Hugging Face Trending Papers

ChainClaw: A Layered Agent Framework for Reliable On-Chain Execution

Read the original on Hugging Face Trending Papers →

General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments. On-chain execution is stateful, adversarial, and economically irreversible, exposing three fundamental gaps: Reactivity, Irreversibility, and Observability.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI
Jul 16

AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities

arXiv:2607. 13705v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.

By Zichen Ding, Jiaye Ge, Shufan Jiang, Kai Chen, Mo Li, Qingqiu Li, Zehao Li, Zonglin Li, Tiaohao Liang, Shudong Liu, Zerun Ma, Zixing Shang, Wenhui Tian, Zun Wang, Liwei Wu, Zhenyu Wu, Jun Xu, Bowen Yang, Dingbo Yuan, Qi Zhang, Songyang Zhang, Peiheng Zhou, Dongsheng Zhu
arXiv AI
Jun 12

The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements

arXiv:2606. 12797v1 Announce Type: new Abstract: Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly deployed in public-facing domains, including government services, healthcare triage, and financial advising.

By Md Jafrin Hossain, Mohammad Arif Hossain, Weiqi Liu, Nirwan Ansari