A deep-dive into the knowledge supply chain for frontier AI.

Model improvement has a supply chain: expert human judgment on one end, the scientific record on the other. Wednesday, the people running both ends take the stage.

Curtis Northcutt — inventor of confident learning, co-founder & CEO of Cleanlab through its 2026 acquisition by Handshake, now Director of AI Research at the company supplying expert data to the frontier labs — opens with where expert judgment still makes the difference in model performance, and debuts unreleased research from his team, presented in this room a week before its public release.

Kalpit Dixit — founder of Paper Lantern, previously the AWS Bedrock scientist whose teams delivered trillions of pretraining tokens — closes with a controlled experiment: give Karpathy's autoresearch agent access to two million research papers, and it rebuilds GPT-2 with 26% less training time. Same agent, same starting conditions — the literature was the difference.

Founders Edition is for researchers who became founders, presenting the technical work inside their companies: methods, results, and open questions. Ten minutes on stage, then extended Q&A that goes as deep as the room wants.

Co-hosted with Ascension by AGI House SF.

The Frontier Research Club is a curated forum for rigorous, technical discussion at the frontier of AI. We convene researchers from the frontier labs, Stanford, Berkeley, and the teams building in production to examine concrete work — papers, methods, and results — with a bias toward assumptions, evaluation methodology, failure modes and convincing evidence.

Presentations are intentionally brief so the majority of time is reserved for questions and critique. Materials are shared in advance so the conversation starts at depth.

Agenda

5:30pm: Doors open5:30pm – 6:30pm: Networking + light dinner6:30pm – 8:00pm: Research presentations + discussion8:00pm – 8:30pm: Networking

Presenters & topics

Talk 1: Expert Human Data for Frontier AI

Curtis Northcutt — Director of AI Research, Handshake · Co-founder, Cleanlab · MIT PhD

Few people have shaped how the field thinks about data quality more than Curtis. He invented confident learning at MIT, built it into the open-source cleanlab library used by ML teams worldwide, co-founded Cleanlab and scaled it to $30M raised and 100+ Fortune 500 customers, and led it through its acquisition by Handshake — where he now runs AI research for the company supplying expert human data to the frontier labs.

Where does expert human judgment still make the difference in model performance — and why do frontier labs pay for it?

Curtis opens the night with what expert human data actually involves, where models continue to need it, and how frontier labs define and evaluate what they buy. He'll also debut new research from his team — presented in this room a week ahead of its public release.

Pre-read: Northcutt, Jiang & Chuang, Confident Learning: Estimating Uncertainty in Dataset Labels, Journal of Artificial Intelligence Research.

Talk 2: Scientific Literature in the Autonomous Research Loop

Kalpit Dixit — Founder of Paper Lantern · Former Senior Applied Scientist, AWS Bedrock · Stanford MS · IIT Bombay

Kalpit has spent his career at the data layer of frontier AI. At AWS Bedrock, his teams delivered trillions of pretraining tokens and shipped multiple AI products to GA; he's published NLP research at Amazon Science; and he now builds Paper Lantern, which puts the world's research literature — more than two million papers — directly inside the reasoning loop of AI agents. Tonight he brings the receipts: a controlled experiment on what the literature is actually worth.

When an autonomous agent improves a training run, is it discovering something new — or finally reading what the field already knew?

Kalpit will present a case study connecting Paper Lantern to Andrej Karpathy's autoresearch framework: two agents, same starting conditions, one difference — access to the literature. After training the best configuration from each run for two hours, the literature-grounded agent reached 3.2% lower validation loss on a roughly seven-million-parameter language model. The discussion will examine the experimental setup, which research-informed changes actually helped, and what the results establish about literature access in autonomous research.

Pre-read: Paper Lantern Improves Autoresearch.

Want to present your work?

If you have a research paper you’d like to discuss at one of our next sessions, please submit it for consideration. ​Submit your paper here!

Who should attend

  • Researchers working on human data, post-training, and evaluation

  • Founders and engineers building AI agents and supporting infrastructure

  • Teams developing autonomous research and model-training workflows

  • Investors focused on AI infrastructure and research-driven companies

Capacity is limited.

We will take photos and short video clips for event recap and promotion. By attending, you consent to being photographed and recorded, and to the use of those images and clips by the organizers on social media and other event marketing channels.

Previous Session Recap — The 20-Watt Problem

At Mission Robotics, FRC #22 examined energy efficiency in biological and artificial intelligence. Marta Gajowa, UC Berkeley neuroscientist and founder of Neuraffica, discussed how living neural circuits represent and process information. Ezra Wolf, founding silicon engineer at Zettascale Computing, examined energy use in AI computation, memory bottlenecks, and accelerator design.

The presentations were followed by discussion with both speakers and robot demonstrations. Recordings will be shared on our YouTube channel.

🌐 Connect with Frontier Research Club

Hosted by

Frontier Syndicate is a venture community connecting frontier tech researchers, builders, and investors through curated convenings and early-stage capital. Across the Bay Area, we host a recurring series of research forums, builder nights, and intimate investor dinners — and back exceptional companies emerging from the labs, communities, and technical networks we convene.

Ascension by AGI House SF is a community of AI founders and researchers accelerating humanity's transition to AGI, hosting merit-based gatherings, hackathons, and technical events that draw leading AI minds from around the world.