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Lars Krupp, Daniel Geißler, Vishal Banwari, Paul Lukowicz, Jakob Karolus

Web agents, like OpenAI's Operator and Google's Project Mariner, are powerful agentic systems pushing the boundaries of Large Language Models (LLM). They can autonomously interact with the internet at the user's behest, such as navigating websites, filling search masks, and comparing price lists. Though web agent research is thriving, induced sustainability issues remain largely unexplored. To highlight the urgency of this issue, we provide an initial exploration of the energy and CO₂ cost associated with web agents from both a theoretical —via estimation— and an empirical perspective —by benchmarking. Our results show how different philosophies in web agent creation can severely impact the associated expended energy, and that more energy consumed does not necessarily equate to better results. We highlight a lack of transparency regarding disclosing model parameters and processes used for some web agents as a limiting factor when estimating energy consumption. Our work contributes towards a change in thinking of how we evaluate web agents, advocating for dedicated metrics measuring energy consumption in benchmarks.

Girish A. Koushik, Helen Treharne, Aditya Joshi, Diptesh Kanojia

Social media memes are a challenging domain for hate detection because they intertwine visual and textual cues into culturally nuanced messages. To tackle these challenges, we introduce TRACE, a hierarchical multimodal framework that leverages visually grounded context augmentation, along with a novel caption-scoring network to emphasize hate-relevant content, and parameter-efficient fine-tuning of CLIP’s text encoder. Our experiments demonstrate that selectively fine-tuning deeper text encoder layers significantly enhances performance compared to simpler projection-layer fine-tuning methods. Specifically, our framework achieves state-of-the-art accuracy (0.807) and F1-score (0.806) on the widely-used Hateful Memes dataset, matching the performance of considerably larger models while maintaining efficiency. Moreover, it achieves superior generalization on the MultiOFF offensive meme dataset (F1-score 0.673), highlighting robustness across meme categories. Additional analyses confirm that robust visual grounding and nuanced text representations significantly reduce errors caused by benign confounders. We publicly release our code to facilitate future research.

Avni Kothari, Patrick Vossler, Jean Digitale, Mohammad Forouzannia, Elise Rosenberg, Michele Lee, Jenneé Bryant, Melanie Molina, James Marks, Lucas Zier 等

Large language models (LLMs) have the potential to address social and behavioral determinants of health by transforming labor intensive workflows in resource-constrained settings. Creating LLM-based applications that serve the needs of underserved communities requires a deep understanding of their local context, but it is often the case that neither LLMs nor their developers possess this local expertise, and the experts in these communities often face severe time/resource constraints. This creates a disconnect: how can one engage in meaningful co-design of an LLM-based application for an under-resourced community when the communication channel between the LLM developer and domain expert is constrained? We explored this question through a real-world case study, in which our data science team sought to partner with social workers at a safety net hospital to build an LLM application that summarizes patients' social needs. Whereas prior works focus on the challenge of prompt tuning, we found that the most critical challenge in this setting is the careful and precise specification of *what* information to surface to providers so that the LLM application is accurate, comprehensive, and verifiable. Here we present a novel co-design framework for settings with limited access to domain experts, in which the summary generation task is first decomposed into individually-optimizable attributes and then each attribute is efficiently refined and validated through a multi-tier cascading approach.

Shufeng Kong, Zijie Wang, Nuan Cui, Hao Tang, Yihan Meng, Yuanyuan Wei, Feifan Chen, Yingheng Wang, Zhuo Cai, Yaonan Wang 等

Automated interpretation of medical images demands robust modeling of complex visual-semantic relationships while addressing annotation scarcity, label imbalance, and clinical plausibility constraints. We introduce MIRNet (Medical Image Reasoner Network), a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning. Tongue image diagnosis is a particularly challenging domain that requires fine-grained visual and semantic understanding. Our approach leverages self-supervised masked autoencoder (MAE) to learn transferable visual representations from unlabeled data; employs graph attention networks (GAT) to model label correlations through expert-defined structured graphs; enforces clinical priors via constraint-aware optimization using KL divergence and regularization losses; and mitigates imbalance using asymmetric loss (ASL) and boosting ensembles. To address annotation scarcity, we also introduce TongueAtlas-4K, a comprehensive expert-curated benchmark comprising 4,000 images annotated with 22 diagnostic labels–representing the largest public dataset in tongue analysis. Validation shows our method achieves state-of-the-art performance. While optimized for tongue diagnosis, the framework readily generalizes to broader diagnostic medical imaging tasks.

Ashley Klein, Edward Raff, Marcia DesJardin

The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effect size and unreliable conclusions. A Bayesian approach may allow analysis to determine if the claim of a positive effect is still warranted, and we build a Bayesian approach to this common medical scenario. To demonstrate its utility, we assist professional OBGYNs in evaluating Trial of Labor After a Cesarean-section (TOLAC) situations where few interventions are available for patients and find the support needed for physicians to advance patient care.

Woojin Kim, Changkwon Lee, Hyeoncheol Kim

Using Artificial Intelligence to improve teaching and learning benefits greater adaptivity and scalability in education. Knowledge Tracing (KT) is recognized for student modeling task due to its superior performance and application potential in education. To this end, we conceptualize and investigate counterfactual explanation as the connection from XAI for KT to education. Counterfactual explanations offer actionable recourse, are inherently causal and local, and easy for educational stakeholders to understand who are often non-experts. We propose KTCF, a counterfactual explanation generation method for KT that accounts for knowledge concept relationships, and a post-processing scheme that converts a counterfactual explanation into a sequence of educational instructions. We experiment on a large-scale educational dataset and show our KTCF method achieves superior and robust performance over existing methods, with improvements ranging from 5.7% to 34% across metrics. Additionally, we provide a qualitative evaluation of our post-processing scheme, demonstrating that the resulting educational instructions help in reducing large study burden. We show that counterfactuals have the potential to advance the responsible and practical use of AI in education. Future works on XAI for KT may benefit from educationally grounded conceptualization and developing stakeholder-centered methods.

Cheol Woo Kim, Shresth Verma, Mauricio Tec, Milind Tambe

We study an LLM fine-tuning task for designing reward functions for sequential resource allocation problems in public health, guided by human preferences expressed in natural language. This setting presents a challenging testbed for alignment due to complex and ambiguous objectives and limited data availability. We propose DPO-PRO, a robust fine-tuning algorithm based on Direct Preference Optimization (DPO), which accounts for uncertainty in the preference distribution using a lightweight Distributionally Robust Optimization (DRO) formulation. Unlike prior DRO-based variants, DPO-PRO focuses solely on uncertainty in preferences, avoiding unnecessary conservatism and incurring negligible computational overhead. We evaluate DPO-PRO on a real-world maternal mobile health program operated by the non-profit organization ARMMAN, as well as on standard alignment benchmarks. Experimental results demonstrate that our method consistently improves robustness to noisy preference signals compared to existing DPO variants. Moreover, DPO-PRO achieves comparable performance to prior self-reflection-based baseline for reward function design, while requiring significantly lower inference-time cost.

Misaal Khan, Mayank Vatsa, Kuldeep Singh, Richa Singh

Child malnutrition remains a global crisis, yet existing screening methods are laborious and poorly scalable, hindering early intervention. In this work, we present NutriScreener, a retrieval-augmented, multi-pose graph attention network that combines CLIP-based visual embeddings, class-boosted knowledge retrieval, and context awareness to enable robust malnutrition detection and anthropometric prediction from children's images, simultaneously addressing generalizability and class-imbalance. In a clinical study, doctors rated it 4.3/5 for accuracy and 4.6/5 for efficiency, confirming its deployment readiness in low-resource settings. NutriScreener was trained and tested on 2,141 children from AnthroVision and additionally evaluated on diverse cross-continent populations, including ARAN and an in-house collected CampusPose dataset, achieving 0.79 recall, 0.82 AUC, and significantly lower anthropometric RMSEs, demonstrating reliable measurement in unconstrained, pediatric settings. Cross-dataset results show up to 25\% recall gain and up to 2.3 cm reduction in head circumference RMSE using demographically matched knowledge bases. NutriScreener offers a scalable and accurate solution for early malnutrition detection in low-resource environments.

Liangyi Kang, Wei Hua, Yan Yang, Jie Liu, Dan Ye

Detecting depression through social media is a complex task, as noisy user-generated content creates significant interference between persistent depressive patterns and transient emotional expressions. Two main challenges arise: First, negative mood indicators are not exclusive to depressed individuals, making it difficult to distinguish between pathological symptoms and situational emotional variations. Second, existing static models fail to adapt to diverse user expression styles and effectively filter out confounding noise from posts by non-depressed individuals. This results in conventional approaches either overfitting to superficial emotional cues or overlooking subtle long-term symptom progression. To address these issues, we propose the Adversarial Learning Enhanced Stability-aware Routing Transformer for Adaptive Depression Detection(ALERT), a novel framework integrating adaptive attention routing and adversarial learning to enhance robustness against confounding mood signals. Specifically, ALERT employs a stability-aware dynamic routing mechanism to annotate user-specific mood valence trends, providing a structured representation of affective progression over time. An adversarial learning module then leverages these mood-based representations to distinguish between expressions indicative of persistent depressive mood and variations in situational mood states, ensuring adaptability to diverse user behaviors. Experimental results on public social media datasets demonstrate that ALERT outperforms state-of-the-art methods in depression detection, effectively reducing false alarm from transient mood states and improving classification accuracy.

William Jurayj, Nils Holzenberger, Benjamin Van Durme

According to the United States Internal Revenue Service, "the average American spends $270 and 13 hours filing their taxes". Even beyond the U.S., tax filing requires complex reasoning, combining application of overlapping rules with numerical calculations. Because errors can incur costly penalties, any automated system must deliver high accuracy and auditability, making modern large language models (LLMs) poorly suited for this task. We propose an approach that integrates LLMs with a symbolic solver to calculate tax obligations. We evaluate variants of this system on the challenging StAtutory Reasoning Assessment (SARA) dataset, and include a novel method for estimating the cost of deploying such a system based on real-world penalties for tax errors. We further show how combining up-front translation of plain-text rules into formal logic programs, combined with intelligently retrieved exemplars for formal case representations, can dramatically improve performance on this task and reduce costs to well below real-world averages. Our results demonstrate the effectiveness of applying semantic parsing methods to statutory reasoning, and show promising economic feasibility of neuro-symbolic architectures for increasing access to reliable tax assistance.

Haoan Jin, Han Ying, Jiacheng Ji, Hanhui Xu, Mengyue Wu

Recent advances in large language models (LLMs) have enabled their application to a range of healthcare tasks. However, aligning LLMs with the nuanced demands of medical ethics, especially under complex real-world scenarios, remains underexplored. In this work, we present MedES, a dynamic, scenario-centric benchmark specifically constructed from 260 authoritative Chinese medical, ethical, and legal sources to reflect the challenges in clinical decision-making. To facilitate model alignment, we introduce a guardian-in-the-loop framework that leverages a dedicated automated evaluator—trained on expert-labeled data and achieving over 97% accuracy within our domain—to generate targeted prompts and provide structured ethical feedback. Using this pipeline, we align a 7B-parameter LLM through supervised fine-tuning and domain-specific preference optimization. Experimental results, conducted entirely within the Chinese medical ethics context, demonstrate that our aligned model outperforms notably larger baselines on core ethical tasks, with observed improvements in both quality and composite evaluation metrics. Our work offers a practical and adaptable framework for aligning LLMs with medical ethics in the Chinese healthcare domain, and suggests that similar alignment pipelines may be instantiated in other legal and cultural environments through modular replacement of the underlying normative corpus.

Bo Jin, ShichaoZhao, Jin Lyu, Bin Zhang, Tao Yu, Liang An, Yebin Liu, Meili Wang

The lactation performance of Saanen dairy goats, renowned for their high milk yield, is intrinsically linked to their body size, making accurate 3D body measurement essential for assessing milk production potential, yet existing reconstruction methods lack goat-specific authentic 3D data. To address this limitation, we establish the FemaleSaanenGoat dataset containing synchronized eight-view RGBD videos of 55 female Saanen goats (6-18 months). Using multi-view DynamicFusion, we fuse noisy, non-rigid point cloud sequences into high-fidelity 3D scans, overcoming challenges from irregular surfaces and rapid movement. Based on these scans, we develop SaanenGoat, a parametric 3D shape model specifically designed for female Saanen goats. This model features a refined template with 41 skeletal joints and enhanced udder representation, registered with our scan data. A comprehensive shape space constructed from 48 goats enables precise representation of diverse individual variations. With the help of SaanenGoat model, we get high-precision 3D reconstruction from single-view RGBD input, and achieve automated measurement of six critical body dimensions: body length, height, chest width, chest girth, hip width, and hip height. Experimental results demonstrate the superior accuracy of our method in both 3D reconstruction and body measurement, presenting a novel paradigm for large-scale 3D vision applications in precision livestock farming.

Zheng Jiang, Wei Wang, Gaowei Zhang, Yi Wang

Sea Surface Temperature (SST) is crucial for understanding upper-ocean thermal dynamics and ocean-atmosphere interactions, which have profound economic and social impacts. While data-driven models show promise in SST prediction, their black-box nature often limits interpretability and overlooks key physical processes. Recently, physics-informed neural networks have been gaining momentum but struggle with complex ocean-atmosphere dynamics due to 1) inadequate characterization of seawater movement (e.g., coastal upwelling) and 2) insufficient integration of external SST drivers (e.g., turbulent heat fluxes). To address these challenges, we propose SSTODE, a physics-informed Neural Ordinary Differential Equations (Neural ODEs) framework for SST prediction. First, we derive ODEs from fluid transport principles, incorporating both advection and diffusion to model ocean spatiotemporal dynamics. Through variational optimization, we recover a latent velocity field that explicitly governs the temporal dynamics of SST. Building upon ODE, we introduce an Energy Exchanges Integrator (EEI)-inspired by ocean heat budget equations-to account for external forcing factors. Thus, the variations in the components of these factors provide deeper insights into SST dynamics. Extensive experiments demonstrate that SSTODE achieves state-of-the-art performances in global and regional SST forecasting benchmarks. Furthermore, SSTODE visually reveals the impact of advection dynamics, thermal diffusion patterns, and diurnal heating-cooling cycles on SST evolution. These findings demonstrate the model's interpretability and physical consistency.

Hao Jiang, Yixin Xu, Pradeep Varakantham

The core of efficient on-demand ride-pooling lies in solving the Ride-Pool Matching Problem (RMP), which involves assigning multiple customer requests to single vehicles under various service constraints (e.g., pickup windows, detour allowances, and vehicle occupancy). A significant missed opportunity in most current RMP approaches is the assumption that passengers must be picked up and dropped off exactly at their requested locations. Allowing passengers to walk even short distances to meet vehicles could unlock substantial improvements in ride-pooling operations. Building upon the limitations of existing Ride-Pool Matching Problem (RMP) solutions that neglect passenger walkability, this paper introduces a novel matching method that strategically incorporates flexible pickup and drop-off locations. Our approach simultaneously determines the optimal assignment of vehicles to requests (one vehicle to potentially multiple requests and each request to at most one vehicle), identifies advantageous meeting points for passengers, and plans efficient vehicle routes. This comprehensive optimization respects all service constraints and considers the long-term implications of routing decisions. To achieve this integrated solution, we first employ a tree-based approach to enumerate all feasible pairings between passengers and vehicles. Subsequently, we calculate an optimal route for each of these feasible matches. Finally, we evaluate the quality of all possible assignments and select the most advantageous matching for implementation. In our experimental evaluation on city-scale taxi datasets, we demonstrate that our method improves the number of served requests by up to 13% and reduces the average vehicle travel distance by up to 21%. By serving more passengers with less driving distance, our approach achieves greater efficiency in a more sustainable manner — using fewer resources to deliver better service and creating a win-win outcome for all stakeholders, including customers, drivers, the aggregator, and the environment.

Pengfei Jia, Jianghong Ma, Baoquan Zhang, Kenghong Lin, Xinyu Zhang, Chuyao Luo, Xutao Li, Yunming Ye

Photovoltaic (PV) power forecasting is critical for the operation of solar power plants and the coordination of energy within power grids. This work aims to predict future PV power time series by leveraging multimodal data. While recent studies have incorporated numerical modalities such as satellite image sequences and numerical weather prediction (NWP) time series, they often overlook textual modalities—such as the spatio-temporal context of PV plants—and the potential of pretrained large language models (LLMs). In this paper, we build upon existing numerical inputs and further explore the use of spatio-temporal text prompts, generated based on plant coordinates and forecast start time, to enhance the forecasting process. We propose PV-LLM, a satellite-text-prompted framework that integrates a pretrained LLM to improve PV power forecasting. The framework consists of three key components: Text Prompt Construction, Modality-Specific Encoding, and Adaptive Prompt Tuning. First, the Text Prompt Construction module generates spatio-temporal prompts that offer high-level semantic guidance. Next, the Modality-Specific Encoding module encodes each modality according to its unique characteristics, capturing modality-specific patterns while managing varying context lengths. Finally, the Adaptive Prompt Tuning module fine-tunes the LLM to integrate multimodal embeddings, while an adaptive gating mechanism retains its pretrained knowledge. We validate the effectiveness of the proposed framework on a real-world dataset containing multiple PV plants. Experimental results demonstrate that our approach outperforms existing state-of-the-art methods.

Sofia Jamil, Kotla Sai Charan, Sriparna Saha, Koustava Goswami, Joseph K J

Indian poetry, known for its linguistic complexity and deep cultural resonance, has a rich and varied heritage spanning thousands of years. However, its layered meanings, cultural allusions, and sophisticated grammatical constructions often pose challenges for comprehension, especially for non-native speakers or readers unfamiliar with its context and language. Despite its cultural significance, existing works on poetry have largely overlooked Indian language poems. In this paper, we propose the Translation and Image Generation (TAI) framework, leveraging Large Language Models (LLMs) and Latent Diffusion Models through appropriate prompt tuning. Our framework supports the United Nations Sustainable Development Goals of Quality Education (SDG 4) and Reduced Inequalities (SDG 10), by enhancing the accessibility of culturally rich Indian-language poetry to a global audience. It includes (1) a translation module that uses an Odds Ratio Preference Alignment Algorithm to accurately translate morphologically rich poetry into English; (2) an image generation module that employs a semantic graph to capture tokens, dependencies, and semantic relationships between metaphors and their meanings, to create visually meaningful representations of Indian poems. Our comprehensive experimental evaluation, including both human and quantitative assessments, demonstrates the superiority of TAI Diffusion in poem image generation tasks, outperforming strong baselines. To further address the scarcity of resources for Indian-language poetry, we introduce the Morphologically Rich Indian Language Poems MorphoVerse Dataset, comprising 1,570 poems across 21 low-resource Indian languages. By addressing the gap in poetry translation and visual comprehension, this work aims to broaden accessibility and enrich the reader’s experience.

Calvin Isley, Joshua Gilbert, Evangelos Kassos, Michaela Kocher, Allen Nie, Emma Brunskill, Ben Domingue, Jake Hofman, Joscha Legewie, Teddy Svoronos 等

While large language models (LLMs) challenge conventional methods of teaching and learning, they present an exciting opportunity to improve efficiency and scale high-quality instruction. One promising application is the generation of customized exams, tailored to specific course content. There has been significant recent excitement on automatically generating questions using artificial intelligence, but also comparatively little work evaluating the psychometric quality of these items in real-world educational settings. Filling this gap is an important step toward understanding generative AI's role in effective test design. In this study, we introduce and evaluate an iterative refinement strategy for question generation, repeatedly producing, assessing, and improving questions through cycles of LLM-generated critique and revision. We evaluate the quality of these AI-generated questions in a large-scale field study involving 91 classes---covering computer science, mathematics, chemistry, and more---in dozens of colleges across the United States, comprising nearly 1700 students. Our analysis, based on item response theory (IRT), suggests that for students in our sample the AI-generated questions performed comparably to expert-created questions designed for standardized exams. Our results illustrate the power of AI to make high-quality assessments more readily available, benefiting both teachers and students.

Md Zahidul Islam, Cameron S. Fletcher, Ke Sun, Amir Dezfouli, Iadine Chades

Biodiversity is declining globally at an unprecedented rate. Managers urgently need to allocate limited resources to control pest species where interventions have the highest ecological impact. However, many species are hard to detect, and data collection is often expensive, irregular, and incomplete, thus posing significant challenges for machine learning models that traditionally require large and regular datasets. We present a novel deep learning architecture that estimates the spatiotemporal abundance of hard-to-detect species from sparse, zero-inflated, and irregular data. Our method combines Graph Convolutional Networks (GCNs) to model spatial dependencies across monitoring sites with Recurrent Neural Networks (RNNs) to capture long-range temporal dynamics explicitly addresses the challenges of data sparsity, heterogeneity, and irregular sampling. We apply our model to the Crown-of-Thorns Starfish (COTS) on Australia's Great Barrier Reef, a species with devastating impact on coral reefs and a major target of pest control programs. Our method significantly outperforms baseline approaches and the current resource-intensive approach, manta-tow surveillance, in both accuracy and detectability. Simulations indicate a 20% increase in starfish removal efficiency over a year, enabling more effective coral protection. This work demonstrates how tailored deep learning methods can overcome ecological data limitations and substantially improve conservation outcomes.

Kazi Ashik Islam, Zakaria Mehrab, Mahantesh M Halappanavar, Henning Mortveit, Katragadda Sridhar, Jon Derek Loftis, Stefan Hoops, Madhav Marathe

Coastal flooding poses increasing threats to communities worldwide, necessitating accurate and hyper-local inundation forecasting for effective emergency response. However, real-world deployment of forecasting systems is often constrained by sparse sensor networks, where only a limited subset of locations may have sensors due to budget constraints. To approach this challenge, we present Diff-Sparse, a masked conditional diffusion model designed for probabilistic coastal inundation forecasting from sparse sensor observations. Diff-Sparse primarily utilizes the inundation history of a location and its neighboring locations from a context time window as spatiotemporal context. The fundamental challenge of spatiotemporal prediction based on sparse observations in the context window is addressed by introducing a novel masking strategy during training. Digital elevation data and temporal co-variates are utilized as additional spatial and temporal contexts, respectively. A convolutional neural network and a conditional UNet architecture with cross-attention mechanism are employed to capture the spatiotemporal dynamics in the data. We trained and tested Diff-Sparse on coastal inundation data from the Eastern Shore of Virginia and systematically assessed the performance of Diff-Sparse across different sparsity levels (0%, 50%, 95% missing observations). Our experiment results show that Diff-Sparse achieves upto 62% improvement in terms of two forecasting performance metrics compared to existing methods, at 95% sparsity level. Moreover, our ablation studies reveal that digital elevation data becomes more useful at high sparsity levels compared to temporal co-variates.

Shuyan Huang, Ying Zheng, Xiaoli Zeng, Zitao Liu

Creating a well-structured lesson plan is essential for improving classroom efficiency, yet it is often a labor-intensive process. Recently, many studies have leveraged large language models (LLMs) to generate lesson plans automatically. However, existing methods heavily rely on LLMs that are pre-trained on large-scale universal corpora, which often lack critical educational theory and textbook-specific information. This can lead to inconsistencies and misalignments with textbook content. To address these challenges, we propose CE-LessonPlan, a novel compress-expand framework to generate lesson plans by effectively combining external lesson plan references and textbook information. The framework consists of two key components: a compressor, which synthesizes multiple retrieved references into a cohesive document, and an expander, which integrates textbook-specific information with the parametric knowledge of LLMs to produce another enriched lesson plan. The outputs of the compressor and expander are then seamlessly integrated to create a comprehensive golden context, further enhancing the lesson plan generation process with LLMs. We conduct extensive experiments to demonstrate that CE-LessonPlan outperforms existing methods for generating lesson plans.