Publications

Communicating about Space: Language-Mediated Spatial Integration Across Partial Views
Ankur Sikarwar
Debangan Mishra
Sudarshan Nikhil
Ponnurangam Kumaraguru
Aishwarya Agrawal
Humans build shared spatial understanding by communicating partial, viewpoint-dependent observations. We ask whether Multimodal Large Langua… (voir plus)ge Models (MLLMs) can do the same, aligning distinct egocentric views through dialogue to form a coherent, allocentric mental model of a shared environment. To study this systematically, we introduce COSMIC, a benchmark for Collaborative Spatial Communication. In this setting, two static MLLM agents observe a 3D indoor environment from different viewpoints and exchange natural-language messages to solve spatial queries. COSMIC contains 899 diverse scenes and 1250 question-answer pairs spanning five tasks. We find a capability hierarchy, MLLMs are most reliable at identifying shared anchor objects across views, perform worse on relational reasoning, and largely fail at building globally consistent maps, performing near chance, even for frontier models. Moreover, we find thinking capability yields gains in anchor grounding, but is insufficient for higher-level spatial communication. To contextualize model behavior, we collect 250 human-human dialogues. Humans achieve 95% aggregate accuracy, while the best model, Gemini-3-Pro-Thinking, reaches 72%, leaving substantial room for improvement. Moreover, human conversations grow more precise as partners align on a shared spatial understanding, whereas MLLMs keep exploring without converging, suggesting limited capacity to form and sustain a robust shared mental model throughout the dialogue. Our code and data is available at https://github.com/ankursikarwar/Cosmic.
2026-03-27
arXiv (prépublication)
doi.org
arxiv.org
Additional file 1 of Impact of WHO AWaRe Antibiotic Handbook training on antibiotics prescribing knowledge among private primary care providers: a vignette-based, prep–post pilot study in Patna, India
Poshan Thapa
Prachi Shukla
Chandrashekhar Joshi
Sena Sayood
P. Sinha
Diwash Timilsina
Mili Dutta
Madhukar Pai
Samira Abbasgholizadeh Rahimi
Sumanth Gandra
Supplementary Material 1
2026-03-26
Figshare (inconnu)
doi.org
Additional file 2 of Impact of WHO AWaRe Antibiotic Handbook training on antibiotics prescribing knowledge among private primary care providers: a vignette-based, prep–post pilot study in Patna, India
Poshan Thapa
Prachi Shukla
Chandrashekhar Joshi
Sena Sayood
P. Sinha
Diwash Timilsina
Mili Dutta
Madhukar Pai
Samira Abbasgholizadeh Rahimi
Sumanth Gandra
Supplementary Material 2
2026-03-26
Figshare (inconnu)
doi.org
Impact of WHO AWaRe Antibiotic Handbook training on antibiotics prescribing knowledge among private primary care providers: a vignette-based, prep–post pilot study in Patna, India
Poshan Thapa
Prachi Shukla
Chandrashekhar Joshi
Sena Sayood
P. Sinha
Diwash Timilsina
Mili Dutta
Madhukar Pai
Samira Abbasgholizadeh Rahimi
Sumanth Gandra
Abstract Introduction Inappropriate antibiotic prescribing is a major concern in low- and middle-income countries (LMICs), particularly at t… (voir plus)he primary care level. The WHO AWaRe Antibiotic Handbook was introduced to promote rational antibiotic use, yet its real-world feasibility and potential impact remain underexplored. Our study evaluated the impact and usefulness of the WHO AWaRe Handbook training among private primary care providers (PCPs) in Patna, India. Methods We conducted a pre–post pilot study among 145 private PCPs (40 formal PCPs (FPs) and 105 informal PCPs (IPs) in Patna, India. Participants received training from an infectious disease physician on the WHO AWaRe Antibiotic Handbook. Antibiotic prescribing knowledge was assessed before and after the intervention using clinical vignettes for four conditions: acute diarrhea, urinary tract infection (UTI), cellulitis, and community-acquired pneumonia (CAP). An endline survey evaluated the perceived usefulness of the intervention. Changes in prescribing knowledge was analyzed using McNemar’s test for paired data. Results The intervention significantly reduced overall antibiotic prescribing knowledge for acute diarrhea ( p = 0.0003) and UTI ( p = 0.0113), with greater reductions among IPs. No significant changes were observed for cellulitis ( p = 0.3692) or CAP ( p = 0.7150). Watch-category antibiotic prescribing significantly decreased for acute diarrhea ( p < 0.0001), with no significant changes for other conditions. IPs showed greater improvements overall compared to FPs. The majority of PCPs (75%; n = 107) rated the training as moderately or very useful. Conclusion Training private PCPs using the WHO AWaRe Handbook improved antibiotic prescribing knowledge for some common conditions, particularly among IPs. Future research should combine training with strategies that address broader contextual barriers, alongside tailored reinforcement interventions, and longer-term follow-up.
2026-03-26
Antimicrobial Resistance & Infection Control (publié)
doi.org
MOOZY: A Patient-First Foundation Model for Computational Pathology
Yousef Kotp
Vincent Quoc-Huy Trinh
Christopher Pal
Mahdi S. Hosseini
Computational pathology needs whole-slide image (WSI) foundation models that transfer across diverse clinical tasks, yet current approaches … (voir plus)remain largely slide-centric, often depend on private data and expensive paired-report supervision, and do not explicitly model relationships among multiple slides from the same patient. We present MOOZY, a patient-first pathology foundation model in which the patient case, not the individual slide, is the core unit of representation. MOOZY explicitly models dependencies across all slides from the same patient via a case transformer during pretraining, combining multi-stage self-supervision with scaled low-cost task supervision. In Stage 1, we pretrain a vision-only slide encoder on 77,134 public slide feature grids using masked self-distillation. In Stage 2, we align these representations with clinical semantics using a case transformer and multi-task supervision over 333 tasks from 56 public datasets, including 205 classification and 128 survival tasks across four endpoints. Across sixteen held-out tasks, MOOZY improves macro weighted F1, balanced accuracy, and macro weighted ROC-AUC relative to PRISM by +4.19\%, +7.93\%, and +6.95\%, respectively. MOOZY is also parameter efficient with 85.77M parameters, 14
2026-03-26
arXiv (prépublication)
doi.org
arxiv.org
Neighbor-Aware Localized Concept Erasure in Text-to-Image Diffusion Models
Zhuan Shi
Alireza Dehghanpour Farashah
Rik de Vries
Golnoosh Farnadi
Concept erasure in text-to-image diffusion models seeks to remove undesired concepts while preserving overall generative capability. Localiz… (voir plus)ed erasure methods aim to restrict edits to the spatial region occupied by the target concept. However, we observe that suppressing a concept can unintentionally weaken semantically related neighbor concepts, reducing fidelity in fine-grained domains. We propose Neighbor-Aware Localized Concept Erasure (NLCE), a training-free framework designed to better preserve neighboring concepts while removing target concepts. It operates in three stages: (1) a spectrally-weighted embedding modulation that attenuates target concept directions while stabilizing neighbor concept representations, (2) an attention-guided spatial gate that identifies regions exhibiting residual concept activation, and (3) a spatially-gated hard erasure that eliminates remaining traces only where necessary. This neighbor-aware pipeline enables localized concept removal while maintaining the surrounding concept neighborhood structure. Experiments on fine-grained datasets (Oxford Flowers, Stanford Dogs) show that our method effectively removes target concepts while better preserving closely related categories. Additional results on celebrity identity, explicit content and artistic style demonstrate robustness and generalization to broader erasure scenarios.
2026-03-26
arXiv (prépublication)
doi.org
arxiv.org
The Cluster Evolutionary Reference Ensemble at Low- <i>z</i> (CEREAL) Sample of Galaxy Clusters. I. X-Ray Morphological Properties and Demographics
L. V. White
Michael McDonald
S. J. Allen
Marshall W. Bautz
Michael S. Calzadilla
G. P. Garmire
Julie Hlavacek-Larrondo
Ralph Kraft
Adam B. Mantz
Taweewat Somboonpanyakul
Alexey Vikhlinin
Abstract With rapid improvements in the assembly of large samples of galaxy clusters, we are approaching the ability to study clusters at z … (voir plus)≳ 2. Evolutionary studies comparing these distant clusters to the clusters in our local Universe depend heavily on the reliability of low-redshift cluster samples, most of which are subject to X-ray selection effects, biasing them to relaxed, cool-core clusters. Here, we introduce the Cluster Evolutionary Reference Ensemble at Low- z (CEREAL) sample, composed of Chandra X-ray observations of 169 galaxy clusters that have been selected from the Planck Sunyaev–Zel’dovich catalog. CEREAL has a simple and well-understood selection function, spans an order of magnitude in mass at z ∼ 0.15, and has uniform, high-resolution X-ray follow-up. We present the full sample and provide results based on X-ray surface brightness properties, finding significantly more non-cool-core systems than in X-ray-selected samples. We use surface brightness concentration ( c SB ) as a proxy for cool-core strength and centroid shift ( w ) to measure dynamical state. Over the full sample, we find a cool-core ( c SB > 0.075) fraction of 0.3 9 − 0.04 + 0.04 , a strong cool-core ( c SB > 0.155) fraction of 0.1 3 − 0.03 + 0.03 , and a dynamically relaxed ( w < 0.01) frac
2026-03-26
Astrophysical Journal (publié)
doi.org
arxiv.org
1113 GPR124 Alleviates Blood-Brain Barrier Disruption Through Improving the Function of Microvascular Endothelial After Traumatic Brain Injury
Chen Wang
Jun Ding
Hengli Tian
Hao Chen
Xinyu Niu
2026-03-25
Neurosurgery (publié)
doi.org
Adversarial-Robust Multivariate Time-Series Anomaly Detection via Joint Information Retention
Hadi Hojjati
Narges Armanfard
Time-series anomaly detection (TSAD) is a critical component in monitoring complex systems, yet modern deep learning-based detectors are oft… (voir plus)en highly sensitive to localized input corruptions and structured noise. We propose ARTA (Adversarially Robust multivariate Time-series Anomaly detection via joint information retention), a joint training framework that improves detector robustness through a principled min-max optimization objective. ARTA comprises an anomaly detector and a sparsity-constrained mask generator that are trained simultaneously. The generator identifies minimal, task-relevant temporal perturbations that maximally increase the detector's anomaly score, while the detector is optimized to remain stable under these structured perturbations. The resulting masks characterize the detector's sensitivity to adversarial temporal corruptions and can serve as explanatory signals for the detector's decisions. This adversarial training strategy exposes brittle decision pathways and encourages the detector to rely on distributed and stable temporal patterns rather than spurious localized artifacts. We conduct extensive experiments on the TSB-AD benchmark, demonstrating that ARTA consistently improves anomaly detection performance across diverse datasets and exhibits significantly more graceful degradation under increasing noise levels compared to state-of-the-art baselines.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned
Maeva Guerrier
Karthik Soma
Jana Pavlasek
Giovanni Beltrame
Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations. Despite growing … (voir plus)real-world deployment, existing evaluations rely almost exclusively on success rate, whether the robot reaches its goal, which conceals trajectory quality, collision behavior, and robustness to environmental change. We present a real-world evaluation of five state-of-the-art VNMs (GNM, ViNT, NoMaD, NaviBridger, and CrossFormer) across two robot platforms and five environments spanning indoor and outdoor settings. Beyond success rate, we combine path-based metrics with vision-based goal-recognition scores and assess robustness through controlled image perturbations (motion blur, sunflare). Our analysis uncovers three systematic limitations: (a) even architecturally sophisticated diffusion and transformer-based models exhibit frequent collisions, indicating limited geometric understanding; (b) models fail to discriminate between different locations that are perceptually similar, however some semantics differences are present, causing goal prediction errors in repetitive environments; and (c) performance degrades under distribution shift. We will publicly release our evaluation codebase and dataset to facilitate reproducible benchmarking of VNMs.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
Collision-Aware Vision-Language Learning for End-to-End Driving with Multimodal Infraction Datasets
Alex Koran
Dimitrios Sinodinos
Hadi Hojjati
Takuya Nanri
Fangge Chen
Narges Armanfard
High infraction rates remain the primary bottleneck for end-to-end (E2E) autonomous driving, as evidenced by the low driving scores on the C… (voir plus)ARLA Leaderboard. Despite collision-related infractions being the dominant failure mode in closed-loop evaluations, collision-aware representation learning has received limited attention. To address this gap, we first develop a Video-Language-Augmented Anomaly Detector (VLAAD), leveraging a Multiple Instance Learning (MIL) formulation to obtain stable, temporally localized collision signals for proactive prediction. To transition these capabilities into closed-loop simulations, we must overcome the limitations of existing simulator datasets, which lack multimodality and are frequently restricted to simple intersection scenarios. Therefore, we introduce CARLA-Collide, a large-scale multimodal dataset capturing realistic collision events across highly diverse road networks. Trained on this diverse simulator data, VLAAD serves as a collision-aware plug-in module that can be seamlessly integrated into existing E2E driving models. By integrating our module into a pretrained TransFuser++ agent, we demonstrate a 14.12% relative increase in driving score with minimal fine-tuning. Beyond closed-loop evaluation, we further assess the generalization capability of VLAAD in an open-loop setting using real-world driving data. To support this analysis, we introduce Real-Collide, a multimodal dataset of diverse dashcam videos paired with semantically rich annotations for collision detection and prediction. On this benchmark, despite containing only 0.6B parameters, VLAAD outperforms a multi-billion-parameter vision-language model, achieving a 23.3% improvement in AUC.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
A Compression Perspective on Simplicity Bias
Tom Marty
Eric Elmoznino
Leo Gagnon
Tejas Kasetty
Mizu Nishikawa-Toomey
Sarthak Mittal
Guillaume Lajoie
Dhanya Sridhar
Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cas… (voir plus)t new light on this phenomenon through the lens of the Minimum Description Length principle, formalizing supervised learning as a problem of optimal two-part lossless compression. Our theory explains how simplicity bias governs feature selection in neural networks through a fundamental trade-off between model complexity (the cost of describing the hypothesis) and predictive power (the cost of describing the data). Our framework predicts that as the amount of available training data increases, learners transition through qualitatively different features -- from simple spurious shortcuts to complex features -- only when the reduction in data encoding cost justifies the increased model complexity. Consequently, we identify distinct data regimes where increasing data promotes robustness by ruling out trivial shortcuts, and conversely, regimes where limiting data can act as a form of complexity-based regularization, preventing the learning of unreliable complex environmental cues. We validate our theory on a semi-synthetic benchmark showing that the feature selection of neural networks follows the same trajectory of solutions as optimal two-part compressors.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org