The paper introduces a reproducible framework that converts existing text-based tool‑calling benchmarks into audio‑based evaluations using text‑to‑speech, speaker variation, and environmental noise, while preserving original annotations. Experiments on seven omni‑modal models show that performance varies strongly by model and task, with notable gaps between text and voice results. The study also includes a stress test, a reference‑free LLM‑as‑judge protocol, and demonstrates that open‑source judges can match proprietary ones, aiding privacy‑preserving evaluation.
By Md Tahmid Rahman Laskar, Xue-Yong Fu, Seyyed Saeed Sarfjoo, Quinten McNamara, Jonas Robertson, Shashi Bhushan TN
The paper introduces Expandable Modality Experts, a medical vision‑language framework that uses an expandable bank of modality experts and a Multiway Transformer to combine shared visual computation with deterministic routing and a separate text pathway. It employs a modality‑aware visual tokenizer for masked pretraining on 19.4 million unpaired image rows across seven modalities, followed by contrastive alignment on 3.23 million image‑text pairs. With a ViT‑Large backbone, the method achieves state‑of‑the‑art results in 24 of 25 benchmark settings across nine modalities, including zero‑shot classification, linear probing, and retrieval tasks, while preserving existing representations.
By DM BAPPY, Eunsu Kim, Hanjoo Lee, Sung Hak Lee, Sangjeong Ahn
Vision foundation models such as DINOv2 enable strong few‑shot anomaly detection (FSAD) using a frozen backbone and a non‑parametric k‑nearest‑neighbor (k‑NN) scorer. The paper introduces a white‑box attack framework for k‑NN memory‑bank detectors and proposes an adversarially robust FSAD method that is training‑free with respect to the backbone. The defense combines a PatchShift input augmentation and a FeaturePurifier residual MLP, achieving significant gains in image‑level AUROC (~27%) and pixel‑level PRO (~51%) on MVTec‑AD, VisA, and MPDD while maintaining clean accuracy within 3%.
whyItMatters:"The work addresses the previously unstudied few‑shot regime for robust anomaly detection, providing a practical, training‑free defense that matches full‑shot adversarially‑trained baselines and improves performance under adversarial attacks."
By Akib Mohammed Khan, Bartosz Krawczyk
NV-Generate-BodyMask is a 3D latent diffusion model that generates whole-body CT segmentation masks with precise control over organ anatomy, tumor burden, and patient demographics. It operates in the compressed latent space of a frozen mask autoencoder, producing 256³ volumetric label maps conditioned on a 19‑dimensional vector via Adaptive Group Normalization and cross‑attention. Trained on 8,571 masks from 26 cohorts, the model achieves high Spearman correlations for organ size, excellent tumor recall for several lesion types, and strong demographic alignment.
By Can Zhao, Pengfei Guo, Dong Yang, Yufan He, Yucheng Tang, Ahmed Harouni, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, Daguang Xu
The paper introduces PARED, an approach that uses inverse reinforcement learning to derive an explicit, interpretable reward from expert demonstrations. By training a lightweight discriminator on response-level features, PARED can guide both inference-time reranking and on-policy reinforcement learning, improving alignment for models that have not undergone supervised fine-tuning and further refining those that have. The method also supports contextual alignment, enabling a single policy to adapt its responses to different audience preferences.
By Micha{\l} Wili\'nski, Liu Leqi, Chirag Nagpal
The paper evaluates the Attention Tracker, a white‑box detection method for indirect prompt injection (IPI) attacks, in realistic agentic settings using the AgentDojo benchmark. It finds that the method’s claimed dataset‑agnostic detection fails in agentic environments, with near‑chance performance across most model families. The authors propose AttnDA, a Fisher Linear Discriminant Analysis classifier that replaces the scalar threshold, which improves detection across multiple benchmarks but still struggles to transfer to AgentDojo, highlighting the importance of training data from the target domain.
By Amelia Kawasaki, Stefan Lee
T-DIG is a text-conditioned diversity framework that enables users to generate multiple images from a single prompt and then request variations along specific axes—such as background, style, or clothing—using natural language. The system employs an LLM policy to produce K axis-conditioned prompts or edit instructions for a frozen image generator or editor, trained with GRPO and a diversity reward. T‑DIG works with open‑weight, distilled, and proprietary models, and the authors release a new dataset of human‑written axes and real user interactions to benchmark text‑conditioned diversity.
By Simran Khanuja, Rohit Gandikota, Gaurav Parmar, Graham Neubig
NeuroCodec is a novel generative framework that uses EEG signals to guide target speaker extraction in multi‑talker environments. It operates in the latent space of a neural audio codec, modeling EEG spatial dependencies with graph convolutions and fusing EEG and speech representations via attention and Mamba‑based temporal modeling. A six‑phase training curriculum and a causal streaming version enable high‑fidelity, 44.1 kHz audio reconstruction up to 20 kHz, outperforming baselines on Cocktail Party and KUL datasets and receiving higher perceptual scores in a listening test.
By Julian Silva, Jaliya Kodithuwakku, Navindu Sirimanna, Shaveen Herath, Wageesha N. Manamperi, Joshua P. Kulasingham
The paper introduces a mechanistic interpretability framework inspired by aphasia to probe large language models (LLMs). Using classifier‑guided lesioning and continuous activation steering, the authors induce aphasia‑like breakdowns and find that language functions can be isolated to discrete structural circuits and continuous vectors, yet these functions are densely entangled with significant overlap across different aphasic profiles. While steering generalizes across downstream tasks, structural lesioning shows asymmetric constraints, highlighting the densely entangled architecture of LLMs that can be mapped through clinically‑informed interventions.
By Muxuan He, Wang Bill Zhu, Yue Chen, Elsi Kaiser, Weizhi Du, Yao Du, Samuel Nastase, Yanjun Gao
The paper introduces P2NPCBench, a benchmark that pairs 12 NP‑complete problems with their polynomial‑time counterparts, using an adversarial multi‑agent system (AgentGAN) to rewrite problem statements until standard code models fail while preserving correctness and complexity class. The benchmark yields 2,114 hardened statements with verified human solutions, and evaluation shows that all tested models perform significantly worse on the NP‑complete versions (35.4% vs. 63.8% pass@1), with most failures due to incorrect or crashing programs rather than timeouts. The hardening process also reduces the performance gap between P and NP‑complete problems, indicating that prior gaps were partly due to familiarity with textbook phrasing.
By Nurul Labib Sayeedi, Md. Faiyaz Abdullah Sayeedi, Zabir Al Nazi, Mahbub E Sobhani, Shubhashis Roy Dipta
The paper introduces an inference‑time defense for Diffusion Language Models that combines adaptive safety steering with safety‑aware remasking. It uses a gating direction to adjust steering strength during denoising and a lightweight response detector to identify unsafe trajectories early, triggering targeted remasking and regeneration under adaptive steering. Experiments on LLaDA and Dream show improved robustness against jailbreak attacks while maintaining benign generation quality.
By Yejin Lee, Ungsik Kim, Yo-Sub Han
HorizonComposer is a new instruction‑guided video editing framework that preserves traffic semantics in driving videos. It uses a hybrid dataset of real and synthetic edits, applies advantage conditioning with dense pixel‑level rewards during training, and introduces thinking frames to reduce temporal shock. Experiments show state‑of‑the‑art spatiotemporal consistency, improved content preservation, and gains in downstream road line segmentation and multi‑object detection.
By Mauricio Soroco, Yuqiu Liu, Zaid Tasneem, Francesco Pittaluga, Abhishek Aich, Wuyang Chen, Manmohan Chandraker, Ziyu Jiang
HumanCentric-Scenes-AI is a human‑curated dataset of 296 AI‑generated stimuli depicting people in four contexts: 85 CCTV‑style images, 128 occupational portraits (balanced male/female pairs across 64 jobs), 27 AI‑edited real photographs, and 56 short videos. Each stimulus is accompanied by its generation prompt and metadata, and the dataset includes analyses of gender distribution, edit‑region size versus detection difficulty, image‑quality metrics, and subject‑category coverage. The collection is designed for human‑perception studies, AI‑detection calibration, and bias analysis of generative models.
By Nima Kamali, Sana Nasiri, Negar Kamali
Melange is a synthetic visual bias benchmark designed to evaluate social biases in multi‑modal large language models (MLLMs). It extends the language‑only BBQ dataset by generating controlled visual scenarios that clearly depict target attributes, and validates images through automated VQA screening and human inspection. Using Melange, 23 open‑source MLLMs were tested in text‑only, image‑only, and image‑with‑context settings, revealing that switching from text to vision reduces fairness by an average of 0.291 and that models often choose stereotypical responses when biased answers are made.
By Razieh Rezaei, Ehsaneddin Asgary, Masoud Jalili Sabet
CausalAlign is a new framework for diffusion‑based video generation models that tackles the problem of drifting camera ego‑motion over long horizons. It first diagnoses how ego‑motion is represented and changes during denoising, revealing a rise‑and‑fall pattern. Then it strengthens ego‑motion by aligning internal representations to a 3D foundation model in a delta‑token space that captures inter‑frame changes, improving motion accuracy, geometric consistency, and temporal smoothness while preserving visual quality.
By Kai Zhu, Yintianrun Zhang, Shuwei Jiang, Ruolin Zuo, Yixin Chen, Duomin Wang, Miao Liu, Yin Li, Daquan Zhou, Yiwu Zhong
The study examines how intergroup identity—specifically shared gender or age—affects commenters’ social judgments and empathy in the r/AmITheAsshole subreddit. Analyzing 126,716 comments on 36,163 posts, the authors find that ingroup commenters are more likely to give a ‘Not the Asshole’ verdict and display greater text-based understanding, as measured by large language models. The research also notes age differences among commenters and shows that the ingroup effect is moderated by the relationship between the poster and another party mentioned in the conflict.
By Yu Hou, Hal Daum\'e III, Rachel Rudinger
The paper introduces PGL-Net, a lightweight physics-inspired network for real‑world single image dehazing. It decouples the task into global distribution rectification and local structural refinement, using a Physics‑Inspired Affine Fusion module for global alignment and a Degradation‑Aware Modulation block for adaptive detail restoration. Experiments show that PGL‑Net, especially its Tiny variant, outperforms recent state‑of‑the‑art methods in PSNR and downstream object detection while reducing inference latency by over tenfold.
By Yifei Qu, Ru Li, Junjie Chen, Jinyuan Wu
The paper introduces ALPS, a pretrained visual state encoder that integrates physical-state information, native language alignment, and pixel reconstruction into a single representation. Trained from scratch on still images and text, ALPS uses language, reconstruction, and spatial correspondence objectives, followed by cross-view self‑distillation. In evaluations across fifteen encoders, ALPS achieves the lowest aggregate LIBERO state‑probe error among ViT‑B models, while also delivering strong language alignment, accurate pixel reconstruction, and high policy success in downstream world‑model tasks.
By Yanqing Liu, Letian Zhang, Chengzhi Liu, Zijun Wang, Jieru Mei, Hongliang Fei, Yuyin Zhou, Cihang Xie
arXiv:2610. 28878v1 Announce Type: new Abstract: We show that every class of distributions that is learnable up to arbitrarily small target error in total variation distance with finitely many samples is also learnable under ($\varepsilon, \delta$)-differential privacy up to arbitrarily small target error with finitely many samples.
By Mohammad Afzali, Hassan Ashtiani, Alex Bie, Gautam Kamath, Peihan Liu, Argyris Mouzakis, Vikrant Singhal
The paper investigates how causal modeling can improve the robustness of world models to physical parameter shifts. By freezing a dependency mask derived from paired interventions, the authors reduce out‑of‑distribution RMSE on a synthetic benchmark from 1.769 to 0.697, nearly matching an oracle mask. In a robotic link‑mass extrapolation task, incorporating explicit affine coefficients for mass effects and using a known PD controller yields a consistent predictive gain, raising median joint R² from 0.609 to 0.727 across all runs, while a learned‑torque model competes with the best recurrent world model when out‑of‑range masses are supplied.
By Chu Lin, Yi Sun