IJCAI 2026 Papers

Fairness auditing aims to assess whether a model is fair, playing a critical role in identifying potential risks in deployed AI systems. In practice, due to limited access, third-party auditors often rely on self-collected datasets (e.g., via sock-puppets), which may differ from real-world deployment scenarios. Such discrepancy can lead to inconsistencies between audit conclusions on the collected data and those in actual deployment, raising concerns about the reliability of third-party audits. This motivates a critical question: When can we trust the fairness audit conclusion derived from third-party datasets? Answering this question is challenging, as the actual deployment distribution is typically inaccessible or unobservable. To tackle this, we introduce the Consistency Radius, a metric that quantifies the maximum distribution shift under which an audit conclusion based on third-party dataset remain consistent. We further propose a convex relaxation optimization-based method to estimate the radius relying solely on model responses over the audit dataset. Leveraging this framework, third-party auditors can provide their datasets to model providers and request the magnitude of distributional discrepancy relative to the deployment distribution, enabling reliable audit conclusions without requiring any direct data access.

Educational recommendations empower personalized learning by suggesting suitable learning resources to learners, and the graph-based recommenders are widely adopted. Existing methods are mainly ID-based, which initialize learners and resources with trainable identifiers and optimize their representations solely from the interaction graph. As a result, the lack of semantic understanding of learning resources and learning paths hinders further improvements in recommendation accuracy. To alleviate the problem, we propose KLU4EduRec, which leverages large language models (LLMs) to understand resource knowledge and learning paths, thereby enhancing traditional graph-based educational recommenders. Specifically, for learning path understanding, we segment a learning path by detecting learning pattern drift in resource knowledge sequence, and prompt LLMs to infer learners' learning patterns within each segment. The segment-level patterns are then chronologically aggregated to represent the overall learning path. Besides, we prompt LLMs to summarize the core knowledge of learning resources from their content as complementary semantic signals. Finally, the resulting semantic representations are aligned and fused with structural representations learned by a graph-based recommender to enable more accurate recommendations. Extensive experiments show that KLU4EduRec greatly outperforms existing methods, including traditional ID-based methods and recent LLM-powered methods. A case study shows how the understanding of pattern drift in a learning path leads to more suitable recommendations. Our codes are available at https://github.com/DaSESmartEdu/KLU4EduRec.

Online learning systems provide accessible learning opportunities for blind or low-vision students. To support access to complex scientific materials, the speech models used in these systems need to deliver accurate scientific formula verbalization. While recent large speech language models (LSLMs) provide remarkable low-latency streaming capabilities, their potential for scientific formula verbalization remains underexplored. In this paper, we propose Formula-Speech, the first end-to-end LSLM designed for scientific formula verbalization. Specifically, we construct two high-quality scientific formula datasets with educational experts to align speech models with scientific formula verbalization patterns. We then adopt a lightweight and effective two-stage training framework, combining supervised fine-tuning for basic formula-to-speech alignment with reinforcement learning guided by a custom reward function to optimize for human-preferred verbalization. Experimental results show that our model significantly improves the verbalization performance of LSLMs and achieves state-of-the-art results across multiple scientific domains. Our code and datasets are available at https://github.com/ai4ed/FormulaSpeech.

Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalable alternative, its performance in the wild is often challenged by environmental variability. Current methods rely heavily on cloud resource, which requires continuous uploading of field data for model retraining. This approach is unsuitable for remote deployments because it consumes limited power and network connectivity. To address these constraints, this research proposes a shift from model adaptation to knowledge adaptation. We introduce an architecture that separates visual perception from reasoning, combining a visual encoder with a dynamic knowledge base. We uses an explicit knowledge base to replace implicitly encoding expert knowledge into model parameters. This method also supports knowledge sustainability by preserving expert insights in a structured form. Through cross-disciplinary collaboration with biologists and Indigenous communities, this work advances ethical AI co-development, fostering responsible and culturally informed ecosystem management.

Effective flood risk management relies on accurate forecasting, yet the ”black box” nature of state-of-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions. While existing Explainable AI (XAI) methods offer local attributions, they fail to provide the verifiable, operationally meaningful causal narratives required by disaster response authorities. To address this societal challenge, we propose Context-Aware Concept Distillation (CACD), a framework developed in collaboration with domain experts to distill opaque LSTMs into interpretable, hydrology-aware surrogate models. We introduce an unsupervised pipeline to discover a ”Hydrological Language” and a Residual Hypernetwork that dynamically modulates these concepts based on static basin characteristics. Evaluated on 5,203 basins globally, our model achieves high fidelity (Median NSE 0.70), significantly outperforming black-box baselines (e.g., Multi Layer Perceptrons) on unseen future data. By demonstrating that human-interpretable concepts are sufficient to reconstruct flood dynamics, this work balances AI accuracy with the transparency required for responsible environmental decision-making.

The rapid evolution of online scams, driven by transnational networks and mass-produced social engineering scenarios, has exposed the speed limitations of conventional detection, necessitating tighter inter-agency coordination. While LLMs show promise in scam identification, their role in accelerating integrated response frameworks remains underexplored. We propose Counter-Scam, a unified LLM-based multi-agent framework that orchestrates end-to-end response from initial detection to crime investigation. The framework first proposes safe data guidelines, emphasizing non-public scam data and secure dataset construction via scam-specific NER. Developed with insights from 37 stakeholders to reduce delays and improve analytical efficiency, the system integrates CSRA (multi-agent mitigation), CSRT (nine role-aligned NLP tasks), and CSRD (a corpus of 185,300 scam cases and 38,587 knowledge entries). Experiments show that fine-tuned sLLMs surpass commercial models with over 10% in all CSRT tasks and a 0.24 F1 improvement in scam-specific NER. This proves the framework's capability for enabling rapid, collaborative mitigation of online scam.

Multimodal meme understanding is increasingly used to analyze socially sensitive content, yet existing models often exhibit biased behavior when interpreting economic dependence and social roles under ambiguity. Many memes express economic relationships through sparse text or symbolic visual cues, providing insufficient evidence for gendered attribution. In such underspecified settings, models tend to rely on pretraining correlations, leading to hallucinated and stereotypical economic role assignments. In this work, we study gendered economic dependence in image-text memes through the lens of contextual sufficiency and identify epistemic overcommitment—inferring roles without adequate evidence—as a primary source of bias. We propose CGER-Net, a context-grounded multimodal framework that estimates whether the input provides sufficient evidence for gendered economic reasoning and applies evidence-gated inference to enable confident attribution when cues are explicit while favoring principled abstention otherwise. We evaluate CGER-Net on EconMeme-GE, a curated dataset of image-text memes annotated as Men, Women, Neutral, or Ambiguous. Across strong contemporary multimodal baselines, CGER-Net reduces Gender Overcommitment Rate by up to 44% on ambiguous instances while maintaining comparable accuracy on unambiguous cases. Human evaluation further shows that 79% of generated rationales are judged as epistemically aligned with the available evidence. These results highlight the importance of modeling when not to infer for reliable and responsible multimodal analysis.

HIV is a retrovirus that attacks the human immune system and can lead to death without proper treatment. In collaboration with the WHO and the University of Witwatersrand, we study how to improve the efficiency of HIV testing with the goal of eventual deployment, directly supporting progress toward UN Sustainable Development Goal 3.3. While prior work has demonstrated the promise of intelligent algorithms for sequential, network-based HIV testing, existing approaches rely on assumptions that are impractical in our real-world implementations. Here, we study sequential testing on incrementally revealed disease networks and introduce Policy-Embedded Graph Expansion (PEGE), a novel framework that directly embeds a generative distribution over graph expansions into the decision-making policy rather than attempting explicit topological reconstruction. We further propose Dynamics-Driven Branching (DDB), a diffusion-based graph expansion model that supports decision making in PEGE and is designed for data-limited settings where forest structures arise naturally, as in our real-world referral process. Experiments on real HIV transmission networks show that the combined approach (PEGE + DDB) consistently outperforms baselines (e.g., 17.3% improvement in discounted reward and 15.4% more HIV detections with 25% of the population tested) and explore key tradeoffs that drive solution quality.

Ocean heat extremes, including marine heatwaves and the El Ni\~no–Southern Oscillation (ENSO), exert profound impacts on marine ecosystems and socio-economic stability. Establishing robust early warning systems is critical for proactive risk management; however, conventional predictive models often fail to generalize to the intensifying, non-stationary extremes driven by rapid global warming. This project introduces a novel AI-Ocean Dynamics synergy designed to provide an integrated early warning system. By synthesizing multi-source observations with physics-informed neural networks, it ensures predictions remain constrained by fundamental physical laws. The system forecasts event onset, intensity, duration, and spatial extent while simultaneously attributing the underlying mechanisms, such as ocean advection and air–sea heat exchange. To validate performance, we establish a specialized ocean heat extremes benchmark to assess predictive skill and attribution reliability. Furthermore, the system incorporates an incremental learning mechanism, enabling continuous adaptation to long-term climatic and environmental evolutions. This project advances the development of reliable, interpretable, and adaptive early warning systems, providing a vital tool for informed policy and maritime decision-making.

Wildfires, especially extreme wildfires, can cause prolonged damage to natural environments, human lives and global economies. To mitigate these impacts, enhancing wildfire preparedness is critical. This research proposal introduces iFire AI, a collaborative project to develop a world-leading 3D immersive visualisation system that enables users to experience and understand extreme wildfire scenarios. iFire AI utilises our advanced 360-degree immersive system AVIE, which visualises interactive landscapes and wildfire events enhanced by AI techniques. Firstly, we propose a deep learning model for wildfire behaviour modelling that generates minute-resolution wildfire progression information for immersive visualisation, supported by a Sim2Real pipeline that integrates simulated and real-world data to address data insufficiency and enhance model development and evaluation. We also explore 3D tree reconstruction using 3D Gaussian splatting, creating visually realistic and computationally efficient tree models. iFire AI can enhance users' risk perception, situational awareness and collaborative decision-making, and thereby reduce risks due to extreme wildfires and promote sustainable development.

Along with the rapid development of new urban mobility options like ride-sharing over the past decade, on-demand micro-transit services stand out as a middle ground, bridging the gap between fixed-line mass transit and single-request ride-hailing, balancing ridership maximization and travel time minimization. Micro-transit adoption can have significant social impact. It improves urban sustainability, through lower energy consumption and reduced emissions, while enhancing equitable mobility access for disadvantaged communities, thanks to its lower vehicle miles per passenger, flexible schedules, and affordable pricing. However, effective operation of micro-transit services requires planning geo-fenced zones in advance, which involves solving a challenging combinatorial optimization problem. Existing approaches enumerate candidate zones first and selects a fixed number of optimal zones in the second step. In this paper, we generalize the Micro-Transit Zoning Problem (MZP) to allow a global budget rather than imposing a size limit for candidate zones. We also design a Column Generation (CG) framework to solve the problem exactly and several pricing heuristics to accelerate computation. Extensive numerical experiments across major U.S. cities demonstrate that our approach produces higher-quality solutions more efficiently and scales better in the generalized setting.

Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: adversarial pattern evolution by malicious actors and the out-of-distribution (OOD) problem caused by varied transaction semantics on blockchains. To address these challenges, we propose a novel framework termed TEmporal Motif-aware Graph Test-Time Adaptation (TEMG-TTA). First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif-aware graph learning. Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real-world datasets demonstrate that our proposed TEMG-TTA outperforms state-of-the-art GAD approaches by an average of 54.88%. A further case study on interpretable motif patterns reveals that TEMG-TTA explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code is publicly available at https://github.com/LuoXishuang0712/TEMG-TTA/.

Credit card fraud threatens global payment ecosystems, causing billions in losses and undermining public trust. Efficient fraud detection remains challenging due to surging transaction volumes and evolving tactics. While Graph Neural Networks (GNNs) excel at modeling structural relationships, they struggle in real-world scenarios characterized by label scarcity and often overlook discriminative feature-level signals, leaving rich risk signals underutilized without costly manual engineering. To address this, we propose DRESS, a Deep Reinforcement Learning (DRL) Enhanced Semi-supervised GNN framework. It employs a DRL agent to automatically capture and enhance feature-level risks, fusing them with graph-based structural risks and propagating via a gated temporal attention network for final prediction. To mitigate inefficient exploration of the DRL module, we incorporate a feature self-attention layer to weigh feature contributions to fraud detection and employ self-supervised intrinsic rewards to help optimize the DRL module efficiently. Extensive experiments on real-world datasets demonstrate that DRESS outperforms state-of-the-art methods, especially in low-label scenarios with only 2%–10% labeled samples. By empowering resource-limited institutions to combat fraud and prevent financial loss, DRESS secures the digital trust essential for inclusive growth, contributing to AI for poverty alleviation and economic development.

Automatically mapping and segmenting global mining footprints using remote sensing and deep learning is critical for monitoring the socio-environmental risks and impacts of mining, yet its progress is hindered by the scarcity of fine-grained annotated data. Although large-scale datasets with coarse boundaries are widely available, leveraging them to improve fine-grained segmentation is challenging due to significant domain shift. To address this, we propose MineC2FNet, a coarse-to-fine domain incremental learning framework that exploits abundant coarse data to enhance fine-grained mining footprint segmentation. MineC2FNet adopts a teacher–student architecture with attentive distillation at both the feature and prediction levels, selectively transferring generalized knowledge from the coarse domain while enabling boundary refinement using limited fine-grained data (fine domain). We further introduce an expertly validated dataset of 219 images with precise boundary annotations across diverse geographies and commodities. Extensive experiments against state-of-the-art approaches, including domain adaptation and domain incremental learning methods, demonstrate that MineC2FNet achieves superior performance while effectively handling domain shift. The dataset and code are publicly available at https://github.com/risqiutama/MineC2FNet.

Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios. This gap is critical in regions like rural India, where patients often express complex medical queries in native Indic languages and rely on multimodal inputs such as medical images. Existing English-centric MLLMs struggle to support such use cases, limiting equitable access to AI-driven healthcare assistance. To address this challenge, we introduce ArogyaBodha, a large-scale multilingual multimodal medical question--answer dataset constructed from eight heterogeneous sources, covering 31 body systems, six imaging modalities, and 21 clinical domains across English and seven major Indian languages. We further propose ArogyaSutra, an actor--critic-based multi-agent framework that integrates tool grounding with dual-memory mechanisms for step-wise, reasoning-aware decision making, and uses stored actor--critic simulation trajectories for distillation. Experiments show that our dataset and framework improve multilingual medical reasoning accuracy across all Indic languages, with ablations validating the contribution of each component. The source code and dataset are available at: https://iitp-cse.github.io/ArogyaSutra/

Monitoring forest functional diversity is essential to understand ecosystem resilience in the face of rapid environmental change. While existing remote sensing approaches primarily track structural attributes such as canopy density and tree height, functional traits like leaf phenology (evergreen vs. deciduous) and leaf type (broadleaf vs. needleleaf) reveal more direct information about adaptive strategies of tree species. This study presents a scalable machine learning framework for mapping these traits across India at 10 m resolution using Google AlphaEarth Foundations (AEF) embeddings, which capture the complete annual spectral reflectance and radar signatures of the land surface. A key contribution we make is to curate an ML-ready training dataset by combining tree traits information with tree species occurrence data, and to obtain a diverse sample from this data based on spectral time-series to ensure the dataset captures a wide range of phenological dynamics. We then build cross-validation folds to specifically test for spatial generalizability across different eco-regions in India and temporal generalizability across different years, for classifiers learned from the data. Multiple classifiers are evaluated: Random Forest models trained on AEF embedding features achieved the best performance for both classification tasks, outperforming models trained on conventional Sentinel-1 and Sentinel-2 time series while offering seamless deployment in Google Earth Engine. Compared to publicly available land-cover products that encode leaf phenology and leaf type, our model yields significantly higher accuracy while providing outputs at substantially finer spatial resolution. We then observe the outputs of our model over several protected forest areas in India to understand their dynamics over the last 8 years. Our contribution is an analysis-ready open dataset to learn tree traits from remote sensed spectral data, a trained model that is spatially and temporally generalizable, and a demonstration of the insights the model can provide to understand the dynamics of protected forest areas, all of which can be replicated in other areas.

Classrooms are increasingly heterogeneous, with learners varying in performance, motivation, language proficiencies, and learning differences such as dyslexia and ADHD. While teachers recognize the need for differentiated instruction, growing workloads make it an ideal that is often unrealized in practice. Current AI educational tools are predominantly student-facing and performance-centric, ignoring other aspects that shape learning outcomes. We introduce Facet, a teacher-facing multi-agent framework that supports differentiation across motivation, performance, and learning differences. Developed with educational stakeholders from the outset, the framework coordinates four specialized agents, including learner simulation, diagnostic assessment, material generation, and evaluation within a teacher-in-the-loop design. School principals (N = 30) shaped system requirements through participatory workshops, while in-service K–12 teachers (N = 70) evaluated material quality. Mixed-methods evaluation demonstrates strong perceived value for inclusive differentiation. Practitioners emphasized both the urgent need arising from classroom heterogeneity and the importance of maintaining pedagogical autonomy as a prerequisite for adoption. We discuss implications for future school deployment and outline partnerships for longitudinal classroom implementation.

The increasing frequency and severity of climate-related disasters have intensified the need for real-time monitoring, early warning, and informed decision-making. Earth Observation (EO), powered by satellite data and Machine Learning (ML), offers powerful tools to meet these challenges. Foundation Models (FMs) have revolutionized EO ML by enabling general-purpose pretraining on large-scale remote sensing datasets. However most existing models rely on high-resolution satellite imagery with low revisit rates, limiting their suitability for fast-evolving phenomena and time-critical emergency response. In this work, we present HighFM, a first cut approach towards a FM for high-temporal-resolution, multispectral EO data. Leveraging over 2 TB of SEVIRI imagery from the Meteosat Second Generation (MSG) platform, we adapt the SatMAE masked autoencoding framework to learn robust spatiotemporal representations. To support real-time monitoring, we enhance the original architecture with fine-grained temporal encodings to capture short-term variability. The pretrained models are then fine-tuned on cloud masking and active fire detection tasks. We benchmark our SEVIRI-pretrained Vision Transformers against traditional baselines and recent geospatial FMs, demonstrating consistent gains across both balanced accuracy and IoU metrics. Our results highlight the potential of temporally dense geostationary data for real-time EO, offering a scalable path toward foundation models for disaster detection and tracking.

Drought prediction is crucial for disaster mitigation, yet it remains challenging because regional droughts reflect nonlinear interactions among precipitation supply, evaporative demand, and slowly varying land atmosphere states. Although time series foundation models (TSFMs) have shown strong zero shot performance in general forecasting tasks, they can exhibit systematic sensitivity mismatch in the prediction of regional precipitation and temperature anomalies in the Standardized Precipitation Evapotranspiration Index (SPEI). We introduce PhyTTA, a physics informed test time adaptation framework that keeps the TSFM backbone frozen and corrects the predictable part of its one step residual through three components: a dynamic forcing correction, a static tracker of slowly varying background bias, and a conditional confidence gate that applies the correction only in supported regimes. Our analysis shows that, when frozen model residuals contain a component predictable from local forcing changes or slowly varying background bias, correcting this component reduces expected squared error in the population setting. PhyTTA implements this principle through a conservative online ridge EMA correction with regime support gating. Across four foundation backbones on three South Australian locations and TimesFM on three cross regional regions, PhyTTA reduces MSE by up to 19.78%. With the TimesFM backbone it gives consistent 12.3% to 15.1% MSE reductions across the three South Australian locations.