A new system called Schema, which coordinates Claude Opus 4.8 and Fable 5 models through improved reasoning processes, has reached 99% accuracy on the ARC-AGI-3 Public benchmark without modifying the underlying model weights.
The harness works by refining how observations are turned into game models, how predictions are tested, and how plans are revised, indicating that orchestration and process can unlock substantial performance gains from existing models.
What happened
A system called Schema, which wraps Claude Opus 4.8 and Fable 5 models without changing their weights, achieved 99% accuracy on the ARC-AGI-3 Public benchmark set. The same harness scored 95.35% using GPT-5.6 Sol. Schema works by improving how observations are converted into a working model of the game, how predictions are tested against history, and how plans are executed and revised.
Why it matters
Schema demonstrates that substantial gains on reasoning tasks can come from better orchestration of existing models rather than from model weights alone. For teams developing AI systems on constrained budgets or timelines, this suggests process improvements may offer comparable returns to larger model training.
What to watch
The ARC Prize president tweeted "Looks cool - need to dig into it," signaling potential interest from the benchmark's leadership. Technical details are available at schema-harness.github.io.
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Schema represents a shift in how reasoning benchmarks can be approached: rather than pushing for larger or more capable base models, the team focused on improving the process layer that surrounds inference. The 99% result on ARC-AGI-3 is particularly noteworthy because it demonstrates that significant accuracy gains are possible through better orchestration—observation parsing, history-based prediction testing, and plan revision—without modifying the underlying model weights. The use of a fallback rule (rerunning games that score below 80 with different model variants and keeping the higher score) is pragmatic but also reveals that performance gains come partly from model diversity and partly from the harness's process improvements.
The ARC Prize president's cautious but positive response ("Looks cool - need to dig into it") suggests the result is being taken seriously within the benchmark community, though the need for deeper investigation indicates the mechanism and generalizability of the gains may not yet be fully established.
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