SE-RRM paper hits ARC-AGI with just 2M params amid GPT-6 recurrent depth speculation
gklambauer · x · 2026-09-04
- Researcher Günter Klambauer speculates (unverified) that GPT-6 Astra may use "recurrent depth": transformer blocks with shared weights applied multiple times, the RRM idea his team proposed earlier this year.
- The new SE-RRM paper enforces permutation equivariance at the architectural level, guaranteeing identical solutions under symbol/color permutations without costly data augmentation.
- Results: outperforms prior RRMs like HRM/TRM on 9x9 Sudoku; generalizes from 9x9 training to 4x4, 16x16 and 25x25; competitive on ARC-AGI-1/2 with only 2M parameters. Code released.
Related event: OpenAI's Astra Reportedly Uses Recurrent Depth, Raising AI Safety Concerns(7 posts)→
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