The paper introduces Align‑LoRA, a unified LoRA framework for multi‑task learning that replaces complex, isolated adapter designs with a single‑adapter model enhanced by a higher rank and an explicit alignment loss. It demonstrates that a router‑free, multi‑head model with high inter‑head redundancy can outperform more elaborate baselines, and that a unified LoRA can achieve competitive performance while enabling weight merging and zero inference latency. Extensive experiments and theoretical analysis confirm that Align‑LoRA surpasses prevailing approaches, offering a simpler, production‑friendly paradigm for parameter‑efficient fine‑tuning of large language models.
By Jinda Liu, Yi Chang, Yuan Wu
arXiv:2608.23012v1 Announce Type: new
Abstract: Image matching is a core component of applications such as Simultaneous Localization and Mapping (SLAM), Visual Localization, and Structure from Motion...
By Francesco Vultaggio, Predrag Djindjic, Markus Gerke, Sebastian Tschiatschek, Phillipp Fanta-Jende
arXiv:2607.27836v2 Announce Type: replace
Abstract: Large language model unlearning is consistently fragile under relearn attacks. On TOFU, fine-tuning on twenty forget examples substantially recover...
By Xiangyu Yin, Jiaxu Liu, Zhen Chen, Chih-Hong Cheng
arXiv:2608.21570v1 Announce Type: new
Abstract: Deploying a safety layer for large language models on commodity hardware is constrained by the guards available to do it: current open guard models hol...
By Edson Rodrigues da Cruz Filho, Paulo Ricardo Ferreira Neves, Paulo Henrique Eleuterio Falsetti, Jo\~ao Vitor Pavan, Ian Degaspari, Henrique Vieira Laturrague, Patrick Vieira Laturrague, Guilherme Nielsen Dias, Marccello Wilson Perez Berto, Gustavo Voltani Von Atzingen
arXiv:2608.21811v1 Announce Type: new
Abstract: Reinforcement learning for vision-language math reasoning starves under sparse reward: on a pool of 20,830 visual-math problems where Qwen2-VL-2B answe...
By Qiqian Fu
arXiv:2608.21487v1 Announce Type: cross
Abstract: Vision-Language Models (VLMs) exhibit strong zero-shot capabilities, making them an attractive solution for continual learning across diverse tasks....
By Chang Sun, Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh
arXiv:2608.22196v1 Announce Type: cross
Abstract: While cascaded multi-talker ASR (MT-ASR) leverages state-of-the-art foundation models, its performance is often capped by speaker leakage during sepa...
By Hermann Yepdjio Nkouanga, Minwei Luo, Maggie Wigness, Suresh Singh
arXiv:2608.23565v1 Announce Type: new
Abstract: An interactive world model must follow the user's actions, remember the places it has shown, and stream in real time. The tension is structural: contro...
By Zhifei Chen, Luozhou Wang, Guibao Shen, Dongyu Yan, Shuai Yang, Tianshuo Xu, Yihua Du, Wei Wang, Tianyi Gui, Lianghua Huang, Yingcong Chen
arXiv:2608.22762v1 Announce Type: new
Abstract: Knowledge graph question answering (KGQA) is a key task for evaluating KG-augmented Large Language Models (LLMs), and complex KGQA that requires multi-...
By Chenhui Liu, Jianpeng Zhou, Jiahai Wang
arXiv:2608.21393v1 Announce Type: new
Abstract: Running large language models inside enterprise environments has always bumped up against a practical wall: the data lives in one place, the AI horsepo...
By Sandeep Bokkasam, Pankaj D
The paper evaluates how well Vision Transformers (ViTs) can handle token merging techniques—specifically ToMe and Mutual Pair Merging—across wheat phenotyping tasks such as growth-stage classification, wheat-head detection, and wheat-organ segmentation. It benchmarks task quality, throughput, token count, and GPU memory usage, including tests on a Raspberry Pi 5. Results show that classification is highly tolerant to token merging, whereas detection and segmentation suffer due to factors like repeated instances, thin organs, dense boundaries, and runtime overhead, and that optimized attention backends can negate apparent speed gains.
By Simon Rav\'e, Pejman Rasti, David Rousseau
VeCAS is a two‑stage framework that generates contrast‑free X‑ray angiograms from non‑contrast images. Stage I localizes vascular structures using a discriminative model and cross‑modality latent distillation, while Stage II synthesizes angiographic appearance within those regions with a vessel‑focused inpainting model. Experiments on a lower‑limb vascular intervention dataset show that VeCAS improves vascular structural fidelity and image quality, and robotic guidewire navigation tests demonstrate significant reductions in time to target and operation steps.
By De-Xing Huang, Chen-Yu Wang, Hao Liang, Xiao-Hu Zhou, Mei-Jiang Gui, Tian-Yu Xiang, Qin-Yi Zhang, Chen Wang, Xiao-Liang Xie, Shi-Qi Liu, Ming-Yuan Liu, Zhen-Chang Wang, Zeng-Guang Hou
arXiv:2608.22753v1 Announce Type: new
Abstract: Large language models (LLMs) excel at text understanding and generation, yet still struggle to reliably understand and apply externally provided proced...
By Bohan Yu, Pengfei Cao, Chen Han, Chenxi Zhou, Zhiheng Zhang, Zhiyang Xie, Wenhao Teng, Xiangwen Liao, Jun Zhao, Kang Liu
Interpretable AI with Local Distillation proposes a method where a black‑box teacher model guides a regularized linear student model at each query point. The teacher defines locality by upweighting training observations with similar predicted outcomes and anchors the fit with its own prediction at the query point, treated as a pseudo‑observation. By adding Gaussian randomization and refitting, the approach identifies reliable features and stable subgroups, achieving near‑teacher accuracy while producing sparse, locally interpretable linear models.
By Erin Craig, Yiling Huang, Snigdha Panigrahi
The paper introduces a training‑free structured suffix modeling technique to accelerate Diffusion Language Models (DLMs). It partitions the suffix into local, middle, and tail regions, retaining varying numbers of tokens per region and incorporating previous decoding results into current token representations. Experiments on three DLMs show significant speedups—up to 72.81× in long‑sequence inference—while often improving performance, and the method is compatible with existing acceleration strategies.
By Zifeng Cheng, Keda Li, Zhiwei Jiang, Cong Wang, Fei Shen, Qing Gu
arXiv:2608.22898v1 Announce Type: new
Abstract: Diffusion language models (DLMs) alleviate the inherent latency bottleneck of autoregressive (AR) large language models (LLMs), but their degraded gene...
By Hyeongsoo Lim, Jinyoung Kim, Eunseo Seo, Minho Jang, Jiwon Yoon
arXiv:2608.23101v1 Announce Type: cross
Abstract: Recent progress in the field of TinyML has demonstrated that low-power hardware based on microcontrollers can achieve bird species monitoring in real...
By Nathan Duboisset, Zhaolan Huang, Felix Bie{\ss}mann, Roudy Dagher, Antoine Lavandier, Emmanuel Baccelli
arXiv:2608.21874v1 Announce Type: cross
Abstract: Deep learning has achieved strong performance in encrypted traffic classification (ETC), yet its computational cost limits deployment on resource-con...
By Yuantu Luo, Jun Tao, Xiangyu Xu, Linxiao Yu, Kangying Li
arXiv:2608.22526v1 Announce Type: new
Abstract: We introduce RS$^3$-Prune, a training-free token-pruning recipe that instantiates as a small set of inference time hooks atop existing video object seg...
By Avilasha Mandal, Sarvesh Shashikumar
arXiv:2608.23253v1 Announce Type: cross
Abstract: Vision-language models typically encode an image into hundreds of visual tokens, incurring substantial inference latency and GPU memory overhead. Exi...
By Taoyu Qian, Qi Wang, Daqian Shi, Yuanhao Jiang, Shang Gao, Hualong Yu