collaborators

11 papers

cs.LG2026

SymbolicArena: A Unified Infrastructure for Benchmark Distillation and Dynamic Evaluation in Symbolic Regression

Ziwen Zhang, Xiju Wu, Yuheng Jing +8

Symbolic regression (SR) seeks concise and interpretable mathematical expressions from data for scientific equation discovery. Existing SR benchmarks face a tradeoff between evalua…

cs.LG2026

Not Every Term Adds New Structure: Sobolev Novelty for Symbolic Regression

Boxiao Wang, Kai Li, Yuheng Jing +4

Symbolic regression (SR) aims to discover compact and meaningful mathematical equations from data, but searching the vast combinatorial space of symbolic structures remains challen…

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.CL2026

SLATE: Are AI-Generated Slides Educationally Effective? A Benchmark for Language Teaching Quality and Learner Knowledge Acquisition

Jingzhuo Wu, Jiajun Zhang, Liu Yi +4

LLMs have achieved remarkable capabilities in generating language teaching slides. However, a critical mismatch persists between visual polish and actual instructional effectivenes…

cs.LG2026

Qwen-CUA: Native Computer Use for (almost) Everything

Dunjie Lu, Shuai Bai, Tianyi Bai +42

Native computer use offers a general interface for agents to operate almost any software available to people, but requires long-horizon state tracking, large-scale interactive expe…

cs.AI2026

The Verification Horizon: No Silver Bullet for Coding Agent Rewards

Binghai Wang, Chenlong Zhang, Dayiheng Liu +10

A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foundation models develop strong…

cs.AI2026

Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork

Yuheng Jing, Kai Li, Ziwen Zhang +8

In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordination with un…

cs.CL2026

Qwen3-Coder-Next Technical Report

Ruisheng Cao, Mouxiang Chen, Jiawei Chen +17

We present Qwen3-Coder-Next, an open-weight language model specialized for coding agents. Qwen3-Coder-Next is an 80-billion-parameter model that activates only 3 billion parameters…

cs.AI2026

K^2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control

Zhe Wu, Donglin Mo, Hongjin Lu +7

Existing mobile device control agents often perform poorly when solving complex tasks requiring long-horizon planning and precise operations, typically due to a lack of relevant ta…

cs.CL2026

Scaling Agentic Verifier for Competitive Coding

Zeyao Ma, Jing Zhang, Xiaokang Zhang +9

Large language models (LLMs) have demonstrated strong coding capabilities but still struggle to solve competitive programming problems correctly in a single attempt. Execution-base…

cs.SE2026

From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning

Jiajun Zhang, Zeyu Cui, Jiaxi Yang +9

The dominant Fill-in-the-Middle (FIM) paradigm for code completion is constrained by its rigid inability to correct contextual errors and reliance on unaligned, insecure Base model…