Search papers, labs, and topics across Lattice.
13 papers from Meta AI (FAIR) on Architecture Design (Transformers, SSMs, MoE)
Scaling linear RNNs with Sparse Delta Memory leads to dramatic gains in long-context recall without increasing computational overhead.
Gemma 4's unified architecture and reasoning mode enable it to outperform larger models in human-rated tasks while maintaining high efficiency.
Controllable 3D generation takes a leap forward with 3D-ReGen, a framework that leverages an initial 3D shape for tasks like enhancement and editing, outperforming existing methods.
Serverless functions can get a 37% density boost and significantly reduced overhead by offloading I/O to a shared backend, without sacrificing ecosystem compatibility.
Training 3D avatar diffusion models on millions of in-the-wild videos is now possible, thanks to a clever 3D tokenization and visibility-aware training strategy that overcomes partial observability.
Forget scaling laws: a large VLM strategically paired with a smaller model's reasoning tokens can rival the performance of a much larger, monolithic model.
Stop avatars from looking like they're having a seizure: this method uses autoregressive prediction of appearance latents to create temporally stable and high-fidelity 3D Gaussian avatars.
Forget imbalanced LoRA usage: ReMix leverages reinforcement learning to route effectively among LoRAs, boosting performance in parameter-efficient fine-tuning.
Forget quadratic complexity: ULTRA-HSTU achieves 21x faster inference and 4-8% better engagement in large-scale recommendation by co-designing input sequences, sparse attention, and model topology.
Achieve zero-collision embedding tables in production recommenders without sacrificing training speed, unlocking better personalization via fresher and higher-quality item embeddings.
Ditch ANN search altogether: MFLI learns a hierarchical index alongside item embeddings, boosting recall by up to 11.8% and cold-content delivery by 57.29% in large-scale recommender systems.
Ditch the pre-trained models: PAST directly learns speech tokens from phonetic data, outperforming existing methods in representation and reconstruction.
Edit the bassline, drums, or other instruments of any song with this new open-source multi-stem music generation model.
