SafeCommit: Certifying When Memory-Grounded Agents May Safely Act
Not on the map. Why?
Abstract (arXiv)
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 α; 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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 20 of its 23 references that are in the map. First posted on arXiv 4 Aug 2026.
Builds on
20 of 23 references.
References · 20
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
- 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
- 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
- 2023Identifying the Risks of LM Agents with an LM-Emulated Sandboxcited by 548
- 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Basescited by 429
- 2025Structured Uncertainty guided Clarification for LLM Agentscited by 41
- 2026Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agentscited by 32
- 2025Active Task Disambiguation with LLMscited by 23
- 2026Agentic Uncertainty Quantificationcited by 19
- 2025Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safetycited by 12
- 2026AgentGuardian: Learning Access Control Policies to Govern AI Agent Behaviorcited by 11
8 more
- 2026Position: LLMs Must Use Functor-Based and RAG-Driven Bias Mitigation for Fairnesscited by 10
- 2026VLA-Forget: Vision-Language-Action Unlearning for Embodied Foundation Modelscited by 10
- 2026Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategiescited by 9
- 2026CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMscited by 9
- 2026RAZOR: Ratio-Aware Layer Editing for Targeted Unlearning in Vision Transformers and Diffusion Modelscited by 8
- 2026G-Drift MIA: Membership Inference via Gradient-Induced Feature Drift in LLMscited by 8
- 2026PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMscited by 6
- 2026Listening with Attention: Entropy-Guided Explainability for Transformer-Based Audio Modelscited by 4
Built on by
Citing papers per year: 2026: 6.
6 papers.
- 2026Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failurescited by 1
- 2026ERRAND: Budgeted Maintenance of Agent Memorycited by 1
- 2026Plan Pointers and Record-Directive Form in Budgeted Verification of Inherited Agent Memorycited by 0
- 2026What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agentscited by 0
- 2026RippleCP: Measuring Counterfactual Checkpoint Advantage in Coding Agentscited by 0
- 2026REPORT-OPS: Hybrid Evidence-Guided Reporting with Human-Supervised Refinement for HPCcited by 0
- 2026REPORT-OPS: Hybrid Evidence-Guided Reporting with Human-Supervised Refinement for HPC13 Oct 2026
- 2026RippleCP: Measuring Counterfactual Checkpoint Advantage in Coding Agents6 Oct 2026
- 2026What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents30 Sep 2026
- 2026Engineering Reliable Commit Gates for Agentic AI: Cost-Aware Verification Portfolios under Common-Mode Data Failures10 Sep 2026
- 2026Plan Pointers and Record-Directive Form in Budgeted Verification of Inherited Agent Memory3 Sep 2026
- 2026ERRAND: Budgeted Maintenance of Agent Memory2 Sep 2026