KbSD: Knowledge Boundary aware Self-Distillation for Behavioral Calibration in Agentic Search
arXiv:2606.29863v1 Announce Type: new Abstract: Agentic search equips large language models with dynamic retrieval abilities, but existing reinforcement learning methods remain limited by reward sparsity in knowledge boundary calibration -- deciding when to trust parametric memory, when to rely on retrieved evidence, and when to abstain. Binary rewards can penalize undesirable outcomes, but provide little guidance on the reasoning process required to make calibrated decisions across different knowledge states. To address this, we propose KbSD (Knowledge boundary Self-Distillation), a framework
The rapid advancement in large language models necessitates improved autonomous decision-making, particularly concerning knowledge calibration and the management of uncertainty.
This research directly addresses a core limitation of current AI agents, enabling more reliable and effective deployment in complex, real-world tasks where trust and accuracy are paramount.
AI agents can now more effectively determine when to use internal knowledge, external data, or when to abstain, leading to fewer errors and more calibrated behavior.
- · AI Agent developers
- · Enterprises deploying AI for critical functions
- · Applied AI researchers
- · Systems with uncalibrated agents
- · Trial-and-error RL approaches
Improved reliability and broader adoption of AI agents in sensitive applications.
Increased efficiency and automation in white-collar tasks, further impacting industries reliant on knowledge work.
Accelerated development of fully autonomous systems with reduced human oversight due to enhanced trustworthiness.
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Read at arXiv cs.CL