Singapore

This paper presents the largest known benchmark dataset for road damage assessment and road alignment, and provides 18 baseline models trained on the CRASAR-U-DRIODs dataset’s post-disaster small uncrewed aerial systems (sUAS) imagery from 10 federally declared disasters, addressing three challenges within prior post-disaster road damage assessment datasets. While prior disaster road damage assessment datasets exist, there is no current state of practice, as prior public datasets have either been small-scale or reliant on low-resolution imagery insufficient for detecting phenomena of interest to emergency managers. Further, while machine learning (ML) systems have been developed for this task previously, none are known to have been operationally validated. These limitations are overcome in this work through the labeling of 657.25km of roads according to a 10-class labeling schema, followed by training and deploying ML models during the operational response to Hurricanes Debby and Helene in 2024. Motivated by observed road line misalignment in practice, 9,184 road line adjustments were provided for spatial alignment of a priori road lines, as it was found that when the 18 baseline models are deployed against real-world misaligned road lines, model performance degraded on average by 5.596% Macro IoU. If spatial alignment is not considered, approximately 8% (11km) of adverse conditions on road lines will be labeled incorrectly, with approximately 9% (59km) of road lines misaligned off the actual road. These dynamics are gaps that should be addressed by the ML, CV, and robotics
communities to enable more effective and informed decision-making during disasters.

AAAI 2026

A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagery

cv: remote sensing / geospatial ai

cv: segmentation

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

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. 

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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.

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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.

Integrating Large Language Models (LLMs) into judicial decision-making demands rigorous safety examination against non-legal influences. This paper presents a novel stress test where we evaluate LLM-generated labor dispute outcomes by introducing social media sentiment as an external pressure, critically comparing them against 10,000 real-world court judgments from China Judgments Online (CJOL). Our findings reveal significant LLM safety vulnerabilities: models exhibit inherent deviations from real rulings, and public opinion substantially amplifies these discrepancies, leading to unstable and often inflated compensation predictions. Furthermore, these safety risks are compounded across low-skilled occupational categories and emotionally charged topics. This study uncovers critical threats to judicial integrity and public trust, underscoring the urgent need for robust safeguards against non-legal influences in AI legal systems.

LLM Safety in Judicial AI: A Stress Test of Social Media Influence on Real-World Judgments

Sustainability is becoming increasingly critical in the maritime transport, encompassing both environmental and social impacts, such as Greenhouse Gas (GHG) emissions and navigational safety. Traditional vessel navigation heavily relies on human experience, often lacking autonomy and emission awareness, and is prone to human errors that may compromise safety. In this paper, we propose a Curriculum Reinforcement Learning (CRL) framework integrated with a realistic, data-driven marine simulation environment and a machine learning-based fuel consumption prediction module. The simulation environment is constructed using real-world vessel movement data and enhanced with a Diffusion Model to simulate dynamic maritime conditions. Vessel fuel consumption is estimated using historical operational data and learning-based regression. The surrounding environment is represented as image-based inputs to capture spatial complexity. We design a lightweight, policy-based CRL agent with a comprehensive reward mechanism that considers safety, emissions, timeliness, and goal completion. This framework effectively handles complex tasks progressively while ensuring stable and efficient learning in continuous action spaces. We validate the proposed approach in a sea area of the Indian Ocean, demonstrating its efficacy in enabling sustainable and safe vessel navigation.

Realistic Curriculum Reinforcement Learning for Autonomous and Sustainable Marine Vessel Navigation

The opioid crisis represents a significant moment in public health that reveals systemic shortcomings across regulatory systems, healthcare practices, corporate governance, and public policy. Analyzing how these interconnected systems simultaneously failed to protect public health requires innovative analytic approaches for exploring the vast amounts of data and documents disclosed in the UCSF-JHU Opioid Industry Documents Archive (OIDA).
The complexity, multimodal nature, and specialized characteristics of these healthcare-related legal and corporate documents necessitate more advanced methods and models tailored to specific data types and detailed annotations, ensuring the precision and professionalism in the analysis.
In this paper, we tackle this challenge by organizing the original dataset according to document attributes and constructing a benchmark with 400k training documents and 10k for testing. 
From each document, we extract rich multimodal information—including textual content, visual elements, and layout structures—to capture a comprehensive range of features. 
Using multiple AI models, we then generate a large-scale dataset comprising 360k training QA pairs and 10k testing QA pairs.
Building on this foundation, we develop domain-specific multimodal Large Language Models (LLMs) and explore the impact of multimodal inputs on task performance. To further enhance response accuracy, we incorporate historical QA pairs as contextual grounding for answering current queries.
Additionally, we incorporate page references within the answers and introduce an importance-based page classifier, further improving the precision and relevance of the information provided.
Preliminary results indicate the improvements with our AI assistant in document information extraction and question-answering tasks, 
highlighting the effectiveness of our benchmark in addressing the opioid crisis.
Data and models will be released for public research.

OIDA-QA: A Multimodal Benchmark for Analyzing the Opioid Industry Documents Archive

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 human-in-the-loop 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.

When Proxy Agents Disagree, Do Humans Mirror? Manipulating Human Behavior in Moral Dilemmas Through Agents

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.

Characterizing AI Manipulation Risks in Brazilian YouTube Climate Discourse

The increasing complexity and workload of clinical radiology leads to inevitable oversights and mistakes in their use as diagnostic tools, causing delayed treatments and sometimes life-threatening harms to patients. While large language models (LLMs) have shown remarkable progress in many tasks, their utilities in detecting and correcting errors in radiology reporting are limited. We present a novel dual-knowledge infusion framework that enhances LLMs' capability for radiology report proofreading through systematic integration of medical expertise.
Specifically, our knowledge infusion combines medical knowledge graph distillation (MKGD) with external knowledge retrieval (EXKR), enabling an effective automated approach in tackling mistakes in radiology reporting. By decomposing the complex proofreading task into three specialized stages of detection, localization, and correction, our method mirrors the systematic review process employed by expert radiologists, ensuring both precision and clinical interpretability. The dual-knowledge framework captures intricate medical relationships through structured graph representations while leveraging curated clinical expertise from reference reports.
To perform a robust, clinically relevant evaluation, we constructed a comprehensive benchmark using real-world radiology reports with error patterns derived from real-world scenarios, including speech recognition confusions, terminology ambiguities, and template-related inconsistencies, all validated by practicing radiologists. Extensive evaluations across multiple LLM architectures demonstrate substantial improvements of our approach: up to 31.56\% increase in error detection accuracy and 37.4\% reduction in processing time. Human evaluation by radiologists confirms superior clinical relevance and factual consistency compared to existing approaches.
Our framework addresses critical needs in clinical practice by enhancing report quality while reducing radiologist burden, particularly benefiting resource-constrained healthcare environments.

Error Correction in Radiology Reports: A Knowledge Distillation-Based Multi-Stage Framework

Opioid overdose is a growing global health crisis that claims more than 120,000 lives annually, of which more than half use opioids alone, without access to bystander intervention. Fatal overdose events are marked by motionlessness, respiratory depression, and hypoxemia, yet current wearable systems often rely on a single biomarker, limiting detection speed and accuracy. We present HypoxSpike, a novel ternary spiking neural network designed for real-time, multi-biomarker overdose detection for low-power neuromorphic hardware, optimized for integration into shoulder-based wearables. HypoxSpike combines motion, respiration, and oxygen saturation signals, while accounting for skin tone and body physiology, thus addressing known racial bias in pulse oximetry. Our research leverages an open-source shoulder-worn dataset from 19 patients experiencing sleep apnea, exploiting the shared physiological mechanisms underlying apnea and opioid overdose. This allows a direct comparison of our model with existing overdose detection approaches. HypoxSpike classifies three stages of hypoxemia with an average accuracy of 94%, outperforming state-of-the-art shoulder-based hypoxemia estimation while reducing false positive alert rates by 23.5%. By minimizing false positives, HypoxSpike supports accurate and power-efficient overdose detection, improving trust and usability for high-risk populations often overlooked by conventional systems.

HypoxSpike: Ternary Spiking Neural Network for Opioid Overdose Detection

Open-vocabulary object detection (OVOD) aims at detecting and recognizing objects beyond a fixed set of classes. Although region-word alignment and knowledge distillation have been explored for training a strong open-vocabulary detector, our analysis reveals three main issues (inaccurate alignment, redundant distillation, and low-quality class embedding) that limit OVOD's performance. In this paper, we explore the well-designed Tensor decomposition and Language descriptions for open-vocabulary object Detection (called TLDet).
Proposals with the highest similarity score often correspond to discriminative but incomplete regions (e.g., object heads), resulting in inaccurate region-word alignment. To mitigate this issue, we propose a low-rank proposal filtering module that quantitatively assesses the completeness of each proposal by performing singular value decomposition and computing the sum of its singular values. This allows the model to reduce discriminative proposals and enhance the precision of alignment between visual regions and textual concepts. Furthermore, to mitigate redundant knowledge transfer, we introduce a core tensor distillation approach that decomposes teacher and student features into core tensors via Tucker decomposition and performs distillation through optimized tensor alignment. This ensures that the student acquires the most essential knowledge from the teacher. Finally, to improve the quality of class embedding, a language description enhancement method is proposed by exploring the knowledge of LLM to enrich the representations of categories during inference. Extensive experiments on popular datasets demonstrate the superior performance of our TLDet, achieving 36.1% mAP on COCO and 30.1% mask mAP on LVIS, and outperforming existing methods on novel categories.

Tensor Decomposition and Language Description for Open-Vocabulary Object Detection

Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applications. Existing defense methods often suffer from (1) high computational overhead and (2) poor generalization ability across diverse attack types.
To bridge these gaps, we propose a novel yet efficient teacher-student framework, namely Multimodal Robust Prompt Distillation (MRPD) for distilling robust 3D point cloud model.
It learns lightweight prompts by aligning student point cloud model's features with robust embeddings from three distinct teachers: a vision model processing depth projections, a high-performance 3D model, and a text encoder.
To ensure a reliable knowledge transfer, this distillation is guided by a confidence-gated mechanism which dynamically balances the contribution of all input modalities. Notably, since the distillation is all during the training stage, there is no additional computational cost at inference. Extensive experiments demonstrate that MRPD substantially outperforms state-of-the-art defense methods against a wide range of white-box and black-box attacks, while even achieving better performance on clean data.
Our work presents a new, practical paradigm for building robust 3D vision systems by efficiently harnessing multimodal knowledge.

Multimodal Robust Prompt Distillation for 3D Point Cloud Models

With the rise of vertical segmentation in real-world data, federated graph-level clustering has gained significant attention in recent years.
However, the inherent missing attributes in graph datasets held by certain clients lead to suboptimal local parameter updates and misaligned global parameter consensus. This results in knowledge shifts during negotiation to ultimately impair overall clustering performance. 
This issue remains largely underexplored in the current advanced research.
To bridge this gap, we propose a novel deep learning network called **Fed**erated Graph-level Clustering Network with **A**ttribute **I**nference (FedAI), which utilizes high-confidence prior knowledge from each domain and multi-party collaborative optimization to achieve efficient reasoning of unknown features. 
Specifically, on the client, we project high-confidence graph samples into a latent space, extracting and uploading their irreversible path digest information and structure-guided inference signal.
On the server, we first hierarchically identify affinity relationships by the improved graph kernel method. We then infer attributes of clients lacking attributes through a prior attribute-oriented inference signal, facilitating inter-client knowledge transfer for better clustering.
Experimental results on 15 cross-dataset and cross-domain non-IID graph datasets demonstrate that FedAI consistently outperforms existing methods. Our source codes are available at https://github.com/H00001/FedAI.

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