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

Summary

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 compre...