8 papers
Evo-Bench: Can Language Models Improve Agent Harness?
Lisheng Huang, Chen Yang, Hao Zhou +6
Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emerging frontier is harness evolu…
Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Model
Nanbeige Lab, :, Chen Yang +23
We present Nanbeige4.2-3B, a compact general agentic model with 3B non-embedding parameters. It delivers strong performance across code-agent, office-agent, and complex tool-use ta…
SWE-Master: Unleashing the Potential of Software Engineering Agents via Post-Training
Huatong Song, Lisheng Huang, Shuang Sun +11
In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents. SWE-Master system…
PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning
Ruixiang Feng, Yuntao Wen, Silin Zhou +14
Language Reasoning Models (LRMs) achieve strong performance by scaling test-time computation but often suffer from ``overthinking'', producing excessively long reasoning traces tha…
SWE-World: Building Software Engineering Agents in Docker-Free Environments
Shuang Sun, Huatong Song, Lisheng Huang +11
Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedb…
Nanbeige4-3B Technical Report: Exploring the Frontier of Small Language Models
Chen Yang, Guangyue Peng, Jiaying Zhu +16
We present Nanbeige4-3B, a family of small-scale but high-performing language models. Pretrained on 23T high-quality tokens and finetuned on over 30 million diverse instructions, w…