Schema tops ARC-AGI-3 benchmark reasoning like physicists

The significant performance gains achieved by Schema on ARC-AGI-3 demonstrate that the immediate path to solving hard reasoning challenges lies in custom agent scaffolding rather than relying solely on raw base model scaling. By structuring model execution to mimic physical reasoning patterns, Schema unlocks latent reasoning capabilities in existing models.

* Custom agent engineering and specialized execution scaffolds are yielding higher returns on reasoning benchmarks than general prompting.

* Integrating domain-specific inductive biases, like inverse graphics and dynamics, provides structured constraints that keep agent logic grounded.

* Achieving near-perfect scores on ARC-AGI-3 suggests that current reasoning benchmarks are increasingly vulnerable to structured agentic workflows.

* This trend shifts the engineering focus from training larger models to building more sophisticated, context-aware runtimes for existing models.