Cover Image

ThinkSwitcher: When to Think Hard, When to Think Fast

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liang, Guosheng, Zhong, Longguang, Yang, Ziyi, Quan, Xiaojun
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Cognitive Decision Routing in Large Language Models: When to Think Fast, When to Think Slow
by: Du, Y., et al.
Published: (2025)

FuseRL: Dense Preference Optimization for Heterogeneous Model Fusion
by: Zhong, Longguang, et al.
Published: (2025)

FuseChat-3.0: Preference Optimization Meets Heterogeneous Model Fusion
by: Yang, Ziyi, et al.
Published: (2025)

When to Think and When to Look: Uncertainty-Guided Lookback
by: Bi, Jing, et al.
Published: (2025)

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

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

Thinkless: LLM Learns When to Think
by: Fang, Gongfan, et al.
Published: (2025)

When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning
by: Wei, Jiaqi, et al.
Published: (2026)

When Thinking Backfires: Mechanistic Insights Into Reasoning-Induced Misalignment
by: Yan, Hanqi, et al.
Published: (2025)

Weighted-Reward Preference Optimization for Implicit Model Fusion
by: Yang, Ziyi, et al.
Published: (2024)

FuseChat: Knowledge Fusion of Chat Models
by: Wan, Fanqi, et al.
Published: (2024)

Adaptive Deep Reasoning: Triggering Deep Thinking When Needed
by: Wang, Yunhao, et al.
Published: (2025)

When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs
by: Li, Xiaomin, et al.
Published: (2025)

When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning
by: Xiang, Yang, et al.
Published: (2026)

Think Only When You Need with Large Hybrid-Reasoning Models
by: Jiang, Lingjie, et al.
Published: (2025)

When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models
by: Li, Yanhong, et al.
Published: (2024)

Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains
by: Tan, Wenhui, et al.
Published: (2025)

Think When You Need: Self-Adaptive Chain-of-Thought Learning
by: Yang, Junjie, et al.
Published: (2025)

Knowledge Fusion of Chat LLMs: A Preliminary Technical Report
by: Wan, Fanqi, et al.
Published: (2024)

Learning When to Think While Listening in Large Audio-Language Models
by: Song, Zhiyuan, et al.
Published: (2026)

Fast Thinking for Large Language Models
by: Zheng, Haoyu, et al.
Published: (2025)

Chain of Thought Still Thinks Fast: APriCoT Helps with Thinking Slow
by: Moore, Kyle, et al.
Published: (2024)

Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking
by: Cheng, Xiaoxue, et al.
Published: (2025)

When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy
by: Qi, Jirui, et al.
Published: (2025)

Thinking Out Loud: Do Reasoning Models Know When They're Right?
by: Zeng, Qingcheng, et al.
Published: (2025)

When Two LLMs Debate, Both Think They'll Win
by: Prasad, Pradyumna Shyama, et al.
Published: (2025)

SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning
by: Shu, Fangxun, et al.
Published: (2025)

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

Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?
by: Wang, Shouren, et al.
Published: (2025)

Can Thinking Models Think to Detect Hateful Memes?
by: Kmainasi, Mohamed Bayan, et al.
Published: (2026)

Thinking Augmented Pre-training
by: Wang, Liang, et al.
Published: (2025)

Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs
by: Sun, Kaiser, et al.
Published: (2026)

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

When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models
by: Wang, Kai, et al.
Published: (2025)

When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?
by: Tian, Yuan, et al.
Published: (2026)

ThinkPatterns-21k: A Systematic Study on the Impact of Thinking Patterns in LLMs
by: Wen, Pengcheng, et al.
Published: (2025)

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

BlockPruner: Fine-grained Pruning for Large Language Models
by: Zhong, Longguang, et al.
Published: (2024)

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
by: Jia, Xinda, et al.
Published: (2025)

ThinkQE: Query Expansion via an Evolving Thinking Process
by: Lei, Yibin, et al.
Published: (2025)