1 citations · 1 across the 7 of their papers we have counts for
12 papers
RIPPLE: Recovery Internalization through Process-Level Policy Learning and Effect-Aware Credit for Long-Horizon Agentic Coding
Jingxuan Xu, Zihang Xu, Yuechen Luo +9
Long-horizon coding agents can observe a failure and generate a plausible critique yet continue along the same incorrect trajectory. We call this the Critique-to-Action Handoff Gap…
WEAVE: Weaving Real-World Code Evolution into Dependency-Structured Tasks for Benchmarking and Training Code Agents
Weihao Li, Zihang Xu, Zhongqiu Wang +8
Real-world software engineering rarely consists of isolated changes. Features, migrations, and systematic fixes often unfold across multiple pull requests (PRs), requiring coordina…
OPRD: On-Policy Reminder Distillation for Long-Horizon Coding Agents
Yuechen Luo, Zhiyi Lai, Miao Ziqi +9
Reliable repository-level coding requires agents not only to generate correct code but also to preserve constraints and respond to tool feedback across long interactions. However,…
SWE-Nexus: Automated Composition of Software Engineering Tasks from Test Execution
Songwei Yu, Ziqi Miao, Jingxuan Xu +10
Historical code changes provide convenient task boundaries, but do not identify which production functions selected tests exercise or support composing validated behaviors beyond a…
Toward General Quantum Control with Physics-Informed Large Language Models
Yusheng Zhao, Han Wang, Xin Liu +10
Quantum control is essential for quantum information science and technology, yet designing high-fidelity control protocols remains challenging due to complex optimization landscape…
Towards Verifiable and Self-Correcting AI Physicists for Quantum Many-Body Simulations
Ken Deng, Xiangfei Wang, Guijing Duan +7
While large language models (LLMs) promise to revolutionize automated scientific discovery, their application in rigorous real-world physical research is stalled by two critical ba…
From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence
Jian Yang, Xianglong Liu, Weifeng Lv +68
Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, dri…
SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
Jingxuan Xu, Ken Deng, Weihao Li +36
Evaluating large language models (LLMs) for software engineering has been limited by narrow task coverage, language bias, and insufficient alignment with real-world developer workf…
KAT-Coder Technical Report
Zizheng Zhan, Ken Deng, Jinghui Wang +37
Recent advances in large language models (LLMs) have enabled progress in agentic coding, where models autonomously reason, plan, and act within interactive software development wor…
HiPO: Hybrid Policy Optimization for Dynamic Reasoning in LLMs
Ken Deng, Zizheng Zhan, Wen Xiang +25
Large Language Models (LLMs) increasingly rely on Chain-of-Thought (CoT) reasoning to improve accuracy on complex tasks. However, always generating lengthy reasoning traces is inef…
ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding
Yuhang Li, Chenchen Zhang, Ruilin Lv +6
While Large Language Models (LLMs) excel at algorithmic code generation, they struggle with front-end development, where correctness is judged on rendered pixels and interaction. W…
SeamlessFlow: A Trainer Agent Isolation RL Framework Achieving Bubble-Free Pipelines via Tag Scheduling
Jinghui Wang, Shaojie Wang, Yinghan Cui +24
We introduce SeamlessFlow, a server based reinforcement learning (RL) framework that addresses two core challenges in industrial scale RL: (1) decoupling RL training from the compl…