activity
20242026
collaborators

14 papers

cs.CL2026

RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents

Shuai Bai, Jiayong Deng, Sicheng Fan +30

Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Real digital work requires both…

cs.AI2026

Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments

Jie Wu, Zhenru Zhang, Beichen Zhang +11

As terminal-based code agents become prevalent, agent trajectories have accumulated at scale, while realistic, executable environments remain scarce. However, environments are what…

cs.LG2026

Gradients Know What Outcomes Don't: Unlocking Reinforcement Learning for LLM Reasoning with Gradient-Aligned Rewards

Leqi Zheng, Jinbo Su, Fang Niu +8

Reinforcement learning from verifiable rewards (RLVR) drives chain-of-thought reasoning in large language models, yet its binary outcome reward cannot distinguish among correct tra…

cs.LG2026

Learning Where Outcomes Change:Credit-Addressable Reasoning for Multimodal Geometry

Jiani Guo, Junjie Wang, Jie Wu +5

Multimodal geometry reasoning requires VLMs to extract precise visual relations and preserve them through multi-step deduction. Existing free-form traces obscure the decisions that…

cs.SE2026

DPIAgent: Divide, Protocol, Isolate for Agentic Reproduction Test Generation

Hao Liu, Steven Liu, Xin Zhang +8

Reproduction test generation, producing a failing-then-passing test that captures a reported bug, is a critical step in automated software engineering. Existing agentic methods tre…

cs.AI2026

VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct

Haoling Li, Kai Zheng, Jie Wu +4

Scaling reinforcement learning for visual mathematical reasoning requires more than generating harder questions: as data volume grows, the reward labels themselves must remain reli…

cs.CV2026

ViPO: Visual Preference Optimization at Scale

Ming Li, Jie Wu, Justin Cui +3

While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference d…

cs.CL2026

X-Coder: Advancing Competitive Programming with Synthetic Tasks, Solutions, and Tests

Jie Wu, Haoling Li, Xin Zhang +8

Competitive programming remains challenging for code LLMs. Despite recent progress, many training pipelines still depend on scarce real-world data, raising concerns about scalabili…

cs.CL2025

ToM: Leveraging Tree-oriented MapReduce for Long-Context Reasoning in Large Language Models

Jiani Guo, Zuchao Li, Jie Wu +5

Large Language Models (LLMs), constrained by limited context windows, often face significant performance degradation when reasoning over long contexts. To address this, Retrieval-A…

cs.CL2025

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation

Jane Luo, Xin Zhang, Steven Liu +11

Large language models excel at generating individual functions or single files of code, yet generating complete repositories from scratch remains a fundamental challenge. This capa…

cs.CV2025

PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning

Yizhen Zhang, Yang Ding, Shuoshuo Zhang +9

Inspired by the impressive reasoning capabilities demonstrated by reinforcement learning approaches like DeepSeek-R1, recent emerging research has begun exploring the use of reinfo…

cs.CL2025

Teaching Your Models to Understand Code via Focal Preference Alignment

Jie Wu, Haoling Li, Xin Zhang +8

Preference learning extends the performance of Code LLMs beyond traditional supervised fine-tuning by leveraging relative quality comparisons. In existing approaches, a set of n ca…

cs.CL2025

EpiCoder: Encompassing Diversity and Complexity in Code Generation

Yaoxiang Wang, Haoling Li, Xin Zhang +10

Existing methods for code generation use code snippets as seed data, restricting the complexity and diversity of the synthesized data. In this paper, we introduce a novel feature t…

cs.CV2024

LayoutDiT: Exploring Content-Graphic Balance in Layout Generation with Diffusion Transformer

Yu Li, Yifan Chen, Gongye Liu +6

Layout generation is a foundation task of graphic design, which requires the integration of visual aesthetics and harmonious expression of content delivery. However, existing metho…