arXiv AI
Jul 8
A toy framework for single and multi-agent human-AI curiosity ecosystems
arXiv:2607. 06214v1 Announce Type: new Abstract: This paper offers a toy framework for considering curiosity as an ecosystem.
A framework for single and multi-agent human-AI curiosity ecosystems
arXiv:2607. 06214v2 Announce Type: replace Abstract: This paper offers a framework for considering curiosity as an ecosystem.
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Hugging Face Trending Papers
Jul 7
A toy framework for single and multi-agent human-AI curiosity ecosystems
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.
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.
Hugging Face Trending Papers
Aug 4
AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions
Identifying promising scientific ideas remains an important challenge in research practice. Researchers commonly rely on small-group discussions or one-to-one interactions with a single large language model, yet these approaches often expose them to only a limited range of perspectives and directions.
arXiv Computation and Language
Sep 22
A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents
arXiv:2601.12538v2 Announce Type: replace-cross Abstract: Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) d...
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.
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.
arXiv AI
Sep 10
Efficient Exploration Is Enough
arXiv:2609.07575v1 Announce Type: cross Abstract: This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic...
arXiv AI
Sep 4
CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery
CORAL is a framework that enables autonomous multi‑agent evolution for open‑ended discovery, replacing rigid heuristics with long‑running agents that explore, reflect, and collaborate via shared memory and asynchronous execution. It incorporates safeguards such as isolated workspaces, evaluator separation, and resource management. In experiments across mathematical, algorithmic, and systems optimization tasks, CORAL achieves 3–10 times higher improvement rates with fewer evaluations than traditional evolutionary baselines, and improves the best known score on Anthropic’s kernel engineering task from 1363 to 1103 cycles.
arXiv AI
Sep 12
Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks
The article "Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks" surveys the lack of a standard definition for AI agents and organizes this ambiguity into five dimensions: environmental interaction, learning and adaptation, autonomy, goal‑directed behavior, and temporal coherence. It reviews how each dimension has been conceptualized in prior work and compiles the metrics, benchmarks, and evaluation frameworks used to assess them. The authors also introduce the Agent Compendium, a public digital resource that extends these evaluation methods, aiming to provide a common structure for evaluating and comparing agent capabilities across AI systems.
arXiv AI
Aug 5
Dr. AGENTONOMICS: A Didactic Experiment of AGENTONOMICS
arXiv:2608. 03524v1 Announce Type: new Abstract: AGENTONOMICS is a framework that treats AI agents as economic entities that can be designed, managed, and governed through an integrated management architecture.
arXiv AI
Jun 19
SIGMA: Search-Augmented On-Demand Knowledge Integration for Agentic Mathematical Reasoning
arXiv:2510. 27568v2 Announce Type: replace Abstract: Solving mathematical reasoning problems requires not only accurate access to relevant knowledge but also careful, multi-step thinking.