Maps are powerful carriers of structured and contextual knowledge, encompassing geography, demographics, infrastructure, and environmental patterns. Reasoning over such knowledge requires models to integrate spatial relationships, visual cues, real-world context, and domain-specific expertise—capabilities that current large language models (LLMs) and vision–language models (VLMs) still struggle to exhibit consistently. Yet, datasets used to benchmark VLMs on map-based reasoning remain narrow in scope, restricted to specific domains, and heavily reliant on artificially generated content (outputs from LLMs or pipeline-based methods), offering limited depth for evaluating genuine geospatial reasoning. To address this gap, we present MAPVERSE, a large-scale benchmark built on real-world maps. It comprises 11,837 human-authored question-answer pairs across 1,025 maps, spanning ten diverse map categories and multiple question categories for each. The dataset provides a rich setting for evaluating map reading, interpretation, and multimodal reasoning. We evaluate ten state-of-the-art models against our benchmark to establish baselines and quantify reasoning gaps. Beyond overall performance, we conduct fine-grained categorical analyses to assess model inference across multiple dimensions and investigate the visual factors shaping reasoning outcomes. Our findings reveal that while current VLMs perform competitively on classification-style tasks, both open-and closed-source models fall short on advanced tasks requiring complex spatial reasoning.

Siddharth Khincha, Tushar Kataria, Ankita Anand, Dan Roth, Vivek Gupta. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Abhilash Shankarampeta, Harsh Mahajan, Tushar Kataria, Dan Roth, Vivek Gupta. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Transformers have emerged as the state-of-the-art architecture in medical image registration, outperforming convolutional neural networks (CNNs) by addressing their limited receptive fields and overcoming gradient instability in deeper models. Despite their success, transformer-based models require substantial resources for training, including data, memory, and computational power, which may restrict their applicability for end users with limited resources. In particular, existing transformer-based 3D image registration architectures face two critical gaps that challenge their efficiency and effectiveness. Firstly, although window-based attention mechanisms reduce the quadratic complexity of full attention by focusing on local regions, they often struggle to effectively integrate both local and global information. Secondly, the granularity of tokenization, a crucial factor in registration accuracy, presents a performance tradeoff: smaller voxel-size tokens enhance detail capture but come with increased computational complexity, higher memory usage, and a greater risk of over-fitting. We present EFFICIENTMORPH, a transformer-based architecture for unsupervised 3D image registration that balances local and global attention in 3D volumes through a plane-based attention mechanism and employs a Hi-Res tokenization strategy with merging operations, thus capturing finer details without compromising computational efficiency. Notably, EFFICIENTMORPH sets a new benchmark for performance on the OASIS dataset with ~16-27x fewer parameters. https://github.com/MedVIC-Lab/Efficient.Morph.Registration

Language models, characterized by their blackbox nature, often hallucinate and display sensitivity to input perturbations, causing concerns about trust.To enhance trust, it is imperative to gain a comprehensive understanding of the model's failure modes and develop effective strategies to improve their performance.In this study, we introduce a methodology designed to examine how input perturbations affect language models across various scales, including pre-trained models and large language models (LLMs).Utilizing fine-tuning, we enhance the model's robustness to input perturbations.Additionally, we investigate whether exposure to one perturbation enhances or diminishes the model's performance with respect to other perturbations.To address robustness against multiple perturbations, we present three distinct fine-tuning strategies.Furthermore, we broaden the scope of our methodology to encompass large language models (LLMs) by leveraging a chain of thought (CoT) prompting approach augmented with exemplars.We employ the Tabular-NLI task to showcase how our proposed strategies adeptly train a robust model, enabling it to address diverse perturbations while maintaining accuracy on the original dataset.

The aim of this paper is to present methods to systematically analyze individual and group behavioral patterns observed in community driven discussion platforms like Reddit where users exchange information and views on various topics of current interest. We conduct this study by analyzing the statistical behavior of posts and modeling user interactions around them. We have chosen Reddit as an example, since it has grown exponentially from a small community to one of the biggest social network platforms in the recent times. Due to its large user base and popularity, a variety of behavior is present among users in terms of their activity. Our study provides interesting insights about a large number of inactive posts which fail to gather attention despite their authors exhibiting Cyborg-like behavior to draw attention. We also present interesting insights about short-lived but extremely active posts emulating a phenomenon like Mayfly Buzz. Further, we present methods to find the nature of activity around highly active posts to determine the presence of Limelight hogging activity, if any. We analyzed over 2 million posts and more than 7 million user responses to them during entire 2008 and over 63 million posts and over 608 million user responses to them from August 2014 to July 2015 amounting to two one-year periods, in order to understand how social media space has evolved over the years.