activity
20232026
most citedBadChain: Backdoor Chain-of-Thought Prompting for Large Language Models

8 citations · 30 across the 24 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

MidTool: Mid-training Data Synthesis for Agentic Tool Use

Fengqing Jiang, Yite Wang, Boyi Liu +5

Mid-training is increasingly recognized as a critical stage for shaping the capabilities of large language models. Recent work has shown that targeted mid-training can strengthen r…

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.AI2024

CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models

Yuetai Li, Zhangchen Xu, Fengqing Jiang +4

The remarkable performance of large language models (LLMs) in generation tasks has enabled practitioners to leverage publicly available models to power custom applications, such as…