FLEET Framework Open-Sourced: Guiding LLM Generation via Logits Entropy, Slashing Code Hallucinations

Core Background & Industry Pain Points Autoregressive generation suffers from compounding errors: when a model samples an erroneous token at a point of high epistemic uncertainty, that token re-enters the prompt, precipitating a cascading hallucination loop. Traditional mitigation strategies like Best-of-N sampling or auxiliary judge models inflate compute costs and introduce severe latency penalties in interactive coding sessions. ### Architecture Highlights & Internals FLEET introduces training-free, real-time decoding intervention. It monitors the Shannon entropy of raw logits vectors during forward passes. When entropy spikes—signaling an inflection point—FLEET expands beam search branches and lowers local temperature, pruning unreliable trajectories before errors contaminate the working context. ### Authoritative Benchmarks & Measured Scores Across multilingual HumanEval-X and GSM8K benchmarks, FLEET elevates Pass@1 rates by 14.8% over standard nucleus sampling, while suppressing hallucination loops by 38.2% across long (>2,000 token) code synthesis tasks. Compute overhead remains under 3.5%. ### Developer Hands-on Guide Developers can integrate FLEET via pip install fleet-decoding using standard Hugging Face generate hooks or deploy official CUDA patches directly to vLLM clusters.