most citedFIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insights

1 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2026

SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills

Enze Ma, Chunyu Miao, Yankai Chen +14

Agent skills turn procedural knowledge into reusable artifacts, allowing LLM agents to accumulate and refine expertise through experience. Building on this, skill evolution iterati…

cs.AI2026

SkillGraph: Self-Evolving Multi-Agent Collaboration with Multimodal Graph Topology

Zheng Nie, Ruolin Shen, Xinlei Yu +3

Scaling vision-language models into Visual Multiagent Systems (VMAS) is hindered by two coupled issues. First, communication topologies are fixed before inference, leaving them bli…

cs.AI2026

CoT2-Meta: Budgeted Metacognitive Control for Test-Time Reasoning

Siyuan Ma, Bo Gao, Zikai Xiao +6

Recent test-time reasoning methods improve performance by generating more candidate chains or searching over larger reasoning trees, but they typically lack explicit control over w…

cs.AI2026

Memento-Skills: Let Agents Design Agents

Huichi Zhou, Siyuan Guo, Anjie Liu +14

We introduce \emph{Memento-Skills}, a generalist, continually-learnable LLM agent system that functions as an \emph{agent-designing agent}: it autonomously constructs, adapts, and…

cs.AI2026★ 1 cited

FIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insights

Zhen Wang, Fan Bai, Zhongyan Luo +9

Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery r…

cs.CV2025★ 1 cited

PAN: A World Model for General, Actionable, and Long-Horizon World Simulation

PAN Team, Zihan Liu, Yi Gu +12

A world model is a cognitive simulator of the real-world environment allowing biological agents to reason about how the world evolves, whether spontaneously or in response to their…