Grid Complexity Metrics Predict Solver Success on ARC-AGI Tasks
Researchers found that structural properties of intermediate grid states can predict whether a symbolic AI solver will succeed on ARC-AGI tasks, achieving a mean within-task AUC of 0.885 across 44,800 runs. The signal generalizes across two architecturally distinct solvers and holds on a pre-registered held-out task set, with most predictive power concentrated along a single grid-complexity axis. The findings enable early stopping strategies that cut beam-search compute by 33.6% and SDFS compute by 65.3% with negligible loss of solved tasks.
A study posted to arXiv examined whether hand-crafted structural descriptors of intermediate grid states during ARC-AGI solving could predict final solver success, framing the question as a conditional mutual information test. Across 44,800 runs using beam search and Stochastic DFS solvers on 400 ARC tasks, descriptors measured at 50% trajectory completion discriminated successful from failed runs within the same task at a mean best-feature AUC of 0.885 (p < 0.001). The predictive signal generalized across solver architectures, with cross-solver transfer AUCs of 0.747–0.762, and was confirmed on a pre-registered held-out set of 41 tasks where the frozen feature n_components_final achieved AUC = 0.765 (95% CI [0.717, 0.810]). Crucially, the signal was not explained by solver configuration capacity, with configuration-residualized AUCs of 0.927 and 0.896 for beam search and SDFS respectively, and was only weakly correlated with score trajectories (R² ≈ 0). Practically, early stopping based on these descriptors reduced beam-search compute by 33.6% while retaining 98.9% of solves, and degenerate-trajectory detection cut SDFS compute by 65.3% with no solve loss. The study also identified a significant DSL coverage limitation: on 229 of 400 evaluation tasks, the primitive library produced no valid transition from the input grid, a failure invariant to search budget that points to gaps in the DSL rather than insufficient search effort.
What's missing
The study does not address whether these structural grid descriptors would transfer to neural or hybrid ARC solvers beyond the two symbolic systems tested. The work is a preprint and has not yet undergone peer review.
What different sources said
- arXiv cs.LGCenter
Structural Grid Descriptors Predict Within-Task Solver Success on ARC-AGI
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