Author

Di He

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#artificial intelligence Preprint Oct 2026

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

In this paper, we argue that looped Transformers need their own residual connections to prevent performance degradation as the number of iterations grows. We observe that increasing loop iterations can reduce reasoning accuracy: noisy state updates overwrite correct intermediate deductions and even undo completed solut...

Peng-Xiang Li, Dilxat Muhtar, Di He et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts

Looped Transformers introduce recurrent depth as a new scaling axis for LLMs: by repeatedly applying shared Transformer blocks, they increase effective depth without increasing parameter count. However, the benefits of looping remain unclear for large MoE LLMs under FLOPs-matched comparisons. The main reason is that th...

Di He, Peng-Xiang Li, Da Chang et al. · 0 citations