GenBio ICML Workshop 2026

AgenticLS @ NeurIPS 2026 — Agentic AI for Biological Discovery

AgenticLS: Agentic AI for Biological Discovery — Toward Closed-Loop Life-Science Intelligence. A NeurIPS 2026 workshop in Sydney, December 11, 2026.

Generative artificial intelligence: The Times and Sunday Times editorial guidelines

As we explore the potential of generative artificial intelligence our commitment is to uphold our high editorial standards.

Use of generative artificial intelligence | ÉPICBiodiversity

An alternative version of this document was initially drafted by Timothée Poisot with input from members of the Viral Emergence Research Initiative, and further revised based on a conversation with group members. For this reason, it is excluded from the CC BY-NC-SA license under which the rest of the website is published, and may not be reproduced without permission.

Laboratory of Evolutionary Design

Developing biological AI for human good at Stanford University

Agentic systems are adept at solving well-scoped, verifiable problems in computational biology

We introduce CompBioBench, a benchmark of 100 diverse tasks for evaluating agentic systems in computational biology. Unlike mathematics and programming, which more readily admit systematic verification, biological data are inherently noisy and open to interpretation.

Library: Faculty Guide to Generative AI: Welcome

Library: Faculty Guide to Generative AI: Welcome

Introducing LM Studio Bionic: the AI agent for open models

The AI agent made for open models, built to get things done.

BioKEA – an AI-driven Bioinformatics OS

BioKEA integrates agent-based bioinformatics and AI-enhanced publishing through Agentis.

Library: Faculty Guide to Generative AI: Detecting AI

Agentic AI Needs a Systems Theory

The endowment of AI with reasoning capabilities and some degree of agency is widely viewed as a path toward more capable and generalizable systems. Our position is that the current development of agentic AI requires a more holistic, systems-theoretic perspective in order to fully understand their capabilities and mitigate any emergent risks. The primary motivation for our position is that AI development is currently overly focused on individual model capabilities, often ignoring broader emergent behavior, leading to a significant underestimation in the true capabilities and associated risks of agentic AI. We describe some fundamental mechanisms by which advanced capabilities can emerge from (comparably simpler) agents simply due to their interaction with the environment and other agents. Informed by an extensive amount of existing literature from various fields, we outline mechanisms for enhanced agent cognition, emergent causal reasoning ability, and metacognitive awareness. We conclude by presenting some key open challenges and guidance for the development of agentic AI. We emphasize that a systems-level perspective is essential for better understanding, and purposefully shaping, agentic AI systems.

AI is turning research into a scientific monoculture

Generative AI deserves scientific attention. But the rush to study it is producing a feedback loop of topical and methodological convergence, flattening scientific imagination and crowding out the pluralism needed to keep research adaptive, resilient, and intellectually generative.

Lightcone Research

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

Ars Technica's policy on generative AI

How Ars Technica uses, and doesn't use, generative AI.

AI creates first synthetic viruses

Genomic language model has huge potential to redesign organisms such as bacteria

Agentic AI gets lost

On the failure of AI to develop world models