We introduce the Visual Implicit Geometry Transformer (ViGT), an autonomous driving geometric model that estimates continuous 3D occupancy fields from surround-view camera rigs. ViGT represents a step towards foundational geometric models for autonomous driving, prioritizing scalability, architectural simplicity, and generalization across diverse sensor configurations. Our approach achieves this through a calibration-free architecture, enabling a single model to adapt to different sensor setups. Unlike general-purpose geometric foundational models that focus on pixel-aligned predictions, ViGT estimates a continuous 3D occupancy field in a bird’s-eye-view (BEV) addressing domain-specific requirements. ViGT naturally infers geometry from multiple camera views into a single metric coordinate frame, providing a common representation for multiple geometric tasks. Unlike most existing occupancy models, we adopt a self-supervised training procedure that leverages synchronized image-LiDAR pairs, eliminating the need for costly manual annotations. We validate the scalability and generalizability of our approach by training our model on a mixture of five large-scale autonomous driving datasets (NuScenes, Waymo, NuPlan, ONCE, and Argoverse) and achieving state-of-the-art performance on the pointmap estimation task, with the best average rank across all evaluated baselines. We further evaluate ViGT on the Occ3D-nuScenes benchmark, where ViGT achieves comparable performance with supervised methods.
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Hyperspectral image (HSI) clustering facilitates the unsupervised discrimination of complex surface materials but traditionally relies on the idealized assumption of centralized data availability. In real-world scenarios, however, this assumption clashes with data privacy regulations and the physical distribution of data across isolated silos. While Federated Learning offers a decentralized solution, existing frameworks in remote sensing are predominantly confined to supervised paradigms and struggle to address the heavy reliance on annotations and Non-IID distributions inherent among clients. To overcome these limitations, we propose a novel framework named Robust Federated HSI Clustering(RFHC) that enables collaborative unsupervised learning without requiring raw data exchange. Specifically, we design a dual-encoder architecture that incorporates a Federated Model Weight Augmentation strategy(FMWA), which generates consistent views through local-global network interactions to mitigate the spectral distortion introduced by traditional data augmentation. Furthermore, we develop a hybrid optimization objective that synergizes prototype relationship optimization with contrastive learning. This mechanism utilizes global prototypes to guide local training, effectively stabilizing feature learning against data heterogeneity while ensuring intra-cluster compactness. Extensive experiments on three benchmark HSI datasets demonstrate the effectiveness and superiority of the proposed method against state-of-the-art model(SOTA) federated approaches.
Event Causality Identification (ECI) is a crucial task in knowledge discovery that extracts structured causal relationships between annotated event mentions from unstructured text. However, existing approaches typically rely on extensive labeled data, which is scarce for specialized domains and topics. Although Large Language Models (LLMs) show strong promise for few-shot and zero-shot information extraction, they are prone to “causal hallucination,” generating unreliable and spurious causal links. To address these limitations, we propose LLM-SD (Large Language Model Self-Debate), a novel framework that formulates ECI as a structured debate among multiple identical instances of a single LLM. Causality is determined through the integration of adversarial evidence. The framework employs LLMs in distinct roles: an affirmative team argues for the existence of causality, a negative team argues against it, and an adjudication committee evaluates the evidence for determination. An evidence strength grading rule guides the quantification and integration of adversarial evidence. The automatic and LLM-driven verification finally produce a reasoned verdict for event causality. LLM-SD reduces spurious causal links resulting from causal hallucination in LLMs and identifies more long-distance causalities by promoting a balanced evaluation of arguments. Extensive experiments on three benchmark datasets demonstrate that LLM-SD achieves state-of-the-art performance in a zero-shot setting.
Edge addition is commonly considered risky in Graph Anomaly Detection (GAD), as random edge addition may induce anomaly–normal connectivity. Consequently, most existing augmentation strategies focus on feature perturbation, edge removal, or subgraph sampling, leaving edge addition largely unexplored. In contrast, we empirically find that moderate and targeted structural completion among normal nodes consistently improves GAD performance, revealing guided edge addition as an overlooked yet effective augmentation dimension. Motivated by this observation, we introduce two adjacency-centered edge generation strategies with complementary mechanisms. One performs a training-free structural completion scheme via spectrum-aware Langevin dynamics, enriching graph connectivity while preserving node features. The other models the joint evolution of node features and graph structure through a stochastic differential equation–based diffusion process, producing structurally coherent and anomaly-aware complementary graphs. Extensive experiments on 12 benchmark datasets with 7 state-of-the-art GAD models demonstrate consistent and substantial improvements in both AUROC and AUPRC. Code and appendices are available at https://github.com/GaoHaokai222/LangGen-JointSDE
We present S³-TIR (Coupled Spatial-Spectral Splatting), a physics-inspired framework for high-fidelity thermal infrared (TIR) novel view synthesis. Existing 3D Gaussian splatting methods struggle to capture the unique duality of TIR imagery: the coexistence of diffusive smoothness from heat conduction and structural sharpness from material discontinuities. To address this issue, we leverage the intrinsic frequency properties of thermal scenes, where conduction creates smooth fields while discontinuities cause sharp signal jumps. Accordingly, S³-TIR couples adaptive spatial support with internal spectral modulation via a unified generalized-exponential Gabor primitive. This design utilizes spatial support as a physical truncation controller, confining high-frequency textures within sharp boundaries while maintaining smooth diffusion elsewhere. Furthermore, we propose a frequency-aware deferred splatting scheme that enforces multi-view frequency consistency, anchoring frequency distributions to the 3D structure rather than view-dependent appearance. Experiments on diverse benchmarks demonstrate that S³-TIR achieves state-of-the-art performance, notably reducing LPIPS by 42.5% and improving PSNR by 3.2% on the RGBT-Scenes dataset, while yielding structurally stable thermal sequences free from jittering artifacts.
Reproducible benchmarking of tools that automatically detect vulnerabilities in source code remains challenging due to inconsistent implementations, varying data preprocessing, and methodological flaws that compromise fair model comparison. In a recent study, 9 in 10 vulnerability detection studies were found to use inappropriate evaluation approaches, with models achieving high scores through spurious correlations rather than actual vulnerability detection. We present VulnBench, an extensible, open-source benchmarking tool that enables fair comparison across models and datasets. Our systematic evaluation of CodeBERT, GraphCodeBERT, CodeT5 (encoder-only and full), and NatGen across eight mostly C/C++ source code datasets reveals that proper threshold optimization can improve F1-scores by up to 54%, as well as wide variation in F1-scores showing the large gap in the difficulty of the vulnerability dataset field. By standardising evaluation protocols, VulnBench enables researchers to distinguish between genuine model improvements and methodological artifacts as well as reducing wasteful duplication of effort spent on reproducing results.
Retrieval-Augmented Generation (RAG) has become the standard approach for integrating domain knowledge into Large Language Models (LLMs). However, fair comparison of RAG pipelines remains difficult: data preparation is often ad hoc, subsampling methods are opaque, parameters vary across implementations, and evaluation is fragmented. We present In-Situ Eval, a unified and reproducible framework that operationalizes the full RAG pipeline with configurable subsampling strategies and both RAG-specific and generic evaluation metrics. The platform supports two execution modes: an offline Dataset mode for evaluating precomputed outputs, and a live Retrieval mode for benchmarking RAG variants with state-of-the-art LLMs. Users can flexibly select datasets, retrieval techniques, models, and metrics, enabling side-by-side comparisons, ablations, and targeted analyses. This holistic approach reduces computational costs, clarifies the impact of subsampling techniques, and provides actionable insights for real-world deployments. By facilitating transparent, customizable, and interactive benchmarking, In-Situ Eval empowers both researchers and practitioners to make informed decisions in adapting RAG pipelines to domain-specific needs.
Post-training quantization is widely used to compress large language models (LLMs) for efficient deployment in resource-constrained environments. However, recent work shows that quantization, especially aggressive schemes such as 4-bit QLoRA, can substantially degrade safety alignment, making models more vulnerable to harmful completions and jailbreaks. In this work, we investigate these safety risks and propose a mitigation strategy: projecting quantized parameters back into safety-aligned subspaces. First, we empirically measure safety degradation on benchmark datasets using both safety and utility metrics. Next, we explore projection-based restoration methods to recover alignment-preserving directions in the LoRA adapters of quantized models. Finally, we study how quantization affects mechanistic safety neurons and how hybrid-precision designs can preserve them. By foregrounding the safety implications of model compression, this work aims to support more robust, deployment-ready, and ethically aligned LLMs.
iCD: An Implicit Clustering Distillation Method for Structural Information Mining (Student Abstract)
PDF ↗Logit Knowledge Distillation has gained substantial research interest in recent years due to its simplicity and lack of requirement for intermediate feature alignment; however, it suffers from limited interpretability in its decision-making process. To address this, we propose implicit Clustering Distillation (iCD): a simple and effective method that mines and transfers interpretable structural knowledge from logits, without requiring ground-truth labels or feature-space alignment. iCD leverages Gram matrices over decoupled local logit representations to enable student models to learn latent semantic structural patterns. Extensive experiments on benchmark datasets demonstrate the effectiveness of iCD across diverse teacher-student architectures, with particularly strong performance in fine-grained classification tasks---achieving a peak improvement of +5.08% over the baseline.
Agentic workflows, where multiple AI agents collaborate to accomplish complex tasks like reasoning or planning, play a substantial role in many cutting-edge commercial applications. These workflows depend critically on the prompts used to provide the roles models play in such workflows. Poorly designed prompts that fail even slightly to guide individual agents can lead to sub-optimal performance that may snowball within a system of agents, limiting their reliability and scalability. To address this important problem of inference-time prompt optimization, we introduce ProRefine, an innovative inference-time optimization method that uses an agentic loop of LLMs to generate and apply textual feedback. ProRefine dynamically refines prompts for multi-step reasoning tasks without additional training or ground truth labels. Evaluated on five benchmark mathematical reasoning datasets, ProRefine significantly surpasses zero-shot Chain-of-Thought baselines by 3 to 37 percentage points. This approach not only boosts accuracy but also allows smaller models to approach the performance of their larger counterparts. This highlights its potential for building cost-effective and powerful hybrid AI systems, thereby democratizing access to high-performing AI.
Synthetic-to-Real Transfer Learning for League of Legends Minimap Object Detection (Student Abstract)
PDF ↗Esports is growing rapidly, yet the data available to researchers is limited due to the game company policies. Consequently, vision-based approaches utilizing game screens are gaining attention as a practical alternative. We focus on the League of Legends minimap and address the challenges of champion detection when extracting champion information from the minimap. The challenges in this domain include small objects, rapid movement, and frequent occlusions. We propose a transfer-learning-based object detection pipeline that combines synthetic data with a subset of replay data. Synthetic data enables the rapid generation of diverse scenarios and improves training scalability, while replay data reduces the data distribution gap. This approach achieves 0.588 mean average precision, improving over replay-only by 0.261 and synthetic-only by 0.312, with 6.4 ms latency. Furthermore, we constructed a dataset encompassing all champions, enabling comparative analysis of detection models and supporting reproducible benchmarking for various application studies.
Adaptive Coreset Selection via Uncertainty-Density for Efficient Spam Detection (Student Abstract)
PDF ↗Efficient spam detection in resource-constrained environments remains challenging due to class imbalance, noisy text, and the computational demands of large Transformer models. We introduce a novel coreset selection framework based on a unified Entropy, Class-Balanced Uncertainty-Density Ranking (CBUDR) scheme. Our method prioritizes highly informative and uncertain samples while ensuring diversity and class balance within the selected subset. The framework flexibly supports multiple selection strategies, including Top-K, Bottom-K, and adaptive class-wise schemes, enabling robust performance even when training on as little as 5% of the dataset. Extensive experiments on benchmark datasets (UCI SMS, UTKML Twitter, LingSpam) show that our ranking scheme achieves competitive accuracy, precision, and recall while significantly reducing computational cost. These results demonstrate that carefully designed coreset strategies can surpass full-data performance in both balanced and imbalanced settings, highlighting the potential for deployment on low-power devices and mobile platforms.
UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data (Student Abstract)
PDF ↗Federated Learning (FL) often suffers from severe performance degradation when faced with non-IID data, largely due to local classifier bias. Traditional remedies such as global model regularization or layer freezing either incur high computational costs or struggle to adapt to feature shifts. In this work, we propose UniVarFL, a novel FL framework that emulates IID-like training dynamics directly at the client level, eliminating the need for global model dependency. UniVarFL leverages two complementary regularization strategies during local training: Classifier Variance Regularization, which aligns class-wise probability distributions with those expected under IID conditions, effectively mitigating local classifier bias; and Hyperspherical Uniformity Regularization, which encourages a uniform distribution of feature representations across the hypersphere, thereby enhancing the model’s ability to generalize under diverse data distributions. Extensive experiments on multiple benchmark datasets demonstrate that UniVarFL outperforms existing methods in accuracy, highlighting its potential as a highly scalable and efficient solution for real-world FL deployments, especially in resource-constrained settings.
Weight Entropy-Maximised Evidential Metamodel for Uncertainty Quantification (Student Abstract)
PDF ↗Reliable uncertainty quantification (UQ) is crucial for deploying deep learning models in safety-critical domains. Existing UQ methods often either rely on multi-pass inference, which increases computational cost, or restrict expressiveness by using only final-layer embeddings. In this work, we propose a lightweight evidential meta-model that leverages multi-layer feature fusion from a pretrained backbone, capturing both low-level features and high-level semantics to better estimate uncertainty. To further enhance epistemic fidelity, we integrate maximum weight-entropy (Max-WEnt) regularization, which encourages hypothesis diversity without altering the base network or adding test-time overhead. Experiments across two benchmark settings, medical (BACH, HAM10000, BreakHIS, DIV2K) and natural (ImageNet, SVHN, Fashion-MNIST, ImageNet-C) datasets, demonstrate consistent improvements in AUROC of out-of-distribution detection compared to prior post-hoc UQ methods. Our findings show that combining multi-layer evidential modeling with Max-WEnt provides a robust, efficient, and practical framework for trustworthy AI in high-stakes applications. The meta-model adds only ~0.8M parameters and trains in under four hours on a single 48GB GPU, making it practical for real-world deployment.
Dynamics-Aware Planning Representation for Zero-Shot Reinforcement Learning (Student Abstract)
PDF ↗Offline Zero-Shot Reinforcement Learning requires an agent to solve unseen tasks using only a fixed offline dataset without explicit rewards. A central challenge is learning representations that capture both high-level long-term planning and low-level physical dynamics. We propose a novel framework, Dynamics-Aware Planning Representation (DAPR), which disentangles these two aspects via complementary contrastive objectives. Specifically, DAPR learns goal-oriented planning directions and local dynamics-consistent directions in the latent space. By jointly enforcing these constraints, DAPR yields representations that balance “where to go” with “how to move.” Experiments on standard locomotion benchmarks (Walker, Cheetah, Quadruped) demonstrate that DAPR consistently improves performance and generalization over strong baselines, achieving substantial gains on precision demanding tasks.
Nature is inherently structured! The entities in the real world are naturally organized in rich relationships. For example, dolphins and sharks, despite their striking visual resemblance in body shape and fins, are actually from entirely different branches of the animal hierarchy, i.e., mammals and fishes, respectively. This remarkable similarity is a prime example of ‘convergent evolution’, where unrelated species develop similar features because they face similar environmental challenges. This illustrates how nature’s underlying organization often transcends superficial visual resemblances. Although humans intuitively grasp and utilize these profound natural constraints, they are typically underutilized in most AI systems. As a result, trained AI models tend to align with statistical patterns in the data, such as sampling biases or class imbalance, rather than adhering to the underlying relational consistency. This thesis argues that AI systems must evolve beyond learning “flat” feature representations, which are domain-agnostic and derived purely from data correlations, to “explicitly model the domain-specific structural relationships”. A key benefit of encoding relational priors in the learning process is that it can inject domain knowledge as an inductive bias, leading to more robust and reliable models. My research investigates incorporating domain knowledge by leveraging “graph-based structural priors” that explicitly model relational constraints in various visual recognition tasks. This work spans three distinct dimensions of visual recognition, progressing from coarse-level (image-level) to fine-grained (scene-level) understanding. My research highlights a crucial limitation in existing AI models: they often fail to incorporate real-world constraints, leading to significant errors. I show that even powerful, pre-trained neural networks can make severe mistakes due to a lack of domain knowledge. I argue that standard metrics like top-1 accuracy, precision, and recall are insufficient for evaluating model robustness, and propose a new metric based on rank order of the predictions as a better indicator of reliability. The benchmark on various large-scale datasets confirms that existing solutions do not sufficiently capture the domain knowledge, which is often available as a taxonomy tree, motivating our design of better learning frameworks. I also examine complex visual re-identification (Re-ID) tasks, such as monitoring animals in the wild. I find that existing foundational models struggle with new species and environments. This challenge is compounded by the high cost of manual annotation for adapting these systems to new settings. While existing unsupervised learning methods can help reduce the need for extensive labeling, they often suffer from under- and over-segmentation errors, which led me to develop more effective active learning strategies. Finally, I address the limitations of the classic Kalman filter, a widely used tool for dynamic systems. I point out that this filter makes a flawed assumption that the movement of each individual object is independent of its dynamic surroundings. In the real world, this is rarely the case. I demonstrate the need for a new filtering mechanism that not only considers an object’s past movements but also its spatial relationship with other dynamic entities in its environment. In my analysis, I observed the vision foundation models for all recognition tasks, i.e., classification, detection, and segmentation, lack the domain knowledge. I believe that our learning framework, which was designed specifically for classification, can be adapted for other recognition tasks. I speculate that a unified learning framework can be designed that can be leveraged for making vision foundation models aware of the available taxonomy.
Time-Series Anomaly Detection with Graph-Based Self-Supervised Learning and Foundation Models: Towards Real-World Applications
PDF ↗Time-series data, which represent the evolution of one or more variables over time, are ubiquitous across domains such as finance, medicine, industry, and security. Time-Series Anomaly Detection (TSAD) is essential for identifying irregular events such as equipment failures, fraudulent activities, and neurological disorders. Despite significant progress, TSAD remains challenging due to the complexity of time-series signals, the diversity of anomaly types, and the scarcity of high-quality labeled data. This thesis contributes: (i) the first comprehensive surveys of Graph-based TSAD (G-TSAD) and Self-Supervised Learning for Anomaly Detection (SSL-AD), showing how graph modeling and SSL proxy tasks yield robust representations for TSAD while mapping limits and future directions; (ii) EEG-CGS, a contrastive–generative SSL framework that encodes fine-grained subgraph structure without anomaly labels, improving multivariate TSAD and localizing anomalous sensors and regions; (iii) TSAD-C, which integrates graph representations with diffusion models to capture long-range temporal and spatial dependencies while explicitly handling contaminated training data; and (iv) extending TSAD beyond benchmark datasets into other impactful domains, and developing foundation models specialized for biosignals to detect novel anomalies in drug-resistant epilepsy patients.
AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition
PDF ↗Automated scoring plays a crucial role in education by reducing the reliance on human raters and offering scalable and immediate evaluation of student work. While large language models (LLMs) have shown strong potential in this task, their use as end-to-end raters faces challenges such as low accuracy, prompt sensitivity, limited interpretability, and rubric misalignment, which hinder practical implementation. To address the limitations, we propose AutoSCORE, a multi-agent LLM framework enhancing automated scoring via rubric-aligned Structured COmponent REcognition. With two agents, AutoSCORE first extracts rubric-relevant components from student responses and encodes them into a structured representation (i.e., Scoring Rubric Component Extraction Agent), which is then used to assign final scores (i.e., Scoring Agent). This design ensures that model reasoning follows a human-like grading process, enhancing interpretability and robustness. We evaluate AutoSCORE on four benchmark datasets from the ASAP benchmark, using both proprietary and open-source LLMs (GPT-4o, LLaMA-3.1-8B, LLaMA-3.1-70B). Across diverse tasks and rubrics, AutoSCORE predominantly improves scoring accuracy, human-machine agreement (QWK, correlations), and reduces error metrics (MAE, RMSE) compared to single-agent baselines, with particularly strong benefits on complex, multidimensional rubrics, and especially large relative gains on smaller LLMs. These results demonstrate that structured component recognition combined with multi-agent design offers a scalable, reliable, and interpretable solution for automated scoring.
A Guardrail Framework for Sensitive Financial Information Protection: A Taxonomy-Driven Approach
PDF ↗The increasing adoption of large language models in the fi-nancial sector introduces significant challenges related to the handling of sensitive financial information (SFI). Existing general-purpose content safety solutions, or guardrails, often fall short in detecting domain-specific risks inherent in finan-cial data processing. This study addresses these gaps by de-veloping a comprehensive taxonomy of SFI, grounded in globally recognized financial, information security, and AI governance standards. Leveraging this taxonomy, we synthe-sized an extensive dataset encompassing diverse categories of SFI and trained GARD (Generative Adversarial network Risk Detection) model to detect sensitive content in both in-puts and outputs of GenAI systems within the financial do-main. Our evaluation compared GARD against commercial guardrail solutions, including the OpenAI Moderation API and Microsoft Azure Content Safety (ACS). The results demonstrated that while commercial solutions maintained high precision, their recall was substantially lower, indicating many risky instances went undetected. In contrast, our model achieved a recall score of 0.98, significantly outperforming the benchmarks and enhancing SFI detection. These findings underscore the necessity of domain-specific guardrails tai-lored to the financial sector to ensure robust AI safety and compliance. In conclusion, this work contributes (1) A de-tailed taxonomy of SFI tailored for GenAI applications, (2) A comprehensive synthetic dataset that encompasses a wide range of sensitive topics relevant to the domain and (3) A high-performance risk detection model that can be deployed independently or alongside existing solutions to improve con-tent safety in financial services. This approach promotes trust, mitigates financial, legal, and reputational risks, and supports the responsible adoption of GenAI technologies in sensitive domains.
Trauma THOMPSON: A Dataset and Realistic Generative Framework for AI Copilots in Emergency Care
PDF ↗We introduce Trauma THOMPSON, a dataset and suite of benchmarks designed to accelerate the development of AI-powered copilots for real-time decision-making in emergency and resource-limited medical settings. This work proposes a method to address a critical bottleneck for future deployment: models trained on simulations may not perform well in the real world. The dataset features 3,717 unscripted, first-person video clips of five emergency procedures, uniquely including "just-in-time" (JIT) interventions that mirror the improvisational nature of field medicine. To obtain realistic patient data without ethical issues and identity concerns that medical data often encounter, we also propose TraumaGen, a novel framework for generating photorealistic patient and wound images from manikins while preserving clinical context. We establish benchmarks for action recognition, anticipation, and visual question answering (VQA), evaluating state-of-the-art models to demonstrate the challenges and potential of our dataset. By focusing on realism and improvisation, Trauma THOMPSON provides a crucial resource and a clear path toward developing and validating robust AI assistants for future deployment in real-world emergency care.