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ESM Cambrian

Also known as ESMC

From talks

Results and examples shared in single talks

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20261 event
  1. as of Jun 2026metric
    Training data increased from about 50 million samples for ESM-2 to 2.8 billion, and the model family showed continued gains with scale.
    “about 50 million training samples ... pushed that to 2.8 billion”
    14:34inSelf-Play for LLMs, AI for Biology, Formal Verification, and MoreYouTube

Talks 1

1:16:55
Self-Play for LLMs, AI for Biology, Formal Verification, and More

The YC Paper Club features four talks on applying AI research and tools: protein language models, LLM self-play, streaming retrieval for voice agents, and Lean-based formal verification. The biology talk explains how scaling protein sequence data improves models and can yield useful structure and function representations, while the self-play talk shows how grounding generated math problems and judging their quality helps avoid useless training tasks. The StreamRAG talk explores retrieving information while a user is still speaking to reduce voice-agent latency. The final talk shares Channel AI’s workflow for coordinating coding agents in parallel, emphasizing fast feedback, visibility, and a linked knowledge base.

Jun 12, 2026· 1 h 17 minDiscussed 9:28