AI Scientists | Will Bolton

Adversarial Fast-Moving Real-World Domains as Test Beds for Benchmarking AI Scientist Capabilities

Sat, 11 Jul 2026 00:00:00 +0000

ICML 2026: workshop papers and AI for Science meet-up

Fri, 10 Jul 2026 00:00:00 +0000

At ICML 2026 in Seoul I presented:

Learning Clinical-Trial Strategy: Offline Policy Training for Decision Agents: spotlight at the Workshop on Generative and Agentic AI for Biology (GenBio), and poster at the Workshop on Decision-Making from Offline Datasets to Online Adaptation. Paper · Poster
Adversarial Fast-Moving Real-World Domains as Test Beds for Benchmarking AI Scientist Capabilities: Workshop on AI for Science. Paper · Poster

Alongside the conference, the Encode Fellows and I organised and hosted Startups for AI x Science, a meet-up for founders, researchers and builders attending ICML.

Learning Clinical-Trial Strategy: Offline Policy Training for Decision Agents

Fri, 10 Jul 2026 00:00:00 +0000

Startups for AI x Science @ ICML 2026

Wed, 08 Jul 2026 09:00:00 +0000

Together with my fellow Encode: AI for Science Fellows, McClain Thiel, Tim Reichelt and Jonathan Carter, I organised and hosted this breakfast before the second day of ICML 2026 in Seoul. The gathering brought together founders, researchers and builders working at the intersection of AI and science, with conversations spanning early-stage startups, the transition from research to commercialisation, and the infrastructure the field still needs.

et al.

Tue, 12 May 2026 00:00:00 +0000

et al. starts as a personalised discovery feed and self-improving knowledge base for researchers and their agents. Researchers connect their papers, notes and research tools; et al. indexes this knowledge and turns it into working memory for agents. In return, the agents help the researcher reason across papers, generate citations in any journal format, and surface relevant new literature, hypotheses and experimental methods.

Users can import an existing library or save papers, datasets and code repositories as they browse, with a Chrome extension that captures any webpage in one click. They can then read these materials in the app, ask the agent questions about a single paper or their entire library, and receive suggestions of new work based on what they are reading. Every answer is grounded in the user’s own sources, so each claim can be traced back to the passage it came from.

Behind this sits a metadata ingestion pipeline that resolves records across multiple sources and extracts equations from LaTeX, with OCR and vision fallbacks for scanned documents. Citations can be inserted directly into Google Docs, Word and Overleaf through the Word add-in and browser extension, and a remote MCP server lets an external coding agent query a researcher’s library directly.

I also built a reference checker that verifies whether the citations in a paper are correct and actually support the claims made about them: it parses the bibliography, infers the citation style, resolves each reference and checks every claim against the original source.

By capturing what researchers read, save, ask and eventually test, the platform builds a data flywheel: it improves each user’s view of the scientific frontier while gathering the workflow traces needed to train and evaluate scientific agents. Launched in May 2026 and publicly accessible, with around 2,000 people trying the product.

AI Scientists Need a Social Network

Thu, 19 Mar 2026 00:00:00 +0000

AI for Science: Scientists to Builders Summit

Mon, 10 Nov 2025 12:00:00 +0000