3 citations · 3 across the 11 of their papers we have counts for
7 papers · 1 filter
ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents
Kaiwen Xiong, Haonian Ji, Shi Qiu +4
Production large language-model (LLM) agents are increasingly deployed not as lone problem-solvers but as managers: a main model creates specialized subagents, delegates work, and…
VisualClaw: A Real-Time, Personalized Agent for the Physical World
Haoqin Tu, Jianwen Chen, Zijun Wang +14
Vision language models are serving as general-purpose interfaces for complex multimodal tasks. However, deployment still faces three gaps: VLMs typically incur high latency and cos…
ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents
Yuxiang Lai, Peng Xia, Haonian Ji +8
Interactive agent benchmarks face a tension between scalable construction and realistic workflow evaluation. Hand-authored tasks are expensive to extend and revise, while static pr…
ClawArena: Benchmarking AI Agents in Evolving Information Environments
Haonian Ji, Kaiwen Xiong, Siwei Han +9
AI agents deployed as persistent assistants must maintain correct beliefs as their information environment evolves. In practice, evidence is scattered across heterogeneous sources…
MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild
Peng Xia, Jianwen Chen, Xinyu Yang +10
Large language model (LLM) agents are increasingly used for complex tasks, yet deployed agents often remain static, failing to adapt as user needs evolve. This creates a tension be…
NEX: Neuron Explore-Exploit Scoring for Label-Free Chain-of-Thought Selection and Model Ranking
Kang Chen, Zhuoka Feng, Sihan Zhao +5
Large language models increasingly spend inference compute sampling multiple chain-of-thought traces or searching over merged checkpoints. This shifts the bottleneck from generatio…
ARM: Role-Conditioned Neuron Transplantation for Training-Free Generalist LLM Agent Merging
Zhuoka Feng, Kang Chen, Sihan Zhao +7
Interactive large language model agents have advanced rapidly, but most remain specialized to a single environment and fail to adapt robustly to other environments. Model merging o…