arXiv:2606.08831v1 Announce Type: new Abstract: Large language models (LLMs) increasingly perform multi-step reasoning, where intermediate claims form implicit directed acyclic graphs whose node correctness is structurally conditioned on their ancestors. This makes factuality uncertainty structural, rather than a trivial accumulation of node-wise errors, and necessitates inference-time uncertainty quantification over the reasoning structure. While conformal prediction (CP) offers flexible user-specified factuality control, existing work remains post-hoc and cannot intervene during generation.
The increasing complexity of LLM reasoning chains necessitates improved factuality control to enable reliable multi-step autonomous operations.
Achieving valid factuality control during inference is crucial for the deployment of truly autonomous AI agents across sensitive domains, mitigating risks of hallucination and unreliable outputs.
The proposed method introduces a way to dynamically intervene during LLM generation to ensure factual accuracy, moving beyond post-hoc corrections.
- · AI Agent Developers
- · Enterprises Adopting LLMs
- · Reliability & Safety Engineers
- · LLM Hallucination Rates
- · Post-hoc Fact-Checking Solutions
Increased trustworthiness and deployment readiness for complex generative AI applications.
Acceleration of AI agent development and adoption in critical business and industrial processes.
Enhanced automation of white-collar tasks, potentially impacting workforce requirements in specific sectors.
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Read at arXiv cs.AI