MCAIR: Monitoring and Correcting Agent Unfaithfulness via Internal Representations
In this work, we propose MCAIR, a runtime monitoring and correction framework that leverages internal representations of LLM-based agents.
ProofPaper ↗
Key points
- Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in completing complex tasks through multi-step reasoning and tool interactions.
- Detecting and mitigating agent completion unfaithfulness is critical yet remains largely unexplored.
- Furthermore, MCAIR introduces a risk-guided correction loop that prompts agents to review their execution history and mitigate potential inconsistencies between their execution trajectories and final reports.
- Additionally, we construct ACUD, a dataset for Agent Completion Unfaithfulness detection that provides step-level execution traces, step-level internal representations, and consistency annotations for training and evaluating runtime detection probes.
Sources (1)
- [1]MCAIR: Monitoring and Correcting Agent Unfaithfulness via Internal RepresentationsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 07:00 AM
In this work, we propose MCAIR, a runtime monitoring and correction framework that leverages internal representations of LLM-based agents.
Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in completing complex tasks through multi-step reasoning and tool interactions.
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