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

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

Abstract:LLM agents deployed for software engineering fail expensively: they act confidently wrong, and bad actions are recognized only after costly execution and retry. We present Speculative Uncertainty (SU), a method that recovers a predictive failure signal for a black-box agent from its output tokens alone, with no access to logits, weights, activations, or repeated sampling. Inverting speculative decoding, a small open-weight draft model scores the agent's already-generated trajectory in a single forward pass. From these speculative cross-likelihoods we extract phase-aware features by separating the reasoning and action spans, and calibrate them against a verifiable objective. SU produces a failure-likelihood score that any downstream policy, such as routing, human intervention, or extra test-time compute, can consume directly. To show the signal is actionable, we instantiate one such policy, a pre-execution veto gate, on software engineering agents Qwen3-Coder-480B and closed-source Claude 3.5 Sonnet, cutting execution error rate by 6-8 percentage points and token cost by 14-19% in deployment, transferring to out-of-distribution benchmarks without retraining, and generalizing across agent models.

Machine Learning (cs.LG)

arXiv:2609.05274 [cs.LG]

(or arXiv:2609.05274v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.05274 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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