arXiv AI June 19, 2026 By Huang Peng, Jiuyang Tang, Weixin Zeng, Hao Xu, Xiang Zhao

Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 9

Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems

arXiv:2605. 12213v2 Announce Type: replace Abstract: LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context.

By Jiazhou Liang, Armin Toroghi, Yifan Simon Liu, Faeze Moradi Kalarde, Liam Gallagher, Scott Sanner

llms rag agents

arXiv AI

Jun 29

CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching

arXiv:2602. 20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather than spurious correlations.

By Yuzhe Wang, Yaochen Zhu, Jundong Li

llms benchmarks

arXiv AI

Jul 1

Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts

arXiv:2508. 01273v3 Announce Type: replace Abstract: Explicit knowledge conflicts, occurring when retrieved contexts contain contradictory information, pose a fundamental challenge for Large Language Models (LLMs) as they integrate increasingly diverse data sources.

By Xianda Zheng, Zijian Huang, Meng-Fen Chiang, Jiamou Liu, Yuan Fang, Michael Witbrock, Kaiqi Zhao

llms reinforcement-learning

arXiv AI

Jul 14

CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps

arXiv:2607. 10562v1 Announce Type: new Abstract: Evaluating the multi-hop reasoning capabilities of large language models remains a significant challenge.

By JungMin Yun, JuneHyoung Kwon, YoungBin Kim

llms nlp

arXiv AI

Jul 21

Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge Graph

arXiv:2607. 17266v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing.

By Peiji Yu, Xin Chen, Tianxing Wu

llms agents nlp benchmarks safety

arXiv AI

Jun 3

From Context to Skills: Can Language Models Learn from Context Skillfully?

arXiv:2604. 27660v3 Announce Type: replace Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge.

By Shuzheng Si, Haozhe Zhao, Yu Lei, Qingyi Wang, Dingwei Chen, Zhitong Wang, Zhenhailong Wang, Kangyang Luo, Zheng Wang, Gang Chen, Fanchao Qi, Minjia Zhang, Maosong Sun

llms agents reinforcement-learning safety

Related stories

Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems

CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching

Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts

CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps

Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge Graph

From Context to Skills: Can Language Models Learn from Context Skillfully?