mlsanityData to 14 Oct 2026

SafeCommit: Certifying When Memory-Grounded Agents May Safely Act

arXiv 2608.04289 ↗First posted 4 Aug 2026cs.AI cs.CLcited by 6

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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

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  3. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
  4. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
  5. 2023Identifying the Risks of LM Agents with an LM-Emulated Sandboxcited by 548
  6. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Basescited by 429
  7. 2025Structured Uncertainty guided Clarification for LLM Agentscited by 41
  8. 2026Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agentscited by 32
  9. 2025Active Task Disambiguation with LLMscited by 23
  10. 2026Agentic Uncertainty Quantificationcited by 19
  11. 2025Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safetycited by 12
  12. 2026AgentGuardian: Learning Access Control Policies to Govern AI Agent Behaviorcited by 11
8 more
  1. 2026Position: LLMs Must Use Functor-Based and RAG-Driven Bias Mitigation for Fairnesscited by 10
  2. 2026VLA-Forget: Vision-Language-Action Unlearning for Embodied Foundation Modelscited by 10
  3. 2026Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategiescited by 9
  4. 2026CatRAG: Functor-Guided Structural Debiasing with Retrieval Augmentation for Fair LLMscited by 9
  5. 2026RAZOR: Ratio-Aware Layer Editing for Targeted Unlearning in Vision Transformers and Diffusion Modelscited by 8
  6. 2026G-Drift MIA: Membership Inference via Gradient-Induced Feature Drift in LLMscited by 8
  7. 2026PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMscited by 6
  8. 2026Listening with Attention: Entropy-Guided Explainability for Transformer-Based Audio Modelscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.