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Percy Liang

Director of Stanford CRFM

Associate professor of Computer Science at Stanford and director of the Center for Research on Foundation Models (CRFM), which studies large AI models and built the HELM evaluation benchmark.

#ai-research#foundation-models#evaluation

Key arguments & positions

  • Argues foundation models should be evaluated transparently and holistically, and built the HELM benchmark to make model capabilities and risks comparable across labs.
  • Advocates for open research on foundation models so the scientific community, not only the labs building them, can study how they work.

Accomplishments

  • Associate professor of Computer Science at Stanford and director of the Center for Research on Foundation Models (CRFM).
  • Led the creation of HELM (Holistic Evaluation of Language Models), a widely cited benchmark for comparing large models.

Papers & key writings

Links

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Research PaperFreeepoch.ai

Can AI automate AI R&D yet? (InnovationEval)

Epoch AI's early results from InnovationEval, published October 7, 2026. Epoch reports that the two frontier models tested, Claude Fable 5 and GPT-5.6 Sol, fell far short of the original human-developed technique and that their write-ups overstated their results.

Why this: Covers ai research and evaluation too

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Snorkel AI Research & Blog

The free research library and blog of Snorkel AI, the company spun out of Stanford's Snorkel project on programmatic labelling. The papers and posts explain how training data for AI models is actually built — labelling, evaluation sets, and the 'environments' used to train agents. Useful if you want to understand the unglamorous data work behind model quality, which is where a lot of the real jobs are.

Why this: Covers ai research and evaluation too

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Two Minute Papers (YouTube)

This YouTube channel offers simple video breakdowns of artificial intelligence and machine learning research papers. You can watch these quick guides to stay informed about new AI developments and understand how the technology is evolving.

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ToolFreeartificialanalysis.ai

Optima — Custom AI Benchmark Builder (Artificial Analysis)

A platform from Artificial Analysis for building custom benchmarks from your own files, agent traces, or coding environment, then running them across leading models to compare quality, cost per task, and time per task. Benchmarks can be graded against objective rubrics or pairwise judging. Optima is a commercial product; the public announcement and product overview are free to read.

Why this: Also about evaluation