efficiency

This paper introduces difference-informed pruning methods (Wisp, Wisp+, Whisper) for large language models, showing that preserving output differences improves sparsification across Llama 2 and 3.1 models up to 405B parameters.

The author open-sources an execution and context layer for coding agents that cuts fresh model traffic by 57-85% while preserving task success in paired smoke tests on GPT-5.6 and Claude Opus 5, and seeks independent evaluation and sponsorship.

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.