Lngram: Revolutionizing Sequence Modeling with Latent Memory

Lngram is redefining sequence modeling. It introduces a latent-space conditional memory module that directly learns discrete symbols from hidden states. This approach marks a significant departure from traditional methods that rely heavily on text tokenization and hash compression.

Lngram's Competitive Edge

Standard Transformers, while powerful, often entangle compositional reasoning with local static knowledge retrieval through dense computation. Lngram, however, sidesteps this by using an N-gram lookup over these learned symbols. Notably, it removes the dependency on tokenizer IDs, offering a versatile approach that extends naturally to non-text modalities.

The benchmark results speak for themselves. In various evaluated settings, Lngram consistently outperforms both Transformer and Engram baselines, notably reducing perplexity in long-context language modeling. This is a essential achievement as it not only enhances language processing but also efficiently injects domain knowledge into pretrained models.

Enhancing Model Depth

Lngram's design allows for joint training with the backbone model. This approach surpasses the effectiveness of full fine-tuning, demonstrating that sometimes less is indeed more. Experiments across vision-language and vision-language-action tasks show overall performance gains. The data shows that Lngram enables prediction-relevant information to emerge earlier, increasing the model's effective depth without significant inference and memory overhead.

But the question remains: will this new model architecture become the new standard? Given its ability to improve on existing frameworks while maintaining lower computational demands, it's hard not to see its potential for widespread adoption.

Why This Matters

Lngram's introduction comes at a time when the demand for more efficient and effective AI models is soaring. Western coverage has largely overlooked this fundamental shift. With its code readily available on GitHub, anyone can explore and potentially build on this innovative approach.

As AI continues to evolve, models like Lngram, which offer both flexibility and efficiency, will likely lead the way. It challenges us to rethink how we approach sequence modeling and pushes the boundaries of what's possible in AI advancements.