activity
20182026
most citedOptimal Secure Control with Linear Temporal Logic Constraints

14 citations · 72 across the 41 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

RAC: A Relational Agent Contract for Constraint-Governed Engineering Tasks

Kaiyuan Zheng, Fengqing Jiang, Yichen Feng +6

Engineering agents must produce artifacts that satisfy exact, coupled constraints. Existing solutions often require agents to make semantic decisions and also derive consequences s…

cs.AI2026

JobBench: Aligning Agent Work With Human Will

Yuetai Li, Yichen Feng, Zhangchen Xu +21

Current benchmarks for occupational AI agents are scoped primarily by economic values, telling a replacement story. We introduce JobBench, which evaluates AI agents on the workflow…

cs.AI2025

Temporal Sampling for Forgotten Reasoning in LLMs

Yuetai Li, Zhangchen Xu, Fengqing Jiang +5

Fine-tuning large language models (LLMs) is intended to improve their reasoning capabilities, yet we uncover a counterintuitive effect: models often forget how to solve problems th…

cs.AI2025

SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities

Fengqing Jiang, Zhangchen Xu, Yuetai Li +5

Emerging large reasoning models (LRMs), such as DeepSeek-R1 models, leverage long chain-of-thought (CoT) reasoning to generate structured intermediate steps, enhancing their reason…

cs.AI2025★ 1 cited

Small Models Struggle to Learn from Strong Reasoners

Yuetai Li, Xiang Yue, Zhangchen Xu +5

Large language models (LLMs) excel in complex reasoning tasks, and distilling their reasoning capabilities into smaller models has shown promise. However, we uncover an interesting…

cs.AI2024

Stronger Models are NOT Stronger Teachers for Instruction Tuning

Zhangchen Xu, Fengqing Jiang, Luyao Niu +2

Instruction tuning has been widely adopted to ensure large language models (LLMs) follow user instructions effectively. The resulting instruction-following capabilities of LLMs hea…

cs.AI2023

Risk-Aware Distributed Multi-Agent Reinforcement Learning

Abdullah Al Maruf, Luyao Niu, Bhaskar Ramasubramanian +2

Autonomous cyber and cyber-physical systems need to perform decision-making, learning, and control in unknown environments. Such decision-making can be sensitive to multiple factor…