The paper introduces Domain Recentering with Confidence Calibration (DRC), a training‑free technique that adapts CLIP to unlabeled target images by fitting a Gaussian mixture and subtracting a posterior‑weighted average of component means from each embedding. It further corrects residual class bias using a log‑prior adjustment based on confidence‑weighted predictions. DRC outperforms other methods, raising average accuracy on cross‑domain datasets by 4.13 and 5.07 points over zero‑shot CLIP for ViT‑B/16 and ResNet‑50, and maintains gains under ImageNet distribution shifts.
By Youngeun Seol, Jimin Shin, Heeseo Yoon, Uiwon Hwang
The paper surveys 160 benchmarks from 2017‑2026 that evaluate predictive embodied intelligence, categorising them into policy suites, embodied agents, world‑model evaluation, and prediction‑to‑action bridges. It finds that most benchmarks are model‑agnostic, rarely compare Vision‑Language‑Action policies to world models, and seldom turn predictions into executed actions. The authors argue that the lack of benchmarks designed to directly test the closed‑loop advantage of world models prevents the field from answering whether such models truly improve robotic performance.
By Gaytri Jena, Kapil Wanaskar, Vinija Jain, Aman Chadha, Vasu Sharma, Amitava Das
GHOST-Q evaluates how post‑training quantization affects visual grounding in vision‑language models. The study compares three 8B VLM families across FP16, INT8, and NF4 precisions, pairing predictions to measure how compression redistributes grounding successes and failures. While most quantized variants maintain overall accuracy, several exhibit significant changes in hallucination‑sensitive conditions, and memory savings do not always translate to lower latency.
By Saim Rehman, Muhammad Shafique
The paper introduces a framework for Flow‑Matching Vision‑Language‑Action (VLA) models that allows independent adjustment of backbone depth, action expert depth, and denoising steps. Lightweight Exit Transformers are added at intermediate layers to enable early exits, and a KV Cache synthesis mechanism manages skipped layers so the action expert can exit deeper than the backbone. Experiments on SmolVLA and π0.5 across LIBERO and Meta‑World show that joint tuning of these compute axes reduces latency by 79.2 % and FLOPs by 31.8 %, while improving mean success rate by 5.6 %.
By Riccardo Andrea Izzo, Rimvydas Rubavicius, Gianluca Bardaro, Subramanian Ramamoorthy, Matteo Matteucci, Alessandro Suglia
WebArxiv is a reproducible benchmark designed to evaluate multimodal web agents on arXiv-related tasks. It consists of 510 static, time‑invariant tasks that require multi‑constraint paper retrieval, fine‑grained content extraction, and cross‑paper comparison, each with a deterministic ground truth. The benchmark highlights challenges for foundation‑model agents, such as over‑reliance on fixed interaction histories, and introduces a lightweight dynamic‑memory mechanism to improve adaptive retrieval and reasoning.
By Zihao Sun, Zijing Shi, Ling Chen
KathDB-FAO is a query evaluation subsystem for the KathDB multimodal DBMS that transforms natural language queries into executable plans. Each operator in the plan is a function synthesized during evaluation, enabling query‑specific optimizations. The system extracts atomic actions, establishes contracts, groups them for efficiency, and synthesizes functions on the fly, achieving an average 58.8% reduction in execution cost on SemBench compared to the next best system.
By Guorui Xiao, Douglas Brown, Artur Borycki, Magdalena Balazinska
M$^2$PFN is an end‑to‑end multimodal framework that extends the TabPFN in‑context learning engine to Alzheimer’s disease diagnosis by aligning 3D‑MRI and tabular features in a shared subspace. It performs differentiable inference through TabPFN’s transformer, back‑propagates gradients into the encoders, and incorporates a frozen tabular‑only prediction via a gated shortcut. On the ADNI cohort it achieves 65.55 % macro‑F1 and 82.21 % macro‑AUC, surpassing unimodal and multimodal baselines, and it generalizes to external cohorts without retraining.
By Lujia Zhong, Shuo Huang, Jianwei Zhang, Xinyu Nie, Yonggang Shi
TOLA is a diffusion‑based text image super‑resolution method that eliminates iterative image‑text diffusion by using a one‑step latent adaptation framework. It employs a confidence‑weighted text conditioning module to build a reliable semantic condition and a lightweight latent residual correction module to fix structured residual errors, thereby preserving text fidelity. Experiments show TOLA outperforms existing diffusion‑based TSR methods, achieving at least 2.72 dB higher PSNR on the CTR‑TSR‑Test benchmark.
By Yike Xu, Yue Shi, Yong Guo, Jiezhang Cao
CrossScale-GLIO is a multimodal framework that aligns magnetic resonance imaging (MRI) and whole‑slide histopathology of diffuse glioma by representing MRI as a tumor‑habitat graph and histology as a cell‑niche graph. Using a structure‑aware optimal transport objective anchored by diagnostic language, the method achieved high predictive performance on glioma subtyping and molecular markers, with a paired‑test subtype macro‑F1 of 0.789 and AUROCs ranging from 0.802 to 0.934. Pathologists found 81.2% of high‑mass habitat‑niche pairs biologically plausible, and experiments showed that preserving relational topology is essential for accurate cross‑scale correspondence.
By Yantong Liu, Zheyu Zhang, Runpeng Liu, Mu Xitang, Seong-Yoon Shin, Hyun-Ae Lee
The paper presents a rapid pipeline for training and deploying machine‑learning models on the WeBe Band, a wrist‑worn wearable device. It automates the creation of hardware‑efficient models, integrates with the Piccolo AI ecosystem, and supports OTA deployment while profiling latency and memory usage. Experimental results show trade‑offs between classical models and lightweight neural networks for real‑time performance on a microcontroller.
By Ehsan Kourkchi, Asmita Asmita, Houman Homayoun, Mahdi Eslamimehr
The paper introduces PixelJev, a native-image decision interface that combines an image, a task instruction, and a runtime candidate set into a structured choice and candidate-conditioned probabilities using small open multimodal models. It unifies recognition and multiple-choice visual question answering via a language-model readout, offering options for frozen inference, language-side adaptation, and held-out calibration. Across seven benchmarks, 64-shot source adaptation significantly boosts Pets accuracy from 60.13% to 92.40%, and the system supports both VQA tasks with frozen inference, while also highlighting areas for improvement such as schema robustness and cross-family transfer.
By Xunlan Zhou, Xianliang Yang, Li Zhao
The paper investigates whether reasoning always benefits universal multimodal embeddings (UMEs). By comparing the discriminative and reasoning-driven branches of UME-R1, the authors find that while reasoning improves positive similarity in 56.6% of cases, it also creates 15.7% false-helpful instances where hard negatives are drawn closer. Diagnostic analyses reveal that reasoning often de‑condenses retrieved neighborhoods and that chain‑of‑thought tokens encode evidence common to both positives and hard negatives. Based on these insights, the authors introduce SURE, a utility router that boosts UME-R1‑7B by 1.5 points and consistently improves other embedding models on MMEB‑V2 without retraining or extra VLM passes.
By Wenxiao Fan, Jingling Fu, Luohang Liu, Xinyuan Shan, Lichen Ma, Yu He, Junshi Huang, Yan Li, Kan Li
Spot, Separate, and Enhance (SSE) is a multimodal, user‑guided generative model for audio remixing and enhancement. It rebalances audio, removes unwanted sources, and reduces reverberation using video and textual guidance. The authors introduce the DegradedMix dataset and adopt generative evaluation metrics, showing SSE outperforms existing baselines in controllability and remixing quality.
By Ilpo Viertola, Giulio Cengarle, Gouthaman KV, Daniel Arteaga, Lie Lu
The paper shows that chain‑of‑thought (CoT) instructions can distort evaluation of vision‑language models (VLMs) when a scorer reads answer‑label logits before the model generates a rationale. On ScienceQA, Qwen2.5‑VL‑7B’s accuracy falls from 80.76% to 45.48% under this CoT‑prefix scoring, and most predictions incorrectly pick the first option. Linear probes and free generation recover most of the lost accuracy, indicating that the answer information remains in the hidden states but is missed by the early readout. The authors explain the mismatch with vocabulary and layer diagnostics, noting that probability mass shifts toward continuation tokens while answer information stays linearly accessible in later layers. The effect varies across datasets and models, but the study demonstrates that CoT‑prefix scoring can misrepresent model knowledge unless the requested and scored outputs are aligned.
By Zeyan Li, Siyuan Qiu, Jianfeng Xu
This study evaluates automatic speech recognition (ASR) for adolescent health communication in Twi, Dagbani, and Ewe by benchmarking five ASR systems on a Bible corpus and a domain-specific ASRH dataset, then performing supervised domain adaptation with a fine‑tuned Qwen3-ASR-0.6B model. Fine‑tuning significantly lowered word and character error rates, especially for Ewe, and the adapted model was deployed in the KasaHealth voice‑first application, which received high user approval and highlighted remaining domain gaps. The work demonstrates that in‑domain data, rather than model size or computational resources, is the primary limitation for effective ASR in these languages.
By Stephen E. Moore, Akwasi Asare, Mich-Seth Owusu, Paul Azunre, Joel Budu, Lawrence A. Adu-Gyamfi
SMILESGNN is a multimodal architecture that fuses a SMILES Transformer encoder with a GATv2 graph encoder through cross‑attention, enabling interpretable clinical toxicity predictions. The model retains an explicit graph branch, allowing GNNExplainer to identify substructures linked to toxicity. On the ClinTox dataset it achieves an AUC‑ROC of 0.987 and F1 of 0.906 with only 0.4 M parameters, while on Tox21 it attains a mean AUC‑ROC of 0.750, comparable to strong single‑modality baselines.
By Quang Minh Nguyen, Thuy Quynh Nguyen, Duc Minh Le, Ho Nhat Minh Nguyen, Thanh Long Dai Doan, Trong Nghia Nguyen
The paper introduces Accent Analogy Guidance (AAG), a training‑free sampling technique that removes accent influence from synthetic voices in cross‑lingual zero‑shot text‑to‑speech. By subtracting an accent direction derived from the model’s own predictions, AAG improves speaker similarity while maintaining the same accent level. Experiments on four open TTS models show that AAG consistently outperforms reweighting methods, achieving higher speaker similarity scores across multiple test sets.
By Yoomee Cho, Jisun Lee
The paper introduces SVGLM, a framework that integrates scalable vector graphics (SVG) primitives into vision‑language models to enable image generation within reasoning tasks. By treating SVG both as image descriptions and text instructions, SVGLM offers a compact and interpretable method for connecting text and image reasoning. The authors provide a curated SVG‑based image editing dataset and demonstrate strong SVG generation and image‑aware reasoning performance on a mathematical benchmark.
By Sunli Chen, Ding Zhong, Ziqiao Ma, Jiaxin Liu, Zeyuan Yang, Hao Zhang, Lie Lu, Joyce Chai, Chuang Gan
CrossSafe proposes embodiment-conditioned safety filtering that uses a Hamilton‑Jacobi reachability value function shared across robots while conditioning on each robot’s morphology and kinematics via a morphology‑aware latent representation. The method performs reachability analysis directly in latent space, enabling a single policy trained on multiple bimanual robot embodiments and manipulation tasks to generalize zero‑shot to a held‑out embodiment and reduce collision rates. Experiments on five embodiments and five tasks demonstrate that training with more embodiments improves generalization.
By Ihab Tabbara, Yuxuan Yang, Hussein Sibai
The paper introduces a multimodal dataset for survival prediction in resected pancreatic ductal adenocarcinoma, comprising 302 patients, 446 H&E whole-slide images, clinicopathological variables, targeted sequencing data for 154 patients, and overall survival outcomes. The authors evaluated fourteen survival‑prediction models, finding that a Ridge Cox regression on numeric clinicopathological variables achieved the highest concordance (≈0.65), while multimodal fusion of image and molecular data reached 0.619. These benchmarks provide a foundation for future research and external validation using this pancreas‑specific dataset.
By Anh-Tien Nguyen, Mawuko Tettey, Jacqueline Michelle Metsch, Teresa Zimmer, Niklas Ullrich, Mario Duker, Sandra Rungeling, Kirsten Reuter-Jessen, Tessa Rosenthal, Lena-Christin Conradi, Michael Ghadimi, Alexander Konig, Elisabeth Hessmann, Volker Ellenrieder, Philipp Strobel, Hanibal Bohnenberger, Anne-Christin Hauschild