Saved in:
| Main Authors: | Zhai, Zhiyuan, You, Xinkai, Yan, Wenjing, Wang, Xin |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.23926 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Think Just Enough: Sequence-Level Entropy as a Confidence Signal for LLM Reasoning
by: Sharma, Aman, et al.
Published: (2025)
by: Sharma, Aman, et al.
Published: (2025)
How Much Data Is Enough? Uniform Convergence Bounds for Generative & Vision-Language Models under Low-Dimensional Structure
by: Thompson, Paul M.
Published: (2025)
by: Thompson, Paul M.
Published: (2025)
Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning
by: Xiang, Violet, et al.
Published: (2025)
by: Xiang, Violet, et al.
Published: (2025)
Think Clearly: Improving Reasoning via Redundant Token Pruning
by: Choi, Daewon, et al.
Published: (2025)
by: Choi, Daewon, et al.
Published: (2025)
How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function
by: Thompson, Paul M.
Published: (2026)
by: Thompson, Paul M.
Published: (2026)
DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models
by: Shen, Yi, et al.
Published: (2025)
by: Shen, Yi, et al.
Published: (2025)
Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards
by: He, Haoran, et al.
Published: (2025)
by: He, Haoran, et al.
Published: (2025)
Quantifying and Understanding Uncertainty in Large Reasoning Models
by: Li, Yangyi, et al.
Published: (2026)
by: Li, Yangyi, et al.
Published: (2026)
How Much Cache Does Reasoning Need? Depth-Cache Tradeoffs in KV-Compressed Transformers
by: Wang, Xiao
Published: (2026)
by: Wang, Xiao
Published: (2026)
How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning
by: Cai, Hongyi James, et al.
Published: (2025)
by: Cai, Hongyi James, et al.
Published: (2025)
Understanding Reasoning in Thinking Language Models via Steering Vectors
by: Venhoff, Constantin, et al.
Published: (2025)
by: Venhoff, Constantin, et al.
Published: (2025)
Infinite Width Models That Work: Why Feature Learning Doesn't Matter as Much as You Think
by: Sernau, Luke
Published: (2024)
by: Sernau, Luke
Published: (2024)
Accordion-Thinking: Self-Regulated Step Summaries for Efficient and Readable LLM Reasoning
by: Yang, Zhicheng, et al.
Published: (2026)
by: Yang, Zhicheng, et al.
Published: (2026)
DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models
by: Yin, Cheng, et al.
Published: (2025)
by: Yin, Cheng, et al.
Published: (2025)
Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Representation Learning
by: Gao, Hang, et al.
Published: (2025)
by: Gao, Hang, et al.
Published: (2025)
Base Models Know How to Reason, Thinking Models Learn When
by: Venhoff, Constantin, et al.
Published: (2025)
by: Venhoff, Constantin, et al.
Published: (2025)
How Much is Too Much? Exploring LoRA Rank Trade-offs for Retaining Knowledge and Domain Robustness
by: Rathore, Darshita, et al.
Published: (2025)
by: Rathore, Darshita, et al.
Published: (2025)
Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory
by: Dong, Songwei, et al.
Published: (2026)
by: Dong, Songwei, et al.
Published: (2026)
Locality-Aware Redundancy Pruning for LLM Depth Compression
by: Yun, Vincent-Daniel, et al.
Published: (2026)
by: Yun, Vincent-Daniel, et al.
Published: (2026)
Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models
by: Balef, Amir Rezaei, et al.
Published: (2026)
by: Balef, Amir Rezaei, et al.
Published: (2026)
Structural Reasoning Improves Molecular Understanding of LLM
by: Jang, Yunhui, et al.
Published: (2024)
by: Jang, Yunhui, et al.
Published: (2024)
ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces
by: Xu, Xin, et al.
Published: (2026)
by: Xu, Xin, et al.
Published: (2026)
When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate
by: Forest, Florent, et al.
Published: (2025)
by: Forest, Florent, et al.
Published: (2025)
Do Large Language Models Know How Much They Know?
by: Prato, Gabriele, et al.
Published: (2025)
by: Prato, Gabriele, et al.
Published: (2025)
How Much Can We Forget about Data Contamination?
by: Bordt, Sebastian, et al.
Published: (2024)
by: Bordt, Sebastian, et al.
Published: (2024)
Thinking with Deltas: Incentivizing Reinforcement Learning via Differential Visual Reasoning Policy
by: Gao, Shujian, et al.
Published: (2026)
by: Gao, Shujian, et al.
Published: (2026)
Think Before You Lie: How Reasoning Leads to Honesty
by: Yuan, Ann, et al.
Published: (2026)
by: Yuan, Ann, et al.
Published: (2026)
Comment on The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
by: Lawsen, A.
Published: (2025)
by: Lawsen, A.
Published: (2025)
How Many Experts Are Enough? Towards Optimal Semantic Specialization for Mixture-of-Experts
by: Park, Sumin, et al.
Published: (2025)
by: Park, Sumin, et al.
Published: (2025)
Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis
by: Zhai, Zhiyuan, et al.
Published: (2026)
by: Zhai, Zhiyuan, et al.
Published: (2026)
Efficient Reasoning with Hidden Thinking
by: Shen, Xuan, et al.
Published: (2025)
by: Shen, Xuan, et al.
Published: (2025)
Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
by: Liu, Siyuan, et al.
Published: (2025)
by: Liu, Siyuan, et al.
Published: (2025)
One Token Embedding Is Enough to Deadlock Your Large Reasoning Model
by: Zhang, Mohan, et al.
Published: (2025)
by: Zhang, Mohan, et al.
Published: (2025)
Think-Augmented Function Calling: Improving LLM Parameter Accuracy Through Embedded Reasoning
by: Wei, Lei, et al.
Published: (2026)
by: Wei, Lei, et al.
Published: (2026)
Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models
by: Guo, Zhenyuan, et al.
Published: (2026)
by: Guo, Zhenyuan, et al.
Published: (2026)
AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking in Large Language Models
by: Wang, Xiangqi, et al.
Published: (2025)
by: Wang, Xiangqi, et al.
Published: (2025)
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options
by: Nair, Lakshmi, et al.
Published: (2025)
by: Nair, Lakshmi, et al.
Published: (2025)
To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models
by: Zhu, Zihao, et al.
Published: (2025)
by: Zhu, Zihao, et al.
Published: (2025)
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model
by: Wan, Xu, et al.
Published: (2025)
by: Wan, Xu, et al.
Published: (2025)
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
by: Ross, Brendan Leigh, et al.
Published: (2025)
by: Ross, Brendan Leigh, et al.
Published: (2025)
Similar Items
-
Think Just Enough: Sequence-Level Entropy as a Confidence Signal for LLM Reasoning
by: Sharma, Aman, et al.
Published: (2025) -
How Much Data Is Enough? Uniform Convergence Bounds for Generative & Vision-Language Models under Low-Dimensional Structure
by: Thompson, Paul M.
Published: (2025) -
Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning
by: Xiang, Violet, et al.
Published: (2025) -
Think Clearly: Improving Reasoning via Redundant Token Pruning
by: Choi, Daewon, et al.
Published: (2025) -
How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function
by: Thompson, Paul M.
Published: (2026)