The paper investigates how different stakeholders—end‑users, AI builders, and policymakers—define and experience transparency in generative AI. It finds that while all groups agree on core goals of accuracy and privacy, end‑users perceive larger gaps between desired and available transparency features, with only 28% feeling well served compared to 53% of builders and 49% of policymakers. The study also shows that transparency is context‑dependent, shifting priorities based on task and role, and calls for pluralistic transparency infrastructures rather than a one‑size‑fits‑all approach.
By Genevieve Smith, Sahiba Chopra, Natalia Luka, Min Kyung Lee
MA‑SafeDiffuser is a multi‑agent extension of SafeDiffuser that incorporates diffusion‑based trajectory planners with finite‑time diffusion invariance guarantees for joint‑agent safety specifications. It formalizes joint safe sets as intersections of per‑agent and pairwise barrier sets, and derives both centralized and decentralized constrained denoising procedures that maintain invariance under mild assumptions. The approach also tackles local‑trap and deadlock issues using time‑varying specifications and liveness CBFs, and introduces a lightweight benchmarking suite including a multi‑agent Maze2D domain, showing reduced violation counts compared to unconstrained diffusion baselines while preserving planning quality.
By Kiran Ravish, Ankita Kushwaha, Preeti, Pawan Kumar
The paper investigates confirmation bias in Agent-as-a-Judge systems, which evaluate open-ended agent tasks by gathering evidence from the agent’s trajectory. It demonstrates that a single false pre‑screening note can overturn up to 40% of correct verdicts across multiple judges and datasets, even when the judge collects decisive evidence. The authors classify the resulting errors into over‑trust, hallucination, and under‑investigation, noting that over‑trust dominates in larger Claude judges, and show that lightweight mitigations reduce but do not eliminate the bias.
By Drake Benjamin Brown, Khushbu Pahwa, Yubin Ge, Salvatore Romeo, Akhil Agnihotri, Yi Zhang, Xiaofei Ma, Daniele Bonadiman
The paper investigates how vision‑language models (VLMs) perceive uncertainty in visualizations and make decisions based on visual data. Two studies were conducted: one on agents’ ability to quantify uncertainty in charts, and another on their decision‑making when presented with images. Results show GPT‑4o best perceives uncertainty in pie and bar charts, while agents align most closely with rational decision theory when uncertainty is shown via circle charts, icon arrays, or text.
By Keyty Loza, Shayan Monadjemi
The paper presents a self‑supervised pretraining approach for facial expression recognition (FER) using optomyographic (OMG) smart glasses like OCOsense. By pretraining a Vision Transformer with a masked‑autoencoder on unlabelled recordings and fine‑tuning for a five‑class FER task, the authors demonstrate that the pretrained model outperforms a from‑scratch model even when only 5% of the labels are used. The study shows that SSL can substantially reduce labeling costs for smart eyewear FER and suggests benefits for future personalization and continual adaptation.
By Zhengtao Gao, Francesco Bombassei De Bona, Leonardo Alchieri, Ivana Kiprijanovska, Simon Stankoski, Bojan Sofronievski, Hristijan Gjoreski, Marc Langheinrich, Martin Gjoreski
UniCompress is a deployment-oriented framework that transforms multi-task vision‑language expertise into a single compact, adapter‑free model suitable for edge intelligence. Starting from a BF16 Qwen3‑VL‑4B teacher, it builds an asymmetric INT2 student using GPTQ initialization and teacher‑guided quantization recovery, jointly compressing both the vision encoder and language backbone. Evaluated on four embodied tasks, UniCompress cuts weight storage from 8.9 GB to 2.1 GB (≈76% reduction) while improving the INT2 macro task score by 34.3 percentage points over GPTQ‑INT2 and staying within 0.9 points of the BF16 teacher.
By Mingkang Liu, Peipei Jiao, Weiming Meng, Tianyuan Liu, Lichen Pang, Junyan Li
The paper introduces a new framework for multi-resident data association in smart homes, framing the problem as a Markov Decision Process and solving it with Behavior Cloning. Two policy architectures are proposed: a lightweight Transformer treating association as multi-label classification over event histories, and a fine-tuned Large Language Model that generates conditional text to leverage semantic knowledge. Experiments on three datasets show the approach outperforms existing baselines, especially for concurrent multi-resident events, and the LLM policy hints at cross-environment transfer potential.
By Xi Chen (M-PSI), Julien Cumin (LIG, M-PSI), Fano Ramparany (LIG, M-PSI), Dominique Vaufreydaz (LIG, M-PSI)
The paper introduces a method called Addition by Subtraction to strengthen safety alignment in large language models. By gradually removing identified safety‑critical neurons during finetuning and re‑optimizing preferences, the authors reduce the model’s reliance on sparse components that can be targeted by pruning or ablation attacks. Experiments on four models from three vendors show that this approach improves robustness to both white‑box and black‑box jailbreaks, lowers attack success rates by up to 90%, and enhances truthfulness while preserving general capabilities.
By Hamid Dashtbani, Alexandra Dmitrienko
BEAKER is a new benchmark for evaluating embodied actionability, knowledge, experimentation, and reasoning in self‑driving chemical laboratories. It contains 1,000 expert‑curated question‑answer pairs across 11 image and video task types, covering affordance grounding, trajectory planning, equipment and state recognition, safety, apparatus setup, operation, step reasoning, and temporal procedure reasoning. The benchmark is manually created and reviewed by chemistry and embodied AI experts, and it tests 28 different multimodal large language models in an offline, single‑turn setting.
By Fei Lin, Tengchao Zhang, Ziyang Gong, Xiaotong Yu, Bohan Zhang, Yifan Zhou, Qihao Yang, Ji Dai, Dong Li, Yining Jiang, Qinghua Ni, Jun Huang, Qiang Zhang, Yue Zhou, Zhihang Zhong, Yonglin Tian, Fei-Yue Wang, Xue Yang
The paper introduces behavior graphs, an unsupervised method that compresses high‑dimensional robot policy rollouts into a single directed graph. By clustering similar behavior modes, linking them temporally, and associating them with outcomes, the graphs provide an interpretable representation of deployment data. In a user study, participants preferred behavior graphs over raw videos, achieving comparable conclusions with less perceived effort, and across many datasets the graphs improved progress prediction accuracy.
By Erik Bauer, Jakob Thumm, Christopher Agia, Rohan Sinha, Chen Xu, Haruki Nishimura, Jeannette Bohg, Marco Pavone, Masha Itkina
The paper introduces Omni-Encoder, a single SigLIP-2 Vision Transformer that jointly processes image, video, audio, and audio‑visual data using fully shared parameters. It replaces separate modality encoders and anchor‑based alignment with a unified architecture that tokenizes audio, applies multimodal rotary position embeddings, and trains all modalities end‑to‑end through alternating‑modality batches. Omni‑Encoder achieves competitive performance on 16 benchmarks across all four modalities, outperforming individually trained counterparts, and the authors plan to release the code and models publicly.
By Xiaofei Sun, Peng Wang, Huayao Liu, Xiaoxiao Xu, Jun Song, Deqing Li, Lin Qu
The study reviews 377 academic papers on large language model (LLM)–based non‑player characters (NPCs) in video games, focusing on 42 key works. It quantitatively maps component adoption, noting that short‑term memory and world‑state grounding are common (60% and 69%), while long‑term memory, reflection, and retrieval‑augmented generation are less frequent. The paper also reports latency ranges (2.6–15 s) and identifies six structural barriers to commercial adoption, offering evidence‑based recommendations for developers and researchers.
By Dilan Mian, Jeremy Bradbury, Yann-Ga\"el Gu\'eh\'eneuc, Fabio Petrillo, Cristiano Politowski
The paper introduces Laplacian-Constrained Stiefel Flow Matching (LaSFM), a method for generating graphs via their Laplacian eigenvalues and eigenvectors while preserving key structural constraints such as eigenvector orthonormality, positive eigenvalues, and the Laplacian nullspace condition. LaSFM formulates flow matching on a constrained Stiefel manifold and extends to property-guided generation, steering samples toward desired graph properties without violating constraints. Experiments show that LaSFM improves both spectral and graph-level metrics and better aligns generated graphs with target properties.
By Anita Yang, Ken-ichi Kawarabayashi, Yohan Jung
Bio‑VQA is a new, large-scale visual question answering benchmark for biology, comprising 1.86 million questions over 217 k images from 11 datasets. It tests four key aspects of biological visual understanding—taxonomic reasoning, attribute grounding, spatial reasoning, and individual re‑identification—using a graded scoring system that rewards near‑misses more than distant ones. Evaluation of 18 open‑weight vision‑language models shows that general visual capabilities do not transfer well to biological tasks, with markedly lower accuracy on counting, taxonomy, trait measurement, and re‑identification compared to everyday object benchmarks.
By S M Rayeed, Qian Ma, Charles V. Stewart, Yao Ma
The study evaluates whether saliency maps—visual explanations for mammography classifiers—perform differently on dense versus non‑dense breasts. Using 1,345 malignant CBIS‑DDSM images, the authors compared Grad‑CAM, Integrated Gradients, and occlusion maps, finding that Grad‑CAM peaks correctly identified 44% of dense‑breast lesions versus 58% of non‑dense ones. After adjusting for case mix (notably more calcifications in dense breasts), the performance gap narrowed, but occlusion maps still showed a significant deficit, and classifiers overall relied less on dense‑breast lesions than on an image‑only conspicuity baseline. The authors recommend reporting density‑specific performance gaps before and after adjustment, along with controls for conspicuity and lesion reliance.
By Ahmed Jaheen
Clips2Story is a training‑free, LLM‑based pipeline that lets users generate short videos from a pool of clips by specifying a keyword. It first extracts multimodal descriptors from each clip, retrieves the most relevant ones, and then uses an LLM to select, arrange, and narrate them into an editable storyboard. The final video is assembled by combining the chosen clips with visuals that match the narration. Evaluation on a synthetic dataset of shuffled clips from 100 long‑form videos shows that Clips2Story achieves better narrative coherence, keyword alignment, and realism than a strong baseline.
By Junnuo Wang, Weihan Xu, Hao-Wen Dong
FedFiOT introduces a Fisher-weighted optimal transport framework for merging client models in one-shot and few-round federated learning. By using Fisher information to assess parameter importance, it aligns mismatched neurons and weights parameters during aggregation. The method alternates between optimizing neuron correspondences and global model parameters, and theoretical analysis shows monotonic improvement of the merging objective with a bound on heterogeneity effects. Experiments on multiple datasets confirm consistent performance gains over existing baselines.
By Quan Minh Nguyen, Hoang M. Ngo, Bhakti Chowkwale, Yasaman Keshtkarjahromi, My T. Thai
UpSafe°C is a unified framework that enhances large language model safety by upcycling safety‑critical layers into a sparse Mixture‑of‑Experts structure, with a router acting as a soft guardrail. It employs a two‑stage supervised fine‑tuning strategy to improve safety discrimination while preserving general capabilities, and introduces a safety temperature mechanism for dynamic inference‑time control of the safety‑utility trade‑off. Experiments across multiple benchmarks and model scales show robust safety improvements against harmful and jailbreak inputs while maintaining competitive general performance.
By Yuhao Sun, Zhuoer Xu, Shiwen Cui, Kun Yang, Lingyun Yu, Yongdong Zhang, Hongtao Xie
The paper introduces Integrated Policy Gradient (IPG), a training‑free framework that applies policy‑based attribution to large language model reasoning. IPG backpropagates outcome‑level, non‑differentiable signals through entire inference trajectories, using path integration to isolate outcome‑relevant mediators at the component level. Empirical results show that IPG precisely localizes sparse internal components linked to specific reasoning behaviors and enables modulation of reasoning capabilities.
By Changming Li, Kaixing Zhang, Haoyun Xu, Yingdong Shi, Zheng Zhang, Kaitao Song, Kan Ren
Certified Circuits introduces a framework that wraps any black‑box circuit discovery algorithm with randomized data subsampling to provide provable stability guarantees. By certifying that inclusion decisions over circuit components are invariant to bounded edit‑distance perturbations of the concept dataset, unstable components are abstained from, producing more compact and accurate mechanistic explanations. Across ResNet, ViT, and GPT‑2 on vision and language tasks, Certified Circuits achieves up to 56% higher accuracy and up to 80% fewer components, maintaining reliability where baseline methods degrade.
By Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz, Jonas Fischer