mlsanityData to 7 Oct 2026

Preemptive Detection and Correction of Misaligned Actions in LLM Agents

arXiv 2407.11843 ↗First posted 16 Jul 2024cs.CL cs.AIcited by 9

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Deploying LLM-based agents in real-life applications often faces a critical challenge: the misalignment between agents' behavior and user intent. Such misalignment may lead agents to unintentionally execute critical actions that carry negative outcomes (e.g., accidentally triggering a "buy-now" in web shopping), resulting in undesirable or even irreversible consequences. Although addressing these issues is crucial, the preemptive detection and correction of misaligned actions remains relatively underexplored. To fill this gap, we introduce InferAct, a novel approach that leverages the belief reasoning ability of LLMs, grounded in Theory-of-Mind, to detect misaligned actions before execution. Once the misalignment is detected, InferAct alerts users for timely correction, preventing adverse outcomes and enhancing the reliability of LLM agents' decision-making processes. Experiments on three widely used tasks demonstrate that InferAct achieves up to 20% improvements on Marco-F1 against baselines in misaligned action detection. An in-depth evaluation of misalignment correction further highlights InferAct's effectiveness in improving agent alignment.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 38 of its 82 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

38 of 82 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Large Language Model-based Human-Agent Collaboration for Complex Task Solving20 Feb
  2. 2023Experiential Co-Learning of Software-Developing Agents28 Dec
  3. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models6 Oct
  4. 2023ExpeL: LLM Agents Are Experiential Learners20 Aug

Other references · 34

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  5. 2017On Calibration of Modern Neural Networkscited by 4,860
  6. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  7. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  8. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  9. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  10. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  11. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
  12. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
22 more
  1. 2024GPT-4V(ision) is a Generalist Web Agent, if Groundedcited by 547
  2. 2024Executable Code Actions Elicit Better LLM Agentscited by 537
  3. 2023Identifying the Risks of LM Agents with an LM-Emulated Sandboxcited by 514
  4. 2023Language Models can Solve Computer Taskscited by 371
  5. 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 288
  6. 2024R-Judge: Benchmarking Safety Risk Awareness for LLM Agentscited by 268
  7. 2024Jailbreak Attacks and Defenses Against Large Language Models: A Surveycited by 215
  8. 2024Trial and Error: Exploration-Based Trajectory Optimization for LLM Agentscited by 194
  9. 2023Automatically Auditing Large Language Models via Discrete Optimizationcited by 187
  10. 2024Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaborationcited by 176
  11. 2023Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Modelscited by 161
  12. 2022Thinking Fast and Slow in Large Language Modelscited by 136
  13. 2024Autonomous Evaluation and Refinement of Digital Agentscited by 136
  14. 2023Retroformer: Retrospective Large Language Agents with Policy Gradient Optimizationcited by 110
  15. 2022Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectlycited by 86
  16. 2018Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systemscited by 53
  17. 2023Exploring the psychology of LLMs' Moral and Legal Reasoningcited by 43
  18. 2021Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedbackcited by 35
  19. 2024AI for social science and social science of AI: A Surveycited by 29
  20. 2020Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstrationcited by 20
  21. 2024Enabling Weak LLMs to Judge Response Reliability via Meta Rankingcited by 9
  22. 2021Addressing the Long-term Impact of ML Decisions via Policy Regretcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.

  1. 2025AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agentscited by 22
  2. 2025WebGuard: Building a Generalizable Guardrail for Web Agentscited by 20
  3. 2026When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agentscited by 8
  4. 2026Safeguarding LLM Agents from Misalignment through Provenance Analysiscited by 4
  5. 2025Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligencecited by 3
  6. 2026No Action Without a NOD: A Heterogeneous Multi-Agent Architecture for Reliable Service Agentscited by 0
  7. 2026Signal-Driven Observation for Long-Horizon Web Agentscited by 0
  8. 2026SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routingcited by 0
  9. 2026What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment
  2. 2025Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents
  3. 2026Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce
  4. 2026PreAct-Bench: Benchmarking Predictive Monitoring in LLMs
  5. 2026Measuring Harness-Induced Belief Divergence in Multi-Step LLM Agents
  6. 2026ReDAct: Uncertainty-Aware Deferral for LLM Agents
  7. 2026ToMAS: A Pilot Failure-Grounded Theory-of-Mind Benchmark from Multi-Agent LLM Failures
  8. 2026LEAP: Learning Efficient Action Proposals For LLM Agents