Hacker News front pageJul 16, 2026, 23:29 UTC+8

Schema harness pushes frontier models to ~99% on ARC-AGI-3 Public

Schema Harness Achieves ~99% on Arc‑AGI‑3 Public

Impossible Research released Schema, a harness that gets Claude Opus 4.8 and GPT-5.6 Sol to 99% and 95.35% RHAE on the ARC-AGI-3 Public set. It doesn't touch model weights. Instead, it makes models act like physicists: turn raw grid observations into an executable state program, discover the game's mechanism, and keep both in one editable program so a failed prediction can revise the state definition itself. Both scores are self-reported and not yet verified by ARC Prize. The post does not disclose inference latency or per-run cost.

Why it matters: 99% on ARC-AGI-3 is a hard number, and the method—a scheduling harness rather than a new model—has direct implications for agent design. Downside: only a project page is available, no full paper yet, so mechanism details need verification.

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The number is what makes you click: 99% RHAE on ARC-AGI-3 Public with Claude Opus 4.8, 95.35% with GPT-5.6 Sol. For context, Sol bare-metal scored 13.33% on Public and 7.78% on Semi-private. That gap is so large you immediately want to verify the scores.

The approach is straightforward: make models act like physicists. Schema turns raw grid observations into an executable state program—which pixels are the player, a wall, a counter—then discovers the game's mechanism and keeps both in one editable program. When a prediction fails, the model can revise its definition of what the state is, not just patch the rules.

I'd discount this a bit. Both scores are self-reported and unverified by ARC Prize. The post doesn't disclose inference latency or per-run cost, and Opus 4.8 isn't cheap to begin with—layering this harness on top likely adds up. Also, the Public set is only 25 games. Whether this generalizes to the Semi-private set is an open question.

Don't read this as "AGI solved." The more sober take: on a tiny test set, a carefully designed orchestration layer squeezed near-maximum reasoning out of already-strong models. The method is interesting, but it's a long way from reliable.

Source: Hacker News front page · schema-harness.github.io
Published: Jul 16, 2026, 23:29 UTC+8 · Jul 16, 2026