id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e3309643bc3fcd71d8e4043679f3ad0cc54123a6bb8495797bb20bdbb6ad3099 | [
"arxiv",
"semantic_scholar"
] | Soft Label Pruning and Quantization for Large-Scale Dataset Distillation | Large-scale dataset distillation requires storing auxiliary soft labels that can be 30-40x larger on ImageNet-1K and 200x larger on ImageNet-21K than the condensed images, undermining the goal of dataset compression. We identify two fundamental issues necessitating such extensive labels: (1) insufficient image diversit... | [
"Xiao Lingao",
"Yang He"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Medicine",
"Computer Science"
] | 2026-04-20T00:00:00 | https://arxiv.org/abs/2604.18135 | https://arxiv.org/pdf/2604.18135v1 | 2604.18135 | 10.1109/TPAMI.2026.3664488 | 1 | 0 | true | https://github.com/he-y/soft-label-pruning-quantization-for-dataset-distillation | IEEE Transactions on Pattern Analysis and Machine Intelligence | 0.85 |
7b4a7be9493a7453dc6a9ceefef97e09a7b065ae832c493c599f7400da41c581 | [
"arxiv",
"semantic_scholar"
] | Six Llamas: Comparative Religious Ethics Through LoRA-Adapted Language Models | We present Six Llamas, a comparative study examining whether large language models fine-tuned on distinct religious corpora encode systematically different patterns of ethical reasoning. Six variants of Meta-Llama-3.1-8B are constructed: one unmodified control and five LoRA-adapted models trained exclusively on the sac... | [
"Chad Coleman",
"W. Russell Neuman",
"Manan Shah",
"Ali Dasdan",
"Matthew Crispi",
"Morris Chiang",
"Zack Leitman",
"Mustafa Poonawala"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-20T00:00:00 | https://arxiv.org/abs/2604.18404 | https://arxiv.org/pdf/2604.18404v1 | 2604.18404 | null | 0 | 0 | false | null | null | 0.35 |
535f207b12e3c5db6d356d572ff15bcfdce5c0c227949ebf7a292ee113dd989e | [
"arxiv",
"semantic_scholar"
] | Domain-Specialized Object Detection via Model-Level Mixtures of Experts | Mixture-of-Experts (MoE) models provide a structured approach to combining specialized neural networks and offer greater interpretability than conventional ensembles. While MoEs have been successfully applied to image classification and semantic segmentation, their use in object detection remains limited due to challen... | [
"Svetlana Pavlitska",
"Malte Stüven",
"Beyza Keskin",
"J. Marius Zöllner"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-20T00:00:00 | https://arxiv.org/abs/2604.18256 | https://arxiv.org/pdf/2604.18256v1 | 2604.18256 | null | 0 | 0 | true | https://github.com/KASTEL-MobilityLab/mixtures-of-experts/ | null | 0.65 |
158dc6ca943fb8437ce10b495f2a7f99848c5c18a831e6c4e8485e87ed02b799 | [
"arxiv",
"semantic_scholar"
] | Towards Joint Quantization and Token Pruning of Vision-Language Models | Deploying Vision-Language Models (VLMs) under aggressive low-bit inference remains challenging because inference cost is dominated by the long visual-token prefix during prefill and the growing KV cache during autoregressive decoding. Token pruning and low-bit quantization are complementary for reducing these costs, ye... | [
"Xinqing Li",
"Xin He",
"Xindong Zhang",
"Ming-Ming Cheng",
"Lei Zhang",
"Yun Liu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-19T00:00:00 | https://arxiv.org/abs/2604.17320 | https://arxiv.org/pdf/2604.17320v1 | 2604.17320 | null | 0 | 0 | false | null | null | 0.35 |
2cc97ea717fd897bfe3ce60c9770134cdb2c18324a5a46980264d32b03298319 | [
"arxiv",
"semantic_scholar"
] | Predictive Multi-Tier Memory Management for KV Cache in Large-Scale GPU Inference | Key-value (KV) cache memory management is the primary bottleneck limiting throughput and cost-efficiency in large-scale GPU inference serving. Current systems suffer from three compounding inefficiencies: (1) the absence of unified KV cache sizing across all attention architectures--particularly multi-head latent atten... | [
"Sanjeev Rao Ganjihal"
] | [
"cs.AR",
"cs.AI",
"cs.DC",
"cs.PF"
] | [
"Computer Science"
] | 2026-04-19T00:00:00 | https://arxiv.org/abs/2604.26968 | https://arxiv.org/pdf/2604.26968v1 | 2604.26968 | null | 0 | 0 | false | null | null | 0.35 |
3d1f5d001658da4a52772636eb74e595d11eddff1c7b5d585dc5ca3684b2f43b | [
"arxiv",
"semantic_scholar"
] | Bit-Flip Vulnerability of Shared KV-Cache Blocks in LLM Serving Systems | Rowhammer on GPU DRAM has enabled adversarial bit flips in model weights; shared KV-cache blocks in LLM serving systems present an analogous but previously unexamined target. In vLLM's Prefix Caching, these blocks exist as a single physical copy without integrity protection. Using software fault injection under ideal b... | [
"Yuji Yamamoto",
"Satoshi Matsuura"
] | [
"cs.CR",
"cs.AR",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-19T00:00:00 | https://arxiv.org/abs/2604.17249 | https://arxiv.org/pdf/2604.17249v2 | 2604.17249 | null | 0 | 0 | false | null | null | 0.35 |
859db27e02a0753772942b853209312b85e46debae8cbaa6f9b1f48bb63c93e4 | [
"arxiv",
"semantic_scholar"
] | MoVE: Translating Laughter and Tears via Mixture of Vocalization Experts in Speech-to-Speech Translation | Recent Speech-to-Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that convey pragmatic intent, which severely limits real-world utility. We address this via three contributions. First, we propose a synthesis pipel... | [
"Szu-Chi Chen",
"I-Ning Tsai",
"Yi-Cheng Lin",
"Sung-Feng Huang",
"Hung-yi Lee"
] | [
"cs.CL",
"cs.AI",
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2026-04-19T00:00:00 | https://arxiv.org/abs/2604.17435 | https://arxiv.org/pdf/2604.17435v1 | 2604.17435 | null | 1 | 0 | false | null | null | 0.35 |
a0ea47cf6f200cf6028fc80134c311b7f04bf507c4c0c0b909d205a05470b913 | [
"arxiv",
"semantic_scholar"
] | HieraSparse: Hierarchical Semi-Structured Sparse KV Attention | The deployment of long-context Large Language Models (LLMs) poses significant challenges due to the intense computational cost of self-attention and the substantial memory overhead of the Key-Value Cache (KV Cache). In this paper, we introduce HieraSparse, a hierarchical KV Cache compression framework with acceleration... | [
"Haoxuan Wang",
"Chen Wang"
] | [
"cs.DC",
"cs.AR"
] | [
"Computer Science"
] | 2026-04-18T00:00:00 | https://arxiv.org/abs/2604.16864 | https://arxiv.org/pdf/2604.16864v1 | 2604.16864 | null | 0 | 0 | true | https://github.com/psl-ntu/HieraSparse | null | 0.65 |
202cadd1a6aebb28f9d7d67988c10bb22a2d8aae4d312d8bdf452787c892ebd2 | [
"arxiv",
"semantic_scholar"
] | Graph-Guided Adaptive Channel Elimination for KV Cache Compression | Large Language Models have revolutionized natural language processing, achieving unprecedented success across a vast range of tasks. However, their practical application in long-context scenarios is severely hampered by the formidable memory footprint of the Key-Value cache. While channel pruning has emerged as a promi... | [
"Enwei Tong",
"Yao Zhu",
"Yuanchao Bai",
"Kai Wang",
"Xianming Liu",
"Xiangyang Ji"
] | [
"eess.SP"
] | [
"Engineering"
] | 2026-04-18T00:00:00 | https://arxiv.org/abs/2604.16983 | https://arxiv.org/pdf/2604.16983v1 | 2604.16983 | null | 0 | 0 | false | null | null | 0.35 |
e5695158bda68c82d5fcd87f732cca08949abdfb6cbab718ec443e10592475e0 | [
"arxiv",
"semantic_scholar"
] | The Illusion of Equivalence: Systematic FP16 Divergence in KV-Cached Autoregressive Inference | KV caching is a ubiquitous optimization in autoregressive transformer inference, long presumed to be numerically equivalent to cache-free computation. This assumption fails under standard FP16 precision: cache-ON and cache-OFF execution paths employ different floating-point accumulation orderings which, due to FP16 non... | [
"Ranjith Chodavarapu",
"Lei Xu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-16T00:00:00 | https://arxiv.org/abs/2604.15409 | https://arxiv.org/pdf/2604.15409v1 | 2604.15409 | null | 0 | 0 | false | null | null | 0.3493 |
a77969075b58e608a7413c17a31718e8aa89e32d39277b2f865d4061d97bfa06 | [
"arxiv",
"semantic_scholar"
] | Dispatch-Aware Ragged Attention for Pruned Vision Transformers | Token pruning methods for Vision Transformers (ViTs) promise quadratic reductions in attention FLOPs by dropping uninformative patches. Yet standard variable-length attention APIs -- including FlashAttention-2's varlen and PyTorch's NestedTensor SDPA -- fail to translate these savings into proportional wall-clock gains... | [
"Seifeldin Abdellatif",
"Ahmad Almasri"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-16T00:00:00 | https://arxiv.org/abs/2604.15408 | https://arxiv.org/pdf/2604.15408v2 | 2604.15408 | null | 0 | 0 | false | null | null | 0.3493 |
c12ba085126bb19f4fab5642a6d2bede59b56436b172294c944991f85470b666 | [
"arxiv",
"semantic_scholar"
] | Geometric Routing Enables Causal Expert Control in Mixture of Experts | Sparse Mixture-of-Experts (MoE) models scale parameters while fixing active computation per token, but the specialization of individual experts remains opaque. In a companion paper we showed that routing topology is quality-neutral: five structurally different configurations converge to statistically equivalent languag... | [
"Ivan Ternovtsii",
"Yurii Bilak"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-15T00:00:00 | https://arxiv.org/abs/2604.14434 | https://arxiv.org/pdf/2604.14434v1 | 2604.14434 | null | 2 | 0 | false | null | null | 0.3485 |
188d979b73119021af4835fed38b86e7ea88d7c79ad04b41645cee6c7771f55d | [
"arxiv",
"semantic_scholar"
] | ART: Attention Run-time Termination for Efficient Large Language Model Decoding | Long-context decoding in Large Language Models (LLMs) is constrained by the cost of accessing and processing the Key-Value (KV) cache. Despite the evidence that attention outputs depend jointly on keys and values, most existing KV management methods rely on key-only pruning, as incorporating values incurs prohibitive a... | [
"Chen Qiu",
"Guozhong Li",
"Cristian McGee",
"Aritra Dutta",
"Panos Kalnis"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-04-15T00:00:00 | https://arxiv.org/abs/2606.00024 | https://arxiv.org/pdf/2606.00024v2 | 2606.00024 | null | 0 | 0 | false | null | null | 0.3485 |
e8074ce7501acb1df0cdf17d52da8641dcfe2259cfef9e8edae5b0cc682aff93 | [
"arxiv",
"semantic_scholar"
] | KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs | Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: reusing a cached document in a new context requires recomputing KV states to account for shifts in attention distribution. Existing solutions such as CacheBlend, EPIC, an... | [
"Chuangtao Chen",
"Grace Li Zhang",
"Xunzhao Yin",
"Cheng Zhuo",
"Bing Li",
"Ulf Schlichtmann"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-14T00:00:00 | https://arxiv.org/abs/2604.13226 | https://arxiv.org/pdf/2604.13226v2 | 2604.13226 | null | 1 | 0 | false | null | null | 0.3478 |
32da91b91c77332e072107884e57c07d2e5adf9e50641274ad5aab9d3a9c68a1 | [
"arxiv",
"semantic_scholar"
] | RetentiveKV: State-Space Memory for Uncertainty-Aware Multimodal KV Cache Eviction | Multimodal Large Language Models face severe challenges in computational efficiency and memory consumption due to the substantial expansion of the visual KV cache when processing long visual contexts. Existing KV cache compression methods typically rely on the "persistence of importance" hypothesis to prune tokens. How... | [
"Sihao Liu",
"YuFan Xiong",
"Zhonghua Jiang",
"Zhaode Wang",
"chengfei lv Shengyu Zhang"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-14T00:00:00 | https://arxiv.org/abs/2605.04075 | https://arxiv.org/pdf/2605.04075v1 | 2605.04075 | null | 0 | 0 | false | null | null | 0.3478 |
250d618f444b25194b1d614c4452b160d50a97b36f468ad705875b94234b2399 | [
"arxiv",
"semantic_scholar"
] | Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning | We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemotron 3 Super is the first model in the Nemotron 3 family to 1) be pre-trained in NVFP4, 2) leverage LatentMoE, a new Mixture-of-Experts arch... | [
" NVIDIA",
" :",
"Aakshita Chandiramani",
"Aaron Blakeman",
"Abdullahi Olaoye",
"Abhibha Gupta",
"Abhilash Somasamudramath",
"Abhinav Khattar",
"Adeola Adesoba",
"Adi Renduchintala",
"Adil Asif",
"Aditya Agrawal",
"Aditya Vavre",
"Ahmad Kiswani",
"Aishwarya Padmakumar",
"Ajay Hotchanda... | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-14T00:00:00 | https://arxiv.org/abs/2604.12374 | https://arxiv.org/pdf/2604.12374v1 | 2604.12374 | null | 9 | 1 | true | null | null | 0.6459 |
9ea6c3415ec79ba1f9a593eb79f8408b74ca0fac1d802159bc8ed7d248305991 | [
"arxiv",
"semantic_scholar"
] | Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework | Early and reliable detection of Alzheimer's disease (AD) is crucial for timely clinical intervention and improved patient management. It also supports the evaluation of emerging therapeutic strategies. In this paper, we propose a Low-Rank Mixture of Experts (LoRA-MoE) deep learning framework for Alzheimer's disease dia... | [
"Wu Wang",
"Yuang Cheng",
"Fouzi Harrou",
"Ying Sun"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-14T00:00:00 | https://arxiv.org/abs/2605.04079 | https://arxiv.org/pdf/2605.04079v1 | 2605.04079 | null | 0 | 0 | false | null | null | 0.3478 |
20733de7296219368726d254373eb2d05e325e8c9d51c44d639a0de467111885 | [
"arxiv",
"semantic_scholar"
] | VFA: Relieving Vector Operations in Flash Attention with Global Maximum Pre-computation | FlashAttention-style online softmax enables exact attention computation with linear memory by streaming score tiles through on-chip memory and maintaining a running maximum and normalizer. However, as attention kernels approach peak tensor-core/cube-core throughput on modern accelerators, non-matmul components of onlin... | [
"Yupeng Sun",
"Yanzhao Li",
"Zhiqiang Zou",
"Bai Du",
"Zhiyuan Zhang",
"Hui Dong",
"Gaoyige Fan",
"Hui Wang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-14T00:00:00 | https://arxiv.org/abs/2604.12798 | https://arxiv.org/pdf/2604.12798v1 | 2604.12798 | null | 0 | 0 | false | null | null | 0.3478 |
db441711f721eaf21d7ec517f2356278827128ba7c329def52275f52ac36afa8 | [
"arxiv",
"semantic_scholar"
] | Quantization Dominates Rank Reduction for KV-Cache Compression | We compare two strategies for compressing the KV cache in transformer inference: rank reduction (discard dimensions) and quantization (keep all dimensions, reduce precision). At matched storage budgets across five models (124M-14B, MHA and GQA), we find that quantization consistently outperforms rank reduction by 4-364... | [
"Samuel Salfati"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-13T00:00:00 | https://arxiv.org/abs/2604.11501 | https://arxiv.org/pdf/2604.11501v1 | 2604.11501 | null | 1 | 0 | false | null | null | 0.3471 |
328931df37dae3043ade81bda07eefbc99e26b02f26672b633e769dfcf46ce90 | [
"arxiv",
"semantic_scholar"
] | Transactional Attention: Semantic Sponsorship for KV-Cache Retention | At K=16 tokens (0.4% of a 4K context), every existing KV-cache compression method achieves 0% on credential retrieval. The failure mode is dormant tokens: credentials, API keys, and configuration values that receive near-zero attention but become essential at generation time. Because these tokens lack the statistical s... | [
"Abhinaba Basu"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-13T00:00:00 | https://arxiv.org/abs/2604.11288 | https://arxiv.org/pdf/2604.11288v1 | 2604.11288 | null | 0 | 0 | false | null | null | 0.3471 |
7f2ad71fb3e250b2d09ea81831e0360fb49920b09e9c8976bdc9feefcf20fd07 | [
"arxiv",
"semantic_scholar"
] | Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models | Vision-Language Models (VLMs) have revolutionized multi-modal learning by jointly processing visual and textual information. Yet, they face significant challenges due to the high computational and memory demands of processing long sequences of vision tokens. Many existing methods rely on local heuristics, such as atten... | [
"Yvon Apedo",
"Martyna Poreba",
"Michal Szczepanski",
"Samia Bouchafa"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-13T00:00:00 | https://arxiv.org/abs/2604.11530 | https://arxiv.org/pdf/2604.11530v2 | 2604.11530 | null | 0 | 0 | false | null | null | 0.3471 |
d7f90706e9b6252fa790b9581d101e0256907f82016c713753de1448d20aac6d | [
"arxiv",
"semantic_scholar"
] | IceCache: Memory-efficient KV-cache Management for Long-Sequence LLMs | Key-Value (KV) cache plays a crucial role in accelerating inference in large language models (LLMs) by storing intermediate attention states and avoiding redundant computation during autoregressive generation. However, its memory footprint scales linearly with sequence length, often leading to severe memory bottlenecks... | [
"Yuzhen Mao",
"Qitong Wang",
"Martin Ester",
"Ke Li"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-12T00:00:00 | https://arxiv.org/abs/2604.10539 | https://arxiv.org/pdf/2604.10539v1 | 2604.10539 | null | 2 | 0 | true | null | null | 0.6432 |
9dc1d3bb0c7037cb3b58fcfd79bfa815247b2d780592aaffe16030d6cc3563b4 | [
"arxiv",
"semantic_scholar"
] | CodeQuant: Unified Clustering and Quantization for Enhanced Outlier Smoothing in Low-Precision Mixture-of-Experts | Outliers have emerged as a fundamental bottleneck in preserving accuracy for low-precision large models, particularly within Mixture-of-Experts (MoE) architectures that are increasingly central to large-scale language modeling. Under post-training quantization (PTQ), these outliers induce substantial quantization error... | [
"Xiangyang Yin",
"Xingyu Liu",
"Tianhua Xia",
"Bo Bao",
"Vithursan Thangarasa",
"Valavan Manohararajah",
"Eric Sather",
"Sai Qian Zhang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-12T00:00:00 | https://arxiv.org/abs/2604.10496 | https://arxiv.org/pdf/2604.10496v1 | 2604.10496 | null | 1 | 0 | true | https://github.com/SAI-Lab-NYU/CodeQuant | null | 0.6432 |
d91605ca1bb23809af027978644388951ac497adf326fc6b78b32796ce6684b0 | [
"arxiv",
"semantic_scholar"
] | LoopGuard: Breaking Self-Reinforcing Attention Loops via Dynamic KV Cache Intervention | Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition loops. We find that this degeneration is driven by collapsed attention patterns, where a subset of heads locks onto a narrow suffix of the history, and is further stabi... | [
"Dongjie Xu",
"Hao Wu",
"Weijie Shi",
"Yue Cui",
"Yuanjun Liu",
"Jiawei Li",
"Haolun Ma",
"An Liu",
"Jia Zhu",
"Jiajie Xu"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-11T00:00:00 | https://arxiv.org/abs/2604.10044 | https://arxiv.org/pdf/2604.10044v1 | 2604.10044 | null | 1 | 1 | false | null | null | 0.3456 |
cf5bfcfeac6d374a606385eac18eedd3c7f9aa80bb3f602ca266d9d73a9c4ae5 | [
"arxiv",
"semantic_scholar"
] | CodeComp: Structural KV Cache Compression for Agentic Coding | Agentic code tasks such as fault localization and patch generation require processing long codebases under tight memory constraints, where the Key-Value (KV) cache becomes the primary inference bottleneck. Existing compression methods rely exclusively on attention signals to estimate token importance, systematically di... | [
"Qiujiang Chen",
"Jing Xiong",
"Chenyang Zhao",
"Sidi Yang",
"Ngai Wong"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-04-11T00:00:00 | https://arxiv.org/abs/2604.10235 | https://arxiv.org/pdf/2604.10235v1 | 2604.10235 | null | 0 | 0 | false | null | null | 0.3456 |
7e126645626e22d06233927d125b67fcddc74d9ff5e1c5489616c789ded2c114 | [
"arxiv",
"semantic_scholar"
] | MoRI: Mixture of RL and IL Experts for Long-Horizon Manipulation Tasks | Reinforcement Learning (RL) and Imitation Learning (IL) are the standard frameworks for policy acquisition in manipulation. While IL offers efficient policy derivation, it suffers from compounding errors and distribution shift. Conversely, RL facilitates autonomous exploration but is frequently hindered by low sample e... | [
"Yaohang Xu",
"Lianjie Ma",
"Gewei Zuo",
"Wentao Zhang",
"Han Ding",
"Lijun Zhu"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2026-04-11T00:00:00 | https://arxiv.org/abs/2604.10165 | https://arxiv.org/pdf/2604.10165v1 | 2604.10165 | null | 0 | 0 | false | null | null | 0.3456 |
70382e8338b39bceeb4a88e95f5dc07733d26ad2fe40e1f033e2b6919a39c82f | [
"arxiv",
"semantic_scholar"
] | Sequential KV Cache Compression via Probabilistic Language Tries: Beyond the Per-Vector Shannon Limit | Recent work on KV cache quantization, culminating in TurboQuant, has approached the Shannon entropy limit for per-vector compression of transformer key-value caches. We observe that this limit applies to a strictly weaker problem than the one that actually matters: compressing the KV cache as a sequence. The tokens sto... | [
"Gregory Magarshak"
] | [
"cs.LG",
"cs.AI",
"cs.IT",
"cs.NE"
] | [
"Computer Science",
"Mathematics"
] | 2026-04-10T00:00:00 | https://arxiv.org/abs/2604.15356 | https://arxiv.org/pdf/2604.15356v1 | 2604.15356 | null | 1 | 0 | false | null | null | 0.3449 |
62f0fbad79b75697b733e3d1e5e929869892cee2399efea8e9411d62e7d0b361 | [
"arxiv",
"semantic_scholar"
] | MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning | Parameter-efficient transfer learning (PETL) has emerged as a pivotal paradigm for adapting pre-trained foundation models to downstream tasks, significantly reducing trainable parameters yet suffering from substantial memory overhead caused by gradient backpropagation during fine-tuning. While memory-efficient transfer... | [
"Yutong Zhang",
"Zimeng Wu",
"Shangcai Liao",
"Shujiang Wu",
"Jiaxin Chen"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-10T00:00:00 | https://arxiv.org/abs/2605.04058 | https://arxiv.org/pdf/2605.04058v1 | 2605.04058 | 10.1609/aaai.v40i34.40084 | 2 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.542 |
192e1da6ca291ee2ed760d051906dc9c663fb8b4db9c7955a5c9df1fab3bbc4f | [
"arxiv",
"semantic_scholar"
] | Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference | Edge devices increasingly run multimodal sensing pipelines that must remain accurate despite fluctuating power budgets and unpredictable sensor dropout. Existing pruning methods fail under these conditions: they generally require fine-tuning after compression, consuming over $10\times$ the deployment energy, and they a... | [
"Yueyuan Sui",
"Payal Mohapatra",
"Doğaç Eldenk",
"Haodong Yang",
"Yiting Zhang",
"Haoyan Zhang",
"Qi Zhu",
"Stephen Xia"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-10T00:00:00 | https://arxiv.org/abs/2604.08971 | https://arxiv.org/pdf/2604.08971v1 | 2604.08971 | null | 0 | 0 | false | null | null | 0.3449 |
9f3b30754b59503a5fb09b8f44edb9d4e81a1d520ab2e4cbe653017dbdd1c6f7 | [
"arxiv",
"semantic_scholar"
] | Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks | Unmanned aerial vehicles serving as aerial base stations can rapidly restore connectivity after disasters, yet abrupt changes in user mobility and traffic demands shift the quality of service trade-offs and induce strong non-stationarity. Deep reinforcement learning policies suffer from plasticity loss under such shift... | [
"Wen Qiu",
"Zhiqiang He",
"Wei Zhao",
"Hiroshi Masui"
] | [
"cs.MA",
"cs.LG",
"cs.NI"
] | [
"Computer Science"
] | 2026-04-10T00:00:00 | https://arxiv.org/abs/2604.09028 | https://arxiv.org/pdf/2604.09028v1 | 2604.09028 | 10.1109/jiot.2026.3696834 | 0 | 0 | false | null | IEEE Internet of Things Journal | 0.542 |
3c29bc3970a7e4bce1ef512ff5c627ee2526ee0c79a7f5d3ccea0eca8d89907f | [
"arxiv",
"semantic_scholar"
] | KV Cache Offloading for Context-Intensive Tasks | With the growing demand for long-context LLMs across a wide range of applications, the key-value (KV) cache has become a critical bottleneck for both latency and memory usage. Recently, KV-cache offloading has emerged as a promising approach to reduce memory footprint and inference latency while preserving accuracy. Pr... | [
"Andrey Bocharnikov",
"Ivan Ermakov",
"Denis Kuznedelev",
"Vyacheslav Zhdanovskiy",
"Yegor Yershov"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-09T00:00:00 | https://arxiv.org/abs/2604.08426 | https://arxiv.org/pdf/2604.08426v4 | 2604.08426 | null | 1 | 0 | false | null | null | 0.3442 |
219ab6374d4916c7e505d4a442b1d9194b034d9b45dbe0e21202df45f44a28bc | [
"arxiv",
"semantic_scholar"
] | Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts | Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon termed Seeing but Not Thinking: models accurately perceive image content yet fail in subsequent reasoning, while correctly solving identical problems presented as pure tex... | [
"Haolei Xu",
"Haiwen Hong",
"Hongxing Li",
"Rui Zhou",
"Yang Zhang",
"Longtao Huang",
"Hui Xue",
"Yongliang Shen",
"Weiming Lu",
"Yueting Zhuang"
] | [
"cs.CV",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-09T00:00:00 | https://arxiv.org/abs/2604.08541 | https://arxiv.org/pdf/2604.08541v1 | 2604.08541 | null | 2 | 0 | false | null | null | 0.3442 |
65aed9717acda0620036cb5f78ef6e1169172f9fdd76d78de1148e6cd5d41a8c | [
"arxiv",
"semantic_scholar"
] | Can LoRA Fusion Support Cross-Domain Tasks in Cloud-Edge Collaboration? | Cloud-hosted large language models (LLMs) commonly rely on LoRA for domain adaptation, yet domain data are distributed across multiple edge devices and cannot be uploaded due to privacy constraints. This raises a fundamental question: how can knowledge from multiple private edges be integrated into a cloud LLM for cros... | [
"Yatong Wang",
"Fali Wang",
"Naibin Gu",
"Zheng Lin",
"Zhengxiao Liu",
"Dingyu Yao",
"Zhiwei Zhang",
"Jianxin Shi",
"Weiping Wang"
] | [
"cs.DC",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-09T00:00:00 | https://arxiv.org/abs/2605.23913 | https://arxiv.org/pdf/2605.23913v1 | 2605.23913 | null | 0 | 0 | false | null | null | 0.3442 |
ccf6bb1000107964a5995f139d4f2cfcbaa98bb25485eb1072079be16f54bff3 | [
"arxiv",
"semantic_scholar"
] | MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization | Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binarization provides extreme efficiency, yet existing binary methods designed for dense LLMs struggle with MoE-specific issues, including cross-expert redundancy, task-agnostic... | [
"Zhixiong Zhao",
"Zukang Xu",
"Zhixuan Chen",
"Dawei Yang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-08T00:00:00 | https://arxiv.org/abs/2604.06798 | https://arxiv.org/pdf/2604.06798v4 | 2604.06798 | null | 0 | 0 | true | https://github.com/Kishon-zzx/MoBiE | null | 0.6378 |
308bfc05e454a554872a2cf602f14ef95577800ee6dcaa03e87b73c2035daeee | [
"arxiv",
"semantic_scholar"
] | AudioKV: KV Cache Eviction in Efficient Large Audio Language Models | Large Audio-Language Models (LALMs) have set new benchmarks in speech processing, yet their deployment is hindered by the memory footprint of the Key-Value (KV) cache during long-context inference. While general KV cache compression techniques excel in LLMs, they often fail in the audio domain by overlooking the intrin... | [
"Yuxuan Wang",
"Peize He",
"Xiyan Gui",
"Xiaoqian Liu",
"Junhao He",
"Xuyang Liu",
"Zichen Wen",
"Xuming Hu",
"Linfeng Zhang"
] | [
"cs.SD"
] | [
"Computer Science"
] | 2026-04-08T00:00:00 | https://arxiv.org/abs/2604.06694 | https://arxiv.org/pdf/2604.06694v1 | 2604.06694 | null | 1 | 0 | false | null | null | 0.3434 |
6cbfcfa948788f6c102de75255ea891114756250d835c043a13f3614ea31947e | [
"arxiv",
"semantic_scholar"
] | Does a Global Perspective Help Prune Sparse MoEs Elegantly? | Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. Sparse Mixture-of-Experts (MoEs) offer a promising alternative by activating only a subset of experts per forward pass, improving efficiency without sacrificing performan... | [
"Zeliang Zhang",
"Nikhil Ghosh",
"Jiani Liu",
"Bin Yu",
"Xiaodong Liu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-04-08T00:00:00 | https://arxiv.org/abs/2604.06542 | https://arxiv.org/pdf/2604.06542v1 | 2604.06542 | null | 0 | 0 | false | null | null | 0.3434 |
17a3008c95aaa5ef05bc9849d9b88037f9e3cdc5efb3c45430990406554f06fa | [
"arxiv",
"semantic_scholar"
] | Region-Graph Optimal Transport Routing for Mixture-of-Experts Whole-Slide Image Classification | Multiple Instance Learning (MIL) is the dominant framework for gigapixel whole-slide image (WSI) classification in computational pathology. However, current MIL aggregators route all instances through a shared pathway, constraining their capacity to specialise across the pathological heterogeneity inherent in each slid... | [
"Xin Tian",
"Jiuliu Lu",
"Ephraim Tsalik",
"Bart Wanders",
"Colleen Knoth",
"Julian Knight"
] | [
"cs.CV",
"cs.AI",
"eess.IV"
] | [
"Computer Science",
"Engineering"
] | 2026-04-08T00:00:00 | https://arxiv.org/abs/2604.07298 | https://arxiv.org/pdf/2604.07298v1 | 2604.07298 | null | 0 | 0 | false | null | null | 0.3434 |
51229331cfd93bfc4842bf4203772d54017495689fe97703c5201ea146216d21 | [
"arxiv",
"semantic_scholar"
] | ForkKV: Scaling Multi-LoRA Agent Serving via Copy-on-Write Disaggregated KV Cache | The serving paradigm of large language models (LLMs) is rapidly shifting towards complex multi-agent workflows where specialized agents collaborate over massive shared contexts. While Low-Rank Adaptation (LoRA) enables the efficient co-hosting of these specialized agents on a single base model, it introduces a critical... | [
"Shao Wang",
"Rui Ren",
"Lin Gui"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2604.06370 | https://arxiv.org/pdf/2604.06370v1 | 2604.06370 | null | 0 | 0 | false | null | null | 0.3427 |
5a4211aedb271a766a363d381406bad463e14e3d760dc9966a47580e74f27e5e | [
"arxiv",
"semantic_scholar"
] | Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees | Sparse Mixture-of-Experts (MoE) allows scaling of language and vision models efficiently by activating only a small subset of experts per input. While this reduces computation, the large number of parameters still incurs substantial memory overhead during inference. Post-training quantization has been explored to addre... | [
"Mohammed Nowaz Rabbani Chowdhury",
"Kaoutar El Maghraoui",
"Hsinyu Tsai",
"Naigang Wang",
"Geoffrey W. Burr",
"Liu Liu",
"Meng Wang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2604.06515 | https://arxiv.org/pdf/2604.06515v1 | 2604.06515 | null | 2 | 0 | false | null | The Fourteenth International Conference on Learning Representations, 2026 | 0.5385 |
1e395dce6b6afe0baf0fb281b998d39c30dad6a021f50e828371daf53c8d0d9d | [
"arxiv",
"semantic_scholar"
] | HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference | Multimodal Large Language Models (MLLMs) have advanced unified reasoning over text, images, and videos, but their inference is hindered by the rapid growth of key-value (KV) caches. Each visual input expands into thousands of tokens, causing caches to scale linearly with context length and remain resident in GPU memory... | [
"Bowen Zeng",
"Feiyang Ren",
"Jun Zhang",
"Xiaoling Gu",
"Ke Chen",
"Lidan Shou",
"Huan Li"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2604.05887 | https://arxiv.org/pdf/2604.05887v1 | 2604.05887 | null | 6 | 0 | false | null | null | 0.3427 |
37a5f2fa34e7cc188961d2c2c2725bf22dc9c89b1f2479af0e936e6f4fc943e8 | [
"arxiv",
"semantic_scholar"
] | Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models | Future sixth-generation (6G) mobile networks are envisioned to be equipped with a diverse set of powerful, yet highly specialized, optimization experts. Such a promising vision is concurrently expected to give rise to the need for scalable mechanisms that can select, combine, and orchestrate such experts based on high-... | [
"Robert-Jeron Reifert",
"Alaa Alameer Ahmad",
"Hayssam Dahrouj",
"Aydin Sezgin"
] | [
"cs.LG",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2605.02911 | https://arxiv.org/pdf/2605.02911v1 | 2605.02911 | null | 0 | 0 | false | null | null | 0.3427 |
6474c051b7f535373c023060984142a39c5237dd3f132abe95ce2f8051a722f1 | [
"arxiv",
"semantic_scholar"
] | ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads | Low-Rank Adaptation (LoRA) is now the dominant method for parameter-efficient fine-tuning of large language models, but achieving a high-quality adapter often requires systematic hyperparameter tuning because LoRA performance is highly sensitive to configuration choices. In practice, this leads to many concurrent LoRA ... | [
"Jingwei Zuo",
"Xinze Feng",
"Zien Liu",
"Kaijian Wang",
"Fanjiang Ye",
"Ye Cao",
"Zhuang Wang",
"Yuke Wang"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2026-04-07T00:00:00 | https://arxiv.org/abs/2604.05426 | https://arxiv.org/pdf/2604.05426v2 | 2604.05426 | null | 2 | 0 | false | null | null | 0.3427 |
36fef3816df51c37bee1ad8250c6c85afc4b0d91665c2044c740b6b9140b7372 | [
"arxiv",
"semantic_scholar"
] | Don't Waste Bits! Adaptive KV-Cache Quantization for Lightweight On-Device LLMs | Large Language Models (LLMs) have achieved remarkable progress across reasoning, generation, and decision-making tasks, yet deploying them on mobile, embedded, and edge devices remains particularly challenging. On-device LLM inference is heavily constrained by the memory and bandwidth overhead of the key-value (KV) cac... | [
"Sayed Pedram Haeri Boroujeni",
"Niloufar Mehrabi",
"Patrick Woods",
"Gabriel Hillesheim",
"Abolfazl Razi"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-06T00:00:00 | https://arxiv.org/abs/2604.04722 | https://arxiv.org/pdf/2604.04722v1 | 2604.04722 | null | 5 | 0 | false | null | null | 0.342 |
6bcc5b6491f28096042705d410186d8320757c3d1bf65176e49602fc85b231d5 | [
"arxiv",
"semantic_scholar"
] | REAM: Merging Improves Pruning of Experts in LLMs | Mixture-of-Experts (MoE) large language models (LLMs) are among the top-performing architectures. The largest models, often with hundreds of billions of parameters, pose significant memory challenges for deployment. Traditional approaches to reduce memory requirements include weight pruning and quantization. Motivated ... | [
"Saurav Jha",
"Maryam Hashemzadeh",
"Ali Saheb Pasand",
"Ali Parviz",
"Min-Joong Lee",
"Boris Knyazev"
] | [
"cs.AI",
"cs.CL",
"cs.LG",
"cs.PF"
] | [
"Computer Science"
] | 2026-04-06T00:00:00 | https://arxiv.org/abs/2604.04356 | https://arxiv.org/pdf/2604.04356v1 | 2604.04356 | null | 2 | 1 | true | https://github.com/SamsungSAILMontreal/ream | null | 0.6351 |
d9dbc10430bf6f734e04ca85badd470cab1a792eba28e69786560fb49a85cc6e | [
"arxiv",
"semantic_scholar"
] | eOptShrinkQ: Near-Lossless KV Cache Compression Through Optimal Spectral Denoising and Quantization | We show that the key-value (KV) cache in transformer attention heads admits a natural decomposition into a low-rank \emph{shared context} component and a full-rank \emph{per-token} residual, well described by the spiked random matrix model. This observation leads to eOptShrinkQ, a two-stage compression pipeline: optima... | [
"Pei-Chun Su"
] | [
"cs.LG",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2026-04-06T00:00:00 | https://arxiv.org/abs/2605.02905 | https://arxiv.org/pdf/2605.02905v1 | 2605.02905 | null | 0 | 0 | false | null | null | 0.342 |
9c0aee3dfabc0aeeb633a14a1afa1e698326f29e460b541457faded15a1621b4 | [
"arxiv",
"semantic_scholar"
] | Comparative Characterization of KV Cache Management Strategies for LLM Inference | Efficient inference with Large Language Models (LLMs) increasingly relies on Key-Value (KV) caches to store previously computed key and value vectors at each layer. These caches are essential to minimize redundant computation during autoregressive token generation, lowering computational complexity from quadratic to li... | [
"Oteo Mamo",
"Olga Kogiou",
"Hyunjin Yi",
"Weikuan Yu"
] | [
"cs.AR",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-06T00:00:00 | https://arxiv.org/abs/2604.05012 | https://arxiv.org/pdf/2604.05012v1 | 2604.05012 | null | 0 | 0 | false | null | null | 0.342 |
5acb70ba0d37755d9dd17c8aa87a6c1028581d0aefa80f36972b3915d2b0696e | [
"arxiv",
"semantic_scholar"
] | Rényi Attention Entropy for Patch Pruning | Transformers are strong baselines in both vision and language because self-attention captures long-range dependencies across tokens. However, the cost of self-attention grows quadratically with the number of tokens. Patch pruning mitigates this cost by estimating per-patch importance and removing redundant patches. To ... | [
"Hiroaki Aizawa",
"Yuki Igaue"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-04T00:00:00 | https://arxiv.org/abs/2604.03803 | https://arxiv.org/pdf/2604.03803v1 | 2604.03803 | null | 0 | 0 | false | null | null | 0.3405 |
093508dcf98330e7145f1b62d18510fb0be2be570700e7ca54d10912969ba7a5 | [
"arxiv",
"semantic_scholar"
] | Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing | Serving transformer language models with high throughput requires caching Key-Values (KVs) to avoid redundant computation during autoregressive generation. The memory footprint of KV caching is significant and heavily impacts serving costs. This work proposes to lessen these memory requirements. While recent work has l... | [
"Anastasiia Filippova",
"David Grangier",
"Marco Cuturi",
"João Monteiro"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-03T00:00:00 | https://arxiv.org/abs/2604.22782 | https://arxiv.org/pdf/2604.22782v1 | 2604.22782 | null | 0 | 0 | false | null | null | 0.3398 |
a1734fcfed2f160ad1549680e3c0d790c51a4eb3268a023689317cac831cdd64 | [
"arxiv",
"semantic_scholar"
] | QAPruner: Quantization-Aware Vision Token Pruning for Multimodal Large Language Models | Multimodal Large Language Models (MLLMs) have shown strong reasoning ability, but their high computational and memory costs hinder deployment in resource-constrained settings. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques, they are usually treated as independent opt... | [
"Xinhao Wang",
"Zhonyu Xia",
"Zhiwei Lin",
"Zhe Li",
"Yongtao Wang"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-03T00:00:00 | https://arxiv.org/abs/2604.02816 | https://arxiv.org/pdf/2604.02816v1 | 2604.02816 | null | 0 | 0 | false | null | null | 0.3398 |
910adb7abaebfa3718d2e3b741bf4c3747dce8c460da00a598cb4e60e5f4acca | [
"arxiv",
"semantic_scholar"
] | TokenDance: Scaling Multi-Agent LLM Serving via Collective KV Cache Sharing | Multi-agent LLM applications organize execution in synchronized rounds where a central scheduler gathers outputs from all agents and redistributes the combined context. This All-Gather communication pattern creates massive KV Cache redundancy, because every agent's prompt contains the same shared output blocks, yet exi... | [
"Zhuohang Bian",
"Feiyang Wu",
"Chengrui Zhang",
"Hangcheng Dong",
"Yun Liang",
"Youwei Zhuo"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2026-04-03T00:00:00 | https://arxiv.org/abs/2604.03143 | https://arxiv.org/pdf/2604.03143v1 | 2604.03143 | null | 1 | 0 | false | null | null | 0.3398 |
a324edd365652390c578a5f03d72bfdc8719e5241e636c188f87adbd45f89fbd | [
"arxiv",
"semantic_scholar"
] | Mixture-of-Experts in Remote Sensing: A Survey | Remote sensing data analysis and interpretation present unique challenges due to the diversity in sensor modalities and spatiotemporal dynamics of Earth observation data. Mixture-of-Experts (MoE) model has emerged as a powerful paradigm that addresses these challenges by dynamically routing inputs to specialized expert... | [
"Yongchuan Cui",
"Peng Liu",
"Lajiao Chen"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-03T00:00:00 | https://arxiv.org/abs/2604.03342 | https://arxiv.org/pdf/2604.03342v1 | 2604.03342 | null | 0 | 0 | false | null | https://www.icck.org/article/abs/jgrs.2025.140654 | 0.534 |
a62dab8eb8e64f4c5e996644409c8b60bd38b04c9876d49cf6b00644f12629f7 | [
"arxiv",
"semantic_scholar"
] | The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level | Mixture-of-Experts (MoE) architectures have become the dominant choice for scaling Large Language Models (LLMs), activating only a subset of parameters per token. While MoE architectures are primarily adopted for computational efficiency, it remains an open question whether their sparsity makes them inherently easier t... | [
"Jeremy Herbst",
"Stefan Wermter",
"Jae Hee Lee"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-02T00:00:00 | https://arxiv.org/abs/2604.02178 | https://arxiv.org/pdf/2604.02178v2 | 2604.02178 | null | 5 | 0 | true | https://github.com/jerryy33/MoE_analysis | null | 0.6297 |
f60562b1504d3ed5cb95bc02897ba005ed8fdaf8f74f86f08730c4fcd0d70658 | [
"arxiv",
"semantic_scholar"
] | Attention to Mamba: A Recipe for Cross-Architecture Distillation | State Space Models (SSMs) such as Mamba have become a popular alternative to Transformer models, due to their reduced memory consumption and higher throughput at generation compared to their Attention-based counterparts. On the other hand, the community has built up a considerable body of knowledge on how to train Tran... | [
"Abhinav Moudgil",
"Ningyuan Huang",
"Eeshan Gunesh Dhekane",
"Pau Rodríguez",
"Luca Zappella",
"Federico Danieli"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-04-01T00:00:00 | https://arxiv.org/abs/2604.14191 | https://arxiv.org/pdf/2604.14191v1 | 2604.14191 | null | 1 | 0 | false | null | null | 0.3383 |
1d2e9986934d368ad386fc2e878f262e2c5616acbfca731dceb5fe1e507ad859 | [
"arxiv",
"semantic_scholar"
] | Routing-Free Mixture-of-Experts | Standard Mixture-of-Experts (MoE) models rely on centralized routing mechanisms that introduce rigid inductive biases. We propose Routing-Free MoE which eliminates any hard-coded centralized designs including external routers, Softmax, Top-K and load balancing, instead encapsulating all activation functionalities withi... | [
"Yilun Liu",
"Jinru Han",
"Sikuan Yan",
"Volker Tresp",
"Yunpu Ma"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-04-01T00:00:00 | https://arxiv.org/abs/2604.00801 | https://arxiv.org/pdf/2604.00801v1 | 2604.00801 | null | 0 | 0 | true | https://github.com/liuyilun2000/RoutingFreeMoE/tree/release | null | 0.6283 |
011fd8755dbdc3b571d133cc955add35d6a97cd5aa0e16945a88b68857cd44de | [
"arxiv",
"semantic_scholar"
] | Cost-Penalized Fitness in FMA-Orchestrated Mixture of Experts: Experimental Evidence for Molecular Memory in Domain Adaptation | We present experimental results from seven controlled runs of nanoFMT, a Free-Market Algorithm (FMA) orchestrated transformer with dynamic Mixture-of-Experts (MoE) management. The experiments address a fundamental question for advanced LLM development: how should an MoE system manage its expert pool when operating at f... | [
"Martin Jaraiz"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-01T00:00:00 | https://arxiv.org/abs/2604.00812 | https://arxiv.org/pdf/2604.00812v1 | 2604.00812 | null | 0 | 0 | false | null | null | 0.3383 |
b8aecc7782f427271f02090f4830d1029c9afffe3a1be42efda589167dbdcb3f | [
"arxiv",
"semantic_scholar"
] | IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models | Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens. Existing token pruning methods mitigate this issue through empirical approaches while overlooking the internal mechanism of attention. In this... | [
"Dong-Jae Lee",
"Sunghyun Baek",
"Junmo Kim"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-01T00:00:00 | https://arxiv.org/abs/2604.00757 | https://arxiv.org/pdf/2604.00757v1 | 2604.00757 | null | 0 | 0 | false | null | null | 0.3383 |
4feedb03b6b9207675d85a30b385886576a915f156dce9f8375467c65f26112c | [
"arxiv",
"semantic_scholar"
] | Quantization with Unified Adaptive Distillation to enable multi-LoRA based one-for-all Generative Vision Models on edge | Generative Artificial Intelligence (GenAI) features such as image editing, object removal, and prompt-guided image transformation are increasingly integrated into mobile applications. However, deploying Large Vision Models (LVMs) for such tasks on resource-constrained devices remains challenging due to their high memor... | [
"Sowmya Vajrala",
"Aakash Parmar",
"Prasanna R",
"Sravanth Kodavanti",
"Manjunath Arveti",
"Srinivas Soumitri Miriyala",
"Ashok Senapati"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-31T00:00:00 | https://arxiv.org/abs/2603.29535 | https://arxiv.org/pdf/2603.29535v1 | 2603.29535 | null | 0 | 0 | false | null | null | 0.3376 |
fd27f171f34c8f163c221f6302243f1560363adf5287ed45cd2482786ee9cd70 | [
"arxiv",
"semantic_scholar"
] | On the Role of Encoder Depth: Pruning Whisper and LoRA Fine-Tuning in SLAM-ASR | Automatic speech recognition (ASR) has advanced rapidly in recent years, driven by large-scale pretrained models and end-to-end architectures such as SLAM-ASR. A key component of SLAM-ASR systems is the Whisper speech encoder, which provides robust acoustic representations. While model pruning has been explored for the... | [
"Ganesh Pavan Kartikeya Bharadwaj Kolluri",
"Michael Kampouridis",
"Ravi Shekhar"
] | [
"cs.CL",
"cs.SD"
] | [
"Computer Science"
] | 2026-03-30T00:00:00 | https://arxiv.org/abs/2603.27981 | https://arxiv.org/pdf/2603.27981v1 | 2603.27981 | null | 0 | 0 | false | null | null | 0.3369 |
c75bedd21fe657046b7aacafac1a6f00ab2e41bcedcf9662dce2ef498807745d | [
"arxiv",
"semantic_scholar"
] | IsoQuant: Hardware-Aligned SO(4) Isoclinic Rotations for LLM KV Cache Compression | Orthogonal feature decorrelation is effective for low-bit online vector quantization, but dense random orthogonal transforms incur prohibitive $O(d^2)$ storage and compute. RotorQuant reduces this cost with blockwise $3$D Clifford rotors, yet the resulting $3$D partition is poorly aligned with modern hardware and offer... | [
"Zhongping Ji"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-03-30T00:00:00 | https://arxiv.org/abs/2603.28430 | https://arxiv.org/pdf/2603.28430v1 | 2603.28430 | null | 2 | 1 | false | null | null | 0.3369 |
6b37f8878e1b8c13df5c169f008fea4531d46e0cd1f1d61cd090a68aa9da925a | [
"arxiv",
"semantic_scholar"
] | Low-Latency Edge LLM Handover via Joint KV Cache Transfer and Token Prefill | Edge deployment of large language models (LLMs) can reduce latency for interactive services, but mobility introduces service interruptions when an user equipment (UE) hands over between base stations (BSs). To promptly resume decoding, the target-side edge server must recover the UE context state, which can be provisio... | [
"Seunghun Lee",
"Jihong Park",
"Ce Zheng",
"Hyuncheol Park"
] | [
"eess.SP"
] | [
"Engineering"
] | 2026-03-30T00:00:00 | https://arxiv.org/abs/2603.28018 | https://arxiv.org/pdf/2603.28018v1 | 2603.28018 | null | 1 | 1 | false | null | null | 0.3369 |
45f5bec4189d537fc0ceb1a84d25d17b1df005f2eab557b46525ebb8b5d668fd | [
"arxiv",
"semantic_scholar"
] | KV Cache Quantization for Self-Forcing Video Generation: A 33-Method Empirical Study | Self-forcing video generation extends a short-horizon video model to longer rollouts by repeatedly feeding generated content back in as context. This scaling path immediately exposes a systems bottleneck: the key-value (KV) cache grows with rollout length, so longer videos require not only better generation quality but... | [
"Suraj Ranganath",
"Vaishak Menon",
"Anish Patnaik"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-29T00:00:00 | https://arxiv.org/abs/2603.27469 | https://arxiv.org/pdf/2603.27469v1 | 2603.27469 | null | 2 | 0 | true | https://github.com/suraj-ranganath/kv-quant-longhorizon/ | null | 0.6243 |
de79a316f833cba93384f952cde4c399b561cc29f2f6bd0d7a84a425e4583bae | [
"arxiv",
"semantic_scholar"
] | KVSculpt: KV Cache Compression as Distillation | KV cache compression is critical for efficient long-context LLM inference. Approaches that reduce the per-pair footprint -- quantization and low-rank decomposition -- are orthogonal to those that reduce the sequence length of the cache. Along the sequence-length dimension, existing methods range from pure eviction -- s... | [
"Bo Jiang",
"Sian Jin"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-03-29T00:00:00 | https://arxiv.org/abs/2603.27819 | https://arxiv.org/pdf/2603.27819v1 | 2603.27819 | null | 0 | 0 | false | null | null | 0.3361 |
195271ef729656e923cf5f02544ee6086b42e078b17382ad0228b602e4cbbd9e | [
"arxiv",
"semantic_scholar"
] | TurboAngle: Near-Lossless KV Cache Compression via Uniform Angle Quantization | We compress KV cache entries by quantizing angles in the Fast Walsh-Hadamard domain, where a random diagonal rotation makes consecutive element pairs approximately uniformly distributed on the unit circle. We extend this angular quantizer with per-layer early-boost, which independently configures K and V codebook sizes... | [
"Dipkumar Patel"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-29T00:00:00 | https://arxiv.org/abs/2603.27467 | https://arxiv.org/pdf/2603.27467v1 | 2603.27467 | null | 2 | 0 | false | null | null | 0.3361 |
dddaee0a13abd30e37ea3ffd2f1facd8d17bdcddb97e29a535dbb97fe6cf0e83 | [
"arxiv",
"semantic_scholar"
] | ScoutAttention: Efficient KV Cache Offloading via Layer-Ahead CPU Pre-computation for LLM Inference | Large language models encounter critical GPU memory capacity constraints during long-context inference, where KV cache memory consumption severely limits decode batch sizes. While existing research has explored offloading KV cache to DRAM, these approaches either demand frequent GPU-CPU data transfers or impose extensi... | [
"Qiuyang Zhang",
"Kai Zhou",
"Ding Tang",
"Kai Lu",
"Cheng Li",
"Zhenyu Yang",
"Peng Xu",
"Jiguang Wan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-28T00:00:00 | https://arxiv.org/abs/2603.27138 | https://arxiv.org/pdf/2603.27138v1 | 2603.27138 | null | 0 | 0 | false | null | null | 0.3354 |
29ac0b5282c6eef232598d0df8e417496472c94112baad74f59aa6a81b4325ab | [
"arxiv",
"semantic_scholar"
] | TurboESM: Ultra-Efficient 3-Bit KV Cache Quantization for Protein Language Models with Orthogonal Rotation and QJL Correction | The rapid scaling of Protein Language Models (PLMs) has unlocked unprecedented accuracy in protein structure prediction and design, but the quadratic memory growth of the Key-Value (KV) cache during inference remains a prohibitive barrier for single-GPU deployment and high-throughput generation. While 8-bit quantizatio... | [
"Yue Hu",
"Junqing Wang",
"Yingchao Liu"
] | [
"q-bio.QM"
] | [
"Biology"
] | 2026-03-27T00:00:00 | https://arxiv.org/abs/2603.26110 | https://arxiv.org/pdf/2603.26110v1 | 2603.26110 | null | 0 | 0 | false | null | null | 0.3347 |
afed2caba7ddda81f5c0da9f292ebf40a68ef5edd83468504f10576a804e3c1a | [
"arxiv",
"semantic_scholar"
] | TTKV: Temporal-Tiered KV Cache for Long-Context LLM Inference | Key-value (KV) caching is critical for efficient inference in large language models (LLMs), yet its memory footprint scales linearly with context length, resulting in a severe scalability bottleneck. Existing approaches largely treat KV states as equally important across time, implicitly assuming uniform precision and ... | [
"Gradwell Dzikanyanga",
"Weihao Yang",
"Hao Huang",
"Donglei Wu",
"Shihao Wang",
"Wen Xia",
"Sanjeeb K C"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-27T00:00:00 | https://arxiv.org/abs/2604.19769 | https://arxiv.org/pdf/2604.19769v1 | 2604.19769 | null | 0 | 0 | false | null | null | 0.3347 |
6c4934016b13a0b356a18c4473ac9eecda572f44dc87c41d9301485ed767a327 | [
"arxiv",
"semantic_scholar"
] | Bayesian estimation of optical constants using mixtures of Gaussian process experts | We propose modeling absorption spectrum measurements as mixtures of Gaussian process experts. This enables us to construct a flexible statistical model for interpolating and extrapolating measurements, facilitating statistical integration of Kramers-Kronig relations to estimate the whole complex refractive index. Addit... | [
"Teemu Härkönen",
"Hui Chen",
"Erik Vartiainen"
] | [
"stat.AP",
"physics.data-an"
] | [
"Mathematics",
"Physics"
] | 2026-03-27T00:00:00 | https://arxiv.org/abs/2603.26334 | https://arxiv.org/pdf/2603.26334v1 | 2603.26334 | null | 0 | 0 | false | null | null | 0.3347 |
f3b5b82a8737ec2dc0b25dce7e024e60ff54d8cf0fdaf797a73a193e81137698 | [
"arxiv",
"semantic_scholar"
] | How Pruning Reshapes Features: Sparse Autoencoder Analysis of Weight-Pruned Language Models | Weight pruning is a standard technique for compressing large language models, yet its effect on learned internal representations remains poorly understood. We present the first systematic study of how unstructured pruning reshapes the feature geometry of language models, using Sparse Autoencoders (SAEs) as interpretabi... | [
"Hector Borobia",
"Elies Seguí-Mas",
"Guillermina Tormo-Carbó"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-26T00:00:00 | https://arxiv.org/abs/2603.25325 | https://arxiv.org/pdf/2603.25325v1 | 2603.25325 | null | 2 | 1 | true | https://github.com/hborobia/sae-pruning-paper | null | 0.6202 |
bcf6a3e028a27d0e6c9e7c0b3c711b4019234fcd93e71ef99136128515d40236 | [
"arxiv",
"semantic_scholar"
] | MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning | Standard LoRA fine-tuning of Mixture-of-Experts (MoE) models applies adapters to every expert, yet our profiling shows that per-layer expert routing is highly skewed: a small subset of experts handles most tokens in each layer, while many others are rarely activated ("cold"). We propose MoE-Sieve, a simple routing-guid... | [
"Andrea Manzoni"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-03-25T00:00:00 | https://arxiv.org/abs/2603.24044 | https://arxiv.org/pdf/2603.24044v1 | 2603.24044 | null | 0 | 0 | false | null | null | 0.3332 |
62df5e004553baa4d283ee0b70c1b4eeeec422d0d171e20a5ce568623093473b | [
"arxiv",
"semantic_scholar"
] | EchoKV: Efficient KV Cache Compression via Similarity-Based Reconstruction | The increasing memory demand of the Key-Value (KV) cache poses a significant bottleneck for Large Language Models (LLMs) in long-context applications. Existing low-rank KV compression methods reduce this footprint by modifying model projections, limiting the flexibility to switch back to standard full-cache inference w... | [
"Shiyu Ji",
"Yixuan Wang",
"Yijun Liu",
"Qingfu Zhu",
"Wanxiang Che"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-24T00:00:00 | https://arxiv.org/abs/2603.22910 | https://arxiv.org/pdf/2603.22910v2 | 2603.22910 | null | 0 | 0 | false | null | null | 0.3325 |
dec77340d7e040a40a873747bdb5bd3cb23b234de68ffb37624da3b3ac4fa688 | [
"arxiv",
"semantic_scholar"
] | PCR: A Prefetch-Enhanced Cache Reuse System for Low-Latency RAG Serving | Retrieval-Augmented Generation (RAG) systems enhance the performance of large language models (LLMs) by incorporating supplementary retrieved documents, enabling more accurate and context-aware responses. However, integrating these external documents often results in very long input sequences, which significantly incre... | [
"Wenfeng Wang",
"Xiaofeng Hou",
"Peng Tang",
"Hengyi Zhou",
"Jing Wang",
"Xinkai Wang",
"Chao Li",
"Minyi Guo"
] | [
"cs.DC"
] | [
"Computer Science"
] | 2026-03-24T00:00:00 | https://arxiv.org/abs/2603.23049 | https://arxiv.org/pdf/2603.23049v1 | 2603.23049 | null | 1 | 0 | false | null | null | 0.3325 |
f25c2bf34020c76bf7e0dd7b28de3e3c3d51026e3ce76f6505ed71994b7adea1 | [
"arxiv",
"semantic_scholar"
] | Knowledge Packs: Zero-Token Knowledge Delivery via KV Cache Injection | RAG wastes tokens. We propose Knowledge Packs: pre-computed KV caches that deliver the same knowledge at zero token cost. For causal transformers, the KV cache from a forward pass on text F is identical to what a joint pass on F+q would produce - this follows directly from the causal mask. The equivalence is exact but ... | [
"Andrey Pustovit"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-22T00:00:00 | https://arxiv.org/abs/2604.03270 | https://arxiv.org/pdf/2604.03270v1 | 2604.03270 | null | 0 | 0 | true | https://github.com/cnails/kv-knowledge-packs | null | 0.6148 |
912b19da08c9ca08ebdcf35648f0ee66f59e9de9f271f3d5d81510bada193aef | [
"arxiv",
"semantic_scholar"
] | HELIX: Scaling Raw Audio Understanding with Hybrid Mamba-Attention Beyond the Quadratic Limit | Audio representation learning typically evaluates design choices such as input frontend, sequence backbone, and sequence length in isolation. We show that these axes are coupled, and conclusions from one setting often do not transfer to others. We introduce HELIX, a controlled framework comparing pure Mamba, pure atten... | [
" Khushiyant",
"Param Thakkar"
] | [
"cs.SD",
"cs.LG",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2026-03-22T00:00:00 | https://arxiv.org/abs/2603.21316 | https://arxiv.org/pdf/2603.21316v1 | 2603.21316 | null | 0 | 0 | false | null | null | 0.331 |
a0e433d3b6b7886b962a68ddb00bfe5a79a718a5e9893fab7604f24f34e805d9 | [
"arxiv",
"semantic_scholar"
] | Beyond Token Eviction: Mixed-Dimension Budget Allocation for Efficient KV Cache Compression | Key-value (KV) caching is widely used to accelerate transformer inference, but its memory cost grows linearly with input length, limiting long-context deployment. Existing token eviction methods reduce memory by discarding less important tokens, which can be viewed as a coarse form of dimensionality reduction that assi... | [
"Ruijie Miao",
"Zhiming Wang",
"Wang Li",
"Shiwei Wu",
"Shufan Liu",
"Yanbing Jiang",
"Tong Yang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-21T00:00:00 | https://arxiv.org/abs/2603.20616 | https://arxiv.org/pdf/2603.20616v1 | 2603.20616 | null | 0 | 0 | false | null | null | 0.3303 |
59ca1cb0036a67455fcc20e04efbce80ffa00238eb739f7304e3bf32f38e62f6 | [
"arxiv",
"semantic_scholar"
] | KV Cache Optimization Strategies for Scalable and Efficient LLM Inference | The key-value (KV) cache is a foundational optimization in Transformer-based large language models (LLMs), eliminating redundant recomputation of past token representations during autoregressive generation. However, its memory footprint scales linearly with context length, imposing critical bottlenecks on GPU memory ca... | [
"Yichun Xu",
"Navjot K. Khaira",
"Tejinder Singh"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-20T00:00:00 | https://arxiv.org/abs/2603.20397 | https://arxiv.org/pdf/2603.20397v1 | 2603.20397 | null | 1 | 0 | false | null | null | 0.3296 |
29d2bec40fe3070d962991082a5d3e20871d93a9cc36aac85d2a82a41a4adc8d | [
"arxiv",
"semantic_scholar"
] | The Residual Stream Is All You Need: On the Redundancy of the KV Cache in Transformer Inference | The key-value (KV) cache is widely treated as essential state in transformer inference, and a large body of work engineers policies to compress, evict, or approximate its entries. We prove that this state is entirely redundant: keys and values at every layer are deterministic projections of the residual stream, and rec... | [
"Kaleem Ullah Qasim",
"Jiashu Zhang",
"Muhammad Kafeel Shaheen",
"Razan Alharith",
"Heying Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-20T00:00:00 | https://arxiv.org/abs/2603.19664 | https://arxiv.org/pdf/2603.19664v1 | 2603.19664 | null | 1 | 0 | true | https://github.com/Kaleemullahqasim/KV-Direct | null | 0.6121 |
184cb8a2cfcc3629fd3cdbf524eea69b8b4bb7fad1d4eff7095a5288de5fa696 | [
"arxiv",
"semantic_scholar"
] | Mixture of Experts with Soft Nearest Neighbor Loss: Resolving Expert Collapse via Representation Disentanglement | The Mixture-of-Experts (MoE) model uses a set of expert networks that specialize on subsets of a dataset under the supervision of a gating network. A common issue in MoE architectures is ``expert collapse'' where overlapping class boundaries in the raw input feature space cause multiple experts to learn redundant repre... | [
"Abien Fred Agarap",
"Arnulfo P. Azcarraga"
] | [
"cs.NE",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-20T00:00:00 | https://arxiv.org/abs/2603.26734 | https://arxiv.org/pdf/2603.26734v1 | 2603.26734 | null | 0 | 0 | false | null | null | 0.3296 |
505e1a8e965845c6e4a5ceb2d5777a1e8a7d9508dda4192e4697f2fdd5caadba | [
"arxiv",
"semantic_scholar"
] | EntropyCache: Decoded Token Entropy Guided KV Caching for Diffusion Language Models | Diffusion-based large language models (dLLMs) rely on bidirectional attention, which prevents lossless KV caching and requires a full forward pass at every denoising step. Existing approximate KV caching methods reduce this cost by selectively updating cached states, but their decision overhead scales with context leng... | [
"Minsoo Cheong",
"Donghyun Son",
"Woosang Lim",
"Sungjoo Yoo"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-19T00:00:00 | https://arxiv.org/abs/2603.18489 | https://arxiv.org/pdf/2603.18489v1 | 2603.18489 | null | 0 | 0 | true | https://github.com/mscheong01/EntropyCache | null | 0.6107 |
c0380f0d11c31a45214ba7131ec6637859c727b80e9233598d9d881bfd15d4ec | [
"arxiv",
"semantic_scholar"
] | Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression | What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve higher efficiency by combining multiple methods such as pruning and quantization. A central but underexplored factor in joint model compressi... | [
"Minjun Kim",
"Jaehyeon Choi",
"Hyunwoo Yang",
"Jongjin Kim",
"Jinho Song",
"U Kang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-03-19T00:00:00 | https://arxiv.org/abs/2603.18426 | https://arxiv.org/pdf/2603.18426v1 | 2603.18426 | null | 1 | 0 | false | null | null | 0.3289 |
2bafc89e70d327ebae6bd46cce96bc73e1cc5b86ca09c6bfd3703d252c892d1e | [
"arxiv",
"semantic_scholar"
] | AIMER: Calibration-Free Task-Agnostic MoE Pruning | Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token compute, but the deployment still requires storing all experts, making expert pruning important for reducing memory and serving overhead. Existing task-agnostic expert pruning methods are typically calibration-dependent:... | [
"Zongfang Liu",
"Shengkun Tang",
"Yifan Shen",
"Huan Wang",
"Xin Yuan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-19T00:00:00 | https://arxiv.org/abs/2603.18492 | https://arxiv.org/pdf/2603.18492v2 | 2603.18492 | null | 1 | 0 | false | null | null | 0.3289 |
d5826824fb9625f03c2bc48b0891d0a16ff27cae15ceb86b1b9e3a7e0dc3eb44 | [
"arxiv",
"semantic_scholar"
] | DyMoE: Dynamic Expert Orchestration with Mixed-Precision Quantization for Efficient MoE Inference on Edge | Despite the computational efficiency of MoE models, the excessive memory footprint and I/O overhead inherent in multi-expert architectures pose formidable challenges for real-time inference on resource-constrained edge platforms. While existing static methods struggle with a rigid latency-accuracy trade-off, we observe... | [
"Yuegui Huang",
"Zhiyuan Fang",
"Weiqi Luo",
"Ruoyu Wu",
"Wuhui Chen",
"Zibin Zheng"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-19T00:00:00 | https://arxiv.org/abs/2603.19172 | https://arxiv.org/pdf/2603.19172v1 | 2603.19172 | null | 0 | 0 | false | null | null | 0.3289 |
edecef9494ffc7ff8a6f1b3e9f2a2e42b0cbd6fdf33f919d51a66512ad4b170e | [
"arxiv",
"semantic_scholar"
] | Path-Constrained Mixture-of-Experts | Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of \emph{expert paths} -- the sequence of expert selections a token makes across all layers. This perspective reveals that, despite $N^L$ possible p... | [
"Zijin Gu",
"Tatiana Likhomanenko",
"Vimal Thilak",
"Jason Ramapuram",
"Navdeep Jaitly"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-18T00:00:00 | https://arxiv.org/abs/2603.18297 | https://arxiv.org/pdf/2603.18297v2 | 2603.18297 | null | 0 | 0 | false | null | null | 0.3281 |
b7ba6ad5fa85713fd7297c51c54422d952690b12f87badb8ec451851174c9f93 | [
"arxiv",
"semantic_scholar"
] | FEMBA on the Edge: Physiologically-Aware Pre-Training, Quantization, and Deployment of a Bidirectional Mamba EEG Foundation Model on an Ultra-low Power Microcontroller | Objective: To enable continuous, long-term neuro-monitoring on wearable devices by overcoming the computational bottlenecks of Transformer-based Electroencephalography (EEG) foundation models and the quantization challenges inherent to State-Space Models (SSMs). Methods: We present FEMBA, a bidirectional Mamba architec... | [
"Anna Tegon",
"Nicholas Lehmann",
"Yawei Li",
"Andrea Cossettini",
"Luca Benini",
"Thorir Mar Ingolfsson"
] | [
"eess.SP",
"cs.LG"
] | [
"Medicine",
"Engineering",
"Computer Science"
] | 2026-03-18T00:00:00 | https://arxiv.org/abs/2603.26716 | https://arxiv.org/pdf/2603.26716v1 | 2603.26716 | 10.1109/tbme.2026.3683482 | 0 | 0 | false | null | null | 0.3281 |
7ecf1ba867b0007e1516b3029303e6402d54604bbb40f1d38c557828acdcbc6e | [
"arxiv",
"semantic_scholar"
] | VQKV: High-Fidelity and High-Ratio Cache Compression via Vector-Quantization | The growing context length of Large Language Models (LLMs) enlarges the Key-Value (KV) cache, limiting deployment in resource-limited environments. Prior training-free approaches for KV cache compression typically rely on low-rank approximation or scalar quantization, which fail to simultaneously achieve high compressi... | [
"Yixuan Wang",
"Qingyu Shi",
"Jiayu Zhou",
"Dianbo Liu",
"Ziwei He",
"Zhouhan Lin"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-17T00:00:00 | https://arxiv.org/abs/2603.16435 | https://arxiv.org/pdf/2603.16435v1 | 2603.16435 | null | 1 | 0 | false | null | null | 0.3274 |
87ad767d85ab2cba95f89ccd152b39026819399144b726e289237fb7f09697ef | [
"arxiv",
"semantic_scholar"
] | Mixture of Style Experts for Diverse Image Stylization | Diffusion-based stylization has advanced significantly, yet existing methods are limited to color-driven transformations, neglecting complex semantics and material details. We introduce StyleExpert, a semantic-aware framework based on the Mixture of Experts (MoE). Our framework employs a unified style encoder, trained ... | [
"Shihao Zhu",
"Ziheng Ouyang",
"Yijia Kang",
"Qilong Wang",
"Mi Zhou",
"Bo Li",
"Ming-Ming Cheng",
"Qibin Hou"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-03-17T00:00:00 | https://arxiv.org/abs/2603.16649 | https://arxiv.org/pdf/2603.16649v3 | 2603.16649 | null | 0 | 0 | false | null | null | 0.3274 |
bbc2f2a580c4694e729e3902134e7945e870c799bbea61f7b4184b4627cd2368 | [
"arxiv",
"semantic_scholar"
] | Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization | Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance. While several compression approaches have been proposed, less emphasis has been placed on selecting the most suitable set of data (the so-called \emph{calibration data}) for fin... | [
"Francesco Pio Monaco",
"Elia Cunegatti",
"Flavio Vella",
"Giovanni Iacca"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-17T00:00:00 | https://arxiv.org/abs/2603.16105 | https://arxiv.org/pdf/2603.16105v3 | 2603.16105 | null | 1 | 0 | true | https://github.com/FrancescoMonaco/ZipCal.} | null | 0.608 |
73a6c57ab61a3d6802ae33462d4ba4d3c57ae63152da5fcbb770245e3be57e25 | [
"arxiv",
"semantic_scholar"
] | Mostly Text, Smart Visuals: Asymmetric Text-Visual Pruning for Large Vision-Language Models | Network pruning is an effective technique for enabling lightweight Large Vision-Language Models (LVLMs), which primarily incorporates both weights and activations into the importance metric. However, existing efforts typically process calibration data from different modalities in a unified manner, overlooking modality-... | [
"Sijie Li",
"Biao Qian",
"Jungong Han"
] | [
"cs.CV",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-16T00:00:00 | https://arxiv.org/abs/2603.16001 | https://arxiv.org/pdf/2603.16001v1 | 2603.16001 | null | 0 | 0 | true | https://github.com/LezJ/ATV-Pruning | null | 0.6067 |
943a64d8001b65702238b54306a6c7b8e336656930e75411ac05ae1dc913342c | [
"arxiv",
"semantic_scholar"
] | Bridging Local and Global Knowledge: Cascaded Mixture-of-Experts Learning for Near-Shortest Path Routing | While deep learning models that leverage local features have demonstrated significant potential for near-optimal routing in dense Euclidean graphs, they struggle to generalize well in sparse networks where topological irregularities require broader structural awareness. To address this limitation, we train a Cascaded M... | [
"Yung-Fu Chen",
"Anish Arora"
] | [
"cs.LG",
"cs.NI"
] | [
"Computer Science"
] | 2026-03-16T00:00:00 | https://arxiv.org/abs/2603.15541 | https://arxiv.org/pdf/2603.15541v1 | 2603.15541 | null | 0 | 0 | false | null | null | 0.3267 |
4fc59cfa68ba8cce5c5e3f44cdf82e25a4ae633fd0a96c9f95dd611ef236dfba | [
"arxiv",
"semantic_scholar"
] | ASAP: Attention-Shift-Aware Pruning for Efficient LVLM Inference | While Large Vision-Language Models (LVLMs) demonstrate exceptional multi-modal capabilities, the quadratic computational cost of processing high-resolution visual tokens remains a critical bottleneck. Though recent token reduction strategies attempt to accelerate inference, such methods inadequately exploit attention v... | [
"Surendra Pathak",
"Bo Han"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-15T00:00:00 | https://arxiv.org/abs/2603.14549 | https://arxiv.org/pdf/2603.14549v2 | 2603.14549 | null | 1 | 0 | false | null | null | 0.3259 |
0a71a7ce56e0e696fe4c5519909e4f79843a225ea39dac30b0e23d3314d40ab1 | [
"arxiv",
"semantic_scholar"
] | OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism | Embodied AI agents increasingly require parallel execution of multiple tasks, such as manipulation, conversation, and memory construction, from shared observations under distinct time constraints. Recent Mixture-of-Transformers (MoT) Vision-Language-Action Models (VLAs) architecturally support such heterogeneous output... | [
"Xiangyu Li",
"Huaizhi Tang",
"Xin Ding",
"Weijun Wang",
"Ting Cao",
"Yunxin Liu"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-15T00:00:00 | https://arxiv.org/abs/2603.14371 | https://arxiv.org/pdf/2603.14371v2 | 2603.14371 | null | 1 | 0 | false | null | null | 0.3259 |
c1302e1af1fe9142aad81a012d3adea61c5891458cc967ac2f3d8f1ae9876018 | [
"arxiv",
"semantic_scholar"
] | Flood Risk Follows Valleys, Not Grids: Graph Neural Networks for Flash Flood Susceptibility Mapping in Himachal Pradesh with Conformal Uncertainty Quantification | Flash floods are the most destructive natural hazard in Himachal Pradesh (HP), India, causing over 400 fatalities and $1.2 billion in losses in the 2023 monsoon season alone. Existing risk maps treat every pixel independently, ignoring the basic fact that flooding upstream raises risk downstream. We address this with a... | [
"Paras Sharma",
"Swastika Sharma"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-03-15T00:00:00 | https://arxiv.org/abs/2603.15681 | https://arxiv.org/pdf/2603.15681v1 | 2603.15681 | null | 0 | 0 | true | https://github.com/Parassharmaa/flash-flood-zones-hp | null | 0.6053 |
c75f50d846f9cb1c4b64eab5fa7875ca11c39204effdbbf0d0f2325a0f6ae2cb | [
"arxiv",
"semantic_scholar"
] | SemantiCache: Efficient KV Cache Compression via Semantic Chunking and Clustered Merging | Existing KV cache compression methods generally operate on discrete tokens or non-semantic chunks. However, such approaches often lead to semantic fragmentation, where linguistically coherent units are disrupted, causing irreversible information loss and degradation in model performance. To address this, we introduce S... | [
"Shunlong Wu",
"Hai Lin",
"Shaoshen Chen",
"Tingwei Lu",
"Yongqin Zeng",
"Shaoxiong Zhan",
"Hai-Tao Zheng",
"Hong-Gee Kim"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-15T00:00:00 | https://arxiv.org/abs/2603.14303 | https://arxiv.org/pdf/2603.14303v1 | 2603.14303 | 10.1109/icassp55912.2026.11464823 | 0 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.5122 |
8a35ce815134b0eb7c9379640f1b13c463bdea33e44353a7b9292700326225fd | [
"arxiv",
"semantic_scholar"
] | LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing | Mixture-of-Experts (MoE) based Large Language Models (LLMs) have demonstrated impressive performance and computational efficiency. However, their deployment is often constrained by substantial memory demands, primarily due to the need to load numerous expert modules. While existing expert compression techniques like pr... | [
"Jiawei Hao",
"Zhiwei Hao",
"Jianyuan Guo",
"Li Shen",
"Yong Luo",
"Han Hu",
"Dan Zeng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-13T00:00:00 | https://arxiv.org/abs/2603.12645 | https://arxiv.org/pdf/2603.12645v1 | 2603.12645 | null | 1 | 0 | false | null | null | 0.3245 |
c5681253aeca3c3526727669d7ccd97b59709a5a19dde3ca709154d43b176ad1 | [
"arxiv",
"semantic_scholar"
] | MoEKD: Mixture-of-Experts Knowledge Distillation for Robust and High-Performing Compressed Code Models | Large language models for code have achieved strong performance across diverse software analytics tasks, yet their real-world adoption remains limited by high computational demands, slow inference speeds, significant energy consumption, and environmental impact. Knowledge distillation (KD) offers a practical solution b... | [
"Md. Abdul Awal",
"Mrigank Rochan",
"Chanchal K. Roy"
] | [
"cs.SE"
] | [
"Computer Science"
] | 2026-03-13T00:00:00 | https://arxiv.org/abs/2603.13213 | https://arxiv.org/pdf/2603.13213v1 | 2603.13213 | null | 1 | 0 | false | null | null | 0.3245 |
62afd323071f0d51198d4e800fa9cd40bb245d855841b80f2031e4e146287b86 | [
"arxiv",
"semantic_scholar"
] | LongFlow: Efficient KV Cache Compression for Reasoning Models | Recent reasoning models such as OpenAI-o1 and DeepSeek-R1 have shown strong performance on complex tasks including mathematical reasoning and code generation. However, this performance gain comes with substantially longer output sequences, leading to significantly increased deployment costs. In particular, long outputs... | [
"Yi Su",
"Zhenxu Tian",
"Dan Qiao",
"Yuechi Zhou",
"Juntao Li",
"Min Zhang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2026-03-12T00:00:00 | https://arxiv.org/abs/2603.11504 | https://arxiv.org/pdf/2603.11504v2 | 2603.11504 | null | 1 | 0 | false | null | null | 0.3237 |
6aff84c78c181251f6eeeb77ad0b6b4ece78cbc5be7da07d040fa99d080eedad | [
"arxiv",
"semantic_scholar"
] | Accelerating Suffix Jailbreak attacks with Prefix-Shared KV-cache | Suffix jailbreak attacks serve as a systematic method for red-teaming Large Language Models (LLMs) but suffer from prohibitive computational costs, as a large number of candidate suffixes need to be evaluated before identifying a jailbreak suffix. This paper presents Prefix-Shared KV Cache (PSKV), a plug-and-play infer... | [
"Xinhai Wang",
"Shaopeng Fu",
"Shu Yang",
"Liangyu Wang",
"Tianhang Zheng",
"Di Wang"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-12T00:00:00 | https://arxiv.org/abs/2603.13420 | https://arxiv.org/pdf/2603.13420v2 | 2603.13420 | null | 0 | 0 | false | null | null | 0.3237 |
5408c2ea0d17de6c9257d3dbc5f23288f22735208c6ff399b70106d19995cfff | [
"arxiv",
"semantic_scholar"
] | Where Matters More Than What: Decoding-aligned KV Cache Compression via Position-aware Pseudo Queries | The Key-Value (KV) cache is crucial for efficient Large Language Models (LLMs) inference, but excessively long contexts drastically increase KV cache memory footprint. Existing KV cache compression methods typically rely on input-side attention patterns within a prompt observation window to estimate token importance du... | [
"Zhenxu Tian",
"Yi Su",
"Juntao Li",
"Min Zhang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-12T00:00:00 | https://arxiv.org/abs/2603.11564 | https://arxiv.org/pdf/2603.11564v1 | 2603.11564 | null | 1 | 0 | false | null | null | 0.3237 |
bf0f16d8e660f4559def9b285d5c616764e7222fa47ee96e3ccdf52716efda04 | [
"arxiv",
"semantic_scholar"
] | Task-Conditioned Routing Signatures in Sparse Mixture-of-Experts Transformers | Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation summarizing expert activation patt... | [
"Mynampati Sri Ranganadha Avinash"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-11T00:00:00 | https://arxiv.org/abs/2603.11114 | https://arxiv.org/pdf/2603.11114v1 | 2603.11114 | null | 0 | 0 | false | null | null | 0.323 |
c9b6dafdbad61df80fa18fcf60b0597295f43516d54b8601ce8d673842787915 | [
"arxiv",
"semantic_scholar"
] | Optimal Expert-Attention Allocation in Mixture-of-Experts: A Scalable Law for Dynamic Model Design | This paper presents a novel extension of neural scaling laws to Mixture-of-Experts (MoE) models, focusing on the optimal allocation of compute between expert and attention sub-layers. As MoE architectures have emerged as an efficient method for scaling model capacity without proportionally increasing computation, deter... | [
"Junzhuo Li",
"Peijie Jiang",
"Changxin Tian",
"Jia Liu",
"Zhiqiang Zhang",
"Xuming Hu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-11T00:00:00 | https://arxiv.org/abs/2603.10379 | https://arxiv.org/pdf/2603.10379v1 | 2603.10379 | null | 0 | 0 | false | null | null | 0.323 |
004c0e3045d469626b727ab0354521a734e9eb15fba6a20724f141dc0a0976b0 | [
"arxiv",
"semantic_scholar"
] | LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation | Transformer-based large language models (LLMs) rely on key-value (KV) caching to avoid redundant computation during autoregressive inference. While this mechanism greatly improves efficiency, the cache size grows linearly with the input sequence length, quickly becoming a bottleneck for long-context tasks. Existing sol... | [
"Jinwoo Ahn",
"Ingyu Seong",
"Akhil Kedia",
"Junhan Kim",
"Hyemi Jang",
"Kangwook Lee",
"Yongkweon Jeon"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-11T00:00:00 | https://arxiv.org/abs/2603.10899 | https://arxiv.org/pdf/2603.10899v1 | 2603.10899 | null | 2 | 1 | true | https://github.com/SamsungLabs/LookaheadKV | null | 0.5999 |
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