most citedUnsafer in Many Turns: Benchmarking and Defending Multi-Turn Safety Risks in Tool-Using Agents

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cs.AI2026

Agents' Last Exam

Yiyou Sun, Xinyang Han, Weichen Zhang +306

Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…

cs.AI2026

The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break

Xinyu Jessica Wang, Haoyue Bai, Yiyou Sun +7

Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, interdependent action sequence…

cs.AI2026

Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance

Weida Liang, Yiyou Sun, Shuyuan Nan +3

Example-based guidance is widely used to improve mathematical reasoning at inference time, yet its effectiveness is highly unstable across problems and models-even when the guidanc…

cs.AI2026

Climbing the Ladder of Reasoning: What LLMs Can-and Still Can't-Solve after SFT?

Yiyou Sun, Georgia Zhou, Haoyue Bai +4

Recent supervised fine-tuning (SFT) approaches have significantly improved language models' performance on mathematical reasoning tasks, even when models are trained at a small sca…

cs.AI2025

MIRAGE-Bench: LLM Agent is Hallucinating and Where to Find Them

Weichen Zhang, Yiyou Sun, Pohao Huang +3

Hallucinations pose critical risks for large language model (LLM)-based agents, often manifesting as hallucinative actions resulting from fabricated or misinterpreted information w…