A Microsoft Research study shows that running physical AI inference solely on robots' onboard GPUs limits performance, battery life and scalability, and offloading inference to edge or cloud GPUs can significantly improve it.
An IBM Research team tested eight models on 585 multi-step tasks in AppWorld using ALTK-Evolve and found that how much memory an agent should get depends on the model's capabilities.
Hugging Face has published a paper that enables offline distillation by caching the teacher's top-100 logits, and introduces a fused chunked KL loss that avoids generating full vocabulary-by-sequence-length matrices.
Researchers extend Berkeley Sky Lab's evolutionary kernel search framework K-Search with an MLX backend, using a structured CUDA-to-MLX translation layer to transfer decades of NVIDIA kernel optimization knowledge to Apple Silicon, achieving near-expert-level kernels.
Google Research's linear elastic caching approach, published at CIDR, models page eviction as a ski rental problem, using a lightweight decision tree model to predict TTLs for cached pages and dynamically resize caches. In Spanner production, it cut memory usage by 15.5% versus fixed-size caches while cache misses rose only 5.5%, lowering TCO by about 5% with only a 0.5% impact on actual I/O costs.
Google DeepMind released the Decoupled DiLoCo distributed training architecture, which splits large model training into asynchronously communicating compute islands, isolating hardware failures and sharply reducing bandwidth requirements.
Google Research introduced TurboQuant, a compression algorithm (to be published at ICLR 2026) that combines Quantized Johnson-Lindenstrauss (QJL) and PolarQuant (to be published at AISTATS 2026) to achieve zero-memory-overhead vector compression, compressing models and KV cache without accuracy loss.