3 citations · 6 across the 12 of their papers we have counts for
15 papers
EGCForest: Structured Agent Memory Organization for Enterprise Group Chats
Lingxi Cui, Jinhao Ruan, Wenqing Wei +4
Enterprise group chats record project progress, decisions, task ownership, and other organizational information valuable to enterprise agents. Reliable retrieval is challenging bec…
RSIGym: A Flexible Environment for Recursive Self-Improvement
Fanqing Meng, Lingxiao Du, Haocheng Lu +6
Recursive self-improvement requires carrying accepted changes into later improvement cycles, while studying agent-proposed changes also requires substantial research infrastructure…
Gödel Forest: Balancing Search Depth and Breadth for Data-Centric Recursive Self-Improvement
Ziqi Zhao, Fanqing Meng, Haocheng Lu +4
Recursive self-improvement (RSI) aims to achieve compounding gains by having models improve themselves. While most existing RSI systems optimize external agent harnesses or prompts…
Agentic Artifact Creation: Systems, Evaluation, Principles, and Opportunities
Tianfu Wang, Zhezheng Hao, Xilin Xia +9
Generative models can turn natural-language prompts into images, text, code, and other content, lowering the cost of producing drafts and components. Their practical impact increas…
RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-Improvement
Fanqing Meng, Lingxiao Du, Qiguang Chen +4
Recursive self-improvement requires turning evidence of model failures into better models. Data-centric post-training research entails diagnosing capability gaps, designing and val…
The Scaling Laws of Skills in LLM Agent Systems
Charles Chen, Qiming Yu, Yuhang Gu +12
As agent systems scale, skills accumulate into large reusable libraries, yet their scaling laws remain poorly understood. Across 15 frontier LLMs, 1,141 real-world skills, and over…
Do Coding Agents Understand Least-Privilege Authorization?
Zheng Yan, Jingxiang Weng, Charles Chen +9
As coding agents gain access to shells, repositories, and user files, least-privilege authorization becomes a prerequisite for safe deployment: an agent should receive enough autho…
ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents
Fanqing Meng, Lingxiao Du, Zijian Wu +46
Language-model agents are increasingly used as persistent coworkers that assist users across multiple working days. During such workflows, the surrounding environment may change in…
MuSEAgent: A Multimodal Reasoning Agent with Stateful Experiences
Shijian Wang, Jiarui Jin, Runhao Fu +11
Research agents have recently achieved significant progress in information seeking and synthesis across heterogeneous textual and visual sources. In this paper, we introduce MuSEAg…
OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents
Yuhang Zhou, Kai Zheng, Qiguang Chen +4
Deep research agents have shown remarkable potential in handling long-horizon tasks. However, state-of-the-art performance typically relies on online reinforcement learning (RL), w…
The Universal Landscape of Human Reasoning
Qiguang Chen, Jinhao Liu, Libo Qin +14
Understanding how information is dynamically accumulated and transformed in human reasoning has long challenged cognitive psychology, philosophy, and artificial intelligence. Exist…
AutoPR: Let's Automate Your Academic Promotion!
Qiguang Chen, Zheng Yan, Mingda Yang +10
As the volume of peer-reviewed research surges, scholars increasingly rely on social platforms for discovery, while authors invest considerable effort in promoting their work to en…
AI4Research: A Survey of Artificial Intelligence for Scientific Research
Qiguang Chen, Mingda Yang, Libo Qin +13
Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in co…
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
Huan-ang Gao, Jiayi Geng, Wenyue Hua +24
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel task…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…