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20242026
most citedYour Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents

1 citations · 1 across the 9 of their papers we have counts for

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cs.CL2026

WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation

Shuangrui Ding, Xuanlang Dai, Long Xing +14

Large language and vision-language models increasingly power agents that act on a user's behalf through command-line interface (CLI) harnesses. However, most agent benchmarks still…

cs.CL2026

DARE: Diffusion Large Language Models Alignment and Reinforcement Executor

Jingyi Yang, Yuxian Jiang, Xuhao Hu +3

Diffusion large language models (dLLMs) are emerging as a compelling alternative to dominant autoregressive models, replacing strictly sequential token generation with iterative de…

cs.CL2026

DeepSight: An All-in-One LM Safety Toolkit

Bo Zhang, Jiaxuan Guo, Lijun Li +17

As the development of Large Models (LMs) progresses rapidly, their safety is also a priority. In current Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) s…

cs.CL2026

-: Training-free Bidirectional Variable-Length Control for Masked Diffusion LLMs

Jingyi Yang, Yuxian Jiang, Jing Shao

Beyond parallel generation and global context modeling, current masked diffusion large language models (masked dLLMs, i.e., LLaDA) suffer from a fundamental limitation: they requir…

cs.CL2025

Taming Masked Diffusion Language Models via Consistency Trajectory Reinforcement Learning with Fewer Decoding Step

Jingyi Yang, Guanxu Chen, Xuhao Hu +1

Masked diffusion language models (MDLMs) have recently emerged as a promising alternative to autoregressive (AR) language models, offering properties such as parallel decoding, fle…

cs.CL2025

Shall Your Data Strategy Work? Perform a Swift Study

Minlong Peng, Jingyi Yang, Zhongjun He +1

This work presents a swift method to assess the efficacy of particular types of instruction-tuning data, utilizing just a handful of probe examples and eliminating the need for mod…