1 citations · 1 across the 13 of their papers we have counts for
14 papers
ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents
Xuhao Hu, Xi Zhang, Haiyang Xu +6
Computer Use Agents (CUAs) can act through both atomic GUI actions, such as click and type, and high-level tool calls, such as API-based file operations, but this hybrid action spa…
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…
UniACE: A Unified Framework for Evaluating LLM Agentic Capabilities
Pengyu Zhu, Lijun Li, Yaxing Lyu +8
Agent benchmarks are increasingly used to compare large language models (LLMs) across domains, yet a reported score reflects a complete model--harness--environment configuration ra…
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…
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…
-: 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…
Analyzing Diffusion and Autoregressive Vision Language Models in Multimodal Embedding Space
Zihang Wang, Siyue Zhang, Yilun Zhao +4
Embedding models are a fundamental component of modern AI systems such as semantic search and retrieval-augmented generation. Recent advances in large foundation models have substa…
FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning
Zhaopeng Qiu, Shuang Yu, Jingqi Zhang +4
Reinforcement learning (RL) for large language models (LLMs) is increasingly bottlenecked by rollout (generation), where long output sequence lengths make attention and KV-cache me…
Conditional Advantage Estimation for Reinforcement Learning in Large Reasoning Models
Guanxu Chen, Yafu Li, Yuxian Jiang +6
Reinforcement Learning with Verifiable Rewards (RLVR) for large language models (LLMs) has achieved remarkable progress in enhancing LLMs' reasoning capabilities on tasks with clea…
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…
Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents
Shuai Shao, Qihan Ren, Chen Qian +8
Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the environment, demonstrating strong c…
RiOSWorld: Benchmarking the Risk of Multimodal Computer-Use Agents
Jingyi Yang, Shuai Shao, Dongrui Liu +1
With the rapid development of multimodal large language models (MLLMs), they are increasingly deployed as autonomous computer-use agents capable of accomplishing complex computer t…
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…
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning
Brianna Mueller, W. Nick Street, Stephen Baek +3
Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. However, existing FL parad…