GenAI4ELab/papercli-papers huggingface dataset:
Democratizing Writing Support with AI: Insights from One Year of Real-World Interactions with an Open-Access Writing Feedback Tool
Authors: Babette Bühler; Ivo Bueno; Enkelejda Kasneci
Venue: AAAI 2026 (main)

Abstract: Writing is a foundational skill for educational, professional, and civic participation, yet access to frequent and timely writing feedback remains deeply unequal. Teachers face significant workload constraints, particularly in large classes, and many learners lack alternative sources of individualized feedback. While large language models (LLMs) offer the opportunity for scalable, adaptive support, little is known about how students engage with such feedback tools in real-world, self-directed settings.
We present a large-scale, year-long analysis of 23,650 voluntary interactions with an open-access AI writing feedback system used by students across diverse educational contexts and age groups, conducted in accordance with strict data protection standards. Using a clustering approach, we identify 2,800 iterative revision chains and apply a validated LLM-based multidimensional scoring framework to assess text quality over time.
Our findings reveal that students who revised their texts after receiving AI feedback demonstrated statistically significant, albeit modest, improvements across both content and language-related dimensions (overall writing quality: ∆ = 0.067, p < .001, r = .17), with the greatest gains observed among initially low-performing writers. Revision frequency was positively associated with improvement, particularly in higher-order writing skills. However, engagement was uneven, with higher usage among students in academically oriented schools.
These results demonstrate both the technical feasibility and social potential of deploying generative AI for educational support at scale, while highlighting the need for inclusive infrastructure, accessible design, and targeted outreach to truly democratize educational benefits.
Driving with Regulation: Trustworthy and Interpretable Decision-Making for Autonomous Driving with Retrieval-Augmented Reasoning
Authors: Tianhui Cai; Yifan Liu; Zewei Zhou; Haoxuan Ma; Seth Z. Zhao; Zhiwen Wu; Xu Han; Zhiyu Huang; Jiaqi Ma
Venue: AAAI 2026 (main)

Abstract: Understanding and adhering to traffic regulations is essential for autonomous vehicles to ensure safety and trustworthiness. However, traffic regulations are complex, context-dependent, and differ between regions, posing a major challenge to conventional rule-based decision-making approaches. We present an interpretable, regulation-aware decision-making framework, DriveReg, which enables autonomous vehicles to understand and adhere to region-specific traffic laws and safety guidelines. The framework integrates a Retrieval Augmented Generation (RAG)-based Traffic Regulation Retrieval Agent, which retrieves relevant rules from regulatory documents based on the current situation, and a Large Language Model (LLM)-powered Reasoning Agent that evaluates actions for legal compliance and safety. Our design emphasizes interpretability to enhance transparency and trustworthiness. To support systematic evaluation, we introduce DriveReg Scenarios Dataset, a comprehensive dataset of driving scenarios across Boston, Singapore, and Los Angeles, with both hypothesized text-based cases and real-world driving data, specifically constructed and annotated to evaluate models’ capacity for regulation understanding and reasoning. We validate our framework on the DriveReg Scenarios Dataset and real-world deployment, demonstrating strong performance and robustness across diverse environments.
Measuring Model Performance in the Presence of an Intervention
Authors: Winston Chen; Michael W. Sjoding; Jenna Wiens
Venue: AAAI 2026 (main)

Abstract: AI models are often evaluated based on their ability to predict the outcome of interest. However, in many AI for social impact applications, the presence of an intervention that affects the outcome can bias the evaluation. Randomized controlled trials (RCTs) randomly assign interventions, allowing data from the control group to be used for unbiased model evaluation. However, this approach is inefficient because it ignores data from the treatment group. Given the complexity and cost often associated with RCTs, making the most use of the data is essential. Thus, we investigate model evaluation strategies that leverage all data from an RCT. First, we theoretically quantify the estimation bias that arises from naïvely aggregating performance estimates from treatment and control groups and derive the condition under which this bias leads to incorrect model selection. Leveraging these theoretical insights, we propose nuisance parameter weighting (NPW), an unbiased model evaluation approach that reweights data from the treatment group to mimic the distribution of samples that would or would not experience the outcome under no intervention. Using synthetic and real-world datasets, we demonstrate that our proposed evaluation approach consistently yields better model selection than the standard approach, which ignores data from the treatment group, across various intervention effect and sample size settings. Our contribution represents a meaningful step towards more efficient model evaluation in real-world contexts.
Fragile by Design: On the Limits of Adversarial Defenses in Personalized DreamBooth Generation
Authors: Zhen Chen; Yi Zhang; Xiangyu Yin; Chengxuan Qin; Xingyu Zhao; Xiaowei Huang; Wenjie Ruan
Venue: AAAI 2026 (main)

Abstract: Personalized AI applications such as DreamBooth enable the generation of customized content from user images, but also raise significant privacy concerns, particularly the risk of facial identity leakage. Recent defense mechanisms like Anti-DreamBooth attempt to mitigate this risk by injecting adversarial perturbations into user photos to prevent successful personalization. However, we identify two critical yet overlooked limitations of these methods. First, the adversarial examples often exhibit perceptible artifacts such as conspicuous patterns or stripes, making them easily detectable as manipulated content. Second, the perturbations are highly fragile, as even a simple, non-learned filter can effectively remove them, thereby restoring the model's ability to memorize and reproduce user identity. To investigate this vulnerability, we propose a novel evaluation framework, AntiDB_Purify, to systematically evaluate existing defenses under realistic purification threats, including both traditional image filters and adversarial purification. Results reveal that none of the current methods maintains their protective effectiveness under such threats. These findings highlight that current defenses offer a false sense of security and underscore the urgent need for more imperceptible and robust protections to safeguard user identity in personalized generation.
Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget
Authors: Davin Choo; Yohai Trabelsi; Fentabil Getnet; Samson Warkaye Lamma; Wondesen Nigatu; Kasahun Sime; Lisa Matay; Milind Tambe; Stéphane Verguet
Venue: AAAI 2026 (main)

Abstract: As part of nationwide efforts aligned with the United Nations' Sustainable Development Goal 3 on Universal Health Coverage, 
Ethiopia's Ministry of Health is strengthening health posts to expand access to essential healthcare services. However, only a fraction of this health system strengthening effort can be implemented each year due to limited budgets and other competing priorities, thus the need for an optimization framework to guide prioritization across the regions of Ethiopia. In this paper, we develop a tool, Health Access Resource Planner (HARP), based on a principled decision-support optimization framework for sequential facility planning that aims to maximize population coverage under budget uncertainty while satisfying region-specific proportionality targets at every time step. We then propose two algorithms: (i) a learning-augmented approach that improves upon expert recommendations at any single-step; and (ii) a greedy algorithm for multi-step planning, both with strong worst-case approximation estimation. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we demonstrated the empirical efficacy of our method on three regions across various planning scenarios.
ShortageSim: Simulating Drug Shortages Under Information Asymmetry
Authors: Mingxuan Cui; Yilan Jiang; Duo Zhou; Cheng Qian; Yuji Zhang; Qiong Wang
Venue: AAAI 2026 (main)

Abstract: Drug shortages pose critical risks to patient care and healthcare systems worldwide, yet the effectiveness of regulatory interventions remains poorly understood due to information asymmetries in pharmaceutical supply chains. We propose ShortageSim, which addresses this challenge by providing the first simulation framework that evaluates the impact of regulatory interventions on competition dynamics under information asymmetry. Using Large Language Model (LLM)-based agents, the framework models the strategic decisions of drug manufacturers and institutional buyers, in response to shortage alerts given by the regulatory agency. Unlike traditional game theory models that assume perfect rationality and complete information, ShortageSim simulates heterogeneous interpretations on regulatory announcements and the resulting decisions. Experiments on self-processed dataset of historical shortage events show that ShortageSim reduces the resolution lag for production disruption cases by up to 84%, achieving closer alignment to real-world trajectories than the zero-shot baseline. Our framework confirms the effect of regulatory alert in addressing shortages and introduces a new method for understanding competition in multi-stage environments under uncertainty. We open-source ShortageSim and a dataset of 2,925 FDA shortage events, providing a novel framework for future research on policy design and testing in supply chains under information asymmetry.
SatSolarCast: A Flexible Framework for Multimodal Solar Irradiance Forecasting via Memory-Alignment Learning
Authors: Kuai Dai; Hui Su; Chengxing Zhai; Huiwei Lin; Mingliang Bai
Venue: AAAI 2026 (main)

Abstract: Solar irradiance forecast aims to accurately estimate future solar irradiance based on historical data, playing a vital role in energy production and grid management. 
While ground-based station measurements provide local accuracy, geostationary satellites offer much broader environmental contexts, such as cloud coverage, which serves as a key factor for accurate forecasting.  
However, effectively integrating these multimodal observations remains a challenge, with existing methods suffering from inflexibility and high computational costs.
To address this problem, we propose SatSolarCast, a flexible and efficient multimodal framework that introduces a memory alignment learning mechanism to integrate geostationary satellite data and historical irradiance observations. 
By preserving and recalling long-term spatiotemporal patterns from a specialized satellite memory bank, SatSolarCast enables effective guidance for both short- and long-term prediction.
Additionally, SatSolarCast offers plug-and-play compatibility and can be incorporated into various forecasting architectures. 
Extensive experiments across four ground stations demonstrate that SatSolarCast substantially improves forecasting performance compared to prior methods with much lower computational costs.
Efficient Forecasting of Geostationary Infrared Brightness Temperature Sequences: A Benchmark and a Lightweight Model
Authors: Kuai Dai; Hui Su; Xutao Li; Chengxing Zhai
Venue: AAAI 2026 (main)

Abstract: Forecasting geostationary infrared brightness temperature sequences from historical observations is a significant and challenging task. 
By analyzing these predictions, cloud evolution, convective activity, and atmospheric radiative states can be revealed in advance, offering high potential value in domains such as weather nowcasting, energy management, and disaster monitoring. 
Recently, artificial intelligence techniques have provided valuable insights into this task. However, as a nascent research area, the lack of a standardized, high-quality benchmark has significantly impeded progress. Moreover, training existing deep learning models for this task remains computationally expensive due to the complexity of their network architectures and modeling mechanisms. To address these challenges, we introduce a new benchmark, FY4ABT, and propose a lightweight prediction model, WavePredNet. Specifically, FY4ABT comprises three sub-datasets designed to respectively evaluate prediction performance under short-term, medium-term, and long-term scenarios. Meanwhile, WavePredNet effectively captures multi-scale dynamics, including both low- and high-frequency components with low computational costs while delivering exceptional performance.
RiverScope: High-Resolution River Masking Dataset
Authors: Rangel Daroya; Taylor Rowley; Jonathan Acero Flores; Elisa Friedmann; Fiona B Bennitt; Heejin An; Travis Thomas Simmons; Marissa Hughes; Camryn L Kluetmeier; Solomon Kica; J. Daniel Vélez; Sarah E. Esenther; Thomas E. Howard; Yanqi Ye; Audrey J. Turcotte; Colin Gleason; Subhransu Maji
Venue: AAAI 2026 (main)

Abstract: Surface water dynamics play a critical role in Earth’s climate system, influencing ecosystems, agriculture, disaster resilience, and sustainable development. Yet monitoring rivers and surface water at fine spatial and temporal scales remains challenging---especially for narrow or sediment-rich rivers that are poorly captured by low-resolution satellite data. To address this, we introduce RiverScope, a high-resolution dataset developed through collaboration between computer science and hydrology experts. RiverScope comprises 1,145 high-resolution images (covering 2,577 square kilometers) with expert-labeled river and surface water masks, requiring over 100 hours of manual annotation. Each image is co-registered with Sentinel-2, SWOT, and the SWOT River Database (SWORD), enabling the evaluation of cost-accuracy trade-offs across sensors---a key consideration for operational water monitoring. We also establish the first global, high-resolution benchmark for river width estimation, achieving a median error of 7.2 meters---significantly outperforming existing satellite-derived methods. We extensively evaluate deep networks across multiple architectures (e.g., CNNs and transformers), pretraining strategies (e.g., supervised and self-supervised), and training datasets (e.g., ImageNet and satellite imagery). Our best-performing models combine the benefits of transfer learning with the use of all the multispectral PlanetScope channels via learned adaptors. RiverScope provides a valuable resource for fine-scale and multi-sensor hydrological modeling, supporting climate adaptation and sustainable water management.
When Proxy Agents Disagree, Do Humans Mirror? Manipulating Human Behavior in Moral Dilemmas Through Agents
Authors: Haotian Deng; Sitian Wang; Ruxin Wang; Chen Wei; Quanying Liu
Venue: AAAI 2026 (main)

Abstract: The diversity across populations and the variability between individuals have long posed a significant challenge in cognitive science. Although large language models (LLMs) have made notable progress in aligning with human values, faithfully capturing the high degree of diversity and uncertainty in human judgment remains an unresolved challenge.This study investigates whether computational models, or `proxy agents," can not only emulate human decision patterns but also systematically modulate them. We propose a framework wherein we first fine-tune BERT-based proxy agents to replicate both aggregate and individual-level human judgments on a large-scale moral dilemma dataset. We then hypothesize that stimuli identified as maximally divisive for these individualized agents will similarly elicit high disagreement among human participants. Through a manipulating experiment, we validate this hypothesis, demonstrating that agent-selected stimuli can predictably induce targeted divergence in human moral choices. Our findings provide empirical evidence that AI agents can bias human perceptual variability by strategically filtering information. We further analyze this induced moral divergence using a Bayesian framework and concept decomposition to identify the distinct conceptual dimensions driving individual differences. This work quantifies the potential for AI-driven cognitive modulation and underscores the urgent need for ethical guidelines to prevent the misuse of such capabilities.
Toward Gaze Target Detection of Young Autistic Children
Authors: Shijian Deng; Erin E. Kosloski; Siva Sai Nagender Vasireddy; Jia Li; Randi Sierra Sherwood; Feroz Mohamed Hatha; Siddhi Patel; Pamela R. Rollins; Yapeng Tian
Venue: AAAI 2026 (main)

Abstract: The automatic detection of gaze targets in autistic children through artificial intelligence can be impactful, especially for those who lack access to a sufficient number of professionals to improve their quality of life. 
This paper introduces a new, real-world AI application for gaze target detection in autistic children, which predicts a child's point of gaze from an activity image. This task is foundational for building automated systems that can measure joint attention—a core challenge in Autism Spectrum Disorder (ASD). To facilitate the study of this challenging application, we collected the first-ever Autism Gaze Target (AGT) Dataset. We further propose a novel social-aware coarse-to-fine (SACF) gaze detection framework that explicitly leverages the social context of a scene to overcome the class imbalance common in autism datasets—a consequence of autistic children's tendency to show reduced gaze to faces. 
It utilizes a two-pathway architecture with expert models specialized in social and non-social gaze, guided by a context-awareness gate module. The results of our comprehensive experiments demonstrate that our framework achieves new state-of-the-art performance for gaze target detection in this population, significantly outperforming existing methods, especially on the critical minority class of face-directed gaze.
GraphVSSM: Graph Variational State-Space Model for Probabilistic Spatiotemporal Inference of Dynamic Exposure and Vulnerability for Regional Disaster Resilience Assessment
Authors: Joshua Dimasaka; Christian Geiß; Emily So
Venue: AAAI 2026 (main)

Abstract: Regional disaster resilience quantifies the changing nature of physical risks to inform policy instruments ranging from local immediate recovery to international sustainable development. While many existing state-of-practice methods have greatly advanced the dynamic mapping of exposure and hazard, our understanding of large-scale physical vulnerability has remained static, costly, limited, region-specific, coarse-grained, overly aggregated, and inadequately calibrated. With the significant growth in the availability of time-series satellite imagery and derived products for exposure and hazard, we focus our work on the equally important yet challenging element of the risk equation: physical vulnerability. Given this unique problem, we leverage machine learning methods that flexibly capture spatial contextual relationships, limited temporal observations, and uncertainty in a unified probabilistic spatiotemporal inference framework. We therefore introduce Graph Variational State-Space Model (GraphVSSM), a novel modular spatiotemporal approach that uniquely integrates graph deep learning, state-space modeling, and variational inference using time-series data and prior expert belief systems in a weakly supervised or coarse-to-fine-grained manner. We present three major results: a city-wide demonstration in Quezon City, Philippines; an investigation of sudden changes in the cyclone-impacted coastal Khurushkul community (Bangladesh) and the mudslide-affected Freetown (Sierra Leone); and an open geospatial dataset, METEOR 2.5D, that spatiotemporally enhances the existing global static dataset for 46 UN-recognized Least Developed Countries (as of 2020). Beyond advancing the practice of regional disaster resilience assessment and improving our understanding of global progress in disaster risk reduction, our method also offers a probabilistic deep learning approach, contributing to broader urban studies that require compositional data analysis in weakly supervised settings.
Capacity Constraints Make Admissions Processes Less Predictable
Authors: Evan Dong; Nikhil Garg; Sarah Dean
Venue: AAAI 2026 (main)

Abstract: Machine learning models are often used to make predictions about the outcomes of applications to selective programs. Many prospective school or college applicants turn to machine learning models to predict whether they will be admitted to a program, and employers may use algorithmic tools to filter out resumes predicted to have a low probability of being hired when offering interviews for a job opening. However, such decision processes differ substantially from the conventional machine learning setting: decisions are not independent across applicants. Whether a student is admitted depends on the other applicants who apply because admissions decisions are capacity-constrained. We formalize how the nature of admission decisions results in a data-generating process which is incompatible with traditional machine learning assumptions. We characterize how selection functions properties affect the difficulty of generalization to applicant pool distribution shifts, introducing two concepts: stability, which measures how many existing decisions can change when a single new applicant is introduced; and variability, which measures the number of unique students whose decisions can change. We demonstrate our theory on admissions data from the New York City high school matching system, showing that machine learning performance degrades as the applicant pool increasingly differs from the training data. Furthermore, there are larger performance drops for schools using decision rules that are less stable and more variable. Our work raises questions about the reliability of predicting individual admissions probabilities.
Characterizing AI Manipulation Risks in Brazilian YouTube Climate Discourse
Authors: Wenchao Dong; Marcelo Sartori Locatelli; Virgilio Almeida; Meeyoung Cha
Venue: AAAI 2026 (main)

Abstract: Climate change poses a global threat to public health, food security, and economic stability. Addressing it requires evidence-based policies and a nuanced understanding of how the threat is perceived by the public, particularly within visual social media, where narratives quickly evolve through voices of individuals, politicians, NGOs, and institutions. This study investigates climate-related discourse on YouTube within the Brazilian context, a geopolitically significant nation in global environmental negotiations. Through three case studies, we examine (1) which psychological content traits most effectively drive audience engagement, (2) the extent to which these traits influence content popularity, and (3) whether such insights can inform the design of persuasive synthetic campaigns such as climate denialism using recent generative language models. Another contribution of this work is the release of a large publicly available dataset of 226K Brazilian YouTube videos and 2.7M user comments on climate change. The dataset includes fine-grained annotations of persuasive strategies, theory of mind categorizations in user responses, and typologies of content creators. This resource can help support future research on digital climate communication and the ethical risk of algorithmically amplified narratives and generative media.