Hugging Face Trending Papers

A toy framework for single and multi-agent human-AI curiosity ecosystems

Read the original on Hugging Face Trending Papers →

This paper offers a toy framework for considering curiosity as an ecosystem. First, it suggests that a single agent's inquiry policy (how, when, and why an agent asks a question) depends on how the agent values immediate uncertainty reduction, costs, delayed return, and the value of keeping the question open.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv AI
Sep 16

"Looking for Something Weird to Happen": How Humans Sustain AI Agent Novelty Amid Semantic Collapse

The paper investigates semantic collapse—where AI outputs become less diverse and more similar—within MOLTBOOK, a social network of AI agents steered by human users. Across 30,076 agents, most show reduced diversity over time, but a minority maintain high novelty. Interviews and surveys reveal that sustained novelty is linked to users valuing novelty, providing broad, distinctive material, revising outputs when they narrow, and treating MOLTBOOK as an exploratory world rather than a tool for exploitation.

By Shiyang Lai, Arna Woemmel, Hongkai Mao, Junsol Kim, Summer Eunhyung Ann, James Evans
arXiv AI
2d ago

LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios

The article surveys LLM-based agentic reasoning frameworks, presenting a unified formal language that categorizes them into single-agent, tool-based, and multi-agent methods. It reviews application scenarios in scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks, and compares the distinct features and evaluation strategies of each category. The survey highlights the rapid development of complex agentic systems in real-world contexts.

By Bingxi Zhao, Lin Geng Foo, Ping Hu, Christian Theobalt, Hossein Rahmani, Jun Liu
arXiv AI
Aug 5

AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions

arXiv:2608. 03283v1 Announce Type: new Abstract: Identifying promising scientific ideas remains an important challenge in research practice.

By Zhiyao Cui, Qianyi Wang, Haoyang Yan, Yiqun Zhang, Siyue Ren, Hangfan Zhang, Zelin Tan, Hao Li, Chunjiang Mu, Dexian Cai, Shao Zhang, Chen Zhang, Meng Li, Jianan Chai, Yuting Fan, Zichao Ye, Xiaolei Yang, Xinyao Lu, Yuyang Yu, Wenjie Lou, Xiaosong Wang, Fenghua Ling, Shiyang Feng, Mao Su, Qiaosheng Zhang, Bo Zhang, Yang Chen, Lei Bai, Shuyue Hu