arXiv Machine Learning

LMM-IR: Large-Scale Netlist-Aware Multimodal Framework for Static IR-Drop Prediction

arXiv:2511. 12581v2 Announce Type: replace Abstract: Static IR drop analysis is a fundamental and critical task in the field of chip design.

arXiv AI

Jun 3

PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers

arXiv:2606. 03428v1 Announce Type: cross Abstract: The large sizes of Spiking Vision Transformers (SViTs) still hinder their embedded implementation, highlighting the need for model compression.

By Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio, Muhammad Shafique

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Aug 4

UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction

arXiv:2608. 01298v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have emerged as a core architecture in generative modeling due to their scalability and adaptability to multimodal tasks.

By Junno Yun, Ya\c{s}ar Utku Al\c{c}alar, Mehmet Ak\c{c}akaya

llms diffusion multimodal safety

Hugging Face Trending Papers

Jun 11

PRISMR: Overcoming Parse Collapse in Multimodal Listwise Ranking via Parameterized Representation Internalization

Generative listwise ranking with Large Multimodal Models (LMMs) aims to capture global list context in a single forward pass, but its effectiveness degrades in long-context multimodal scenarios. We identify a recurring failure mode, parse collapse, where the autoregressive decoder produces fluent yet incomplete rankings by silently omitting candidates and terminating early.

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 12

PRISMR: Overcoming Parse Collapse in Multimodal Listwise Ranking via Parameterized Representation Internalization

arXiv:2606. 12942v1 Announce Type: new Abstract: Generative listwise ranking with Large Multimodal Models (LMMs) aims to capture global list context in a single forward pass, but its effectiveness degrades in long-context multimodal scenarios.

By Hao Jiang, Xin Li, Annan Wang, Zhi Yang, Haoxiang Zhang, Yichi Zhang, Weisi Lin

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 3

PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers

arXiv:2606. 03257v1 Announce Type: cross Abstract: Spiking Vision Transformer (SViT) models are promising low-power ViT models for solving vision-based tasks with state-of-the-art performance.

By Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio, Muhammad Shafique

llms computer-vision fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 2

OP-LoRA: The Blessing of Dimensionality

arXiv:2412. 10362v2 Announce Type: replace Abstract: Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters.

By Piotr Teterwak, Kate Saenko, Bryan A. Plummer, Ser-Nam Lim

diffusion fine-tuning

arXiv AI

Jul 16

Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

arXiv:2607. 13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.

By Guoxuan Chen, Chufeng Xiao, Haoran Yang, Siyue Xie, Binxiao Huang, Ming Zhang, Cheuk Him Chau, Xinyu Fu, Yingzhao Lian, Tom S. Y. Li, Jintao Lin, Bowen Dong, Zian Qian, Yuhao Liu, Yuxuan Hu, Weikang Shi, Bin Zou, Bowen Zheng, Haoxuan Che, Chang Chen, Yuyang He, Heyang Sun, Tianyu Huang, Chong Hou Choi, Cheng Gong, Han Shi, Haoli Bai, Xihui Liu, Hongsheng Li, Qifeng Chen, Chao Huang, Rui Liu, Chenyang Lei

llms agents diffusion multimodal benchmarks data-engineering

arXiv Machine Learning

Jul 9

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI

arXiv:2607. 06982v1 Announce Type: cross Abstract: Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks.

By Hao Kong, Di Liu, Shuo Huai, Xiangzhong Luo, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu

computer-vision efficiency benchmarks

arXiv AI

Jul 29

At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference

arXiv:2607. 25504v1 Announce Type: cross Abstract: Fine-grained weight pruning and activation sparsification have emerged as effective approaches for reducing the compute and memory cost of inference for Transformer models.

By Bowen Wang, Chi Zhang, Diyou Shen, Renzo Andri, Navaneeth Kunhi Purayil, Luca Benini

llms efficiency

arXiv AI

Jun 8

E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory

arXiv:2601. 16622v2 Announce Type: replace-cross Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems.

By Lin Huang, Chengxiang Huang, Ziang Wang, Yiyue Du, Chu Wang, Haocheng Lu, Yunyang Li, Xiaoli Liu, Arthur Jiang, Jia Zhang

llms efficiency

arXiv AI

Jun 10

NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices

arXiv:2504. 03118v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) often need to be compressed for deployment on resource-constrained edge devices like drones and smart vehicles.

By Ziteng Wei, Qiang He, Bing Li, Feifei Chen, Hai Jin, Yun Yang

llms efficiency benchmarks

arXiv AI

Jul 21

Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget

arXiv:2607. 13125v2 Announce Type: replace-cross Abstract: We introduce Boogu-Image-0.

llms agents diffusion multimodal benchmarks data-engineering

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