🎤 Invited Talk at EvoAgentX Community!

Honoured to be invited by the EvoAgentX community to present our latest project at this week’s EvoAgentX Talk! As a core contributor to the EvoAgentX community, I shared the design and progress behind EvoScientist — an open-source, long-horizon agent system for autonomous scientific discovery.

💡 Talk Title

EvoScientist: Toward Long-Horizon Agent Systems for Scientific Discovery

🖇️ Abstract

AI is rapidly shifting from dialogue models to agent systems. Platforms like OpenClaw have already demonstrated that AI can execute real-world tasks beyond simple Q&A. However, when we move to the scientific research domain, the challenges multiply dramatically. Research tasks are inherently long-horizon, multi-stage, strongly interdependent, and continuously iterative — far from a single-turn reasoning problem, they demand sustained decision-making and an evolving system architecture.

EvoScientist explores a long-horizon agent design paradigm tailored to research scenarios. Through multi-agent collaboration, the system progressively completes the full pipeline from idea generation to experiment execution to result summarization — an approach we call Vibe Research. Unlike many agent systems that remain at the demo stage, EvoScientist emphasizes executability in complex tasks and the ability to continuously evolve in real-world settings.

In this talk, I address three key questions: why current agent systems fall short in research scenarios, what the core design philosophy of EvoScientist is, and whether we truly need a new long-horizon agent paradigm to support increasingly complex task systems in the future.

📺 Slides

🔗 Project Link

EvoScientist — Harness Vibe Research with Self-evolving AI Scientists

EvoScientist aims to harness vibe research by enabling self-evolving AI scientists that autonomously explore, generate insights, and iteratively improve. It is designed to be opinionated and ready to use out of the box, offering a living research system that grows alongside evolving agent skills, toolsets, and memory bases. Moving beyond traditional human-in-the-loop systems, EvoScientist adopts a human-on-the-loop paradigm — AI acts as a research buddy that co-evolves with human researchers and internalizes scholarly taste and scientific judgment.