJt1F
|
Faithful Dynamic Imitation Learning from Human Intervention with Dynamic Regret Minimization
|
[
"Bo Ling",
"Zhengyu Gan",
"Wanyuan Wang",
"Guanyu Gao",
"Weiwei Wu",
"Yan Lyu"
] |
Human-in-the-loop (HIL) imitation learning enables agents to learn complex behaviors safely through real-time human intervention. However, existing methods struggle to efficiently leverage agent-generated data due to dynamically evolving trajectory distributions and imperfections caused by human intervention delays, often failing to faithfully imitate the human expert policy. In this work, we propose Faithful Dynamic Imitation Learning (FaithDaIL) to address these challenges. We formulate HIL imitation learning as an online non-convex problem and employ dynamic regret minimization to adapt to the shifting data distribution and track high-quality policy trajectories.
To ensure faithful imitation of the human expert despite training on mixed agent and human data, we introduce an unbiased imitation objective and achieve it by weighting the behavior distribution relative to the human expert's as a proxy reward.
Extensive experiments on MetaDrive and CARLA driving benchmarks demonstrate that FaithDaIL achieves state-of-the-art performance in safety and task success with significantly reduced human intervention data compared to prior HIL baselines.
|
https://openreview.net/forum?id=udHMDBrfTv
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Main
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Poster
|
udHMDBrfTv
|
Benign Overfitting in Single-Head Attention
|
[
"Roey Magen",
"Shuning Shang",
"Zhiwei Xu",
"Spencer Frei",
"Wei Hu",
"Gal Vardi"
] |
The phenomenon of benign overfitting, where a trained neural network perfectly fits noisy training data but still achieves near-optimal test performance, has been extensively studied in recent years for linear models and fully-connected/convolutional networks. In this work, we study benign overfitting in a single-head softmax attention model, which is the fundamental building block of Transformers. We prove that under appropriate conditions, the model exhibits benign overfitting in a classification setting already after two steps of gradient descent. Moreover, we show conditions where a minimum-norm/maximum-margin interpolator exhibits benign overfitting. We study how the overfitting behavior depends on the signal-to-noise ratio (SNR) of the data distribution, namely, the ratio between norms of signal and noise tokens, and prove that a sufficiently large SNR is both necessary and sufficient for benign overfitting.
|
https://openreview.net/forum?id=ud7VVZ693U
|
Main
|
Poster
|
ud7VVZ693U
|
Learning Provably Improves the Convergence of Gradient Descent
|
[
"Qingyu Song",
"Wei Lin",
"Hong Xu"
] |
Learn to Optimize (L2O) trains deep neural network-based solvers for optimization, achieving success in accelerating convex problems and improving non-convex solutions. However, L2O lacks rigorous theoretical backing for its own training convergence, as existing analyses often use unrealistic assumptions-a gap this work highlights empirically. We bridge this gap by proving the training convergence of L2O models that learn Gradient Descent (GD) hyperparameters for quadratic programming, leveraging the Neural Tangent Kernel (NTK) theory. We propose a deterministic initialization strategy to support our theoretical results and promote stable training over extended optimization horizons by mitigating gradient explosion.
Our L2O framework demonstrates over 50% better optimality than GD and superior robustness over state-of-the-art L2O methods on synthetic datasets.
The code of our method can be found from https://github.com/NetX-lab/MathL2OProof-Official.
|
https://openreview.net/forum?id=uc1js1lBlB
|
Main
|
Poster
|
uc1js1lBlB
|
Regional Explanations: Bridging Local and Global Variable Importance
|
[
"Salim I. Amoukou",
"Nicolas J-B. Brunel"
] |
We analyze two widely used local attribution methods, Local Shapley Values and LIME, which aim to quantify the contribution of a feature value $x_i$ to a specific prediction $f(x_1, \dots, x_p)$. Despite their widespread use, we identify fundamental limitations in their ability to reliably detect locally important features, even under ideal conditions with exact computations and independent features. We argue that a sound local attribution method should not assign importance to features that neither influence the model output (e.g., features with zero coefficients in a linear model) nor exhibit statistical dependence with functionality-relevant features. We demonstrate that both Local SV and LIME violate this fundamental principle. To address this, we propose R-LOCO (Regional Leave Out COvariates), which bridges the gap between local and global explanations and provides more accurate attributions. R-LOCO segments the input space into regions with similar feature importance characteristics. It then applies global attribution methods within these regions, deriving an instance's feature contributions from its regional membership. This approach delivers more faithful local attributions while avoiding local explanation instability and preserving instance-specific detail often lost in global methods.
|
https://openreview.net/forum?id=ubrecCeZrc
|
Main
|
Poster
|
ubrecCeZrc
|
Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL
|
[
"Ruitao Wu",
"Yifan Zhao",
"Guangyao Chen",
"Jia Li"
] |
Few-Shot Class-Incremental Learning (FSCIL) challenges models to sequentially learn new classes from minimal examples without forgetting prior knowledge, a task complicated by the stability-plasticity dilemma and data scarcity. Current FSCIL methods often struggle with generalization due to their reliance on limited datasets. While diffusion models offer a path for data augmentation, their direct application can lead to semantic misalignment or ineffective guidance. This paper introduces Diffusion-Classifier Synergy (DCS), a novel framework that establishes a mutual boosting loop between diffusion model and FSCIL classifier. DCS utilizes a reward-aligned learning strategy, where a dynamic, multi-faceted reward function derived from the classifier's state directs the diffusion model. This reward system operates at two levels: the feature level ensures semantic coherence and diversity using prototype-anchored maximum mean discrepancy and dimension-wise variance matching, while the logits level promotes exploratory image generation and enhances inter-class discriminability through confidence recalibration and cross-session confusion-aware mechanisms. This co-evolutionary process, where generated images refine the classifier and an improved classifier state yields better reward signals, demonstrably achieves state-of-the-art performance on FSCIL benchmarks, significantly enhancing both knowledge retention and new class learning.
|
https://openreview.net/forum?id=ub5QBBQ47S
|
Main
|
Poster
|
ub5QBBQ47S
|
LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis
|
[
"Berkay Döner",
"Thorir Mar Ingolfsson",
"Luca Benini",
"Yawei Li"
] |
Electroencephalography (EEG) offers a non-invasive lens into human brain activity, but building large‐scale models is hampered by $\textit{topological heterogeneity}$: each public corpus defines its own electrode layout, limiting generalization. We introduce $\textbf{LUNA}$ ($\textbf{L}$atent $\textbf{U}$nified $\textbf{N}$etwork $\textbf{A}$rchitecture), a self-supervised foundation model that reconciles disparate electrode geometries while scaling linearly---not quadratically---with channel count. LUNA compresses multi-channel EEG into a fixed-size, topology-agnostic latent space via learned queries and cross-attention. Downstream transformer blocks then operate exclusively on this latent representation using patch-wise temporal self-attention, decoupling computation from electrode count. Pre-trained on TUEG and Siena ($\>$21,000 h raw EEG across diverse montages) using a masked-patch reconstruction objective, LUNA transfers effectively to four downstream tasks: abnormality detection, artifact rejection, slowing classification, and emotion recognition. It demonstrates highly competitive performance across several benchmarks, achieving state-of-the-art results on TUAR and TUSL, e.g., $\textbf{0.921 AUROC}$ on TUAR, while reducing FLOPs by $\textbf{300}$$\times$ and trimming GPU memory use by up to $\textbf{10}$$\times$. Critically, these gains are consistent across all evaluated electrode configurations. Code is available at https://github.com/pulp-bio/biofoundation
|
https://openreview.net/forum?id=uazfjnFL0G
|
Main
|
Poster
|
uazfjnFL0G
|
FairImagen: Post-Processing for Bias Mitigation in Text-to-Image Models
|
[
"Zihao Fu",
"Ryan Brown",
"Shun Shao",
"Kai Rawal",
"Eoin D. Delaney",
"Chris Russell"
] |
Text-to-image diffusion models, such as Stable Diffusion, have demonstrated remarkable capabilities in generating high-quality and diverse images from natural language prompts. However, recent studies reveal that these models often replicate and amplify societal biases, particularly along demographic attributes like gender and race. In this paper, we introduce FairImagen (https://github.com/fuzihaofzh/FairImagen), a post-hoc debiasing framework that operates on prompt embeddings to mitigate such biases without retraining or modifying the underlying diffusion model. Our method integrates Fair Principal Component Analysis to project CLIP-based input embeddings into a subspace that minimizes group-specific information while preserving semantic content. We further enhance debiasing effectiveness through empirical noise injection and propose a unified cross-demographic projection method that enables simultaneous debiasing across multiple demographic attributes. Extensive experiments across gender, race, and intersectional settings demonstrate that FairImagen significantly improves fairness with a moderate trade-off in image quality and prompt fidelity. Our framework outperforms existing post-hoc methods and offers a simple, scalable, and model-agnostic solution for equitable text-to-image generation.
|
https://openreview.net/forum?id=uaQWgFk2Pf
|
Main
|
Poster
|
uaQWgFk2Pf
|
Learning Expandable and Adaptable Representations for Continual Learning
|
[
"Ruilong Yu",
"Mingyan Liu",
"Fei Ye",
"Adrian G. Bors",
"Rongyao Hu",
"Jingling sun",
"shijie zhou"
] |
Extant studies predominantly address catastrophic forgetting within a simplified continual learning paradigm, typically confined to a singular data domain. Conversely, real-world applications frequently encompass multiple, evolving data domains, wherein models often struggle to retain many critical past information, thereby leading to performance degradation. This paper addresses this complex scenario by introducing a novel dynamic expansion approach called Learning Expandable and Adaptable Representations (LEAR). This framework orchestrates a collaborative backbone structure, comprising global and local backbones, designed to capture both general and task-specific representations. Leveraging this collaborative backbone, the proposed framework dynamically create a lightweight expert to delineate decision boundaries for each novel task, thereby facilitating the prediction process. To enhance new task learning, we introduce a novel Mutual Information-Based Prediction Alignment approach, which incrementally optimizes the global backbone via a mutual information metric, ensuring consistency in the prediction patterns of historical experts throughout the optimization phase. To mitigate network forgetting, we propose a Kullback–Leibler (KL) Divergence-Based Feature Alignment approach, which employs a probabilistic distance measure to prevent significant shifts in critical local representations. Furthermore, we introduce a novel Hilbert-Schmidt Independence Criterion (HSIC)-Based Collaborative Optimization approach, which encourages the local and global backbones to capture distinct semantic information in a collaborative manner, thereby mitigating information redundancy and enhancing model performance. Moreover, to accelerate new task learning, we propose a novel Expert Selection Mechanism that automatically identifies the most relevant expert based on data characteristics. This selected expert is then utilized to initialize a new expert, thereby fostering positive knowledge transfer. This approach also enables expert selection during the testing phase without requring any task information. Empirical results demonstrate that the proposed framework achieves state-of-the-art performance.
|
https://openreview.net/forum?id=uXKgVqYTJ2
|
Main
|
Poster
|
uXKgVqYTJ2
|
Group-Level Data Selection for Efficient Pretraining
|
[
"Zichun Yu",
"Fei Peng",
"Jie Lei",
"Arnold Overwijk",
"Wen-tau Yih",
"Chenyan Xiong"
] |
The efficiency and quality of language model pretraining are largely determined by the way pretraining data are selected. In this paper, we introduce *Group-MATES*, an efficient group-level data selection approach to optimize the speed-quality frontier of language model pretraining. Specifically, Group-MATES parameterizes costly group-level selection with a relational data influence model. To train this model, we sample training trajectories of the language model and collect oracle data influences alongside. The relational data influence model approximates the oracle data influence by weighting individual influence with relationships among training data. To enable efficient selection with our relational data influence model, we partition the dataset into small clusters using relationship weights and select data within each cluster independently. Experiments on DCLM 400M-4x, 1B-1x, and 3B-1x show that Group-MATES achieves 3.5\%-9.4\% relative performance gains over random selection across 22 downstream tasks, nearly doubling the improvements achieved by state-of-the-art individual data selection baselines. Furthermore, Group-MATES reduces the number of tokens required to reach a certain downstream performance by up to 1.75x, substantially elevating the speed-quality frontier. Further analyses highlight the critical role of relationship weights in the relational data influence model and the effectiveness of our cluster-based inference. Our code is open-sourced at https://github.com/facebookresearch/Group-MATES.
|
https://openreview.net/forum?id=uX4dyc7Z5Z
|
Main
|
Poster
|
uX4dyc7Z5Z
|
Mean Flows for One-step Generative Modeling
|
[
"Zhengyang Geng",
"Mingyang Deng",
"Xingjian Bai",
"J Zico Kolter",
"Kaiming He"
] |
We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the \textit{MeanFlow} model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256$\times$256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.
|
https://openreview.net/forum?id=uWj4s7rMnR
|
Main
|
Oral
|
uWj4s7rMnR
|
Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization
|
[
"Marius Potfer",
"Vianney Perchet"
] |
Repeated multi-unit auctions, where a seller allocates multiple identical items over many rounds, are common mechanisms in electricity markets and treasury auctions. We compare the two predominant formats: uniform-price and discriminatory auctions, focusing on the perspective of a single bidder learning to bid against stochastic adversaries. We characterize the learning difficulty in each format, showing that the regret scales similarly for both auction formats under both full-information and bandit feedback, as $\tilde{\Theta} ( \sqrt{T} )$ and $\tilde{\Theta} ( T^{2/3} )$, respectively. However, analysis beyond worst-case regret reveals structural differences: uniform-price auctions may admit faster learning rates, with regret scaling as $\tilde{\Theta} ( \sqrt{T} )$ in settings where discriminatory auctions remain at $\tilde{\Theta} ( T^{2/3} )$. Finally, we provide a specific analysis for auctions in which the other participants are symmetric and have unit-demand, and show that in these instances a similar regret rate separation appears.
|
https://openreview.net/forum?id=uWMF3MuEk2
|
Main
|
Poster
|
uWMF3MuEk2
|
MuSLR: Multimodal Symbolic Logical Reasoning
|
[
"Jundong Xu",
"Hao Fei",
"Yuhui Zhang",
"Liangming Pan",
"Qijun Huang",
"Qian Liu",
"Preslav Nakov",
"Min-Yen Kan",
"William Yang Wang",
"Mong-Li Lee",
"Wynne Hsu"
] |
Multimodal symbolic logical reasoning, which aims to deduce new facts from multimodal input via formal logic, is critical in high-stakes applications such as autonomous driving and medical diagnosis, as its rigorous, deterministic reasoning helps prevent serious consequences. To evaluate such capabilities of current state-of-the-art vision language models (VLMs), we introduce the first benchmark MuSLR for multimodal symbolic logical reasoning grounded in formal logical rules. MuSLR comprises 1,093 instances across 7 domains, including 35 atomic symbolic logic and 976 logical combinations, with reasoning depths ranging from 2 to 9. We evaluate 7 state-of-the-art VLMs on MuSLR and find that they all struggle with multimodal symbolic reasoning, with the best model, GPT-4.1, achieving only 46.8%.
Thus, we propose LogiCAM, a modular framework that applies formal logical rules to multimodal inputs, boosting GPT-4.1’s Chain-of-Thought performance by 14.13%, and delivering even larger gains on complex logics such as first-order logic. We also conduct a comprehensive error analysis, showing that around 70% of failures stem from logical misalignment between modalities, offering key insights to guide future improvements.
|
https://openreview.net/forum?id=uWEcZkrSkZ
|
Main
|
Poster
|
uWEcZkrSkZ
|
3D Gaussian Flats: Hybrid 2D/3D Photometric Scene Reconstruction
|
[
"Maria Taktasheva",
"Lily Goli",
"Alessandro Fiorini",
"Zhen Li",
"Daniel Rebain",
"Andrea Tagliasacchi"
] |
Recent advances in radiance fields and novel view synthesis enable creation of realistic digital twins from photographs. However, current methods struggle with flat, texture-less surfaces, creating uneven and semi-transparent reconstructions, due to an ill-conditioned photometric reconstruction objective. Surface reconstruction methods solve this issue but sacrifice visual quality. We propose a novel hybrid 2D/3D representation that jointly optimizes constrained planar (2D) Gaussians for modeling flat surfaces and freeform (3D) Gaussians for the rest of the scene. Our end-to-end approach dynamically detects and refines planar regions, improving both visual fidelity and geometric accuracy. It achieves state-of-the-art depth estimation on ScanNet++ and ScanNetv2, and excels at mesh extraction without overfitting to a specific camera model, showing its effectiveness in producing high-quality reconstruction of indoor scenes.
|
https://openreview.net/forum?id=uVxQEIgXfL
|
Main
|
Poster
|
uVxQEIgXfL
|
ComRank: Ranking Loss for Multi-Label Complementary Label Learning
|
[
"Jing-Yi Zhu",
"Yi Gao",
"Miao Xu",
"Min-Ling Zhang"
] |
Multi-label complementary label learning (MLCLL) is a weakly supervised paradigm that addresses multi-label learning (MLL) tasks using complementary labels (i.e., irrelevant labels) instead of relevant labels. Existing methods typically adopt an unbiased risk estimator (URE) under the assumption that complementary labels follow a uniform distribution. However, this assumption fails in real-world scenarios due to instance-specific annotation biases, making URE-based methods ineffective under such conditions. Furthermore, existing methods underutilize label correlations inherent in MLL. To address these limitations, we propose ComRank, a ranking loss framework for MLCLL, which encourages complementary labels to be ranked lower than non-complementary ones, thereby modeling pairwise label relationships. Theoretically, our surrogate loss ensures Bayes consistency under both uniform and biased cases. Experiments demonstrate the effectiveness of our method in MLCLL tasks. The code is available at https://github.com/JellyJamZhu/ComRank.
|
https://openreview.net/forum?id=uVjuiPP4aP
|
Main
|
Poster
|
uVjuiPP4aP
|
Scaling Offline RL via Efficient and Expressive Shortcut Models
|
[
"Nicolas Espinosa-Dice",
"Yiyi Zhang",
"Yiding Chen",
"Bradley Guo",
"Owen Oertell",
"Gokul Swamy",
"Kianté Brantley",
"Wen Sun"
] |
Diffusion and flow models have emerged as powerful generative approaches capable of modeling diverse and multimodal behavior. However, applying these models to offline RL remains challenging due to the iterative nature of their noise sampling processes, making policy optimization difficult. In this paper, we introduce Scalable Offline Reinforcement Learning (SORL), a new offline RL algorithm that leverages shortcut models – a novel class of generative models – to scale both training and inference. SORL's policy can capture complex data distributions and can be trained simply and efficiently in a one-stage training procedure. At test time, SORL supports both sequential and parallel inference scaling by using the learned Q-function as a verifier. We demonstrate that SORL achieves strong performance across a range of offline RL tasks and exhibits positive scaling behavior with increased test-time compute.
|
https://openreview.net/forum?id=uVarpp7fhU
|
Main
|
Poster
|
uVarpp7fhU
|
Towards Implicit Aggregation: Robust Image Representation for Place Recognition in the Transformer Era
|
[
"Feng Lu",
"Tong Jin",
"Canming Ye",
"Xiangyuan Lan",
"Yunpeng Liu",
"Chun Yuan"
] |
Visual place recognition (VPR) is typically regarded as a specific image retrieval task, whose core lies in representing images as global descriptors. Over the past decade, dominant VPR methods (e.g., NetVLAD) have followed a paradigm that first extracts the patch features/tokens of the input image using a backbone, and then aggregates these patch features into a global descriptor via an aggregator. This backbone-plus-aggregator paradigm has achieved overwhelming dominance in the CNN era and remains widely used in transformer-based models. In this paper, however, we argue that a dedicated aggregator is not necessary in the transformer era, that is, we can obtain robust global descriptors only with the backbone. Specifically, we introduce some learnable aggregation tokens, which are prepended to the patch tokens before a particular transformer block. All these tokens will be jointly processed and interact globally via the intrinsic self-attention mechanism, implicitly aggregating useful information within the patch tokens to the aggregation tokens. Finally, we only take these aggregation tokens from the last output tokens and concatenate them as the global representation. Although implicit aggregation can provide robust global descriptors in an extremely simple manner, where and how to insert additional tokens, as well as the initialization of tokens, remains an open issue worthy of further exploration. To this end, we also propose the optimal token insertion strategy and token initialization method derived from empirical studies. Experimental results show that our method outperforms state-of-the-art methods on several VPR datasets with higher efficiency and ranks 1st on the MSLS challenge leaderboard. The code is available at https://github.com/lu-feng/image.
|
https://openreview.net/forum?id=uVYqwEgIpE
|
Main
|
Poster
|
uVYqwEgIpE
|
Noise Injection Reveals Hidden Capabilities of Sandbagging Language Models
|
[
"Cameron Tice",
"Philipp Alexander Kreer",
"Nathan Helm-Burger",
"Prithviraj Singh Shahani",
"Fedor Ryzhenkov",
"Fabien Roger",
"Clement Neo",
"Jacob Haimes",
"Felix Hofstätter",
"Teun van der Weij"
] |
Capability evaluations play a crucial role in assessing and regulating frontier AI systems. The effectiveness of these evaluations faces a significant challenge: strategic underperformance, or ``sandbagging'', where models deliberately underperform during evaluation.
Sandbagging can manifest either through explicit developer intervention or through unintended model behavior, presenting a fundamental obstacle to accurate capability assessment. We introduce a novel sandbagging detection method based on injecting noise of varying magnitudes into model weights. While non-sandbagging models show predictable performance degradation with increasing noise, we demonstrate that sandbagging models exhibit anomalous performance improvements, likely due to disruption of underperformance mechanisms while core capabilities remain partially intact. Through experiments across various model architectures, sizes, and sandbagging techniques, we establish this distinctive response pattern as a reliable, model-agnostic signal for detecting sandbagging behavior. Importantly, we find noise-injection is capable of eliciting the full performance of Mistral Large 120B in a setting where the model underperforms without being instructed to do so. Our findings provide a practical tool for AI evaluation and oversight, addressing a challenge in ensuring accurate capability assessment of frontier AI systems.
|
https://openreview.net/forum?id=uUWb5eawL9
|
Main
|
Poster
|
uUWb5eawL9
|
Wavelet Canonical Coherence for Nonstationary Signals
|
[
"Haibo Wu",
"Marina I. Knight",
"Keiland W. Cooper",
"Norbert J. Fortin",
"Hernando Ombao"
] |
Understanding the evolving dependence between two sets of multivariate signals is fundamental in neuroscience and other domains where sub-networks in a system interact dynamically over time. Despite the growing interest in multivariate time series analysis, existing methods for between-clusters dependence typically rely on the assumption of stationarity and lack the temporal resolution to capture transient, frequency-specific interactions. To overcome this limitation, we propose scale-specific wavelet canonical coherence (WaveCanCoh), a novel framework that extends canonical coherence analysis to the nonstationary setting by leveraging the multivariate locally stationary wavelet model. The proposed WaveCanCoh enables the estimation of time-varying canonical coherence between clusters, providing interpretable insight into scale-specific time-varying interactions between clusters. Through extensive simulation studies, we demonstrate that WaveCanCoh accurately recovers true coherence structures under both locally stationary and general nonstationary conditions. Application to local field potential (LFP) activity data recorded from the hippocampus reveals distinct dynamic coherence patterns between correct and incorrect memory-guided decisions, illustrating capacity of the method to detect behaviorally relevant neural coordination. These results highlight WaveCanCoh as a flexible and principled tool for modeling complex cross-group dependencies in nonstationary multivariate systems. Code for implementing WaveCanCoh is available at https://github.com/mhaibo/WaveCanCoh.git.
|
https://openreview.net/forum?id=uUIgxjWkCI
|
Main
|
Spotlight
|
uUIgxjWkCI
|
Auto-Connect: Connectivity-Preserving RigFormer with Direct Preference Optimization
|
[
"Guojingfeng",
"Jian Liu",
"Jinnan Chen",
"Shiwei Mao",
"Changrong Hu",
"Puhua Jiang",
"Junlin Yu",
"Jing Xu",
"Qi Liu",
"LiXin Xu",
"Zhuo Chen",
"Chunchao Guo"
] |
We introduce Auto-Connect, a novel approach for automatic rigging that explicitly preserves skeletal connectivity through a connectivity-preserving tokenization scheme. Unlike previous methods that predict bone positions represented as two joints or first predict points before determining connectivity, our method employs special tokens to define endpoints for each joint's children and for each hierarchical layer, effectively automating connectivity relationships. This approach significantly enhances topological accuracy by integrating connectivity information directly into the prediction framework.
To further guarantee high-quality topology, we implement a topology-aware reward function that quantifies topological correctness, which is then utilized in a post-training phase through reward-guided Direct Preference Optimization.
Additionally, we incorporate implicit geodesic features for latent top-$k$ bone selection, which substantially improves skinning quality. By leveraging geodesic distance information within the model's latent space, our approach intelligently determines the most influential bones for each vertex, effectively mitigating common skinning artifacts.
This combination of connectivity-preserving tokenization, reward-guided fine-tuning, and geodesic-aware bone selection enables our model to consistently generate more anatomically plausible skeletal structures with superior deformation properties.
|
https://openreview.net/forum?id=uUHNt5CoaL
|
Main
|
Poster
|
uUHNt5CoaL
|
Generalized Category Discovery under Domain Shift: A Frequency Domain Perspective
|
[
"Wei Feng",
"Zongyuan Ge"
] |
Generalized Category Discovery (GCD) aims to leverage labeled samples from known categories to cluster unlabeled data that may include both known and unknown categories. While existing methods have achieved impressive results under standard conditions, their performance often deteriorates in the presence of distribution shifts. In this paper, we explore a more realistic task: Domain-Shifted Generalized Category Discovery (DS\_GCD), where the unlabeled data includes not only unknown categories but also samples from unknown domains. To tackle this challenge, we propose a \textbf{\underline{F}}requency-guided Gene\textbf{\underline{r}}alized Cat\textbf{\underline{e}}gory Discov\textbf{\underline{e}}ry framework (FREE) that enhances the model's ability to discover categories under distributional shift by leveraging frequency-domain information. Specifically, we first propose a frequency-based domain separation strategy that partitions samples into known and unknown domains by measuring their amplitude differences. We then propose two types of frequency-domain perturbation strategies: a cross-domain strategy, which adapts to new distributions by exchanging amplitude components across domains, and an intra-domain strategy, which enhances robustness to intra-domain variations within the unknown domain. Furthermore, we extend the self-supervised contrastive objective and semantic clustering loss to better guide the training process. Finally, we introduce a clustering-difficulty-aware resampling technique to adaptively focus on harder-to-cluster categories, further enhancing model performance. Extensive experiments demonstrate that our method effectively mitigates the impact of distributional shifts across various benchmark datasets and achieves superior performance in discovering both known and unknown categories.
|
https://openreview.net/forum?id=uUBQ96zs48
|
Main
|
Poster
|
uUBQ96zs48
|
SPARTAN: A Sparse Transformer World Model Attending to What Matters
|
[
"Anson Lei",
"Bernhard Schölkopf",
"Ingmar Posner"
] |
Capturing the interactions between entities in a structured way plays a central role in world models that flexibly adapt to changes in the environment. Recent works motivate the benefits of models that explicitly represent the structure of interactions and formulate the problem as discovering local causal structures. In this work, we demonstrate that reliably capturing these relationships in complex settings remains challenging. To remedy this shortcoming, we postulate that sparsity is a critical ingredient for the discovery of such local structures. To this end we present the SPARse TrANsformer World model (SPARTAN), a Transformer-based world model that learns context-dependent interaction structures between entities in a scene. By applying sparsity regularisation on the attention patterns between object-factored tokens, SPARTAN learns sparse, context-dependent interaction graphs that accurately predict future object states. We further extend our model to adapt to sparse interventions with unknown targets on the dynamics of the environment. This results in a highly interpretable world model that can efficiently adapt to changes. Empirically, we evaluate SPARTAN against the current state-of-the-art in object-centric world models on observation-based environments and demonstrate that our model can learn local causal graphs that accurately reflects the underlying interactions between objects and achieve significantly improved few-shot adaptation to dynamics changes as well as robustness against distractors.
|
https://openreview.net/forum?id=uS5ch7GjZ4
|
Main
|
Poster
|
uS5ch7GjZ4
|
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