The paper introduces Model-to-Data (M2D) distillation, a method that transfers properties learned by a complex graph neural network (GNN) teacher into the graph data itself. By jointly learning augmented node features and graph structure, M2D encodes the teacher’s behavior, allowing simpler GNNs to recover high predictive performance, fairness, and robustness. The resulting graph can be reused with various downstream models, enabling them to approximate sophisticated teachers such as fairness-aware GNNs, Graph Attention Networks, and Graph Transformers.
By Debolina Halder Lina, Arlei Silva
GPEC is a lightweight pre‑LLM Gaussian Process Embedding Correction that refines visual representations for cardiac ultrasound caption generation. By inserting a residual correction layer between the visual projection and the language model, it aligns projected embeddings with annotation‑guided targets derived from structured video annotations. Experiments show that GPEC improves caption similarity and content alignment while adding less than 0.05 s of inference overhead, all without fine‑tuning the pretrained multimodal backbone.
By Arefeh Rezaei
The paper introduces a prototype‑based fuzzy‑rule framework that interprets patch‑level features from pretrained foundation models without fine‑tuning. By clustering class‑specific prototypes in the feature space, the method translates latent representations into human‑readable IF‑THEN rules, achieving accuracy comparable to black‑box classifiers on gastrointestinal imaging tasks. The extracted rules are also used to analyze synthetic medical images, revealing where generators diverge from real tissue.
By Michael D. Vasilakakis (Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece), Dimitris K. Iakovidis (Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece)
The paper introduces RateAR, a benchmark of AR images and videos that captures perceptual factors such as object placement, scale, and shadow consistency. Eleven commercial vision‑language models (VLMs) are evaluated on this benchmark, with VLM‑based quality predictions showing strong correlation with human judgments (Spearman’s ρ up to 0.8695). Using contextual prompting, the authors build an automated AR content adjustment system that, in a 21‑participant study, improved placement and size coherence of virtual content for over 90% of users.
By Elias Rotondo (Duke University), Lin Duan (Duke University), Yanming Xiu (Duke University), Sangjun Eom (Duke University), Conrad Li (Duke University), Maria Gorlatova (Duke University)
The paper introduces Align Then Reason (ATR), a multilingual lip‑sync judge that aligns frame‑level lip representations with phonetic units of a candidate text line and then uses a language model to evaluate both content and timing. ATR achieves significant improvements over existing baselines on a seven‑language benchmark, with mean AUC gains of up to 59.4% for 2B reasoners and similar gains across other LLM families. The method also transfers well to unseen languages and outperforms lip‑reading baselines on real dubbing tasks such as dub‑line reranking and script‑to‑clip assignment.
By Rui Liu, Bhavin Jawade, Haoqi Li, Shivam Mehta, Karan Saxena, Yinghong Lan, Cameron R. Wolfe
The paper introduces HierGF, a method for sparse‑view 3D reconstruction that uses a hierarchical geometry‑perception backbone to generate structured pseudo‑supervision from limited views. It incorporates a learnable confidence network to focus gradients on cross‑view consistent content and a geometrically consistent densification module to improve alignment and fill under‑sampled regions. This approach aims to overcome the challenges of few images and incomplete coverage that typically lead to overfitting and structural holes in existing methods.
By Bi'an Du, Zhimin Zhang, Daizong Liu, Baoquan Chen, Wei Hu
Ego2Act is a new benchmark that tests video generation models on goal‑directed, egocentric manipulation tasks. It contains 2,640 videos from 110 real‑world tasks, each requiring multiple steps of object manipulation to achieve a high‑level goal. The benchmark includes Ego2ActJudge, a reference‑free evaluation pipeline that better aligns with human judgments of task completion and physics plausibility.
By Patrick Amadeus Irawan, Iskandar Muda Rizky Parlambang, Rava Maulana, Qinrong Cui, Erland Hilman Fuadi, Zayd M. K. Zuhri, Nanda Ryaas Absar, Ahmed Elshabrawy, Wilfried Ariel Mulyawan, Shoubin Yu, Yue Zhang, Mohit Bansal, Alham Fikri Aji
FedCKA introduces a Centered Kernel Alignment (CKA)-based method for federated 3D perception that dynamically balances personalization and globalization. By computing layer-wise feature similarities between local client models and a global consensus model, FedCKA generates client‑specific aggregation masks to selectively share representation‑consistent layers. Experiments on a unified multi‑domain nuScenes benchmark demonstrate that FedCKA surpasses established federated baselines, improving average NDS by 7 percentage points.
By Jolle Verhoog, Ali Burak \"Unal, Holger Caesar
PAGER is a label‑free adaptation method that aligns partial, viewpoint‑dependent 3D observations with a frozen global semantic space. It uses matched‑point feature alignment and relational supervision to anchor partial features to their global counterparts while preserving similarity structure, all without altering the pretrained encoder or global probe. Experiments show PAGER outperforms label‑supervised PEFT on Sonata and Concerto, and achieves superior zero‑shot transfer from ScanNet to ScanNet++ compared to fully fine‑tuned Sonata.
By Akira-Miranda Adeyomi Adeniran-Lowe, Binod Singh, Lars Arnold Dethlefsen, Lazaros Nalpantidis, Theodora Kontogianni
The paper introduces Anti-Persona, an image-level defense that prevents unauthorized identity binding and recognition in personalized vision–language models. By aggregating visual features from multiple reference images into an identity prototype and applying subtle perturbations, the method disrupts prototype alignment in the vision‑encoder space while maintaining visual fidelity. Experiments on two personalized LVLMs show protection rates up to 95.0% and robustness across prompt variations and encoder mismatches.
By Abhishek Basu, Fahad Shamshad, Karthik Nandakumar
UnifiedAttack introduces a benchmark for testing the safety of large multimodal models (LMMs) in tasks that combine text and image to produce harmful content. The benchmark focuses on the additional harm that arises from cross‑modal synergy and includes filtered multimodal samples and synthesized disinformation queries. A synergistic hijacking framework—comprising In‑Context Reskinning (ICR) and Cognitive Planning Injection (CPI)—is proposed to expose vulnerabilities, and extensive evaluations show that UnifiedAttack consistently bypasses current alignment defenses in state‑of‑the‑art architectures.
By Bingjun Luo, Jialin Guo, Tony Wang, Siqi Li
The paper introduces Adaptive Tversky Policy Optimization (ATPO), a reinforcement learning framework for multi‑label video safety detection. ATPO uses an Adaptive Tversky Reward (ATR) that dynamically adjusts false‑positive and false‑negative penalties, allowing controllable precision‑recall trade‑offs. Experiments on SafeWatch‑Bench and XD‑Violence demonstrate significant performance gains, raising the Jaccard Index from 40.66 to 75.44 on SafeWatch‑Bench‑Real.
By Guangyu Yang, Jingbiao Mei, Mingsheng Sun, Jinghong Chen, Yingtong Bu, Pengda Qin, Da Chen, Bill Byrne
SR‑Ground is a large‑scale dataset created to enable fine‑grained segmentation of visual artifacts in super‑resolved images. It contains 63,000 images processed by various state‑of‑the‑art SR models, each annotated at the pixel level for six distinct artifact types, validated through a crowdsourcing study with 1,062 participants. The dataset improves the training of image quality assessment models with grounding capabilities and supports a fine‑tuning pipeline that reduces perceptible artifacts in SR outputs, outperforming no‑reference methods on both benchmark and real‑world low‑resolution datasets.
By Artem Borisov, Evgeney Bogatyrev, Khaled Abud, Dmitriy Vatolin
SlotVLA proposes a slot-attention framework that models both objects and their relations for robotic manipulation, using a relation‑centric decoder and an LLM‑driven action translator. The authors introduce LIBERO+, a fine‑grained benchmark with object‑centric annotations, box and mask labels, and instance‑level temporal tracking to evaluate object‑relation reasoning. Experiments on LIBERO+ show that object‑centric and object‑relation slot representations reduce visual token counts while maintaining competitive generalization, offering a compact and interpretable foundation for multitask visuomotor control.
By Taisei Hanyu, Nhat Chung, Huy Le, Toan Nguyen, Yuki Ikebe, Anthony Gunderman, Duy Nguyen Ho Minh, Khoa Vo, Tung Kieu, Kashu Yamazaki, Chase Rainwater, Anh Nguyen, Ngan Le
DynamicVLA is a latency‑aware Vision‑Language‑Action model designed for dynamic object manipulation, featuring a compact 0.4B architecture and a convolutional vision encoder for efficient multimodal inference. It employs a continuous inference schedule that overlaps reasoning and execution, and a Latent‑aware Action Streaming mechanism that discards stale action prefixes to maintain action‑time alignment. The authors also introduce the Dynamic Object Manipulation (DOM) benchmark, comprising 200K synthetic episodes and 2K real‑world episodes, and demonstrate that DynamicVLA improves dynamic manipulation success in simulation and on real robots.
By Haozhe Xie, Beichen Wen, Jiarui Zheng, Zhaoxi Chen, Fangzhou Hong, Haiwen Diao, Ziwei Liu
The paper introduces Tail‑Corrected Top‑k On‑Policy Distillation (TT‑OPD), a method that improves on existing Top‑k OPD by combining the selected top‑k tokens with a sampled token from the student’s rollout. This hybrid approach recovers the probability mass discarded by limiting to top‑k, yielding an unbiased estimator of the reverse KL divergence gradient while maintaining low computational cost. Experiments show TT‑OPD outperforms other OPD variants in accuracy.
By Linjian Meng, Siyuan Gan, YuHan Li, Xiran Wang, Ziyang Ding, Ditang Gou, Yiming Wu, Zhen Zhao
The article discusses how the rapid development of artificial intelligence (AI) and machine learning (ML) is transforming chemical engineering by influencing problem formulation, analysis, and solution across a wide range of applications, from atomic-scale simulations to industrial operations. It highlights recent methodological advances and representative uses, noting a shift from purely black-box models to hybrid and physics-informed frameworks that incorporate conservation laws, thermodynamic consistency, and structural constraints. These integrated approaches enhance robustness, reliability, and human-AI collaboration, ultimately amplifying rather than replacing core chemical engineering principles.
By Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou, Akhilesh Jain, Ankur Kumar, Thomas A. Kwan, F\`elix Llovell, Andrew J. Medford, Ilias Mitrai, Joel Paulson, Junyi Qiao, Matthew P. Rivera, Kirti C. Sahu, Lev Sarkisov, Zachary P. Smith, Calvin Tsay, Ching-Mei Wen, Victor M. Zavala, Huacheng Zhang, Dan Zhao
MOMAT (Mixture of Multiple Atlases) is a hardware‑enhanced safety framework designed to defend quantized large language models (qLLMs) on edge devices against jailbreak attacks. It uses a collection of semantic atlases—each containing harmful or benign sample clusters and policy templates—to perform domain‑localized Retrieval‑Augmented Generation. A lightweight Mixture of Experts detector evaluates top‑k similarity features retrieved by a Compute‑in‑Memory (CiM) accelerated engine, achieving a 4.69 × 10⁶‑fold speedup and a 2.5 × 10⁵‑fold energy reduction compared to DRAM‑based baselines while matching state‑of‑the‑art defense performance.
By Boyang Li, Bingyu Shen, Weihao Hong, Zhiyuan Jiang, Xinlei Guan, Yan Ma, Miles Q. Li, Yi Sheng, Ruiyang Qin
TensorCommitments (TCs) is a lightweight, tensor-native proof‑of‑inference scheme that enables verifiable inference for large language models (LLMs) without requiring the verifier to rerun the model or possess a powerful GPU. By binding each inference to a commitment stored in multivariate Terkle Trees, TCs detect tampering with only a 0.97% overhead for the prover and 0.12% for the verifier on LLaMA2. The approach improves robustness against tailored LLM attacks by up to 48% compared to previous methods that needed a verifier GPU.
By Oguzhan Baser, Elahe Sadeghi, Eric Wang, Nico Vergauwen, Sam Kazemian, Hong Kang, Sandeep P. Chinchali, Sriram Vishwanath
The paper introduces a formal framework for measuring AI propensities—tendencies of models to exhibit particular behaviours—using a bilogistic formulation that identifies an "ideal band" of success probability. It estimates the limits of this band with task‑agnostic rubrics and applies the method to six families of LLMs, showing how shifts in propensity affect task performance. The study finds that propensity estimates from one benchmark predict behaviour on held‑out tasks and that combining propensity with capability metrics yields stronger predictive power than either alone.
By Daniel Romero-Alvarado, Fernando Mart\'inez-Plumed, Lorenzo Pacchiardi, Hugo Save, Siddhesh Milind Pawar, Behzad Mehrbakhsh, Pablo Antonio Moreno Casares, Ben Slater, Paolo Bova, Peter Romero, Zachary R. Tidler, Jonathan Prunty, Luning Sun, Jose Hernandez-Orallo