The paper introduces MR‑DiffuSR, a 3‑D latent diffusion framework that uses high‑resolution T1w structural priors to guide super‑resolution of thick‑slice FLAIR MRI scans. By applying cross‑modality structural swin attention and a mixed‑scale degradation strategy, the method avoids hallucinations and remains robust across varying slice thicknesses. On ADNI datasets, MR‑DiffuSR outperforms CNN and 2‑D diffusion baselines, achieving high PSNR, SSIM, and low LPIPS, and maintains strong white‑matter hyperintensity segmentation performance even at 7 mm equivalent slice thickness.
By Haoyu Lan, Jiazhen Zhang, John Onofrey, Bino Varghese, Nasim Sheikh-Bahaei, Arthur W. Toga, Jeiran Choupan
OpenVAM is a new framework for visual attention modeling that combines a dense saliency map with language‑based explanations. It uses a decoupled design: a visual pathway for precise localization and a vision‑language head that generates grounded what/why explanations. The method is trained in three stages to preserve localization while adding language grounding, and a scalable pipeline creates multi‑domain annotations for evaluation.
By Kiana Hooshanfar, Amirhossein Kazerouni, Alireza Hosseini, Michael Brudno, Babak Taati
The paper investigates how to adapt visualization techniques—such as saliency maps and Grad‑CAM—to convolutional‑recurrent neural networks (CNN+RNN) used for time‑series anomaly detection in video data. It combines VGG19 for feature extraction with a GRU for sequential analysis, noting that the TimeDistributed layer complicates gradient propagation and weakens the link between spatial and temporal information, thereby reducing the effectiveness of standard visualization methods. The authors propose adaptations of these techniques to better interpret models that incorporate a temporal dimension, highlighting both the challenges and the potential of extending static‑image interpretation strategies to temporal models.
By Fabien Poirier, Myriam Lamolle
Spectral Feedback is a new algorithm for aligning discrete diffusion models at test time by iteratively revisiting and editing token positions rather than only steering the reverse process. It selects edit-sets—groups of token positions to re-mask and re-sample—using sparse Fourier representations of edit-set value functions, enabling efficient optimization of which tokens to revisit. The method is model-agnostic and improves alignment performance across pretrained, test‑time aligned, and fine‑tuned diffusion models, achieving significant gains in protein stability for inverse folding tasks.
By Shai Dickman, Mert Cemri, Landon Butler, Kannan Ramchandran
The paper introduces a new target‑speaker unlearning task for automatic speech recognition (TSU‑ASR) that allows certain speakers to opt out of transcription while still indicating their presence. A lightweight Enrollment‑Conditioned Gating (ECG) module is added to a frozen dual‑stream speech LLM, enabling dynamic unlearning of new opt‑out speakers during inference. Experiments on AMI and AliMeeting datasets show significant drops in transcription accuracy for opt‑out speakers while preserving performance for retained speakers.
By Bo Su, Yueru Yan, Thai Le
The paper investigates whether training small decoder-only transformers on code‑switched text can induce cross‑lingual alignment. Using two 100‑million‑word multilingual corpora—one a mix of English, Dutch, and Chinese BabyBabelLM data, and another generated by inserting word‑ and sentence‑level code‑switching via an LLM—the authors find that code‑switched training aligns representations of parallel text, especially across different scripts, and that this alignment persists when later training on monolingual documents. A curriculum that progresses from word‑level code‑switching to sentence‑level code‑switching and finally to monolingual data yields models that outperform baselines on the BabyLM evaluation suite, demonstrating that code‑switching curriculum learning is an effective data augmentation strategy for multilingual pretraining.
By Dries Rooryck, Alex Cai, Yonatan Belinkov, David Alvarez-Melis, Kiant\'e Brantley
CG-Probes are a method for extracting guardrail directions—Medical Urgency, Psychological Urgency, and Topic Sensitivity—from patient query embeddings. By treating each risk axis as a linear direction in the normalized embedding space, the approach uses difference‑in‑means probing and contrastive pairs generated from clustered Czech oncology search queries. Evaluated on 200 queries graded by oncologists, the probes perform comparably to open‑weight and frontier LLMs while offering lower latency and requiring only search logs, axis definitions, and black‑box embedding access.
By Marko \v{R}eh\'a\v{c}ek, V\'it\v{e}zslav Du\v{s}ek, Martin Rusinko, V\'it Nov\'a\v{c}ek
RupeeBias is a new benchmark that audits demographic bias in large language models (LLMs) when they provide economic guidance in India. It contains 39,150 prompts across four use cases—salary estimation, salary increment estimation, counter‑offer recommendation, and service pricing recommendation—varying 87 India‑specific demographic identifiers such as caste, religion, regional identity, gender, disability, and urban‑rural location. Evaluations of nine LLMs show that outputs differ by an average of 20.2% when only the demographic identifier changes, revealing systematic disparities across all six axes.
By Pavithra P M Nair, Bhavik Talaviya, Shourya Bhushan, Rahul Pankajakshan, Seema Guruvadoo, Avinash Agarwal, Gilad Gressel, Krishnashree Achuthan
The paper introduces "stale‑document poisoning," a temporal alignment failure where outdated retrieval evidence causes retrieval‑augmented generation models to produce incorrect answers even when the model could answer correctly without retrieval. A benchmark of 317 verified knowledge reversals across medicine, law, software, and platform policy shows that outdated evidence flips 30–37% of Llama and Qwen answers, rising to 66–75% when models are explicitly instructed to trust the document. The study demonstrates that providing explicit validity information and a recency‑aware re‑ranker can substantially reduce poisoning, highlighting the need for models to assess whether retrieved evidence remains applicable.
By Md Shamim Ahmed, Lukas Galke Poech, Richard R\"{o}ttger
MexHat is a newly released video dataset aimed at improving hate‑speech detection in Mexican Spanish. It contains roughly 1,000 clips annotated for three broad categories—no negative content, offensive content, and hate‑speech—as well as a finer classification into three hate‑speech sub‑categories. The paper presents dataset statistics and baseline results, underscoring the challenges of detecting culturally and contextually nuanced hate speech in multimodal content.
By Itzel Tlelo-Coyotecatl, Hugo Jair Escalante
The paper challenges the assumption that improving cross‑lingual alignment automatically enhances cross‑lingual transfer. Using XLM‑R models aligned on token, sentence, and masked‑language‑modeling objectives across four language pairs, the authors evaluate zero‑shot transfer on part‑of‑speech tagging and sentence classification. They find that embedding‑based alignment metrics poorly predict downstream performance and that alignment and task gradients are often nearly orthogonal, especially when operating at different representational levels.
By Yana Veitsman, Yihong Liu, Hinrich Sch\"utze
The paper introduces Cross-Scale Channel-wise Knowledge Distillation (CSCWD), a training-time framework that transfers high‑resolution spatial representations from a YOLO11m‑P2 teacher to a lightweight YOLO11n student without changing the student’s inference architecture. CSCWD aligns teacher P2 features with student P3 while also applying same‑scale distillation at deeper pyramid levels, yielding a 2.92‑point mAP@0.5 improvement over the baseline and a 2.09‑point gain over same‑scale distillation alone. In zero‑shot tests on DUT‑Anti‑UAV and on a Raspberry Pi 5, the 2.58‑million‑parameter student reaches 50.32% mAP@0.5 at 82.32 ms latency (12.15 fps) with negligible runtime or memory increase.
By Amir Zamani, Zeinab Ghasemi-Naraghi
The paper introduces Timo, a kinematics-aware multimodal diffusion transformer designed for human motion generation. Timo employs fully shared multimodal attention, flow matching, and geometric/rotational-kinematics supervision to better coordinate articulated motion, and uses a two-stage curriculum to align motion with text captions. The authors also present a new benchmark of 40,025 clips from six datasets, showing that Timo outperforms state‑of‑the‑art methods, achieving a 40.8% relative improvement over Kimodo on average.
By Zhao Wang, Jiangtao Hu, Jack Yu, Tao Yu
The paper introduces PIMDE, a self‑supervised monocular depth estimation framework that decomposes input images into perceptual feature maps, each encoding a specific visual cue. Separate depth branches process these maps to produce individual depth estimates, which are then fused explicitly. Experiments on the KITTI benchmark show that PIMDE matches the accuracy of existing self‑supervised methods while offering clearer insight into how each perceptual cue contributes to depth prediction.
By Zain Ul Abidin, George Dimas, Dimitris K. Iakovidis
arXiv:2609.31558v1 Announce Type: new
Abstract: Contrastive Language--Image Pretraining (CLIP) has emerged as a dominant vision backbone due to its strong transferability and zero-shot capabilities....
By Ahmed Abdelnaby, Mohamed Elmahallawy
arXiv:2609.31595v1 Announce Type: new
Abstract: 3D scene graphs provide a structured representation of complex environments by encoding objects, their semantic attributes, and the spatial and functio...
By Luka Milivojevic, Nikola Popovic, Sayan Deb Sarkar, Sebastian Koch, Iro Armeni, Luc Van Gool, Danda Pani Paudel
arXiv:2609.31032v1 Announce Type: cross
Abstract: Existing text-to-video (T2V) jailbreak methods mainly seek more effective or stealthier attack candidates. In guarded T2V systems, however, video gen...
By Tianmeng Fang, Jiancheng Wang, Chen Wang, Liming Wang, Wei Wang, Jiayang Liu, Xiaochun Cao
arXiv:2406.09896v3 Announce Type: replace
Abstract: Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safe...
By Brun\'o B. Englert, Fabrizio J. Piva, Tommie Kerssies, Daan de Geus, Gijs Dubbelman
arXiv:2506.02015v4 Announce Type: replace
Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled unified multimodal understanding and generation. However, they still strug...
By Yoonjin Oh, Yongjin Kim, Hyomin Kim, Donghwan Chi, Sungwoong Kim
arXiv:2511.09239v2 Announce Type: replace
Abstract: Deep neural networks typically learn spatially entangled representations that conflate discriminative foreground features with spurious background...
By Kaixiang Shu