collaborators

7 papers

cs.CL2026

OmniBrain-0: A Unified Agentic Model for Embodied Intelligence

Xiaomi Embodied Intelligence Team, Yuechen Luo, Chong He +29

General-purpose embodied intelligence requires robots to coordinate diverse skills over long horizons, track task progress, and recover from failures. Specialist policies alone do…

cs.AI2026

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…

cs.CL2026

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,…

cs.CL2026

ANCHOR: Assessing Naturalistic Constraint Hierarchies and Ordering in Repository-Grounded Agentic Coding

Wenqiang Zhu, Xinping Lei, Zhiyi Lai +17

For repository-grounded coding agents, a functionally correct patch is not necessarily an acceptable one: developers also constrain how the task must be carried out-modification sc…

cs.SE2025

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…

cs.CL2025

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…

cs.CL2025

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…