Lngram: N-gram Conditional Memory in Latent Space
For sequence modeling tasks requiring both compositional reasoning and local static knowledge retrieval, Lngram offers a more flexible retrieval mechanism that extends to non-text modalities and improves efficiency.
Lngram introduces a latent-space conditional memory module that learns discrete symbols from hidden states for N-gram lookup, removing dependence on tokenization. It consistently reduces perplexity in long-context language modeling and improves performance on vision-language and vision-language-action tasks, surpassing Transformer and Engram baselines.
Sequence modeling requires both compositional reasoning and local static knowledge retrieval, yet standard Transformers handle both through dense computation. Engram partially decouples retrieval from the backbone, but its token-based keys remain tied to text tokenization and hash compression. We propose Lngram, a latent-space conditional memory module that learns discrete symbols directly from hidden states and performs N-gram lookup over these symbols. This design removes the dependence on tokenizer IDs and naturally extends to non-text modalities. In our evaluated settings, Lngram outperforms Transformer and Engram baselines, consistently reduces perplexity in long-context language modeling, and effectively injects domain knowledge when added post hoc to pretrained models. Joint training with the backbone further surpasses full fine-tuning, while experiments on vision-language and vision-language-action tasks show overall gains. Analyses with LogitLens and CKA suggest that Lngram enables prediction-relevant information to emerge earlier, increasing effective depth with limited inference and memory overhead. Code is available at https://github.com/zyaaa-ux/Lngram.