Saved in:
| Main Authors: | Kwon, Yongchan, Zou, James |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.01689 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Reasoning Models Don't Always Say What They Think
by: Chen, Yanda, et al.
Published: (2025)
by: Chen, Yanda, et al.
Published: (2025)
xAI-Drop: Don't Use What You Cannot Explain
by: De Luca, Vincenzo Marco, et al.
Published: (2024)
by: De Luca, Vincenzo Marco, et al.
Published: (2024)
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025)
by: Park, Young-Jin, et al.
Published: (2025)
Know What You Don't Know: Selective Prediction for Early Exit DNNs
by: Bajpai, Divya Jyoti, et al.
Published: (2025)
by: Bajpai, Divya Jyoti, et al.
Published: (2025)
Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025)
by: Schaeffer, Rylan, et al.
Published: (2025)
Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)
by: Lade, Ankit Hemant, et al.
Published: (2026)
by: Lade, Ankit Hemant, et al.
Published: (2026)
ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning
by: Kwon, Yongchan, et al.
Published: (2025)
by: Kwon, Yongchan, et al.
Published: (2025)
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
by: Cooper, A. Feder, et al.
Published: (2024)
by: Cooper, A. Feder, et al.
Published: (2024)
Large Language Models Must Be Taught to Know What They Don't Know
by: Kapoor, Sanyam, et al.
Published: (2024)
by: Kapoor, Sanyam, et al.
Published: (2024)
What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction
by: Li, Lehui, et al.
Published: (2026)
by: Li, Lehui, et al.
Published: (2026)
MedCalc-Bench Doesn't Measure What You Think: A Benchmark Audit and the Case for Open-Book Evaluation
by: Krohn-Grimberghe, Artus
Published: (2026)
by: Krohn-Grimberghe, Artus
Published: (2026)
Think When You Need: Self-Adaptive Chain-of-Thought Learning
by: Yang, Junjie, et al.
Published: (2025)
by: Yang, Junjie, et al.
Published: (2025)
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
by: Sun, Yifan, et al.
Published: (2024)
by: Sun, Yifan, et al.
Published: (2024)
AdaptThink: Reasoning Models Can Learn When to Think
by: Zhang, Jiajie, et al.
Published: (2025)
by: Zhang, Jiajie, et al.
Published: (2025)
What Large Language Models Know and What People Think They Know
by: Steyvers, Mark, et al.
Published: (2024)
by: Steyvers, Mark, et al.
Published: (2024)
What Can You Do When You Have Zero Rewards During RL?
by: Prakash, Jatin, et al.
Published: (2025)
by: Prakash, Jatin, et al.
Published: (2025)
ResNets Are Deeper Than You Think
by: Mehmeti-Göpel, Christian H. X. Ali, et al.
Published: (2025)
by: Mehmeti-Göpel, Christian H. X. Ali, et al.
Published: (2025)
Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models
by: Jha, Basab, et al.
Published: (2025)
by: Jha, Basab, et al.
Published: (2025)
You Don't Need Prompt Engineering Anymore: The Prompting Inversion
by: Khan, Imran
Published: (2025)
by: Khan, Imran
Published: (2025)
Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?
by: Wang, Shouren, et al.
Published: (2025)
by: Wang, Shouren, et al.
Published: (2025)
mHC-lite: You Don't Need 20 Sinkhorn-Knopp Iterations
by: Yang, Yongyi, et al.
Published: (2026)
by: Yang, Yongyi, et al.
Published: (2026)
What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective
by: Li, Ming, et al.
Published: (2024)
by: Li, Ming, et al.
Published: (2024)
Tell What You Hear From What You See -- Video to Audio Generation Through Text
by: Liu, Xiulong, et al.
Published: (2024)
by: Liu, Xiulong, et al.
Published: (2024)
When Two LLMs Debate, Both Think They'll Win
by: Prasad, Pradyumna Shyama, et al.
Published: (2025)
by: Prasad, Pradyumna Shyama, et al.
Published: (2025)
When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?
by: Tian, Yuan, et al.
Published: (2026)
by: Tian, Yuan, et al.
Published: (2026)
What Cohort INRs Encode and Where to Freeze Them
by: Sideri-Lampretsa, Vasiliki, et al.
Published: (2026)
by: Sideri-Lampretsa, Vasiliki, et al.
Published: (2026)
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
by: Johnson, Daniel D., et al.
Published: (2024)
by: Johnson, Daniel D., et al.
Published: (2024)
LoRA Is Slower Than You Think
by: Ko, Seokmin
Published: (2025)
by: Ko, Seokmin
Published: (2025)
Optimisation Is Not What You Need
by: Ibias, Alfredo
Published: (2025)
by: Ibias, Alfredo
Published: (2025)
Rethinking Thinking Tokens: LLMs as Improvement Operators
by: Madaan, Lovish, et al.
Published: (2025)
by: Madaan, Lovish, et al.
Published: (2025)
Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models
by: An, Sohyun, et al.
Published: (2025)
by: An, Sohyun, et al.
Published: (2025)
Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds
by: Zhang, Jiefu, et al.
Published: (2026)
by: Zhang, Jiefu, et al.
Published: (2026)
Voice "Cloning" is Style Transfer
by: Zhou, Kaitlyn, et al.
Published: (2026)
by: Zhou, Kaitlyn, et al.
Published: (2026)
What Radio Waves Tell Us about Sleep
by: He, Hao, et al.
Published: (2024)
by: He, Hao, et al.
Published: (2024)
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning
by: Lee, Dongsu, et al.
Published: (2024)
by: Lee, Dongsu, et al.
Published: (2024)
Do Multilingual LLMs Think In English?
by: Schut, Lisa, et al.
Published: (2025)
by: Schut, Lisa, et al.
Published: (2025)
When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models
by: Wang, Kai, et al.
Published: (2025)
by: Wang, Kai, 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)
Forget What You Know about LLMs Evaluations -- LLMs are Like a Chameleon
by: Cohen-Inger, Nurit, et al.
Published: (2025)
by: Cohen-Inger, Nurit, et al.
Published: (2025)
Think Before You Lie: How Reasoning Leads to Honesty
by: Yuan, Ann, et al.
Published: (2026)
by: Yuan, Ann, et al.
Published: (2026)
Similar Items
-
Reasoning Models Don't Always Say What They Think
by: Chen, Yanda, et al.
Published: (2025) -
xAI-Drop: Don't Use What You Cannot Explain
by: De Luca, Vincenzo Marco, et al.
Published: (2024) -
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025) -
Know What You Don't Know: Selective Prediction for Early Exit DNNs
by: Bajpai, Divya Jyoti, et al.
Published: (2025) -
Position: Model Collapse Does Not Mean What You Think
by: Schaeffer, Rylan, et al.
Published: (2025)