Up to 3.2x Faster Inference with LFM2.5-DSpark

The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction

arXiv:2606. 25986v1 Announce Type: new Abstract: We study whether a scaling-law-style inference-compute frontier appears in limit order book prediction.

Accelerated Inference with Optimum and Transformers Pipelines

Fast Inference on Large Language Models: BLOOMZ on Habana Gaudi2 Accelerator

Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models

arXiv:2605. 06485v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have transformed artificial intelligence, but their computational requirements remain prohibitive for most users.

Quantize the Target, Quantize the Drafter: Efficient Inference with Qwen3.5-4B

arXiv:2607. 04244v1 Announce Type: new Abstract: This report describes our approach to the Efficient Qwen Competition, where the goal is to enable low-latency serving of Qwen3.

Thought-Level Beam Search for Reasoning

arXiv:2608. 08020v1 Announce Type: new Abstract: Test-time compute scaling is a primary driver of performance in large reasoning models (LRMs), but extreme inefficiency bounds current approaches, shifting the critical question from \emph{how much} compute to spend, to \emph{where} to allocate it.

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference

arXiv:2607. 17733v1 Announce Type: cross Abstract: 4-bit quantization enables efficient LLM inference, but suffers from significant accuracy degradation due to outliers.

Seeing the End at Step Zero: Accelerating Diffusion MLLMs via MLP Sparsity-Aware Truncation

arXiv:2607. 14557v1 Announce Type: new Abstract: Diffusion Multimodal Large Language Models (DMLLMs) are highly effective for multimodal reasoning, yet their inference efficiency is significantly hindered by fixed-length generation constraints.

Improving TensorSketch Using Complex Random Variables

arXiv:2608. 10523v1 Announce Type: cross Abstract: \texttt{TensorSketch} by~\cite{pham2013fast,kar2012random} provides efficient sketching algorithms for high-dimensional polynomial kernels $\vec{x}^{\otimes p} \in \R^{d^p}$.

LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models

arXiv:2606. 04485v1 Announce Type: new Abstract: Tabular foundation models (TFMs) increasingly rival tree ensembles, but their performance is often compute-inefficient: with standard affine scalar tokenization, each feature injects value variation through an essentially one-dimensional channel, and feature IDs/positional signals cannot increase within-feature value degrees of freedom, yielding weak early-layer value sensitivity and redundant hidden states.

Multipole Semantic Attention: A Fast Approximation of Softmax Attention for Pretraining

arXiv:2509. 10406v4 Announce Type: replace Abstract: Pretraining transformers on long sequences (entire code repositories, collections of related documents) is bottlenecked by quadratic attention costs.

CurveFP: Co-Designing Numerical Representation and Product Arithmetic for Language Models

arXiv:2608. 10010v2 Announce Type: replace Abstract: Low-precision formats usually optimize scalar fidelity while inheriting conventional product arithmetic.