arXiv:2605.29796v1 Announce Type: cross Abstract: Agentic search enables LLMs to solve complex multi-hop questions through iterative reasoning and external search. Despite the effectiveness, these systems often suffer from a critical limitation in practice: agents fail to recognize their own knowledge boundaries, blindly triggering searches when internal knowledge suffices and failing to terminate search even when adequate evidence has been collected. The lack of self-awareness leads to severe \textbf{over-search}, incurring substantial inference latency and prohibitive computational cost. To
The proliferation of LLM-powered agentic systems is highlighting practical limitations such as inefficient 'over-search,' necessitating immediate research into self-correction mechanisms to improve their real-world viability.
This development addresses a critical efficiency and cost bottleneck in advanced AI agents, which are central to collapsing workflows and scaling AI applications, directly impacting their commercial viability and adoption.
AI agents are moving towards more self-aware and resource-efficient operation, shifting from brute-force search to intelligent termination and internal knowledge utilization, making them more practical and economical.
- · AI Agent developers
- · Cloud providers (reduced inference cost)
- · Enterprises adopting AI agents
- · Inefficient AI agent models
- · Computational resource providers (if efficiency gains are dramatic)
More cost-effective and faster AI-driven automation becomes feasible across various industries.
Increased adoption of AI agents could accelerate the displacement of human-led white-collar tasks.
The development of truly 'self-aware' AI could accelerate ethical and regulatory discussions surrounding advanced AI capabilities.
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