GenAI4ELab/papercli-papers huggingface dataset:
What Are They Filtering Out? An Experimental Benchmark of Filtering Strategies for Harm Reduction in Pretraining Datasets
Authors: Marco Antonio Stranisci; Christian Hardmeier
Venue: AAAI 2026 (main)

Abstract: Data filtering strategies are a crucial component to develop safe Large Language Models (LLM), since they support the removal of harmful contents from pretraining datasets. There is a lack of research on the actual impact of these strategies on vulnerable groups to discrimination, though, and their effectiveness has not been yet systematically addressed. In this paper we present a benchmark study of data filtering strategies for harm reduction aimed at providing a systematic evaluation on these approaches. We provide an overview 55 technical reports of English LMs and LLMs to identify the existing filtering strategies in literature and implement an experimental setting to test their impact against vulnerable groups. Our results show that the positive impact that strategies have in reducing harmful contents from documents has the side effect of increasing the underrepresentation of vulnerable groups to discrimination in datasets.
BSAN: Behavioral State Attention Network for Modeling Mosquito Host-Seeking Behavior
Authors: Haotian Sun; Jessie Zixin Li; John M. Marshall
Venue: AAAI 2026 (main)

Abstract: Understanding the complex host-seeking behavior of disease vectors such as mosquito is critical for predicting disease transmission and vector control. This behavior arises from a dynamic interplay between multi-modal sensory cues and internal behavioral states, a process challenging traditional ODE frameworks due to its inherent stochasticity and discrete, state-based nature. We introduce the Behavioral State Attention Network (BSAN), a deep learning architecture designed to model the underlying sensorimotor computations of this behavior. BSAN utilizes a recurrent neural network (RNN) with an LSTM core to process temporal sequences, incorporating a variational encoder to capture the randomness of flight paths and a Mixture Density Network (MDN) to predict multi-modal velocity distributions. The architecture explicitly models distinct behavioral states, such as $CO_2$ plume tracking and thermal approach, through a Mixture-of-Experts (MoE) framework, and learns to interpretably integrate olfactory, thermal, and visual inputs using a cross-modal attention mechanism. The network generates realistic flight trajectories that exhibit emergent host-seeking behaviors. By providing both trajectory predictions and interpretable behavioral primitives, BSAN serves as a framework for downstream applications in landscape genomics and vector control, enabling the prediction of mosquito population connectivity through environment-specific movement kernels.
ESG-Bench: Benchmarking Long-Context ESG Reports for Hallucination Mitigation
Authors: Siqi Sun; Ben Peng Wu; Mali Jin; Peizhen Bai; Hanpei Zhang; Xingyi Song
Venue: AAAI 2026 (main)

Abstract: As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal requirement in many regions and a key channel for documenting sustainability practices and assessing firms’ long-term and ethical performance. However, the length and complexity of ESG disclosures make them difficult to interpret and automate the analysis reliably. To support scalable and trustworthy analysis, this paper introduces ESG-Bench, a benchmark dataset for ESG report understanding and hallucination mitigation in large language models (LLMs). ESG-Bench contains human-annotated question–answer (QA) pairs grounded in real-world ESG report contexts, with fine-grained labels indicating whether model outputs are factually supported or hallucinated. Framing ESG report analysis as a QA task with verifiability constraints enables systematic evaluation of LLMs’ ability to extract and reason over ESG content and provides a new use case: mitigating hallucinations in socially sensitive, compliance-critical settings. We design task-specific Chain-of-Thought (CoT) prompting strategies and fine-tune multiple state-of-the-art LLMs on ESG-Bench using CoT-annotated rationales. Our experiments show that these CoT-based methods substantially outperform standard prompting and direct fine-tuning in reducing hallucinations, and that the gains transfer to existing QA benchmarks beyond the ESG domain.
Achieving Fairness Without Harm via Selective Demographic Experts
Authors: Xuwei Tan; Yuanlong Wang; Thai-Hoang Pham; Ping Zhang; Xueru Zhang
Venue: AAAI 2026 (main)

Abstract: As machine learning systems become increasingly integrated into human-centered domains such as healthcare, ensuring fairness while maintaining high predictive performance is critical. Existing bias mitigation techniques often impose a trade-off between fairness and accuracy, inadvertently degrading performance for certain demographic groups. In high-stakes domains like clinical diagnosis, such trade-offs are ethically and practically unacceptable. In this study, we propose a fairness-without-harm approach by learning distinct representations for different demographic groups and selectively applying demographic experts consisting of group-specific representations and personalized classifiers through a no-harm constrained selection. We evaluate our approach on three real-world medical datasets—covering eye disease, skin cancer, and X-ray diagnosis—as well as two face datasets. Extensive empirical results demonstrate the effectiveness of our approach in achieving fairness without harm.
AI in the Wild: A Meta-Analytic Evaluation of Depression Detection from Social Media Data
Authors: Xianglu Tang; Joyee W. Jin; Emily Ma; Xingyu Li
Venue: AAAI 2026 (main)

Abstract: As AI moves into high-stakes, human-centered settings, we still lack clear evidence on when and why these systems succeed or fail. This meta-analysis synthesizes all empirical studies published between 2022 and 2025 that use social-media data to predict depression, quantifying pooled accuracy and testing study-level moderators. By showing how data sources and model architecture shape outcomes, we offer an empirical foundation for a more reliable, socially aware deployment of AI in mental health.

Across 67 studies, overall performance is strong (pooled r ≈ 0.80) and climbs even higher in 2024, driven by deep, transformer-based and multimodal systems. The gains, however, are uneven: post-level binary detectors improve the most, user-level severity estimation still lags, and results hinge as much on label provenance and platform context as on model size—highlighting a persistent gap between leaderboard success and clinically meaningful reliability.

To address that gap, we propose a Psych-Aligned Evaluation Framework that maps predictions onto validated symptom dimensions and adds three deployment-critical tests—PHQ error, temporal stability, and clinician agreement. This framework converts single-number benchmarks into a multidimensional yardstick for real-world, psychologically meaningful depression detection.
ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model
Authors: Krishu K Thapa; Supriya Savalkar; Bhupinderjeet Singh; Trong Nghia Hoang; Kirti Rajagopalan; Ananth Kalyanaraman
Venue: AAAI 2026 (main)

Abstract: Various complex water management decisions are made in snow-dominant watersheds with the knowledge of Snow-Water Equivalent (SWE)---a key measure widely used to estimate the water content of a snowpack. However, forecasting SWE is challenging because SWE is influenced by various factors including topography and an array of environmental conditions, and has therefore been observed to be spatio-temporally variable. Classical approaches to SWE forecasting have not adequately utilized these spatial/temporal correlations, nor do they provide uncertainty estimates --- which can be of significant value to the decision maker. In this paper, we present ForeSWE, a new probabilistic spatio-temporal forecasting model that integrates deep learning and classical probabilistic techniques. The resulting model features a combination of an attention mechanism to integrate spatiotemporal features and interactions, alongside a Gaussian process module that provides principled quantification of prediction uncertainty. We evaluate the model on data from 512 Snow Telemetry (SNOTEL) stations in the Western US. The results show significant improvements in both forecasting accuracy and prediction interval compared to state-of-the-art  approaches. The results also serve to highlight the efficacy in uncertainty estimates between different approaches. Collectively, these findings have provided a platform for deployment and feedback by the water management community.
BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models
Authors: Catherine Villeneuve; Benjamin Akera; Mélisande Teng; David Rolnick
Venue: AAAI 2026 (main)

Abstract: Species distribution models (SDMs), which aim to predict species occurrence based on environmental variables, are widely used to monitor and respond to biodiversity change. Recent deep learning advances for SDMs have been shown to perform well on complex and heterogeneous datasets, but their effectiveness remains limited by spatial biases in the data. In this paper, we revisit deep SDMs from a Bayesian perspective and introduce BATIS, a novel and practical framework wherein prior predictions are updated iteratively using limited observational data. Models must appropriately capture both aleatoric and epistemic uncertainty to effectively combine fine-grained local insights with broader ecological patterns. We benchmark an extensive set of uncertainty quantification approaches on a novel dataset including citizen science observations from the eBird platform. Our empirical study shows how Bayesian deep learning approaches can greatly improve the reliability of SDMs in data-scarce locations, which can contribute to ecological understanding and conservation efforts.
RipAlert: A Future-Frame-Aware Framework for Rip Current Forecasting and Early Alerting
Authors: Meng Wan; Qi Su; Zhixin Xia; Kanglin Chen; Jue Wang; Tiantian Liu; Rongqiang Cao; Hui Cui; Peng Shi; Yangang Wang; Liqiang Feng; Zhenbing Zhao
Venue: AAAI 2026 (main)

Abstract: Rip currents cause over 100 drowning deaths and more than 30,000 rescues annually in the United States, posing a severe threat to beach safety worldwide. However, most existing detection methods are reactive, identifying rip currents only after they form, leaving limited time for intervention. We propose RipAlert, a future-frame-aware framework that forecasts near-future coastal dynamics and proactively identifies rip current risks. We design a region-sensitive optical flow prediction method with a novel entropy-based object detector to capture early-stage reverse-flow anomalies. Unlike static-image approaches, RipAlert leverages temporal motion patterns to detect rip currents up to 5 seconds before they visibly form. To support real-world deployment, we design a lightweight mobile application and release a curated dataset with over 2,000 annotated images. Experiments on the RipVIS benchmark show that our approach achieves state-of-the-art performance. The system has been deployed at high-risk beaches in China, issuing successful early warnings over real-world events. Our work advances AI-driven coastal safety and contributes to SDG 3 (Good Health and Well-Being) and SDG 13 (Climate Action).
CARE-Bench: A Benchmark of Diverse Client Simulations Guided by Expert Principles for Evaluating LLMs in Psychological Counseling
Authors: Bichen Wang; Yixin Sun; Junzhe Wang; Hao Yang; Xing Fu; Yanyan Zhao; Si Wei; Shijin Wang; Bing Qin
Venue: AAAI 2026 (main)

Abstract: The mismatch between the growing demand for psychological counseling and the limited availability of services has motivated research into the application of Large Language Models (LLMs) in this domain. Consequently, there is a need for a robust and unified benchmark to assess the counseling competence of various LLMs. Existing works, however, are limited by unprofessional client simulation, static question-and-answer evaluation formats, and unidimensional metrics. These limitations hinder their effectiveness in assessing a model's comprehensive ability to handle diverse and complex clients. To address this gap, we introduce CARE-Bench, a dynamic and interactive automated benchmark. It is built upon diverse client profiles derived from real-world counseling cases and simulated according to expert guidelines. CARE-Bench provides a multidimensional performance evaluation grounded in established psychological scales. Using CARE-Bench, we evaluate several general-purpose LLMs and specialized counseling models, revealing their current limitations. In collaboration with psychologists, we conduct a detailed analysis of the reasons for LLMs' failures when interacting with clients of different types, which provides directions for developing more comprehensive, universal, and effective counseling models.
Multi-Agent VLMs Guided Self-Training with PNU Loss for Low-Resource Offensive Content Detection
Authors: Han Wang; Deyi Ji; Junyu Lu; Lanyun Zhu; Hailong Zhang; Haiyang Wu; Liqun Liu; Peng Shu; Roy Ka-Wei Lee
Venue: AAAI 2026 (main)

Abstract: Accurate detection of offensive content on social media demands high-quality labeled data; however, such data is often scarce due to the low prevalence of offensive instances and the high cost of manual annotation. To address this low-resource challenge, we propose a self-training framework that leverages abundant unlabeled data through collaborative pseudo-labeling. Starting with a lightweight classifier trained on limited labeled data, our method iteratively assigns pseudo-labels to unlabeled instances with the support of Multi-Agent Vision-Language Models (MA-VLMs). Unlabeled data on which the classifier and MA-VLMs agree are designated as the Agreed-Unknown set, while conflicting samples form the Disagreed-Unknown set. To enhance label reliability, MA-VLMs simulate dual perspectives, moderator and user, capturing both regulatory and subjective viewpoints. The classifier is optimized using a novel Positive-Negative-Unlabeled (PNU) loss, which jointly exploits labeled, Agreed-Unknown, and Disagreed-Unknown data while mitigating pseudo-label noise. Experiments on benchmark datasets demonstrate that our framework substantially outperforms baselines under limited supervision and approaches the performance of large-scale models.
EA: Managing Green Data Centers Using Deep Reinforcement Learning Without Discounting
Authors: Peijian Wang; Thu Nguyen
Venue: AAAI 2026 (main)

Abstract: For the past two decades,  sustainability and carbon reduction have emerged as critical factors for data center (DC) design and operation.
A set of advances has been encapsulated in green DCs with the onsite generation of renewable energy and efficient cooling systems.
In this paper, we study how to apply Deep Reinforcement Learning (DRL) to optimize green DC operation. 
Green DC management is typically an infinite-horizon problem with exogenous stochastic input processes. 
We propose EA, a framework that applies DRL to the typical infinite-horizon problem without discounting.
EA approximates the infinite-horizon problem with a finite-horizon one. In this approach, it is important to avoid actions optimized for the end of the finite-horizon problem but inappropriate for the true infinite-horizon one. EA addresses this challenge by combining a stationary policy with the fact that green DC management has repeating patterns (e.g., daily temperature, solar energy generation, and workload). 
We apply EA to the management of a green DC with onsite solar energy generation and a hybrid cooling system that includes ``free'' cooling. Evaluation results show that EA successfully learns important principles such as delaying deferrable jobs to solar-rich times and gracefully maintaining inside temperature. Further, EA outperforms three state-of-the-art DRL algorithms, realizing the greatest benefits on days with high outside temperature and high solar generation. While we evaluate EA in the specific context of a green DC, we believe that EA is a promising approach for more general system management settings.
Shaping Human–AI Collaboration in Education: Effects of AI-Assisted Decision-Making Paradigms and Human–AI Decision Consistency on Pre-Service Teachers’ Psychological States and Performance
Authors: Yingying Wang; Qin Ni; Haoxin Xu; Jiaqi Yin; Tingjiang Wei
Venue: AAAI 2026 (main)

Abstract: Artificial intelligence is playing an increasingly important role in supporting decision-making, particularly in educational contexts, where it serves as a critical tool to assist teacher judgment and optimize instructional decisions. However, limited research has examined how different AI-assisted decision-making paradigms influence the Performance of human-AI collaboration, as well as the underlying psychological mechanisms and causal pathways. Therefore, this study investigated 59 pre-service teachers to examine how AI-assisted decision-making paradigms and human-AI consistency influenced their psychological states and task performance. Specifically, this study employed a two-factor mixed experimental design, with the AI-assisted decision-making paradigms as the between-subjects factor and human-AI consistency as the within-subjects factor. Data were analyzed using the Bayesian cumulative link mixed model and structural equation modeling. 
The results reveal that AI-assisted decision-making paradigms do not have a significant direct effect on task performance. However, when the moderating role of human-AI decision consistency is taken into account, the effect of AI-assisted decision-making paradigms on task performance can exert its influence indirectly through a sequential psychological pathway involving users’ confidence and their trust in the AI. Consistency between human and AI decisions not only significantly enhances users’ trust in AI, confidence, and task performance, but the proportion of consistent decisions also significantly moderates the impact of AI-assisted decision-making paradigms on users’ confidence levels. Notably, our findings indicate that users maintain a moderately level of trust in AI even when their decisions diverge from those of AI. In summary, this study highlights the mediating mechanism by which AI-assisted decision-making paradigms influence task performance through psychological states and identifies the moderating role of human-AI consistency in this pathway. These findings advance the theoretical understanding of human-AI interaction models in educational contexts and offer mechanistic insights to guide the optimization of instructional AI systems.
Trustworthy Classification for Complex Social Surveys: A Memory-Enhanced Hierarchical Framework with Calibrated Uncertainty
Authors: Zeqiang Wang; Rebecca Oldroyd; Yuqi Wang; Jiageng Wu; Jie Yang; Wei Wang; Nishanth R. Sastry; Jon Johnson; Suparna De
Venue: AAAI 2026 (main)

Abstract: Automated classification of complex social survey questionnaires is crucial for large-scale social science research but faces significant reliability challenges due to intricate hierarchical label structures, severe class imbalance, semantic ambiguity, and incomplete data coverage. Conventional classification methods often struggle with these combined complexities, yielding results that lack trustworthiness. We introduce HOCM, a framework designed for trustworthy classification in complex, real-world taxonomies. It features two synergistic components: (1) memory-enhanced contrastive learning, tailored to learn robust representations from noisy, imbalanced data by leveraging quality-aware category memory banks; and (2) hierarchical uncertainty calibration, which enforces taxonomic consistency while providing reliable confidence estimates and identifying inputs falling outside well-represented known categories. Our evaluation on a large-scale, real-world social survey dataset—a challenging exemplar of our target problem class—demonstrates that HOCM maintains strong accuracy on known classes while effectively identifying uncertain cases, significantly boosting accuracy on confident predictions. Furthermore, it adeptly detects low-resource/unknown categories. HOCM provides a more reliable automated classification tool, enabling efficient expert review and enhancing the trustworthiness of analysis in domains with complex, hierarchical data.
Fair Graph Learning with Limited Sensitive Attribute Information
Authors: Zichong Wang; Jie Yang; Jun Zhuang; Puqing Jiang; Mingzhe Chen; Ye Hu; Wenbin Zhang
Venue: AAAI 2026 (main)

Abstract: Graph neural networks (GNNs) excel at modeling graph-structured data but often inherit and amplify biases, leading to substantial efforts in developing fair GNNs. However, most existing approaches assume full access to sensitive attribute information, which is often impractical in real-world scenarios due to privacy concerns or risks of discrimination. To address this limitation, this paper focuses on graph fairness with limited sensitive attribute information, ensuring applicability to real-world contexts where current methods fall short. Specifically, we introduce an innovative fairness optimization strategy, propose a novel framework named FGLISA, and provide a theoretical perspective linking limited sensitive attribute information access to fairness objectives, thus enabling fair graph learning in real-world applications with limited sensitive attribute information. Experiments on diverse real-world datasets and tasks validate the effectiveness of our approach in achieving both fairness and predictive performance.
EgoEMS: A High-Fidelity Multimodal Egocentric Dataset for Cognitive Assistance in Emergency Medical Services
Authors: Keshara Weerasinghe; Xueren Ge; Tessa Heick; Lahiru Nuwan Wijayasingha; Anthony Cortez; Abhishek Satpathy; John Stankovic; Homa Alemzadeh
Venue: AAAI 2026 (main)

Abstract: Emergency Medical Services (EMS) are critical to patient survival in emergencies, but first responders often face intense cognitive demands in high-stakes situations. AI cognitive assistants, acting as virtual partners, have the potential to ease this burden by supporting real-time data collection and decision making. In pursuit of this vision, we introduce EgoEMS, the first end-to-end, high-fidelity, multimodal, multiperson dataset capturing over 20 hours of realistic, procedural EMS activities from an egocentric view in 233 simulated emergency scenarios performed by 62 participants, including 46 EMS professionals. Developed in collaboration with EMS experts and aligned with national standards, EgoEMS is captured using an open-source, low-cost, and replicable data collection system and is annotated with keysteps, timestamped audio transcripts with speaker diarization, action quality metrics, and bounding boxes with segmentation masks. Emphasizing realism, the dataset includes responder-patient interactions reflecting real-world emergency dynamics. 
We also present a suite of benchmarks for real-time multimodal keystep recognition and action quality estimation, essential for developing AI support tools for EMS. 
We hope EgoEMS inspires the research community to push the boundaries of intelligent EMS systems and ultimately contribute to improved patient outcomes.