Buchumschlag

To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Gong, Nanxu, Li, Haotian, Dong, Sixun, Lian, Jianxun, Fu, Yanjie, Xie, Xing
Format:	Preprint
Veröffentlicht:	2026
Schlagworte:	Artificial Intelligence Computation and Language
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Efficient Post-Training Refinement of Latent Reasoning in Large Language Models
von: Wang, Xinyuan, et al.
Veröffentlicht: (2025)

Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations
von: Gong, Nanxu, et al.
Veröffentlicht: (2026)

Thinking Fast, Thinking Wrong: Intuitiveness Modulates LLM Counterfactual Reasoning in Policy Evaluation
von: He, Yanjie
Veröffentlicht: (2026)

MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models?
von: Yong, Xixian, et al.
Veröffentlicht: (2025)

Controlling Thinking Speed in Reasoning Models
von: Lin, Zhengkai, et al.
Veröffentlicht: (2025)

Fast-Slow-Thinking: Complex Task Solving with Large Language Models
von: Sun, Yiliu, et al.
Veröffentlicht: (2025)

RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models
von: Gong, Zhaoyan, et al.
Veröffentlicht: (2025)

Modeling Hierarchical Thinking in Large Reasoning Models
von: Shahariar, G M, et al.
Veröffentlicht: (2025)

To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models
von: Zhu, Zihao, et al.
Veröffentlicht: (2025)

DynamicMind: A Tri-Mode Thinking System for Large Language Models
von: Li, Wei, et al.
Veröffentlicht: (2025)

When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning
von: Zhang, Xiaoyun, et al.
Veröffentlicht: (2025)

GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
von: Luo, Zihan, et al.
Veröffentlicht: (2024)

Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models
von: Guo, Zhenyuan, et al.
Veröffentlicht: (2026)

Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models
von: Nickel, Christian, et al.
Veröffentlicht: (2026)

ThinkPilot: Steering Reasoning Models via Automated Think-prefixes Optimization
von: Li, Sunzhu, et al.
Veröffentlicht: (2025)

Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study
von: Hu, Zhengyu, et al.
Veröffentlicht: (2025)

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
von: Zhu, Jason, et al.
Veröffentlicht: (2025)

Think or Not? Exploring Thinking Efficiency in Large Reasoning Models via an Information-Theoretic Lens
von: Yong, Xixian, et al.
Veröffentlicht: (2025)

Thinking About Thinking: Evaluating Reasoning in Post-Trained Language Models
von: Singla, Pratham, et al.
Veröffentlicht: (2025)

MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning?
von: Xu, Zhe, et al.
Veröffentlicht: (2025)

Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning
von: Cheng, Xiaoxue, et al.
Veröffentlicht: (2025)

Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models
von: Elenjical, Abraham Paul, et al.
Veröffentlicht: (2026)

Exploring the System 1 Thinking Capability of Large Reasoning Models
von: Zhang, Wenyuan, et al.
Veröffentlicht: (2025)

AdaptThink: Reasoning Models Can Learn When to Think
von: Zhang, Jiajie, et al.
Veröffentlicht: (2025)

Algorithmic Thinking Theory
von: Bateni, MohammadHossein, et al.
Veröffentlicht: (2025)

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories
von: Gong, Nanxu, et al.
Veröffentlicht: (2025)

Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld's Episode Theory
von: Li, Ming, et al.
Veröffentlicht: (2025)

AdapThink: Adaptive Thinking Preferences for Reasoning Language Model
von: Wan, Xu, et al.
Veröffentlicht: (2025)

Large Model Strategic Thinking, Small Model Efficiency: Transferring Theory of Mind in Large Language Models
von: Lore, Nunzio, et al.
Veröffentlicht: (2024)

MixReasoning: Switching Modes to Think
von: Lu, Haiquan, et al.
Veröffentlicht: (2025)

Reasoning Models Can Be Effective Without Thinking
von: Ma, Wenjie, et al.
Veröffentlicht: (2025)

Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
von: Liu, Runxuan, et al.
Veröffentlicht: (2025)

When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning
von: Zhu, Rongzhi, et al.
Veröffentlicht: (2025)

Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
von: Kim, Hyunwoo, et al.
Veröffentlicht: (2025)

Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Time
von: Yang, Wang, et al.
Veröffentlicht: (2025)

Thinking Out of Order: When Output Order Stops Reflecting Reasoning Order in Diffusion Language Models
von: Yu, Longxuan, et al.
Veröffentlicht: (2026)

Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition
von: Sarangi, Sneheel, et al.
Veröffentlicht: (2025)

Efficient Reasoning with Balanced Thinking
von: Li, Yulin, et al.
Veröffentlicht: (2026)

Visuospatial Perspective Taking in Multimodal Language Models
von: Prunty, Jonathan, et al.
Veröffentlicht: (2026)

InvThink: Premortem Reasoning for Safer Language Models
von: Kim, Yubin, et al.
Veröffentlicht: (2025)