:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Unlu, Eren
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2604.16753
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Don't Start What You Can't Finish: A Counterfactual Audit of Support-State Triage in LLM Agents
by: Unlu, Eren
Published: (2026)

Preservation Is Not Enough for Width Growth: Regime-Sensitive Selection of Dense LM Warm Starts
by: Unlu, Eren
Published: (2026)

Geotokens and Geotransformers
by: Unlu, Eren
Published: (2024)

Architecting Trust in Artificial Epistemic Agents
by: Marchal, Nahema, et al.
Published: (2026)

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'
by: Manchingal, Shireen Kudukkil, et al.
Published: (2025)

Epistemic Deep Learning: Enabling Machine Learning Models to Know When They Do Not Know
by: Manchingal, Shireen Kudukkil
Published: (2025)

Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs
by: Cheng, Myra, et al.
Published: (2026)

When Single-Agent with Skills Replace Multi-Agent Systems and When They Fail
by: Li, Xiaoxiao
Published: (2026)

From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?
by: Xu, Binyan, et al.
Published: (2026)

Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees
by: Gui, Yu, et al.
Published: (2024)

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)

Do LLM Agents Know How to Ground, Recover, and Assess? A Benchmark for Epistemic Competence in Information-Seeking Agents
by: Shao, Jiaqi, et al.
Published: (2025)

CaRT: Teaching LLM Agents to Know When They Know Enough
by: Liu, Grace, et al.
Published: (2025)

SafeGround: Know When to Trust GUI Grounding Models via Uncertainty Calibration
by: Wang, Qingni, et al.
Published: (2026)

Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
by: Wang, Hongru, et al.
Published: (2025)

Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty
by: Machcha, Sravanthi, et al.
Published: (2026)

When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning
by: Hao, Chenjie, et al.
Published: (2026)

The AI Cognitive Trojan Horse: How Large Language Models May Bypass Human Epistemic Vigilance
by: Maynard, Andrew D.
Published: (2026)

AgentTrap: Measuring Runtime Trust Failures in Third-Party Agent Skills
by: Zhuang, Haomin, et al.
Published: (2026)

Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees
by: Chen, Jiechen, et al.
Published: (2023)

Trust & Safety of LLMs and LLMs in Trust & Safety
by: You, Doohee, et al.
Published: (2024)

Do LLMs Know When to Flip a Coin? Strategic Randomization through Reasoning and Experience
by: Yang, Lingyu
Published: (2025)

On the Performance of LLMs for Real Estate Appraisal
by: Geerts, Margot, et al.
Published: (2025)

More Skills, Worse Agents? Skill Shadowing Degrades Performance When Expanding Skill Libraries
by: Song, Hongwen, et al.
Published: (2026)

Epistemic Context Learning: Building Trust the Right Way in LLM-Based Multi-Agent Systems
by: Zhou, Ruiwen, et al.
Published: (2026)

When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
by: Xiao, Boyu, et al.
Published: (2026)

HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?
by: Trinh, Tu, et al.
Published: (2026)

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)

Do Retrieval Augmented Language Models Know When They Don't Know?
by: Zhou, Youchao, et al.
Published: (2025)

When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
by: McGovern, Hope, et al.
Published: (2026)

When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems
by: Wang, Su, et al.
Published: (2026)

Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-Agent Debates
by: Zou, Rui, et al.
Published: (2024)

Epistemic Skills: Reasoning about Knowledge and Oblivion
by: Liang, Xiaolong, et al.
Published: (2025)

CAREBench: Evaluating LLMs' Emotion Understanding by Assessing Cognitive Appraisal Reasoning
by: Sun, Zhaoyue, et al.
Published: (2026)

When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
by: Wang, Zehao, et al.
Published: (2026)

When Agents Persuade: Rhetoric Generation and Mitigation in LLMs
by: Jose, Julia, et al.
Published: (2026)

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
by: Mei, Zhiting, et al.
Published: (2025)

The Confidence Paradox: Can LLM Know When It's Wrong
by: Tripathi, Sahil, et al.
Published: (2025)

Agents Need Not Know Their Purpose
by: Garcia, Paulo
Published: (2024)

AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents
by: Wang, Zhun, et al.
Published: (2025)