arXiv:2606. 20245v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning ability, enabling them to incorporate external information provided in the input prompt.
By Huang Peng, Jiuyang Tang, Weixin Zeng, Hao Xu, Xiang Zhao
KC-Bench is a dynamic interactive benchmark designed to evaluate how large language model agents reconcile user instructions, internal knowledge, and real‑time environmental observations. It contains 238 manually curated multi‑turn tasks that test world‑knowledge conflicts, input inconsistencies, and multi‑source temporal conflicts, using a user simulator, stateful tools, deterministic environment assertions, an open‑source natural‑language evaluator, and human trajectory verification. Evaluation of nine models—including DeepSeek‑V4‑Flash, GLM‑5.2, and MiniMax‑M3—reveals significant cross‑domain variation, with no model reliably handling factual correction, identity consistency checking, and temporal conflict resolution across all settings, and shows that missed conflicts can propagate to tool calls or synthetic protected‑data flows.
By Yaxing Lyu, Shengjie Zhou, Binbin Toh, Pengyu Zhu, Lijun Li
The paper introduces a taxonomy of six types of contextual knowledge conflicts—factual, inferential, temporal, granularity, perspective, and ambiguity—and presents the ContextConflict dataset with 5,781 samples covering reasoning and summarization tasks. Experiments on nine large language models reveal that current models struggle to resolve these conflicts, exhibit a bias toward earlier evidence, and show latent awareness of conflicts in their internal representations. The authors propose a training‑free, label‑free steering method that adjusts activations to better incorporate evidence, consistently improving reasoning accuracy and producing higher‑quality, balanced summaries on the dataset.
By Xinye Yang, Zhenyang Liu, Ruisi Li, Yuanyuan Lei
The paper examines how large language models resolve conflicts that arise within contextual knowledge, rather than between internal knowledge and external context. It introduces a taxonomy of six contextual conflict types and presents the ContextConflict dataset with 5,781 samples covering reasoning and summarization tasks. Experiments on nine LLMs reveal persistent shortcomings in conflict resolution, uncover a bias toward earlier evidence, and propose a training‑free steering method that improves accuracy and summary quality.
KC-Bench is a dynamic interactive benchmark designed to evaluate how large language model agents reconcile user instructions, internal knowledge, and real‑time environmental observations. It consists of 238 manually curated multi‑turn tasks that test world‑knowledge conflicts, input inconsistencies, and multi‑source temporal conflicts, using a user simulator, stateful tools, deterministic environment assertions, an open‑source natural‑language evaluator, and human trajectory verification. Evaluation of nine models—including DeepSeek‑V4‑Flash, GLM‑5.2, and MiniMax‑M3—reveals significant cross‑domain variation, with no model reliably handling factual correction, identity consistency, and temporal conflict resolution across all settings, and shows that missed conflicts can propagate to tool calls or synthetic protected‑data flows.
arXiv:2608. 13921v1 Announce Type: new Abstract: LLM agents increasingly maintain personal memory across sessions, but it can conflict.
By Lu Yang, Shusheng Xu, Zhuoran Li, Tongkai Yang, Longbo Huang