LLMs Know When They Know, but Do Not Act on It: A Metacognitive Harness for Test-time Scaling
Fuente:
arXiv
Saved in:
| Main Authors: | Cao, Qi, Wang, Yufan, Qin, Peijia, Zhang, Shuhao, Xie, Pengtao |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ATLAS: Agentic Test-time Learning-to-Allocate Scaling
by: Qin, Peijia, et al.
Published: (2026)
by: Qin, Peijia, et al.
Published: (2026)
DAJ: Data-Reweighted LLM Judge for Test-Time Scaling in Code Generation
by: Qin, Peijia, et al.
Published: (2026)
by: Qin, Peijia, et al.
Published: (2026)
Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning
by: Cao, Qi, et al.
Published: (2026)
by: Cao, Qi, et al.
Published: (2026)
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation
by: Qin, Peijia, et al.
Published: (2024)
by: Qin, Peijia, et al.
Published: (2024)
DreamPRM-Code: Function-as-Step Process Reward Model with Label Correction for LLM Coding
by: Zhang, Ruiyi, et al.
Published: (2025)
by: Zhang, Ruiyi, et al.
Published: (2025)
FunPRM: Function-as-Step Process Reward Model with Meta Reward Correction for Code Generation
by: Zhang, Ruiyi, et al.
Published: (2026)
by: Zhang, Ruiyi, et al.
Published: (2026)
When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning
by: Hao, Chenjie, et al.
Published: (2026)
by: Hao, Chenjie, et al.
Published: (2026)
Epistemic Deep Learning: Enabling Machine Learning Models to Know When They Do Not Know
by: Manchingal, Shireen Kudukkil
Published: (2025)
by: Manchingal, Shireen Kudukkil
Published: (2025)
Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense
by: Zhang, Shuhao, et al.
Published: (2026)
by: Zhang, Shuhao, et al.
Published: (2026)
DreamPRM-1.5: Unlocking the Potential of Each Instance for Multimodal Process Reward Model Training
by: Cao, Qi, et al.
Published: (2025)
by: Cao, Qi, et al.
Published: (2025)
Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals
by: Dong, Zihan, et al.
Published: (2026)
by: Dong, Zihan, et al.
Published: (2026)
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?
by: Li, Xiang, et al.
Published: (2025)
by: Li, Xiang, 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)
CaRT: Teaching LLM Agents to Know When They Know Enough
by: Liu, Grace, et al.
Published: (2025)
by: Liu, Grace, et al.
Published: (2025)
Explorations of the Softmax Space: Knowing When the Neural Network Doesn't Know
by: Sikar, Daniel, et al.
Published: (2025)
by: Sikar, Daniel, et al.
Published: (2025)
Bayesian Mixture-of-Experts: Towards Making LLMs Know What They Don't Know
by: Li, Albus Yizhuo
Published: (2025)
by: Li, Albus Yizhuo
Published: (2025)
Generative Models: What Do They Know? Do They Know Things? Let's Find Out!
by: Du, Xiaodan, et al.
Published: (2023)
by: Du, Xiaodan, et al.
Published: (2023)
When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
by: McGovern, Hope, et al.
Published: (2026)
by: McGovern, Hope, et al.
Published: (2026)
Know When You're Wrong: Aligning Confidence with Correctness for LLM Error Detection
by: Xiaohu, Xie, et al.
Published: (2026)
by: Xiaohu, Xie, et al.
Published: (2026)
Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory
by: Cacioli, Jon-Paul
Published: (2026)
by: Cacioli, Jon-Paul
Published: (2026)
Knowing but Not Correcting: Routine Task Requests Suppress Factual Correction in LLMs
by: Chen, Zixuan, et al.
Published: (2026)
by: Chen, Zixuan, et al.
Published: (2026)
Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment
by: Kumar, Sayantan, et al.
Published: (2026)
by: Kumar, Sayantan, et al.
Published: (2026)
Knowing When to Defer: Selective Prediction for Responsible Knowledge Tracing
by: Mitton, Joshua, et al.
Published: (2025)
by: Mitton, Joshua, et al.
Published: (2025)
Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees
by: Gui, Yu, et al.
Published: (2024)
by: Gui, Yu, et al.
Published: (2024)
Know When to Abstain: Optimal Selective Classification with Likelihood Ratios
by: Heng, Alvin, et al.
Published: (2025)
by: Heng, Alvin, et al.
Published: (2025)
W2T: LoRA Weights Already Know What They Can Do
by: Han, Xiaolong, et al.
Published: (2026)
by: Han, Xiaolong, et al.
Published: (2026)
NanoKnow: How to Know What Your Language Model Knows
by: Gu, Lingwei, et al.
Published: (2026)
by: Gu, Lingwei, et al.
Published: (2026)
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)
Base Models Know How to Reason, Thinking Models Learn When
by: Venhoff, Constantin, et al.
Published: (2025)
by: Venhoff, Constantin, et al.
Published: (2025)
Knowing When to Quit: Probabilistic Early Exits for Speech Separation
by: Olsen, Kenny Falkær, et al.
Published: (2025)
by: Olsen, Kenny Falkær, et al.
Published: (2025)
Regression Trees Know Calculus
by: Wycoff, Nathan
Published: (2024)
by: Wycoff, Nathan
Published: (2024)
Distributional Autoencoders Know the Score
by: Leban, Andrej
Published: (2025)
by: Leban, Andrej
Published: (2025)
Knowing What You Know Is Not Enough: Large Language Model Confidences Don't Align With Their Actions
by: Pal, Arka, et al.
Published: (2025)
by: Pal, Arka, et al.
Published: (2025)
Do Androids Know They're Only Dreaming of Electric Sheep?
by: CH-Wang, Sky, et al.
Published: (2023)
by: CH-Wang, Sky, et al.
Published: (2023)
Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization
by: He, Langzhou, et al.
Published: (2025)
by: He, Langzhou, et al.
Published: (2025)
Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
by: Arora, Vinam, et al.
Published: (2025)
by: Arora, Vinam, 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)
To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity
by: Sedova, Anastasiia, et al.
Published: (2024)
by: Sedova, Anastasiia, et al.
Published: (2024)
Knowing When to Ask: Segment-Level Credit Assignment for LLM Tool Use
by: Kumar, Abhijit, et al.
Published: (2026)
by: Kumar, Abhijit, et al.
Published: (2026)
Knowing When to Answer: Adaptive Confidence Refinement for Reliable Audio-Visual Question Answering
by: Tran, Dinh Phu, et al.
Published: (2026)
by: Tran, Dinh Phu, et al.
Published: (2026)
Similar Items
-
ATLAS: Agentic Test-time Learning-to-Allocate Scaling
by: Qin, Peijia, et al.
Published: (2026) -
DAJ: Data-Reweighted LLM Judge for Test-Time Scaling in Code Generation
by: Qin, Peijia, et al.
Published: (2026) -
Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning
by: Cao, Qi, et al.
Published: (2026) -
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation
by: Qin, Peijia, et al.
Published: (2024) -
DreamPRM-Code: Function-as-Step Process Reward Model with Label Correction for LLM Coding
by: Zhang, Ruiyi, et al.
Published: (2025)