Schema Achieves High Score on ARC-AGI-3

xiuyu_l · x · 2026-07-17

This post discusses the performance of a method/framework called schema on the ARC-AGI-3 benchmark.

Key information from the original post:

By injecting analysis-by-synthesis into the model, the authors claim to have achieved 99% RHAE on ARC-AGI-3.
Specific results show that using Opus 4.8 + Fable 5 reaches 99%, while GPT-5.6 Sol achieves 95.35% (public set).
The author describes this approach as enabling LLMs to "think like physicists."

The reposter summarizes it further:

schema is essentially akin to VIGA²: performing inverse graphics first, then moving up a level for inverse dynamics.
The core takeaway is that "analysis by synthesis" proves to be highly effective.

This is a research and benchmark-focused post, highlighting the methodology and scoring performance on ARC-AGI-3.

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