Show HN: Entropy-Guided Loop – How to make small models reason

Logprobs Reasoning Loop with Weights & Biases Weave, an observability tool Uncertainty-Aware Generation with OpenAI's Responses API This project demonstrates a novel approach to improving AI model reasoning by leveraging token-level uncertainty metrics (logprobs) to create self-correcting generation loops. We compare this uncertainty-aware approach against traditional reasoning models to test whether explicit uncertainty handling can match or exceed the performance of dedicated reasoning architectures. Core Concept Modern transformers typically discard valuable uncertainty information during inference. This project explores whether we can harness this discarded information—specifically logprobs and top-k alternatives—to create more reliable and accurate AI responses without requiring specialized reasoning models. Key Innovation We implement an uncertainty-aware generation loop that: Generates an initial response while tracking token-level uncertainty (perplexity) Automatically identifies regions of high uncertainty using logprobs Triggers a refinement pass when uncertainty exceeds a threshold Provides the model with explicit information about uncertain tokens and their alternatives Produces a refined, more accurate final response What We're Testing Hypothesis Uncertainty metrics (logprobs) and top-k alternatives contain valuable reasoning signals that current transformer frameworks underutilize. Comparison Non-reasoning models with uncertainty loops (e.g., gpt-4.1-mini with our framework) (e.g., gpt-4.1-mini with our framework) Native reasoning models (e.g., o4-mini) - Note: These don't expose logprobs, so uncertainty analysis is not available Metrics Tracked Token-level perplexity Average log probabilities Response accuracy Token usage and costs Generation time Technical Implementation The project uses: OpenAI Responses API with include=["message.output_text.logprobs"] with Weave by Weights & Biases for comprehensive experiment tracking and visualization for comprehensive experiment tracking and visualization Perplexity-based thresholds for triggering refinement for triggering refinement Top-k alternatives for informing the model about uncertainty regions Why Weave? Weave is essential for this project because it provides: Persistent experiment tracking - Every run, metric, and decision is logged and queryable - Every run, metric, and decision is logged and queryable Hierarchical operation tracing - See exactly how the uncertainty loop makes decisions - See exactly how the uncertainty loop makes decisions Production-ready observability - Transform research experiments into deployable products - Transform research experiments into deployable products Free tier available - Get started without any cost commitment Get your free Weave API key at: https://wandb.ai/authorize Weave enables us to: Track every token's uncertainty metrics across experiments Compare refinement decisions and their impacts Build a dataset of uncertainty patterns for future research Create reproducible experiments with full lineage tracking Visualize the relationship between uncertainty and answer quality Core Components @ weave . op () def answer_difficult_question_with_uncertainty ( question : str , model : str = "gpt-4.1-mini" , top_k : int = 5 , threshold : float = 1.4 , temperature : float = 0.2 ): # Initial generation with logprobs # Calculate multiple uncertainty metrics: # - Perplexity from average logprobs # - Maximum entropy across tokens # - Count of low-confidence tokens # Multi-metric refinement trigger # Conditional refinement with detailed uncertainty report # Returns structured metrics and final answer Enhanced Uncertainty Detection Our implementation now uses multiple complementary metrics: Perplexity: exp(-mean(log_probabilities)) - Overall uncertainty measure Token-level Entropy: -sum(p * log(p)) across top-k alternatives Confidence Distribution: Count of tokens below confidence thresholds Contextual Analysis: Shows uncertain tokens with surrounding context Getting Started Prerequisites This project includes a vendorized version of polyfile-weave with fixes for Python 3.9+ compatibility. Setting up Virtual Environment (Required) # Create a virtual environment python3 -m venv venv # Activate the virtual environment # On macOS/Linux: source venv/bin/activate # On Windows: # venv\Scripts\activate # Install dependencies (includes local polyfile-weave) pip install -r requirements.txt # Set up environment variables cp env.example .env # Edit .env with your API keys Setting up Weave Tracking (Recommended) Weave provides essential observability for understanding how the uncertainty loop works: Get your free API key: Visit https://wandb.ai/authorize Add to your .env file: WANDB_API_KEY=your-api-key-here WEAVE_PROJECT=weave-intro-notebook # or your custom project name View your experiments: After running, visit the URL printed in console to explore: Token-by-token uncertainty metrics Refinement decision rationale Cost and performance comparisons Full conversation traces with hierarchical operations The free tier includes: Unlimited public projects 100GB of storage Full access to Weave features No credit card required Note: The vendorized polyfile-weave package is included to fix compatibility issues with reserved keywords in the upstream package. package is included to fix compatibility issues with reserved keywords in the upstream package. The script includes a runtime patch for Weave to enable gql 4.0+ compatibility (see our PR for the permanent fix). Running Locally (Python Script) # Option 1: Use .env file (recommended) # Edit .env with your OPENAI_API_KEY python wb-logprobs.py # Option 2: Export environment variable export OPENAI_API_KEY= " sk-your-key-here " python wb-logprobs.py # Option 3: Pass a custom question python wb-logprobs.py " Explain the halting problem and its implications " Troubleshooting Weave Initialization Error: If you encounter a TypeError when initializing Weave: # Option 1: Install compatible gql version pip install gql==3.4.1 # Option 2: Simply run the notebook - it will automatically handle the error # The notebook includes fallback handling and can run without W&B tracking Reasoning Model Compatibility: The code automatically handles differences between reasoning models (o1, o4) and standard models: Reasoning models don't support temperature or logprobs parameters or parameters The code detects model type and adjusts API calls accordingly Reasoning models won't have uncertainty metrics or refinement loops (no logprobs available) Both model types will run successfully for comparison purposes The notebook is designed to run even if Weave initialization fails, so you can proceed with the uncertainty experiments regardless of tracking setup. Running the Notebook jupyter notebook wb-logprobs.ipynb Results & Insights Performance Benchmarks Our comprehensive testing reveals impressive results: Cost Efficiency gpt-4.1-mini with uncertainty loop : 30-43% of o4-mini reasoning model cost : 30-43% of o4-mini reasoning model cost Average cost per complex question: $0.0007-$0.0011 vs $0.0019-$0.0058 Quality Metrics Testing on controversial and complex questions (AGI predictions, ethical implications, cryptocurrency debates): Comparable answer quality to reasoning models to reasoning models Improved confidence calibration through explicit uncertainty handling through explicit uncertainty handling Reduced hallucination via targeted refinement Refinement Triggers Our multi-metric approach catches uncertainty that single metrics miss: Perplexity threshold (>1.4) Maximum entropy (>1.5) High uncertainty token count (≥3 tokens <50% confidence) API Performance Analysis Discovered significant performance characteristics: Simple questions: 2-6 seconds (faster than reasoning models) Complex technical questions: 54-67 seconds (API limitation, not our code) The more powerful the model, the slower the response (gpt-4.1: 99s, gpt-4o: 61s, gpt-4.1-mini: 67s) Key Findings 2.75x cost reduction compared to reasoning models while maintaining quality Intelligent refinement - only triggers when genuinely uncertain (not for all responses) Rich uncertainty analysis provides context about specific uncertain tokens and alternatives Hierarchical logging via Weave enables deep analysis of the decision process Future Roadmap Phase 1: Extended Uncertainty Metrics Integrate pre-softmax hidden states Incorporate raw logits analysis Develop multi-layer uncertainty aggregation Phase 2: Full Inference Framework Build a production-ready inference server Implement streaming with real-time uncertainty monitoring Create adaptive thresholds based on task complexity Phase 3: Model-Agnostic Implementation Extend beyond OpenAI to open-source models Support for local inference with uncertainty extraction Develop uncertainty-aware fine-tuning methods Phase 4: Advanced Applications Multi-turn conversation uncertainty tracking Uncertainty-guided retrieval augmentation Collaborative uncertainty resolution across model ensembles Key Insights Why This Matters Current transformer architectures make discrete token selections, discarding the rich probability distributions that could inform better reasoning. By capturing and utilizing this uncertainty information, we can: Reduce hallucinations by identifying when models are uncertain Improve accuracy through targeted refinement Lower costs compared to dedicated reasoning models Provide transparency about model confidence The Power of Observable AI with Weave This project demonstrates how Weave transforms experimental AI research into production-ready systems: For Researchers: Every experiment is automatically versioned and comparable Uncertainty patterns become queryable datasets Collaborate with full experiment reproducibility Build on previous results without losing context For Product Builders: Monitor uncertainty metrics in production Set alerts for high-uncertainty responses A/B test different uncertainty thresholds Track cost-performance tradeoffs in real-time Data Persistence Benefits: All logprobs and uncertainty metrics are stored permanently Build training datasets from real uncertainty patterns Analyze long-term trends in model confidence Create uncertainty benchmarks for new models The Transformer Framework Gap The standard transformer inference pipeline: Discards logprobs after token selection Ignores uncertainty signals during generation Lacks self-correction mechanisms Provides no confidence metrics to downstream systems Our approach addresses these limitations by treating uncertainty as a first-class citizen in the generation process. Technical Details For a comprehensive technical deep-dive including: Mathematical formulas and derivations Complete implementation details API response processing Example uncertainty reports Performance analysis See TECHNICAL.md Quick Overview Perplexity: exp(-mean(log_probabilities)) - Overall uncertainty measure Entropy: -sum(p * log(p)) - Token-level uncertainty quantification Decision Logic: Refinement triggers if: Perplexity > 1.4 OR Max entropy > 1.5 OR 3+ tokens with <50% confidence Observability: Hierarchical @weave.op() tracking captures every decision and metric Contributing We welcome contributions! Areas of particular interest: Alternative uncertainty metrics Multi-model uncertainty aggregation Visualization improvements Benchmark datasets for uncertainty-aware generation References License MIT License - See LICENSE file for details Acknowledgments OpenAI for providing logprobs access via their APIs Weights & Biases team for the Weave framework The broader AI research community exploring uncertainty quantification Project Status: Active Development (Phase 1: Benchmark Validation in Progress - August 2025) Contact: [email protected] or open an issue for questions or collaboration opportunities Citation: If you use this work in your research, please cite: @software { weave_logprobs_reasoning , title = { Uncertainty-Aware Generation with Logprobs } , author = { Monostate } , year = { 2025 } , url = { https://github.com/monostate/weave-logprobs-reasoning-loop } } Roadmap: Next Steps & Validation Immediate Next Steps (August 2025) We are currently working on: Running ARC-AGI benchmarks to validate abstract reasoning capabilities Testing on LogiQA 2.0 for logical reasoning validation GSM8K evaluation to compare math problem-solving with o4-mini Setting up automated benchmark pipeline with Weave tracking Phase 1: Benchmark Validation (Q3 2025 - Current) Reasoning Benchmarks ARC-AGI - Abstract reasoning corpus - Abstract reasoning corpus LogiQA 2.0 - Logical reasoning in natural language - Logical reasoning in natural language GSM8K - Grade school math word problems - Grade school math word problems MATH - Competition mathematics - Competition mathematics BigBench Hard - Challenging tasks from BIG-Bench - Challenging tasks from BIG-Bench MMLU - Massive multitask language understanding - Massive multitask language understanding HumanEval - Code generation benchmarks Goal: Demonstrate that uncertainty-aware loops achieve comparable or superior performance to reasoning models at 30-40% of the cost. Phase 2: Agentic Applications (Q4 2025) Browser Automation Tasks WebArena - Realistic web navigation tasks - Realistic web navigation tasks Mind2Web - Web interaction benchmarks - Web interaction benchmarks Custom browser automation with uncertainty-driven exploration Tool Use & Function Calling API integration with uncertainty-aware retries Database query generation with confidence metrics File system operations with safety checks based on uncertainty Multi-Step Planning Task decomposition with uncertainty propagation Hierarchical planning with confidence thresholds Rollback mechanisms triggered by high uncertainty Phase 3: Chain-of-Thought Enhancement (Q4 2025 - Q1 2026) Explicit Reasoning Traces Uncertainty-guided CoT : Use logprobs to identify where reasoning needs expansion : Use logprobs to identify where reasoning needs expansion Selective verbalization : Only elaborate on uncertain reasoning steps : Only elaborate on uncertain reasoning steps Confidence-weighted chains: Weight reasoning paths by aggregate certainty Comparison Studies Standard CoT vs Uncertainty-aware CoT Few-shot prompting with uncertainty examples Zero-shot reasoning with automatic uncertainty detection Phase 4: Advanced Techniques (Q1 2026) Self-Consistency with Uncertainty Multiple sampling with uncertainty aggregation Weighted voting based on path confidence Early stopping when uncertainty converges Uncertainty-Aware Ensembles Multi-model uncertainty aggregation Cross-model confidence calibration Selective model routing based on uncertainty profiles Active Learning Integration Identify high-uncertainty examples for human annotation Build uncertainty-aware training datasets Fine-tune models on uncertainty patterns Phase 5: Production Systems (Q1-Q2 2026) Real-World Deployments Customer Support : Route uncertain queries to human agents : Route uncertain queries to human agents Content Generation : Flag potentially problematic content based on uncertainty : Flag potentially problematic content based on uncertainty Medical/Legal AI : Mandatory uncertainty disclosure for high-stakes decisions : Mandatory uncertainty disclosure for high-stakes decisions Educational Tools: Adapt explanations based on model confidence Infrastructure Development Streaming uncertainty detection Real-time refinement triggers Uncertainty-aware caching strategies Cost optimization with dynamic thresholds Phase 6: Research Extensions (Q2 2026 - Ongoing) Theoretical Analysis Information-theoretic bounds on uncertainty reduction Optimal threshold learning algorithms Uncertainty propagation in multi-turn conversations Novel Architectures Uncertainty-aware transformer variants Built-in refinement mechanisms Native uncertainty quantification layers Cross-Domain Transfer Uncertainty patterns across different domains Domain-specific threshold calibration Transfer learning for uncertainty detection Validation Metrics Performance Targets Accuracy : Match or exceed reasoning model baselines : Match or exceed reasoning model baselines Cost : Maintain 30-40% cost ratio vs reasoning models : Maintain 30-40% cost ratio vs reasoning models Latency : Optimize for <2x latency of single-pass generation : Optimize for <2x latency of single-pass generation Reliability: <5% false positive refinement rate Success Criteria Benchmark Performance: Within 5% of reasoning model scores Cost Efficiency: Consistent 2.5-3x cost reduction User Studies: Preference for uncertainty-aware responses in blind tests Production Metrics: Reduced error rates in deployed systems Community Collaboration We invite researchers and practitioners to: Contribute benchmark results with your models and domains with your models and domains Share uncertainty patterns discovered in your applications discovered in your applications Propose new metrics for uncertainty quantification for uncertainty quantification Build integrations with other frameworks and tools Join our efforts to make AI systems more reliable through uncertainty awareness!