activity
20232026
most citedThe Rise and Potential of Large Language Model Based Agents: A Survey

256 citations · 285 across the 19 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

Scaling Verifiable Environments for Long-horizon Work Agents

Jiazheng Zhang, Long Ma, Yunxian Yang +24

Work agents operate over digital artifacts to execute professional knowledge-intensive work, requiring training environments that support long-horizon interaction and trustworthy v…

cs.AI2026

ReDraft, Don't Just Distill: Reference-Driven Revision for Continual VLLM Post-Training

Zhihao Zhang, Mingqi Wu, Qiaole Dong +15

Continual post-training of large multimodal models should add new capabilities while preserving those from pre-training, and the two goals pull in opposite directions. SFT gives ex…

cs.AI2026

ContextWeave: A Real-World Workflow Benchmark

Bo Wang, Yuqian Yao, Enxi Wang +25

Memory is essential as language agents move from isolated tasks to long-horizon, stateful workflows, yet existing evaluations often reduce it to retrieval or question answering. We…

cs.CL2026

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning

Jiahang Lin, Kai Hu, Binghai Wang +12

Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass retrieval. We introduce MM-Doc-…

cs.AI2026

JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees

Yuhui Wang, Zhixiong Yang, Ming Zhang +10

In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by la…

cs.AI2026

Steering LLMs via Scalable Interactive Oversight

Enyu Zhou, Zhiheng Xi, Long Ma +9

As Large Language Models increasingly automate complex, long-horizon tasks such as \emph{vibe coding}, a supervision gap has emerged. While models excel at execution, users often s…

cs.RO2026

FRoM-W1: Towards General Humanoid Whole-Body Control with Language Instructions

Peng Li, Zihan Zhuang, Yangfan Gao +16

Humanoid robots are capable of performing various actions such as greeting, dancing and even backflipping. However, these motions are often hard-coded or specifically trained, whic…