Tilde introduces Wall Attention, allowing RoPE-free models trained on 4,000 tokens to generalize to 200,000-token contexts without retraining

Introducing Wall Attention. Diagonal forget gates enable RoPE-free attention with exceptional length generalization.

Wall outperforms the dominant method RoPE and sophisticated data-dependent methods like Forgetting Attention (FoX). We trained models with Wall on 4k sequence length and they generalized without further training to 200k+ tokens.

Wall generalizes diagonal forget gates from linear RNNs (KDA, RWKV 7, GLA) to softmax attention through a principled induced action framework. It enables transformers to selectively remember or forget per-channel within the attention head, dramatically boosting expressivity.

Wall is production-ready. Wall retains the parallel structure of vanilla attention, is compatible with GQA & MLA, and we open-source reference Triton kernels for training and decoding. Our WallDecode kernel achieves SOTA-level decode throughput.

Continual learning over long-context is fundamentally about selective forgetting → and Wall attention is all about selective forgetting.