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People who stutter (PWS) face systemic exclusion in today’s voice-driven society, where access to voice assistants, authentication systems, and remote work tools increasingly depends on fluent speech. Current automatic speech recognition (ASR) systems, trained predominantly on fluent speech, fail to serve millions of PWS worldwide. We present STEAMROLLER, a real time system that transforms stuttered speech into fluent output through a novel multi-stage, multi-agent AI pipeline. Our approach addresses three critical technical challenges: (1) the difficulty of direct speech to speech conversion for disfluent input, (2) semantic distortions introduced during ASR transcription of stuttered speech, and (3) latency constraints for real time communication. STEAMROLLER employs a three stage architecture comprising ASR transcription, multi-agent text repair, and speech synthesis, where our core innovation lies in a collaborative multi-agent framework that iteratively refines transcripts while preserving semantic intent. Experiments on the FluencyBank dataset and a user study demonstrates clear word error rate (WER) reduction and strong user satisfaction. Beyond immediate accessibility benefits, fine tuning ASR on STEAMROLLER repaired speech further yields additional WER improvements, creating a pathway toward inclusive AI ecosystems.

AAAI 2026 Main Conference

STEAMROLLER: A Multi-Agent System for Inclusive Automatic Speech Recognition for People Who Stutter

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

In applications across agriculture, ecology, and human development, machine learning with satellite imagery (SatML) is limited by the sparsity of labeled training data. While satellite data cover the globe, labeled training datasets for SatML are often small, spatially clustered, and collected for other purposes (e.g., administrative surveys or field measurements). Despite the pervasiveness of this issue in practice, past SatML research has largely focused on new model architectures and training algorithms to handle scarce training data, rather than modeling data conditions directly. This leaves scientists and policymakers who wish to use SatML for large-scale monitoring uncertain about whether and how to collect additional data to maximize performance. Here, we present the first problem formulation for the optimization of spatial training data in the presence of heterogeneous data collection costs and realistic budget constraints, as well as novel methods for addressing this problem. In experiments simulating different problem settings across three continents and four tasks, our strategies reveal substantial gains from sample optimization. Further experiments delineate settings for which optimized sampling is particularly effective. The problem formulation and methods we introduce are designed to generalize across application domains for SatML; we put special emphasis on a specific problem setting where our coauthors can immediately use our findings to augment clustered agricultural surveys for SatML monitoring in Togo.

Mapping on a Budget: Optimizing Spatial Data Collection for ML

Electric vehicles (EVs) are essential for sustainable mobility and combating climate change. EV performance heavily relies on lithium-ion batteries (LIBs), which degrade over time, reducing driving range and increasing maintenance costs. Prolonged exposure to high states of charge (SOC) accelerates battery degradation, which can be mitigated by delaying full charging (\ours). However, successful implementation of \ours requires accurate predictions of user departure times to ensure vehicles reach full charge precisely before use. In this work, we propose Transformer-based real-time-to-event (TTE) model for accurate EV departure prediction. Our approach models each day as a TTE sequence by discretizing the timeline into grids, which are represented as tokens. Unlike previous methods primarily dependent on temporal dependency from historical patterns, our method leverages streaming contextual behavioral and environmental information to predict departures. Evaluation on a real-world study involving 93 users and passive smartphone data demonstrates that our method effectively captures irregular departure patterns within individual routines, significantly outperforming baseline models. Personalized fine-tuning further improves prediction accuracy, highlighting our approach’s potential for practical deployment of the \ours algorithm and its contribution to sustainable transportation systems.

Enabling Delayed-Full Charging Through Transformer-Based Real-Time-to-Departure Modeling for EV Battery Longevity

As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal obligation in many regions. These reports serve as a primary mechanism for organizations to document sustainability practices and for stakeholders to evaluate long-term viability and ethical performance. Ensuring regulatory compliance demands disclosures that are accurate, transparent, and verifiable. However, the complexity and scale of ESG disclosures present challenges for interpretation and automated analysis. To facilitate scalable and trustworthy analysis of these reports, this paper introduces ESG-Bench, a novel benchmark dataset aimed at advancing research in ESG report understanding and hallucination mitigation for large language models (LLMs). ESG-Bench consists of human-annotated question–answer (QA) pairs grounded in real-world ESG report contexts, along with fine-grained labels indicating whether model responses are factually supported or hallucinated. By framing ESG report analysis as a QA task with verifiability constraints, ESG-Bench enables systematic evaluation of LLMs' ability to extract and reason over ESG content.
We also uncover a previously unexplored use case: applying ESG-Bench to mitigate hallucinations in socially sensitive and compliance-critical contexts. To this end, 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. Experimental results demonstrate that these CoT-based strategies substantially outperform standard prompting and direct fine-tuning, effectively mitigating hallucinations across benchmarks and highlighting the unique challenges of long-context document reasoning in the ESG setting. We also evaluate our approach across existing QA benchmarks to assess generalization beyond the ESG domain.

ESG-Bench: Benchmarking Long-Context ESG Reports for Hallucination Mitigation

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 ill-suited for 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.

BSAN: Behavioral State Attention Network for Modeling Mosquito Host-Seeking Behavior

Understanding human attitudes, preferences, and behaviors through social surveys is essential for academic research and policymaking. Yet traditional surveys face persistent challenges, including fixed-question formats, high costs, limited adaptability, and difficulties ensuring cross-cultural equivalence. While recent studies explore large language models (LLMs) to simulate survey responses, most are limited to structured questions, overlook the entire survey process, and risks under-representing marginalized groups due to training data biases. We introduce AlignSurvey, the first benchmark that systematically replicates and evaluates the full social survey pipeline using LLMs. It defines four tasks aligned with key survey stages: social role modeling, semi-structured interview modeling, attitude stance modeling and survey response modeling. It also provides task-specific evaluation metrics to assess alignment fidelity, consistency, and fairness at both individual and group levels, with a focus on demographic diversity. To support AlignSurvey, we construct a multi-tiered dataset architecture: (i) the Social Foundation Corpus, a cross-national resource with 44K+ interview dialogues and 400K+ structured survey records; and (ii) a suite of Entire-Pipeline Survey Datasets, including the expert-annotated AlignSurvey-Expert (ASE) and two nationally representative surveys for cross-cultural evaluation. We release the SurveyLM family, obtained through two-stage fine-tuning of open-source LLMs, and offer reference models for evaluating domain-specific alignment. All datasets, models, and tools are available at github and huggingface to support transparent and socially responsible research.

AlignSurvey: A Comprehensive Benchmark for Human Preferences Alignment in Social Surveys

Image classification systems often inherit biases from uneven group representation in training data. For example, in face datasets for hair color classification, blond hair may be disproportionately associated with females, reinforcing stereotypes. A recent approach leverages the Stable Diffusion model to generate balanced training data, but these models often struggle to preserve the original data distribution. In this work, we explore multiple diffusion-finetuning techniques, e.g., LoRA and DreamBooth, to generate images that more accurately represent each training group by learning directly from their samples. Additionally, in order to prevent a single DreamBooth model from being overwhelmed by excessive intra-group variations, we explore a technique of clustering images within each group and train a DreamBooth model per cluster. These models are then used to generate group-balanced data for pretraining, followed by fine-tuning on real data. Experiments on multiple benchmarks demonstrate that the studied finetuning approaches outperform vanilla Stable Diffusion on average and achieve results comparable to SOTA debiasing techniques like Group-DRO, while surpassing them as the dataset bias severity increases. Code will be made public upon acceptance.

Harnessing Diffusion-Generated Synthetic Images for Fair Image Classification

Personalized insulin therapy for individuals with Type 1 Diabetes via closed‑loop artificial pancreas systems requires rapid adaptation of dosing strategies to each patient's unique insulin response. However, learning patient‑specific policies from scratch demands extensive exploration, which is often impractical. In this work, we study a framework that integrates insulin-response-informed transfer learning with model-based reinforcement learning for insulin dosing. We first train an LSTM‑based insulin responsiveness predictor on virtual patients, using their glucose, insulin, and meal history to forecast future glucose levels. Analysis of insulin responsiveness of in-silico patients uncovers natural insulin‑response groups characterized by similar sensitivity and dynamics profiles. For a new patient, we identify a representative model from their response group and use it to generate synthetic trajectories. These trajectories are integrated into an enhanced H-step Deep Dyna-Q algorithm, enabling accelerated policy optimization through model-based planning. The dynamics model trained entirely in simulation achieves 91.31\% accuracy in predicting blood glucose ranges on the Ohio Type 1 Diabetes dataset, indicating strong zero-shot generalization. Additionally, we find that bootstrapping a new patient with a physiologically-matched reference model accelerates convergence of effective dosing policies across in-silico cohorts of children, adolescents, and adults. These findings suggest that leveraging response-group-specific synthetic experience can expedite personalized insulin therapy, offering a promising pathway towards clinical validation.

Bootstrapping Personalized Insulin Therapy via Model-Based Reinforcement Learning: An In Silico Study

Existing approaches to complaint analysis largely rely on unimodal, short-form content such as tweets or product reviews. This work advances the field by leveraging multimodal, multi-turn customer support dialogues—where users often share both textual complaints and visual evidence (e.g., screenshots, product photos)—to enable fine-grained classification of complaint aspects and severity. We introduce $\textit{VALOR}$, a Validation-Aware Learner with Expert Routing, tailored for this multimodal setting. It employs a multi-expert reasoning setup using large-scale generative models with Chain-of-Thought (CoT) prompting for nuanced decision-making. To ensure coherence between modalities, a semantic alignment score is computed and integrated into the final classification through a meta-fusion strategy. In alignment with the United Nations Sustainable Development Goals (UN SDGs), the proposed framework supports SDG 9 (Industry, Innovation and Infrastructure) by advancing AI-driven tools for robust, scalable, and context-aware service infrastructure. Further, by enabling structured analysis of complaint narratives and visual context, it contributes to SDG 12 (Responsible Consumption and Production) by promoting more responsive product design and improved accountability in consumer services. We evaluate $\textit{VALOR}$ on a curated multimodal complaint dataset annotated with fine-grained aspect and severity labels, showing that it consistently outperforms baseline models, especially in complex complaint scenarios where information is distributed across text and images. This study underscores the value of multimodal interaction and expert validation in practical complaint understanding systems. Resources related to data and codes are available here: https://anonymous.4open.science/r/672.

Talk, Snap, Complain: Validation-Aware Multimodal Expert Framework for Fine-Grained Customer Grievances

Although Large language models (LLMs) are increasingly implicated in interpersonal and societal decision-making, their ability to navigate explicit conflicts between legitimately different cultural value systems remains largely unexamined. Existing benchmarks predominantly target cultural knowledge (CulturalBench), value prediction (WorldValuesBench), or single-axis bias diagnostics (CDEval); none, however, evaluate how LLMs adjudicate when multiple culturally grounded values directly clash. We address this gap with CCD-Bench, a benchmark that assesses LLM decision-making under overt cross-cultural value conflict. CCD-Bench comprises 2,182 open-ended dilemmas spanning seven domains, each paired with exactly ten anonymized response options corresponding to the ten GLOBE cultural clusters, which represent the organizational behavior of 62 societies. These dilemmas are presented using a Stratified Latin Square to mitigate ordering effects. We evaluate 17 leading non-reasoning LLMs. LLMs disproportionately prefer Nordic Europe (mean 20.2\%) and Germanic Europe (12.4\%), while the options for Eastern Europe and the Middle East \& North Africa are underrepresented (5.6–5.8\%). Although 87.9\% of rationales reference two or more GLOBE dimensions, this apparent pluralism is largely superficial: LLMs repeatedly recombine a narrow subset of Future Orientation and Performance Orientation, and rarely ground choices in Assertiveness or Gender Egalitarianism (both $<$3\%). Ordering effects are negligible (Cramér’s $V < 0.10$), and symmetrized KL divergence indicates LLMs clustering by developer lineage rather than geography. Taken together, these patterns suggest that contemporary alignment pipelines encourage a consensus-oriented, progress-centric worldview that underserves scenarios demanding explicit power negotiation, rights-based reasoning, or gender-aware analysis. CCD-Bench thus shifts evaluation from isolated bias detection to pluralistic decision making, revealing that current LLMs maintain Western-centric, consensus-oriented preferences even when confronted with ten equally valid, culturally diverse alternatives, and underscoring the need for alignment strategies that substantively engage with diverse worldviews.

CCD-Bench: Probing Cultural Conflict in Large Language Model Decision-Making

Classifiers trained on historical data are deployed in the real-world to automate decisions from hiring to loan issuance. Judging the fairness and efficiency of these systems, and their human counterparts, is a complex and important topic studied across both computational and social sciences. One common way to address bias in classifiers is to resample the training data to offset distributional disparities. In the hiring domain, where results may vary by a protected class, many interventions from the literature equalize the hiring rate within the training set to alleviate bias in the resulting classifier. While simple and seemingly effective, these methods have typically only been evaluated using data obtained through convenience samples, e.g., results of some real world hiring process, introducing selection and label bias into the evaluation. In the social and health sciences, audit studies, in which fictitious ``testers'' (resumes) are sent to subjects (job openings) in a randomized control trial, provide high quality data that support rigorous estimates of discrimination by controlling for confounding factors. In this paper, we investigate how data from audit studies can be used to improve our ability to both train and evaluate automated hiring algorithms. We find that audit data of real-world hiring reveals cases where equalizing base rates across classes \emph{appears} to achieve parity using traditional measures, but in fact has $\approx$ 10\% disparity when measured appropriately. We also show that corrections based on individual treatment effect estimation methods combined with audit study data can overcome these issues, underscoring the need for rigorous data collection in fairness research.

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