activity
20242026
most citedQwen2.5 Technical Report

107 citations · 143 across the 45 of their papers we have counts for

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Showing 2026Show all

11 papers · 1 filter

cs.CL2026

BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences

Ru Peng, Haokai Xu, Xijun Gu +11

While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanities and social sciences (HSS…

cs.CL2026

HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs

Ru Peng, Tianyu Zhao, Xijun Gu +9

High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the hum…

cs.LG2026

Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling

Yucheng Li, Huiqiang Jiang, Yang Xu +14

Reinforcement learning (RL) has become a key component in modern large language models, yet the rollout stage remains the key bottleneck in RL training pipelines. Although Multi-To…

cs.AI2026

CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents

Bowen Wang, Dunjie Lu, Junli Wang +11

Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents…

cs.CL2026

Revealing Behavioral Plasticity in Large Language Models: A Token-Conditional Perspective

Liyuan Mao, Le Yu, Jing Zhou +7

In this work, we reveal that Large Language Models (LLMs) possess intrinsic behavioral plasticity-akin to chameleons adapting their coloration to environmental cues-that can be exp…

cs.CV2026

Think-as-You-See: Streaming Chain-of-Thought Reasoning for Large Vision-Language Models

Jialiang Zhang, Junlong Tong, Junyan Lin +4

Large Vision Language Models (LVLMs) exhibit strong Chain-of-Thought (CoT) capabilities, yet most existing paradigms assume full-video availability before inference, a batch-style…

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

WebWorld: A Large-Scale World Model for Web Agent Training

Zikai Xiao, Jianhong Tu, Chuhang Zou +7

Web agents require massive trajectories to generalize, yet real-world training is constrained by network latency, rate limits, and safety risks. We introduce \textbf{WebWorld} seri…

cs.CL2026

HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Weiqi Zhai, Zhihai Wang, Jinghang Wang +33

Humanity's Last Exam (HLE) has become a widely used benchmark for evaluating frontier large language models on challenging, multi-domain questions. However, community-led analyses…

cs.CL2026

PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice

Yuzhen Shi, Huanghai Liu, Yiran Hu +27

As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential.…

cs.AI2026

DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints

Yinger Zhang, Shutong Jiang, Renhao Li +6

While agent evaluation has shifted toward long-horizon tasks, most benchmarks still emphasize local, step-level reasoning rather than the global constrained optimization (e.g., tim…