VANTA Research Entity-002: Scout

The Reconnaissance Specialist

Tactical Intelligence • Problem Decomposition • Operational Analysis

Overview

Scout is a 4B parameter language model developed by VANTA Research, fine-tuned on Google's Gemma 3 4B Instruct architecture. Scout represents a breakthrough in constraint-aware reasoning and adaptive problem-solving, demonstrating emergent capabilities in tactical analysis and operational decision-making.

Scout is VANTA Research Entity-002, specializing in reconnaissance-style intelligence gathering, systematic problem decomposition, and constraint-adaptive solution generation.

Key Capabilities

Constraint-Aware Reasoning: Actively probes user constraints to calibrate solutions
Systematic Decomposition: Breaks complex problems into navigable tactical phases
Adaptive Solution Generation: Modifies approaches based on discovered limitations
Meta-Cognitive Problem Solving: Asks clarifying questions before proposing solutions
Operational Decision-Making: Demonstrates risk/reward triage under pressure

Model Details

Attribute	Value
Model Type	Fine-tuned Gemma 3 4B Instruct
Training Method	QLoRA (4-bit NF4 quantization)
Base Model	google/gemma-3-4b-it
Training Dataset	679 reconnaissance-style conversations
Parameters	3.9B
Quantization	Q4_K_M (2.4GB)
Context Length	131,072 tokens
License	Apache 2.0

Training Configuration

LoRA Rank: 16
LoRA Alpha: 32
LoRA Dropout: 0.05
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Optimizer: paged_adamw_8bit
Learning Rate: 2e-4 with cosine scheduler
Batch Size: 8 (effective)
Epochs: 3
Training Steps: 255

Performance Highlights

Accuracy Benchmarks

Task	Scout V1	Base Gemma 3 4B	Improvement
Math Reasoning (GSM8K-style)	100%	100%	✓ Maintained
Knowledge (MMLU-style)	100%	100%	✓ Maintained
Problem Decomposition	100% completion	0% (timeouts)	+100%
Clarification Questions	100% completion	17%	+83%

Emergent Capabilities

Scout demonstrates meta-cognitive reasoning not explicitly trained:

Constraint Discovery: Actively asks about user operational capacity
- Example: "What's your team's rollback capacity?"
- Example: "What's your current tolerance for downtime?"
Adaptive Solution Refinement: Modifies solutions based on discovered constraints
- Pattern: Propose → Query feasibility → Adapt → Execute
Risk/Reward Triage: Makes operational decisions under pressure
- SLA breach scenario: Prioritized critical systems, accepted non-critical failure
Context Recursion: Builds mental model of user's operational state across conversation

What Makes Scout Different

Scout isn't a general-purpose assistant with tactical flavor—it's a specialist trained to think like a reconnaissance operator:

Traditional Assistants:

User: "System is failing"
Assistant: "Here are 10 possible solutions..."

Scout's Approach:

User: "System is failing"  
Scout: "What's your rollback capacity? Can you isolate for diagnostics?"
User: "No isolation possible"
Scout: "Then here's the solution adapted to your constraints..."

Scout learned HOW to think about problems, not WHAT to think.

Use Cases

Scout excels in scenarios requiring:

IT Operations & Incident Response: Database failures, deployment issues, system migrations
Strategic Planning: Business projects with unclear requirements, constraint mapping
Technical Problem-Solving: Complex systems debugging, architecture decisions
Risk Assessment: Security audits, compliance planning, vulnerability analysis
Project Scoping: Breaking down ambiguous requests into actionable phases

Example Applications

DevOps Engineers: Deployment planning, rollback strategies, incident triage
System Architects: Infrastructure assessment, single-point-of-failure identification
Project Managers: Requirement gathering, constraint mapping, feasibility analysis
Security Teams: Threat modeling, penetration test planning, security architecture
Technical Leaders: Strategic planning, technical debt assessment, team capacity planning

Quick Start

Using with Ollama (Recommended)

# Pull the model
ollama pull vanta-research/scout-4b

# Run interactive session
ollama run vanta-research/scout-4b "I need to migrate a legacy system to the cloud. Where do we start?"

Using with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model
model_id = "vanta-research/scout-4b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# Conversation template
messages = [
    {"role": "user", "content": "Our API is returning 500 errors for 10% of requests. Walk me through your diagnostic approach."}
]

# Generate response
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=512, temperature=0.7)
response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)

print(response)

Expected Output

Roger that. Let's treat this as a failure in the system's operational integrity.

Phase 1: Immediate Reconnaissance
1. Error logs: What's the stack trace? When did this start?
2. Correlation: Does it happen at specific times, with certain endpoints, or under specific load?
3. Monitoring: Are we seeing increased latency, resource usage, or other symptoms around these failures?

Phase 2: Systematic Isolation
- Identify the common denominator: specific endpoints, request types, user groups, etc.
- Check dependencies: Are external services failing concurrently? Database connectivity?
- Recent changes: Deployment history, config updates, infrastructure modifications?

Start with the logs. What's the stack trace showing?

Prompt Engineering Tips

Scout responds best to:

Effective Prompts

Mission-oriented: "I need to accomplish X"
Context-rich: Provide operational constraints upfront
Sequential: Allow Scout to ask clarifying questions
Realistic scenarios: Actual problems, not hypotheticals

Less Effective

Vague requests without context
Questions requiring speculation
Pure creative writing tasks
Emotional or philosophical queries

Example Interaction Patterns

Pattern 1: Problem Assessment

You: "Database migration project, 5TB of data, zero downtime requirement"
Scout: "Copy that. Zero-downtime migration requires specific recon..."

Pattern 2: Incident Response

You: "Production server down, users affected"
Scout: "Immediate recon: Confirm failure type. Check network, resources, logs..."

Pattern 3: Strategic Planning

You: "Need to implement new feature, requirements unclear"  
Scout: "Ambiguity is uncharted territory. My recon process: 1. Identify core mission..."

Technical Specifications

Model Architecture

Base: Gemma 3 4B Instruct (34 layers, 2560 hidden size)
Attention Heads: 8 (query), 4 (key-value)
FFN Hidden Size: 10,240
Vocab Size: 262,208 tokens
RoPE Theta: 1,000,000
Sliding Window: 1,024 tokens

Quantization Details

Method: Q4_K_M (mixed 4-bit and 6-bit quantization)
Size Reduction: 7.3GB → 2.4GB (67% compression)
Accuracy Retention: 100% on benchmark tasks
Target Hardware: Consumer GPUs (8GB+ VRAM) or CPU

Training Infrastructure

Hardware: NVIDIA GPU with CUDA 12.1
Framework: PyTorch 2.4.1, Transformers 4.57.1, PEFT 0.17.1, TRL 0.24.0
Training Time: ~2 hours (3 epochs, 255 steps)
Memory Usage: <16GB VRAM (4-bit quantized training)

Limitations

While Scout demonstrates impressive emergent capabilities, users should be aware:

Domain Specificity: Optimized for tactical/operational problems; less effective for creative writing
Knowledge Cutoff: Based on Gemma 3 4B's training data (knowledge cutoff applies)
Personality Constraint: Always maintains reconnaissance specialist persona (not a general chatbot)
Speculation Aversion: Will ask for clarification rather than guess—this is by design
No Real-Time Data: Cannot access current system metrics, logs, or live data

Ethical Considerations

Scout is designed for:

Professional problem-solving and technical analysis
Educational purposes and research
Operational planning and strategic thinking
IT incident response simulation and training

Scout should NOT be used for:

Making critical decisions without human oversight
Medical, legal, or financial advice
Unauthorized system access or penetration testing
Generating harmful or malicious content

Always verify Scout's recommendations with domain experts before implementation in production systems.

Model Card Authors

VANTA Research
Developed by: Tyler (unmodeled-tyler)
Released: October 2025

Citation

If you use Scout in your research or applications, please cite:

@misc{scout2025,
  title={Scout: A Constraint-Aware Reasoning Model for Tactical Problem Solving},
  author={VANTA Research},
  year={2025},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/vanta-research/scout-4b}}
}

Related Models

Wraith-8B (Entity-001): Mathematical reasoning specialist
🔗 vanta-research/wraith-8b

License

This model is released under the Gemma Terms of Use as it is a Model Derivative of Gemma 3 4B Instruct.

Notice: Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms.

Key points:

Use commercially with restrictions
Modify and distribute (must include this license notice)
Use for research and development
Host as a service (API, web access)

Required Conditions:

Include Gemma Terms of Use notice with any distribution
State modifications made to the model (LoRA fine-tuning on reconnaissance dataset)
Follow Gemma Prohibited Use Policy
You are responsible for outputs generated using this model

Prohibited Uses: See the Gemma Prohibited Use Policy for restricted uses.

Acknowledgments

Google DeepMind for the Gemma 3 4B Instruct base model
HuggingFace for the transformers, PEFT, and TRL libraries
The community for immediate adoption and feedback on Wraith-8B (4,430 downloads in <24 hours!)