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arXiv Machine Learning By Konstantin Grotov, Valentin Malykh

How to Speculate about Uncertainty in Agentic Coding? A Draft-Model Gate Method

Read the original on arXiv Machine Learning →

The paper introduces Speculative Uncertainty (SU), a technique that infers a failure likelihood for black‑box LLM agents by evaluating their generated token sequences with a lightweight draft model, without needing internal model details. SU extracts phase‑aware features from reasoning and action spans, calibrates them against verifiable outcomes, and produces a failure‑likelihood score usable by downstream policies. Applying a pre‑execution veto gate based on SU to software‑engineering agents such as Qwen3‑Coder‑480B and Claude 3.5 Sonnet reduced execution error rates by 6‑8 percentage points and token costs by 14‑19 %, while maintaining performance on out‑of‑distribution benchmarks and across different agent models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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