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20242026
most citedDoes Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning

1 citations · 3 across the 30 of their papers we have counts for

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14 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

Context Language Models

Rulin Shao, Shannon Zejiang Shen, Junjie Oscar Yin +10

We introduce Context Language Models (CLMs), language models that natively manage their own context. We implement this by treating the context as a file and allowing the model to m…

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

Agents' Last Exam

Yiyou Sun, Xinyang Han, Weichen Zhang +378

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

AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?

Zhangchen Xu, Junda Chen, Yue Huang +16

Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifac…

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

Simulating Environments with Reasoning Models for Agent Training

Yuetai Li, Huseyin A Inan, Xiang Yue +6

LLM agents excel in compact environments requiring deep reasoning but remain brittle when operating in broader, more complex contexts that demand robustness across diverse tools an…

cs.AI2025★ 1 cited

Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning

Maggie Huan, Yuetai Li, Tuney Zheng +6

Math reasoning has become the poster child of progress in large language models (LLMs), with new models rapidly surpassing human-level performance on benchmarks like MATH and AIME.…

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.AI2025★ 1 cited

ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning

Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson +4

We investigate the logical reasoning capabilities of large language models (LLMs) and their scalability in complex non-monotonic reasoning. To this end, we introduce ZebraLogic, a…

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…