Magnitude Profile Pruning introduces a training‑free, calibration‑free method for removing attention heads in Transformer models by statistically detecting outliers in weight row norms. Heads whose projection weights fall within the bulk of the distribution are pruned, while outlier heads are retained. Across several models, the MP‑G variant achieves superior perplexity at various sparsity levels and yields significant parameter and FLOP reductions without requiring forward passes, calibration data, or gradient computations.
By Kasun Dewage, Marianna Pensky, Heranga K. Rathnasekara, Suranadi De Silva
arXiv:2608.23921v1 Announce Type: new
Abstract: Recent Vision-Language Models encode high-resolution images into long visual token sequences, incurring prohibitive prefill costs. To compress them, ex...
By Yuanhao Sun, Huawei Ji, Yuan Jin, Cheng Deng, Luoyi Fu, Xinbing Wang
arXiv:2610.28385v1 Announce Type: new
Abstract: Head-dimension compression can reduce the memory required by the key-value (KV) cache, but existing methods often reconstruct full-dimensional represen...
By Irene Tenison, Abdulrahman Alabdulkareem, Daniel Karl I. Weidele, Nima Dehmamy, Dan Gutfreund
Sparse attention reduces the KV-cache traffic that dominates long-context LLM decoding, but practical methods largely operate at coarse granularity. Token-level selection offers finer control, yet pre...
arXiv:2606. 19150v1 Announce Type: new Abstract: The remarkable success of Transformer-based models in natural language processing stems from architectural scaling, which leads to a large number of parameters and hinders deployment in resource-constrained environments.
By Yaniv Livertovsky, Shahar Somin, Gonen Singer
arXiv:2606. 04032v1 Announce Type: cross Abstract: Transformers have become the standard solution for various AI tasks, with the query, key, and value (QKV) attention formulation playing a central role.
By Ali Kayyam, Anusha Madan Gopal, M Anthony Lewis
arXiv:2601.06787v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are known to contain significant redundancy, yet a systematic explanation for why certain components, particularly in...
By Jaewon Sok, Jewon Yeom, Seonghyeon Park, Jeongjae Park, Taesup Kim
The paper presents a retraining‑free technique that transforms pre‑trained transformers into elastic models capable of adapting to any computational budget on the fly. By jointly ranking every MLP neuron and attention head on a common scale and applying soft pruning across multiple sparsity targets, the method achieves smooth performance degradation up to 60% sparsity while outperforming one‑shot baselines. Experiments on 27 vision and text transformer encoders show significant parameter reductions with minimal accuracy loss and notable throughput and latency gains on GPUs, all produced in minutes.
By Walter Simoncini, Dheeraj Varghese, Gertjan J. Burghouts, Cees G. M. Snoek
Page-EntroKV is a new KV‑cache eviction framework designed for grouped‑query attention (GQA) in long‑context autoregressive language models. It pools token importance scores across query heads within each physical group using sink‑isolated Renyi‑2 entropy, then projects these pooled scores onto page frames and performs eviction at the hardware tuple level. The authors prove theoretical guarantees on union overhead, budget preservation, and needle‑retention, and demonstrate on a Qwen2.5‑1.5B‑Instruct pilot that Page‑EntroKV achieves exact union overhead control, eliminates sink masquerade, and retains 100% of critical tokens compared to mean‑pooling baselines.
By Inbasekaran S
The paper introduces Pre-hoc Sparsity (PrHS), a method that selects key-value (KV) cache entries before attention scoring to avoid posterior bias in large language model inference. By bounding mutual‑information loss through the dropped attention mass, PrHS offers explicit accuracy control and implements three orthogonal selectors across time, depth, and layer. Experiments on LLaMA and Mistral models show that PrHS cuts retrieval overhead by over 90%, achieves higher sparsity than HShare, and delivers significant speedups and reduced FLOPs on NVIDIA A100 GPUs while maintaining near‑dense accuracy.
By Yifei Gao, Lei Wang, Rong-Cheng Tu, Qixin Zhang, Jun Cheng, Dacheng Tao
arXiv:2608. 10989v1 Announce Type: cross Abstract: Token-pruning policies are usually designed for a single recognition pipeline, but pretrained Vision Transformers are reused across tasks with different spatial demands.
By Hongsen Cao, Mona Jaber, Shanxin Yuan, Ahmed Sayed
Joint Subspace Sparse Attention (JSSA) is a new method that reduces KV‑cache traffic in long‑context LLM decoding by selecting tokens at the token level while preserving query–key structure. It learns a shared low‑dimensional query–key subspace through a tractable spectral solution and implements a gather‑free indexed‑attention kernel that supports per‑KV‑head selections. Across various attention architectures, JSSA maintains near full‑KV accuracy, avoids generation‑length inflation, and can achieve up to 6.43× decoding throughput on a single GPU for 128K context, even at 700B‑parameter scale.
By Jiwon Song, Yoongon Kim, Jae-Joon Kim