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COMPANY · ENTITY #5629

Abstraction and Reasoning Corpus (ARC)

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

PotARCin extends ARC with five-dimensional evaluation of abstract-reasoning skills

PotARCin is a new benchmark that extends the Abstraction and Reasoning Corpus (ARC) by testing understanding of a task's underlying rule across five dimensions—Definition, Classification, Constrained Generation, Editing, and Inversion—using programmatic instance generation. Evaluated on the ARC-AGI-1 training set, five modern models show a 25–52 percentage-point drop relative to standard ARC accuracy, and a held-out P-ARC test set yields 1–8% accuracy across the five dimensions, with models often contradicting their own stated rules.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Embed-TTT: two-step test-time task embedding improves rule induction in VARC (arXiv:2609.21181v1)

The paper introduces Embed-TTT, a two-step test-time training protocol for Vision ARC (VARC) where a task embedding is first finetuned alone and then the backbone is finetuned. Across ARC-AGI-1, ConceptARC, and controlled rule-known datasets, Embed-TTT yields task embeddings that better match underlying rules, improves embedding-based retrieval, enables accurate linear probing of known rules, and—using embeddings that are <0.01% of model parameters—already solves a non-trivial fraction of tasks; the full two-step pipeline further boosts performance and shows compositional interpolation but not extrapolation.

5.0