Entropy-Guided Loop: Achieving Reasoning through Uncertainty-Aware Generation · HackerPapers discussions

Entropy-Guided Loop: Achieving Reasoning through Uncertainty-Aware Generation — mentioned by andrewmonostate

Mon, 23 Feb 2026 00:24:49 GMT

Treni is a runtime project, not an agent framework. The problem we’re targeting is that most agents today operate on serialized tool outputs (HTTP -> JSON -> string parsing). That means the policy often cannot observe execution quality directly. Our thesis is “seeing is believing”: keep models + routing + tokenization + tool execution + state in one GPU runtime so the agent can branch on execution signals (entropy/logprobs, retrieval distance/quality, route confidence) before committing the next step. What we set out to prove (in order): A) Speed: is a unified runtime materially faster than…

Paper and discussion history. A mention is not an endorsement.

Entropy-Guided Loop: Achieving Reasoning through Uncertainty-Aware Generation — HN submission

Wed, 03 Sep 2025 17:19:10 GMT

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

TLDR: A small, vendor-agnostic inference loop that turns token logprobs/perplexity/entropy into an extra pass and reasoning for LLMs. - Captures logprobs/top-k during generation, computes perplexity and token-level entropy. - Triggers at most one refine when simple thresholds fire; passes a compact “uncertainty report” (uncertain tokens + top-k alts + local context) back to the model. - In our tests on technical Q&A / math / code, a small model recovered much of “reasoning” quality at ~⅓ the cost while refining ~⅓ of outputs. I kept seeing “reasoning” models behave like expensive black boxes.…

33 points; 4 discussion replies on HN. Paper and source history. A submission is not an endorsement.