Author

Hong-Jun Liu

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#artificial intelligence Preprint Oct 2026

Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflict

When retrieved evidence contradicts an agent's prior beliefs, does it revise its answer, acknowledge uncertainty, or persist with an incorrect conclusion? Existing evaluations of agentic systems focus primarily on task success, offering limited insight into how agents handle such conflicts. We propose to evaluate agent...

Kaiser Sun, Bernal Jiménez Gutiérrez, Hong-Jun Liu et al. · 0 citations

#artificial intelligence Review May 2026

Harnessing LLM Agents with Skill Programs

This work introduces HASP (Harnessing LLM Agents with Skill Programs), a new framework that upgrades skills into executable Program Functions (PFs) that act as executable guardrails that activate on failure-prone states and modify the next action or inject corrective context.

Hong-Jun Liu, Yifei Ming, Shafiq Joty et al. · arXiv.org · 6 citations · ⚡1

#artificial intelligence Preprint Sep 2026

Memory Is a Derivation: The Distributed-Evidence Paradox in Long-Term Agents

DerivAudit is introduced, a framework for auditing whether a memory is actually supported by the history available when it was written, whether supporting evidence lies beyond writer-provided citations, whether the composed memory introduces unsupported meaning, and how write-time admission decisions affect later memor...

Hong-Jun Liu, Chen Zhao · 0 citations