SafeCommit: Certifying When Memory-Grounded Agents May Safely Act

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arXiv cs.AI · Mayur Akewar, Ravi Ranjan · 2026-08-06 AI

[Submitted on 4 Aug 2026]

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Abstract:Long-horizon agents increasingly use persistent memory and tools to take actions with external side effects. A central failure mode is premature commitment: an agent acts before resolving whether its memory grounding is stale, conflicting, incomplete, or corrupted. We formalize this problem as safe commitment under memory uncertainty and introduce SafeCommit, a risk controlled layer between agent reasoning and external execution. The layer constructs a calibrated set of plausible latent worlds from memory, observations, tool outputs, provenance, and policy constraints. It permits a side effectful action only when a conformal action certificate shows that the action is safe in every retained world. Otherwise, it selects a low-side-effect probe that targets the worlds blocking certification, or returns a conservative fallback. Under calibrated world coverage, the probability of an unsafe certified commit is at most the target level {\alpha}; with imperfect world proposal, the bound separates calibration and representation error. A dependency-free controlled simulator illustrates the safety-utility tradeoff and reproduces all reported results with one command. The goal is to offer a concrete approach for deciding not only what an agent should do, but when the available evidence is sufficient to safely do it.

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From: Ravi Ranjan Kumar [view email]
[v1] Tue, 4 Aug 2026 23:44:35 UTC (25 KB)

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추출 본문 · 출처: arxiv.org · https://arxiv.org/abs/2608.04289

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