arXiv:2601.22436v3 Announce Type: replace Abstract: Self-evolving large language model (LLM) agents continually improve by accumulating and reusing past experience, yet it remains unclear whether they faithfully rely on that experience to guide their behavior. We present the first systematic investigation of experience faithfulness, the causal dependence of an agent's decisions on the experience it is given, in self-evolving LLM agents. Using controlled causal interventions on both raw and condensed forms of experience, we comprehensively evaluate four representative frameworks across 13 LLM b
The rapid advancement and deployment of LLM agents make understanding their limitations and operational fidelity critical for reliable application.
This research highlights a key vulnerability in self-evolving AI agents, indicating that their accumulated 'experience' may not always reliably guide their behavior, impacting trust and performance.
The assumption that LLM agents faithfully learn and apply past experience is challenged, requiring more robust design and evaluation for autonomous systems.
- · AI researchers focusing on interpretability and robust agent design
- · Companies developing verification and validation tools for AI agents
- · Users prioritizing transparency and control in AI systems
- · Developers deploying unverified self-evolving LLM agents
- · Applications relying solely on LLMs' self-improvement without external validatio
- · The 'black box' approach to AI development
Increased scrutiny and demand for explainable AI in agentic systems.
Development of new methodologies and frameworks to ensure 'experience faithfulness' in LLMs and AI agents.
Potential slowdown in the adoption of fully autonomous LLM agents until fidelity concerns are addressed, fostering human-in-the-loop systems.
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Read at arXiv cs.CL