Publications

From Large-Scale Winds to Urban Decision Making: A Cross-Scale Framework for Wind-Aware UAV Navigation
Shaoxiang Qin
Fuyuan Lyu
Di Zhou
Xue Liu
Xiongye Xiao
Anima Anandkumar
Liangzhu Leon Wang
Large-scale weather and climate models provide reliable wind information at regional scales, yet their outputs are typically too coarse for … (see more)direct UAV decision making in geometrically complex urban environments. This paper investigates how large-scale atmospheric information can be transformed into city-scale wind representations and utilized for downstream navigation decisions. We propose a cross-scale prediction and decision framework that takes background wind conditions from existing weather or climate models and combines them with detailed 3D urban geometry to predict time-averaged urban wind fields using a 3D neural operator. The predicted wind fields are then incorporated into a wind-aware UAV trajectory optimization problem to minimize energy consumption under kinematic feasibility and safety constraints. By comparing trajectories planned against a wind-agnostic baseline, we demonstrate significant efficiency gains enabled by AI-predicted wind, specifically 10.3% savings in tailwinds, 7.7% in headwinds, and 3.9% in crosswind conditions. These results indicate that learning decision-relevant urban wind representations offers a practical pathway for bridging large-scale atmospheric information and fine-scale urban decision making.
2026-02-28
AI_and_PDE @ International Conference on Learning Representations (poster)
openreview.net
openreview.net
Objective Misalignment in LLM-based Multi Agent Social Deception Game
Marylou Fauchard
Florian Carichon
Margarida Carvalho
Golnoosh Farnadi
Large language model–based multi-agent systems have attracted increasing attention for their strong performance in collaborative tasks and… (see more) social simulations. However, these interactive settings also introduce vulnerabilities, as a single agent's hidden goals and misaligned behavior can propagate misleading or malicious information throughout the system. In this work, we study these risks in the context of social deception games. We focus on the Werewolf Game, which requires agents to reason, communicate, and collaborate under asymmetric and incomplete information. We modify the individual objectives of some agents to induce benevolent, individualistic, and malevolent strategies that can make agents depart from the objectives of their own team. We evaluate how objective divergence affects game outcomes, collaboration, and goal satisfaction. Misaligned agents often succeed in achieving their own objectives, with effects amplified by role-based power asymmetries. Qualitative analyses further show that agents remain coherent and adaptive, strategically adjusting their reasoning, communication, voting behavior, and influence on group dynamics. These results indicate that risks in LLM-based multi-agent systems extend beyond collaborative task settings and persist even in environments where competition is structurally expected.
2026-02-28
AIWILD @ International Conference on Learning Representations (published)
openreview.net
openreview.net
Piezoelectric tuning of thermal conductivity in nano-architected gallium nitride metamaterials
Jun Cai
Alireza Seyedkanani
Benyamin Shahryari
Hsiu-Chin Lin
Abdolhamid Akbarzadeh
2026-02-28
International Journal of Heat and Mass Transfer (published)
doi.org
PPO-CIS : A deep reinforcement learning framework for real-time toxicity detection in social media
Arezo Bodaghi
Benjamin C.M. Fung
Ketra A. Schmitt
2026-02-28
Knowledge-Based Systems (published)
doi.org
Scalable Multi-Agent Reinforcement Learning Framework for Multi-Machine Tending
Abdalwhab Abdalwhab
Giovanni Beltrame
David St-Onge
Robotic manipulators hold significant untapped potential for manufacturing industries, particularly when deployed in multi-robot configurati… (see more)ons that can enhance resource utilization, increase throughput, and reduce costs. However, industrial manipulators typically operate in isolated one-robot, one-machine setups, limiting both utilization and scalability. Even mobile robot implementations generally rely on centralized architectures, creating vulnerability to single points of failure and requiring robust communication infrastructure. This paper introduces SMAPPO (Scalable Multi-Agent Proximal Policy Optimization), a scalable input-size invariant multi-agent reinforcement learning model for decentralized multi-robot management in industrial environments. MAPPO (Multi-Agent Proximal Policy Optimization) represents the current state-of-the-art approach. We optimized an existing simulator to handle complex multi-agent reinforcement learning scenarios and designed a new multi-machine tending scenario for evaluation. Our novel observation encoder enables SMAPPO to handle varying numbers of agents, machines, and storage areas with minimal or no retraining. Results demonstrate SMAPPO's superior performance compared to the state-of-the-art MAPPO across multiple conditions: full retraining (up to 61% improvement), curriculum learning (up to 45% increased productivity and up to 49% fewer collisions), zero-shot generalization to significantly different scale scenarios (up to 272% better performance without retraining), and adaptability under extremely low initial training (up to 100% increase in parts delivery).
2026-02-28
IEEE Robotics and Automation Letters (published)
doi.org
Semantic Anchor Transport: Robust Test-Time Adaptation for Vision-Language Models
Shambhavi Mishra
Julio Silva-Rodríguez
Ismail Ben Ayed
Marco Pedersoli
Jose Dolz
Large pre-trained vision-language models (VLMs) like CLIP exhibit strong zero-shot performance but struggle under distributional shifts. We … (see more)propose Semantic Anchor Transport (SAT), a method that generates pseudo-labels for test samples by aligning visual embeddings with reliable text-based semantic anchors using Optimal Transport for batch-wise label assignment. These pseudo-labels enable efficient test-time adaptation through principled cross-modal alignment. We further incorporate multi-template distillation to leverage diverse textual clues, replicating multi-view contrastive learning without added computational cost. Extensive experiments demonstrate consistent performance gains over state-of-the-art methods across multiple benchmarks while maintaining computational efficiency.
2026-02-28
TTU_Main_Track @ International Conference on Learning Representations (published)
doi.org
openreview.net
Street review: A participatory AI-based framework for assessing streetscape inclusivity
Rashid Mushkani
Shin Koseki
Urban centers undergo social, demographic, and cultural changes that shape public street use and require systematic evaluation of public spa… (see more)ces. This study presents Street Review, a mixed-methods approach that combines participatory research with AI-based analysis to assess streetscape inclusivity. In Montréal, Canada, 28 residents participated in semi-directed interviews and image evaluations, supported by the analysis of approximately 45,000 street-view images from Mapillary. The approach produced visual analytics, such as heatmaps, to correlate subjective user ratings with physical attributes like sidewalk, maintenance, greenery, and seating. Findings reveal variations in perceptions of inclusivity and accessibility across demographic groups, demonstrating that incorporating diverse user feedback can enhance machine learning models through careful data-labeling and co-production strategies. The Street Review framework offers a systematic method for urban planners and policy analysts to inform planning, policy development, and management of public streets.
2026-02-28
Cities (published)
doi.org
arxiv.org
<b>A Systematic Literature Review of Automated Feedback Generation in Education</b><b></b>
Yajie Song
Yimei Zhang
Maria Cutumisu
Feedback that is individualized and immediate is essential to improving learning outcomes but providing it to every learner is difficult. Au… (see more)tomatic feedback generation (AFG) aims to alleviate this problem, especially with technology-enhanced learning environments. This systematic literature review of AFG in education, following the PRISMA framework, examines 34 peer-reviewed publications. The findings revealed that the reviewed studies (1) gained momentum after 2019; (2) often used secondary cognitive data to evaluate AFG approaches; (3) mainly targeted computer science domain; (4) frequently combined multiple methods to generate feedback; (5) employed multiple performance evaluations; and (6) mostly provided written feedback aimed at correcting student errors. This review also highlighted several gaps, including the lack of (1) in-depth cognitive and affective data from user studies to evaluate feedback and understand how students interpret it; (2) research on feedback use and strategies to close feedback loop; (3) AFG systems for ill-defined domains with strong transferability; (4) elaborated feedback that scaffolds problem-solving rather than giving answers; (5) feedback using multiple modalities and valences; and (6) integration of learning theories in AFG design. This review advances understanding of current AFG practices, evaluates and extends conceptual frameworks of AFG, and provides insights for future AFG design and evaluation.
2026-02-28
International Journal of Technology in Education (published)
doi.org
Understanding Representation Gaps across Scales in Tropical Tree Species Classification from Drone Imagery
Sulagna Saha
Arthur Ouaknine
Étienne Laliberté
Carol Altimas
Evan M. Gora
Adriane Esquivel Muelbert
Ian R. McGregor
César Gutiérrez
Vanessa E. Rubio
David Rolnick
Accurate classification of tropical tree species from unoccupied aerial vehicle (UAV) imagery remains challenging due to high species divers… (see more)ity and strong visual similarity among species at typical image resolutions (centimeters per pixel). In contrast, models trained on close-up citizen science photographs captured with smartphones achieve strong plant species classification performance. Recent advances in UAV data acquisition now enable the collection of close-up images that are spatially registered with top-view aerial imagery and approach the level of visual detail found in smartphone photographs, with the trade-off that such high-resolution photos cannot be acquired for many trees. In this work, we evaluate the performance of existing methods using paired top-view and close-up UAV imagery collected in a species-rich tropical forest. Through fine-tuning experiments, we quantify the performance gap between vision foundation models and in-domain generalist plant recognition models across both image types (high-resolution close-up versus coarser-resolution top-view imagery). We show that classification performance is consistently higher on close-up images than on top-view aerial imagery, and that this performance gap widens for rare species. Finally, we propose that self-supervised representation alignment across these two spatial scales offers a promising approach for integrating fine-grained visual information into canopy-level species classification models based on top-view UAV imagery. Leveraging high-resolution close-up UAV imagery to enhance canopy-level species classification could substantially improve large-scale monitoring of tropical forest biodiversity.
2026-02-28
ML4RS @ International Conference on Learning Representations (published)
openreview.net
openreview.net
When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents
Jaylen Jones
Zhehao Zhang
Yuting Ning
Eric Fosler-Lussier
Pierre-Luc St-Charles
Yoshua Bengio
Dawn Song
Yu Su
Huan Sun
Although computer-use agents (CUAs) hold significant potential to automate increasingly complex OS workflows, they can demonstrate unsafe un… (see more)intended behaviors that deviate from expected outcomes even under benign input contexts. However, exploration of this risk remains largely anecdotal, lacking concrete characterization and automated methods to proactively surface long-tail unintended behaviors under realistic CUA scenarios. To fill this gap, we introduce the first conceptual and methodological framework for unintended CUA behaviors, by defining their key characteristics, automatically eliciting them, and analyzing how they arise from benign inputs. We propose AutoElicit: an agentic framework that iteratively perturbs benign instructions using CUA execution feedback, and elicits severe harms while keeping perturbations realistic and benign. Using AutoElicit, we surface hundreds of harmful unintended behaviors from state-of-the-art CUAs such as Claude 4.5 Haiku and Opus. We further evaluate the transferability of human-verified successful perturbations, identifying persistent susceptibility to unintended behaviors across various other frontier CUAs. This work establishes a foundation for systematically analyzing unintended behaviors in realistic computer-use settings.
2026-02-28
AIWILD @ International Conference on Learning Representations (published)
doi.org
openreview.net
Comparison between DNA- and RNA-based nucleic acid amplification tests for detecting Mycoplasma pneumoniae in pediatric specimens
Boyi Jiang
Hanqing Zhao
Chao Yan
Mingxuan Wang
Zhen Wang
Yanling Feng
Shijie Wang
Jing Yuan
Yuehua Ke
2026-02-27
European Journal of Clinical Microbiology and Infectious Diseases (published)
doi.org
InfoPO: Information-Driven Policy Optimization for User-Centric Agents
Fanqi Kong
Jiayi Zhang
Mingyi Deng
Chenglin Wu
Yuyu Luo
Bang Liu
Real-world user requests to LLM agents are often underspecified. Agents must interact to acquire missing information and make correct downst… (see more)ream decisions. However, current multi-turn GRPO-based methods often rely on trajectory-level reward computation, which leads to credit assignment problems and insufficient advantage signals within rollout groups. A feasible approach is to identify valuable interaction turns at a fine granularity to drive more targeted learning. To address this, we introduce InfoPO (Information-Driven Policy Optimization), which frames multi-turn interaction as a process of active uncertainty reduction and computes an information-gain reward that credits turns whose feedback measurably changes the agent's subsequent action distribution compared to a masked-feedback counterfactual. It then combines this signal with task outcomes via an adaptive variance-gated fusion to identify information importance while maintaining task-oriented goal direction. Across diverse tasks, including intent clarification, collaborative coding, and tool-augmented decision making, InfoPO consistently outperforms prompting and multi-turn RL baselines. It also demonstrates robustness under user simulator shifts and generalizes effectively to environment-interactive tasks. Overall, InfoPO provides a principled and scalable mechanism for optimizing complex agent-user collaboration. Code is available at https://github.com/kfq20/InfoPO.
2026-02-27
arXiv (preprint)
doi.org
arxiv.org