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.