YC Paper Club: Same Model Weights Score 30% on ARC-AGI but 95% With a Better Harness
Y Combinator · youtube · 2026-09-07
Y Combinator's Paper Club convenes researchers and founders for a deep dive on agent harnesses, arguing they are real research, not mere prompt engineering. The anchor claim: identical model weights score 30% on ARC-AGI but 95% with a better harness, so harnesses should be as expressive as possible.
Key segments:
- Self-improving harnesses: Seth Karten presents Prime Agent, a self-improving RLM harness; discussions of context as an L1/L2/L3 cache, a Turing-machine-to-von-Neumann framing, agent messaging, and results on ARC-AGI, Emulator Bench, and GPU kernels.
- Local personal AI: Jon Saad-Falcon demos OpenJarvis, a personal AI on personal devices — how far behind local models are, five primitives of a personal AI stack, letting cloud models optimize the local stack, and a claimed 800x cost reduction versus the cloud.
- YC's work agent: Josh France and Regan Bell present QM, YC's internal agent harness for every employee — history of YC's internal agents, OpenClaw and a fleet of 50 agents, pulling the brain out of the sandbox, letting agents choose their own sandbox and model, the "grind tool" for goal budgets, and why agents don't understand social context.
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