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
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