arXiv:2606.29713v1 Announce Type: cross Abstract: Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers emit only opaque binary labels, leaving agents unable to self-correct and operators unable to audit. We present SEVA, a structured verification agent that emits evidence alignments, step-by-step reasoning chains, calibrated confidence, and a six-category error diagnosis with actionable fixes. Training such an agent with RL is non-trivial: standard binary reward on multi-component output triggers
The proliferation of LLM-based agents necessitates advanced verification mechanisms to address persistent hallucination issues, pushing the development of sophisticated self-correction tools.
Reliable AI agents are crucial for their broader adoption and for collapsing white-collar workflows, making robust fact attribution and self-correction a critical capability.
Agents can now not only detect errors but also diagnose them with actionable fixes and clear confidence measures, moving beyond opaque binary labels.
- · AI agent developers
- · Enterprises deploying LLM agents
- · AI safety researchers
- · End-users of AI applications
- · Providers of unverified LLM outputs
- · Traditional, opaque AI verification methods
Improved reliability and trust in LLM-based AI applications and autonomous agents.
Accelerated adoption of AI agents in mission-critical applications.
Reduced need for human oversight in certain AI-driven decision-making processes, shifting roles in knowledge work.
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Read at arXiv cs.LG