Author

Ming-Xuan Li

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#artificial intelligence Preprint Sep 2026

"You're Right, Let Me Fix It": How LLM Agents Damage Correct Work When Falsely Accused

Results show that preserving already-correct work under unsupported accusation is a distinct safety challenge for long-lived agents, and introduces CAVE-Bench, a benchmark of 365 agentic tasks across six domains built around opaque tasks.

Xu-Tao Mao, Rui Qian, Long-Xiang Wang et al. · 0 citations

Conference Open access Sep 2026

Information-Needs-Guided Virtual Knowledge Graph Enrichment via Large Language Models

This work forms the task of Information-Needs-Guided VKG Enrichment (IN-VKGE), and proposes an iterative framework that leverages large language models to assess whether information needs can be supported using SPARQL execution feedback and generate ontology and mapping enrichment proposals.

Lin Ren, Guohui Xiao, Guilin Qi et al. · Proceedings of the Thirty-Fi... · 0 citations