efficiency

The author shares insights from training a small model with DeepSeek's latent attention, observing layer-dependent latent usage and a test-time trick that reduces KV cache 4x without loss change.

Jeff Geerling reviews the Dell XPS 13 with Intel Core 5 320, highlighting its impressive power efficiency and Linux compatibility, and expresses optimism about Intel's low-end processor direction.

InclusionAI (Ant Group's lab) launched Ling 3.0 Tiny, a closed API model with 1.3B activated params out of 7.9B total, 256K context, native function calling, and a thinking/instant mode, aimed at multi-turn agent tool loops. The article questions the efficiency curve and notes no independent evals or open weights.

Introduces GROM, a gradient-free one-shot machine unlearning method that computes a closed-form additive weight update via ridge-regularized least squares, achieving state-of-the-art forgetting-utility trade-offs on benchmarks like TOFU and WMDP, and resisting quantization-based recovery attacks.

A new sub-6B sparse activation AI model built with a fusion architecture combines weights from LFM2.5-2.6B and Qwen3.6-35B-A3B, achieving near-Qwen3.6-35B performance at a fraction of the size.

The paper proposes EASy, a trainable agentic framework that uses reinforcement learning to jointly optimize task performance and computational efficiency, introducing a milestone-plan-act workflow, dependency-aware execution graphs, and tree-structured rollout for training.

Introduces BinaryPC, a training-free hashing-based sparse attention method for long-context LLMs that uses binary principal components to construct hash codes, preserving accuracy while improving decoding throughput by 3.56x over FlashAttention.

This paper introduces EvtGraph, a framework for efficient multimodal temporal graph learning that compresses sequences into event-level tokens under a budget constraint, demonstrating improved performance and efficiency over Transformer and recurrent baselines on clinical and cross-domain benchmarks.

This paper proposes an attention-only white-box Transformer trained with LeJEPA-based self-supervised pretraining, achieving competitive accuracy on CIFAR-10/100 while cutting parameters by ~31% compared to CRATE, and further shows MLP redundancy in standard ViTs.

Introduces Recurrent Residual Quantization (RRQ), a post-training quantization framework that enables multiple effective precisions from a single LLM checkpoint via additive residual refinement, improving flexibility and construction speed.

PaDoc introduces a layout-grounded parallel decoding method for end-to-end document parsers, decoupling layout and content decoding to reduce decoding depth and improve throughput. It achieves state-of-the-art results on OmniDocBenchFull and significantly speeds up inference compared to sequential baselines.

VisionPsy-Nano-460M-Flash is a new 460M vision-language model that uses only 64 visual tokens per image, cutting first-token latency to 0.3s on iPhone while retaining roughly 99% of the full model's benchmark score. The optimization trades off some OCR and fine-detail quality.

AcceptMoE is a verifier-side expert selection method for speculative decoding of MoE LLMs, which reduces expert-weight traffic and improves throughput by up to 2.06x under expert offloading with minimal accuracy loss.

ATFlash introduces a per-RoPE-wavelength distance window that prunes query-key inner-product terms proportional to each frequency pair's wavelength, cutting 37-48% of attention compute with minimal quality loss and up to 1.31x speedups on long-context LLM inference.

Intern-S2 Mobius is a Qwen3.5-35B derived model with an architectural difference claimed to improve throughput and reduce token consumption.

Introduces OoO-Spec, a method that accelerates LLM tool calling by computing semantic slots out of order via a small sidecar model, achieving up to 5.34x speedup over autoregressive decoding and outperforming existing drafter methods across multiple targets and benchmarks.

SparseKAN is a unified compression method for Kolmogorov–Arnold Networks that prunes basis functions, neurons, and numerical precision under learnable gates, achieving up to 73% parameter reduction and significant latency improvements on software and FPGA hardware.

OmniPack proposes a training-free token compression framework for omni-modal LLMs, combining structural pre-LLM compression with task-relevant inner-LLM semantic refinement, achieving strong performance-efficiency trade-offs on multiple benchmarks.

Frac-Connections is a new method that partitions hidden states instead of expanding them, extending Hyper-Connections to fractional expansion rates. It achieves stronger downstream performance than residual connections with virtually no extra compute, as validated on up to 7B MoE language models.

Introduces implicit machine learning force fields (I-MLFFs) that replace deep neural network stacks with fixed-point equations, enabling warm-starting and up to 5x compute/memory savings across graph neural network architectures for molecular dynamics simulations.