1 min readfrom Machine Learning

New Fable5/Opus4.8 harness called "Schema" claims 99% on ARC-3 [R]

Schema, the harness we introduce today, reaches 99% on the ARC‑AGI‑3 Public set using Claude Opus 4.8 and Fable 5, and 95.35% using GPT‑5.6 Sol. It does not change the underlying model weights. Instead, it changes the process around them: how observations are turned into a working model of the game, how predictions are tested against the interaction history, and how plans are executed and revised.

Both scores come from a fixed fallback rule: Opus 4.8 and Sol xhigh run first; games scoring below 80 are rerun with Fable 5 and Sol max, respectively, and the higher per-game score is retained.

https://schema-harness.github.io/

The president of ARC Prize tweeted this saying "Looks cool - need to dig into it"


I'm not affiliated with ARC Prize, or with this team. I'm posting this to try to bring back technical discussions to this community.

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#Schema
#ARC-3
#ARC-AGI-3
#Fable 5
#Opus 4.8
#Claude
#GPT-5.6 Sol
#Harness
#Model Weights
#Observations
#Predictions
#Interaction History
#Plans
#Revision
#Fallback Rule
#Game Scoring
#ARC Prize
#AGI
#Sol xhigh
#Machine Learning