AI

Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 03:18:24 UTC.

Briefings

A 2026 study introduces InfiLoop, a loop-native residual connection for Transformers that prevents performance degradation during extended reasoning. The research demonstrates that standard recursive architectures fail as iterations exceed 20,000, while InfiLoop maintains accuracy by selectively retaining past computations. On Sudoku-Extreme, the model achieved 97.9% exact accuracy, and on ARC-AGI-2, it reached 13.6% pass@2, outperforming existing recursive systems.

Key points

• InfiLoop introduces a loop-native residual connection that learns which past computations to retain and how much to accept from each new update.
• The model achieves 97.9% exact accuracy on Sudoku-Extreme and 13.6% pass@2 on ARC-AGI-2, surpassing existing recursive architectures.

Context

Transformers require loop-native residual connections to prevent performance degradation as the number of iterations grows. This finding is critical for scaling reasoning capabilities beyond standard iteration limits.

Limitations

Evidence is limited to a single paper from 2026. Results are based on extensive reasoning tasks but do not include performance data for other reasoning benchmarks or real-world applications.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 14/11/2026, 08:25:03 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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