FineVerify: Scaling Test-Time Compute with Fine-Grained Self-Verification for Agentic Search
arXiv:2606.00660v1 Announce Type: new Abstract: Agentic search requires language model agents to explore many sources and answer complex information-seeking questions. Scaling test-time compute is a promising way to improve these agents, but current approaches can fail, because correct answers are often sparse and score-based selection depends on model calibration. We propose FineVerify, a fine-grained self-verification framework that decomposes each question into checkable sub-questions, verifies sampled candidates against each sub-question, and selects the candidate with the highest aggregat
The rapid advancement in Large Language Models necessitates more robust and scalable ways to ensure the accuracy and reliability of agentic systems, particularly as their deployment becomes more widespread.
Improving the trustworthiness and efficiency of AI agents directly impacts their adoption and the scope of tasks they can reliably undertake, driving productivity gains across various industries.
This development suggests a more reliable pathway for scaling AI agent capabilities, moving beyond simple score-based selection to a fine-grained, verifiable approach that enhances decision-making confidence.
- · AI Agent Developers
- · Enterprises Adopting AI Agents
- · AI Infrastructure Providers
- · Knowledge Work Automation
- · Inefficient Agentic Search Systems
- · Manual Information Verification Processes
AI agents become more accurate and capable, reducing errors in complex information-seeking tasks.
Increased trust in AI agents accelerates their integration into critical workflows and decision-making processes.
The enhanced autonomy and reliability of AI agents could significantly disrupt white-collar industries and existing SaaS layers.
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Read at arXiv cs.CL