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…
ClawRec: A Claw-Native Recommender System
Chenghao Wu, Kesha Ou, Xiaolei Wang +8
Recommender systems have become integral to navigating the modern digital ecosystem. Yet most deployed systems remain confined within single-platform boundaries, observing localize…
Controlled LLM Training on Spectral Sphere
Tian Xie, Haoming Luo, Haoyu Tang +9
Scaling large models requires optimization strategies that ensure rapid convergence grounded in stability. Maximal Update Parametrization (P) provides a theoretical…
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…
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…
ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset
Chen Yang, Ran Le, Yun Xing +5
Large Language Model (LLM) agents have developed rapidly in recent years to solve complex real-world problems using external tools. However, the scarcity of high-quality trajectori…