Multi-Turn Language Model Reasoning

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9 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving
Oct 12, 2026Xin Cheng, Junming Yang, Ning XuMulti-Turn Language Model Reasoning LLM Reasoning
School of Computer Science and Engineering, Southeast University, Nanjing, China.
Geometry of Divergence: Tracking Hidden-State Trajectories for Adaptive Multi-Turn Reasoning
Aug 31, 2026Jie Liang, Zhengxin Yu, Hamid Nasiri +1Multi-Turn Language Model Reasoning Long-Horizon LLM Agents
School of Computing and Communications, Lancaster University, UK · School of Computing and Engineering, University of Huddersfield, UK
Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference
Aug 4, 2026Shahrukh Mohiuddin, Chalamalasetti Kranti, Sherzod Hakimov +1LLM Evaluation Multi-Turn Language Model Reasoning
Computational Linguistics, Department of Linguistics University of Potsdam, Germany · German Research Center for Artificial Intelligence (DFKI), Berlin, Germany
BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation
Jun 29, 2026Ankur Samanta, Akshayaa Magesh, Tal Lancewicki +7LLM Evaluation Multi-Turn Language Model Reasoning
Meta AI · Columbia University · Work done at Meta +2
When the Chain of Thought Knows Better: Failure Modes in Multi-Turn Reasoning Models
Jun 9, 2026Sai Kartheek Reddy Kasu, Nils Lukas, Samuele PoppiCoT Faithfulness LLM Alignment
Independent Researcher · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
Same Evidence, Different Answers: Canonical-Context On-Policy Distillation for Multi-Turn Language Models
May 28, 2026Zizhuo Lin, Quanling Liu, Jinsheng Quan +6LLM Grounding Multi-Turn Language Model Reasoning
Zhejiang University · University of Science and Technology of China
Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents
Apr 6, 2026Neharika Jali, Anupam Nayak, Gauri JoshiMulti-Turn Language Model Reasoning Efficient Language Model Reasoning
Carnegie Mellon University
Adaptive Stopping for Multi-Turn LLM Reasoning
Apr 1, 2026Xiaofan Zhou, Huy Nguyen, Bo Yu +2Multi-Turn Language Model Reasoning Efficient Language Model Reasoning
Department of Computer Science, University of Illinois Chicago, Chicago, IL 60607, USA · Data science, Augustana College, Rock Island, IL 61201, USA · Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA
Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction
Mar 5, 2026Xingwu Chen, Zhanqiu Zhang, Yiwen Guo +1Multi-Turn Language Model Reasoning RL for Language Model Reasoning
School of Computing and Data Science, The University of Hong Kong · LIGHTSPEED · Independent Researcher

Oct 12, 2026J/K move · Enter open · S save

Query Your Way Out: Benchmarking Multi-Turn Knowledge Reasoning through Generalized Puzzle Solving

Xin Cheng, Junming Yang, Ning Xu

School of Computer Science and Engineering, Southeast University, Nanjing, China.

Multi-Turn Language Model Reasoning LLM Reasoning

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Puzzles offer a natural testbed for Large Language Models (LLMs) reasoning because they pose verifiable goals under partial information and require multi-step decisions to reach a solution. However, existing puzzle benchmarks typically focus on narrow environments with rigid mechanics and tightly constrained solution targets. To bridge this gap, we introduce the Active Probing Benchmark (APB), which generalizes puzzle-solving by casting concept identification as an interactive reasoning task that integrates specialized knowledge assessment into the puzzle setting. Specifically, APB evaluates whether models can acquire information via multi-turn active probing and inference to pinpoint 960 target concepts of specialized difficulty across eight diverse academic domains. Our evaluation reveals a counter-intuitive phenomenon: most open-source reasoning models significantly underperform their standard instruction counterparts. This finding suggests that in interactive settings, these models struggle to leverage procedural reasoning to reliably access and apply specialized domain knowledge. Beyond these empirical insights, APB provides a scalable pipeline for generating benchmarks that probe knowledge reasoning under interaction across diverse settings.