Saved in:
| Main Authors: | Zhao, Qiannian, Yang, Chen, Jing, Jinhao, Zhang, Yunke, Ren, Xuhui, Yu, Lu, Zhang, Shijie, Yin, Hongzhi |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.22751 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
KnowRL: Teaching Language Models to Know What They Know
by: Kale, Sahil, et al.
Published: (2025)
by: Kale, Sahil, et al.
Published: (2025)
Efficient Data Selection for Multimodal Models via Incremental Optimization Utility
by: Jing, Jinhao, et al.
Published: (2026)
by: Jing, Jinhao, 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)
Can AI Assistants Know What They Don't Know?
by: Cheng, Qinyuan, et al.
Published: (2024)
by: Cheng, Qinyuan, et al.
Published: (2024)
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)
Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
by: Mei, Zhiting, et al.
Published: (2025)
by: Mei, Zhiting, et al.
Published: (2025)
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)
KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality
by: Ren, Baochang, et al.
Published: (2025)
by: Ren, Baochang, et al.
Published: (2025)
Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method
by: Zhao, Yukun, et al.
Published: (2023)
by: Zhao, Yukun, et al.
Published: (2023)
KnowRL: Boosting LLM Reasoning via Reinforcement Learning with Minimal-Sufficient Knowledge Guidance
by: Yu, Linhao, et al.
Published: (2026)
by: Yu, Linhao, et al.
Published: (2026)
SnapKV: LLM Knows What You are Looking for Before Generation
by: Li, Yuhong, et al.
Published: (2024)
by: Li, Yuhong, et al.
Published: (2024)
World Models That Know When They Don't Know - Controllable Video Generation with Calibrated Uncertainty
by: Mei, Zhiting, et al.
Published: (2025)
by: Mei, Zhiting, et al.
Published: (2025)
KnowBias: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement
by: Pan, Jinhao, et al.
Published: (2026)
by: Pan, Jinhao, 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)
LiveBrowseComp: Are Search Agents Searching, or Just Verifying What They Already Know?
by: Fan, HuiMing, et al.
Published: (2026)
by: Fan, HuiMing, et al.
Published: (2026)
Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing
by: Yang, Diji, et al.
Published: (2025)
by: Yang, Diji, et al.
Published: (2025)
K^2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control
by: Wu, Zhe, et al.
Published: (2026)
by: Wu, Zhe, et al.
Published: (2026)
A Primer in Post-Training Reasoning Data: What We Know About How It Works
by: Li, Yaoming, et al.
Published: (2026)
by: Li, Yaoming, et al.
Published: (2026)
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)
Fine-Tuning Language Models to Know What They Know
by: Park, Sangjun, et al.
Published: (2026)
by: Park, Sangjun, et al.
Published: (2026)
GeoSym127K: Scalable Symbolically-verifiable Synthesis for Multimodal Geometric Reasoning
by: Jing, Jinhao, et al.
Published: (2026)
by: Jing, Jinhao, et al.
Published: (2026)
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)
Do Large Language Models Know What They Don't Know? Kalshibench: A New Benchmark for Evaluating Epistemic Calibration via Prediction Markets
by: Nel, Lukas
Published: (2025)
by: Nel, Lukas
Published: (2025)
Visually Dehallucinative Instruction Generation: Know What You Don't Know
by: Cha, Sungguk, et al.
Published: (2024)
by: Cha, Sungguk, et al.
Published: (2024)
What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"
by: Lee, Joosung, et al.
Published: (2026)
by: Lee, Joosung, et al.
Published: (2026)
EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs
by: Yeom, Jewon, et al.
Published: (2026)
by: Yeom, Jewon, et al.
Published: (2026)
KnowCoder-V2: Deep Knowledge Analysis
by: Li, Zixuan, et al.
Published: (2025)
by: Li, Zixuan, et al.
Published: (2025)
Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing
by: Liu, Wenhao, et al.
Published: (2024)
by: Liu, Wenhao, et al.
Published: (2024)
You Don't Know Until You Click:Automated GUI Testing for Production-Ready Software Evaluation
by: Bian, Yutong, et al.
Published: (2025)
by: Bian, Yutong, et al.
Published: (2025)
Do Retrieval Augmented Language Models Know When They Don't Know?
by: Zhou, Youchao, et al.
Published: (2025)
by: Zhou, Youchao, et al.
Published: (2025)
Does Your Reasoning Model Implicitly Know When to Stop Thinking?
by: Huang, Zixuan, et al.
Published: (2026)
by: Huang, Zixuan, et al.
Published: (2026)
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)
Specifying What You Know or Not for Multi-Label Class-Incremental Learning
by: Zhang, Aoting, et al.
Published: (2025)
by: Zhang, Aoting, et al.
Published: (2025)
KnowCoder-A1: Incentivizing Agentic Reasoning Capability with Outcome Supervision for KBQA
by: Chen, Zhuo, et al.
Published: (2025)
by: Chen, Zhuo, et al.
Published: (2025)
Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning
by: Stoisser, Josefa Lia, et al.
Published: (2025)
by: Stoisser, Josefa Lia, et al.
Published: (2025)
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
by: Jiang, Jinhao, et al.
Published: (2024)
by: Jiang, Jinhao, et al.
Published: (2024)
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)
I Know What I Don't Know: Latent Posterior Factor Models for Multi-Evidence Probabilistic Reasoning
by: Alege, Aliyu Agboola
Published: (2026)
by: Alege, Aliyu Agboola
Published: (2026)
Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals
by: Chen, Sirui, et al.
Published: (2026)
by: Chen, Sirui, et al.
Published: (2026)
Similar Items
-
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025) -
KnowRL: Teaching Language Models to Know What They Know
by: Kale, Sahil, et al.
Published: (2025) -
Efficient Data Selection for Multimodal Models via Incremental Optimization Utility
by: Jing, Jinhao, et al.
Published: (2026) -
Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory
by: Cacioli, Jon-Paul
Published: (2026) -
Can AI Assistants Know What They Don't Know?
by: Cheng, Qinyuan, et al.
Published: (2024)