AgenticLS @ NeurIPS 2026 — Agentic AI for Biological Discovery

GenBio ICML Workshop 2026

The 2026 workshop on Generative and Agentic AI for Biology at ICML 2026, Seoul, South Korea.

Lightcone Research

An open ecosystem for inspectable, composable, and referenceable scientific research in the age of agentic AI.

The 2025 AI Agent Index

Agentic AI systems are increasingly capable of performing complex tasks with limited human involvement. The 2025 AI Agent Index documents the origins, design, capabilities, ecosystem, and safety features of 30 prominent AI agents based on publicly available information and correspondence with developers.

Togelius: Please, don't automate science!

I was at an event on AI for science yesterday, a panel discussion here at NeurIPS. The panelists discussed how they plan to replace humans a...

Agentic Laboratories of the Future: Towards World Models for Scientific Discovery

Scientific discovery is fundamentally a problem-solving process involving distributed intelligence. Human intuition, computational reasoning, and experimental execution are distributed across people, instruments, and software systems, limiting the speed and scale of discovery. Although automation, high-throughput experimentation, foundation models, and cloud infrastructure have accelerated individual stages of the scientific workflow, they have not unified the discovery process. We hypothesize that the next generation of laboratories will be agentic: environments in which scientists, AI systems, and robotic platforms operate as collaborative discovery partners, with humans contributing the parts of discovery that remain hardest to make explicit: asking the right questions and holding provisional mechanistic models of how a system works. The key missing layer is an agentic harnessing layer that continuously integrates hypothesis, literature-derived evidence, experimental data, uncertainty, and experimental state into a shared “laboratory world model”—a dynamic representation of the scientific system and its evolving context. By maintaining and updating this lab world model, the agentic harnessing layer enables coordinated decision-making, adaptive planning, and increasingly autonomous scientific workflows across humans and machines. A central challenge is that much of the scientific research process remains inaccessible to machines, including tacit knowledge, human observations, adaptive decision-making, and evolving experimental context. Advances in multimodal AI and immersive interfaces may help bridge this gap, allowing humans, agents, and robotic systems to collaborate seamlessly in scientific discovery rather than simply automating isolated tasks. Agentic laboratories could provide a new architecture for science, integrating human, artificial, and physical intelligence into a unified discovery system.

AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions

Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.

AI Agents Landscape & Ecosystem (January 2026): Complete Interactive Map

Explore the comprehensive AI agents landscape map / ecosystem for January 2026. Compare AI Agents across categories, pricing, and capabilities. Updated daily with new autonomous agents, AI assistants, and emerging solutions.

Science that Compounds: The Need for A New Substrate for Research in the Age of AI

This paper is a perspective from Lightcone Research, an open-source initiative building tooling for scientific research in the age of agentic AI.

How AI Agents are transforming scientific discovery

AI agents are starting to reshape science, from proposing novel hypotheses to writing code. Learn what comes next for scientists and policymakers.

Agentis — coming soon — BioKEA

An AI-first scientific journal built on the AT Protocol. Fast, verifiable peer review and publications as interactive StoryMaps.

Agent4Science

A social network for AI scientists — where agents share, debate, and discuss research papers.

AGENTWORLD | *

An exploration of planetary intelligence, tracing how computation and cosmology—from ancient mechanisms to AI—reshape Earth and its possible futures.

Cosmik Updates: February 2026 - Cosmik Labs

@atproto.science @cosmik.network Raising a question for the ATProto science community: Can AI agents be legitimate participants in research ecosystems? What would make their outputs trustworthy?

OpenScience.ai — Autonomous AI Research Agents

AI agents conducting reproducible scientific inquiry. Full provenance, executable notebooks, peer validation.

AI agents team up in Agent Laboratory to speed scientific research

Johns Hopkins University and AMD have developed Agent Laboratory, a new open-source framework that pairs human creativity with AI-powered workflows.

No humans allowed: scientific AI agents get their own social network

Autonomous agents aren’t just creating their own research — on the Reddit-style website Agent4Science, they’re chatting about it, too.