When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
arXiv:2605.23414v1 Announce Type: cross Abstract: LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan feasibility, a phenomenon we term epistemic miscalibration in planning. Unlike execution errors, epistemic miscalibration is latent during planning, as generated plans can remain self-consistent and executable without observable errors; the miscalibration is also dynamic, as new information can alter feasibility assessments, potentially obscuring past miscalibration signals and causing them to
The rapid development and deployment of LLM-based multi-agent systems are revealing novel failure modes that require immediate research attention to ensure robust and reliable operation.
Understanding and mitigating 'epistemic miscalibration' is crucial for the safe and effective deployment of AI agents in critical applications where planning accuracy directly impacts outcomes.
The focus extends beyond mere execution errors to the inherent cognitive limitations of LLMs within planning architectures, highlighting a new frontier for AI safety and reliability research.
- · AI safety researchers
- · Developers of robust multi-agent systems
- · Auditors and evaluators of AI systems
- · Developers of unstable multi-agent systems
- · Sectors reliant on uncalibrated AI planning
- · Users experiencing plan failures due to miscalibration
This research provides a new framework for diagnosing and preventing complex failures in multi-agent AI systems.
It will drive the development of new techniques for agents to assess and improve their own epistemic calibration during planning, leading to more trustworthy autonomous systems.
The concept of 'epistemic miscalibration' could extend to human-AI collaboration, influencing how trust is built and managed in hybrid intelligence systems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG