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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

arXiv

AI for Mycetoma Diagnosis in Histopathological Images: The MICCAI 2024 Challenge

Hyam Omar Ali, Sahar Alhesseen, Lamis Elkhair, Adrian Galdran, Ming Feng, Zhixiang Xiong, Zengming Lin, Kele Xu, LIANG HU, Benjamin Keel, Oliver Mills, James Battye, Akshay Kumar, Asra Aslam, Prasad Dutande, Ujjwal Baid, Bhakti Baheti, Suhas Gajre, Aravind Shrenivas Murali, Eung-Joo Lee, Ahmed Fahal, Rachid Jennane

Mycetoma is a neglected tropical disease caused by fungi or bacteria leading to severe tissue damage and disabilities. It affects poor and rural communities and presents medical challenges and socioeconomic burdens on patients and healthcare systems in endemic regions worldwide. Mycetoma diagnosis is a major challenge in mycetoma management, particularly in low-resource settings where expert pathologists are limited. To address this challenge, this paper presents an overview of the Mycetoma MicroImage: Detect and Classify Challenge (mAIcetoma) which was organized to advance mycetoma diagnosis through AI solutions. mAIcetoma focused on developing automated models for segmenting mycetoma grains and classifying mycetoma types from histopathological images. The challenge attracted the attention of several teams worldwide to participate and five finalist teams fulfilled the challenge objectives. The teams proposed various deep learning architectures for the ultimate goal of this challenge. Mycetoma database (MyData) was provided to participants as a standardized dataset to run the proposed models. Those models were evaluated using evaluation metrics. Results showed that all the models achieved high segmentation accuracy, emphasizing the necessitate of grain detection as a critical step in mycetoma diagnosis. In addition, the top-performing models show a significant performance in classifying mycetoma types.

arXiv

RadarGen: Automotive Radar Point Cloud Generation from Cameras

Tomer Borreda, Fangqiang Ding, Sanja Fidler, Shengyu Huang, Or Litany

We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery. RadarGen adapts efficient image-latent diffusion to the radar domain by representing radar measurements in bird's-eye-view form that encodes spatial structure together with radar cross section (RCS) and Doppler attributes. A lightweight recovery step reconstructs point clouds from the generated maps. To better align generation with the visual scene, RadarGen incorporates BEV-aligned depth, semantic, and motion cues extracted from pretrained foundation models, which guide the stochastic generation process toward physically plausible radar patterns. Conditioning on images makes the approach broadly compatible, in principle, with existing visual datasets and simulation frameworks, offering a scalable direction for multimodal generative simulation. Evaluations on large-scale driving data show that RadarGen captures characteristic radar measurement distributions and reduces the gap to perception models trained on real data, marking a step toward unified generative simulation across sensing modalities.

arXiv

The N-Body Problem: Parallel Execution from Single-Person Egocentric Video

Zhifan Zhu, Yifei Huang, Yoichi Sato, Dima Damen

Humans can intuitively parallelise complex activities, but can a model predict this from observing a single person? Given one egocentric video, we introduce the N-Body Problem: predicting how N individuals, can hypothetically perform the same set of tasks. The goal is to maximise speed-up, but naive assignment of video segments to individuals often violates real-world constraints, leading to physically impossible scenarios like two people using the same object or occupying the same space. To quantify this, we formalise the N-Body Problem and propose a suite of metrics to evaluate both performance (speed-up, task coverage) and feasibility (spatial collisions, object conflicts and causal constraints). As a proof of concept, we introduce a structured prompting strategy that guides a Vision-Language Model (VLM) to reason about the 3D environment, object usage, and temporal dependencies, producing a viable parallel execution. On 100 videos from EPIC-Kitchens and HD-EPIC, for $N = 2$, our structured prompt improves action coverage by 45% over a baseline prompt for Gemini 2.5 Pro, while simultaneously slashing collision rates, object and causal conflicts by 51%, 52% and 55% respectively.

arXiv

UltraFast-LiNET: Light-weight multi-scale shift convolutional network for real-time low-light image enhancement

Yuhan Chen, Yicui Shi, Guofa Li, Guangrui Bai, Wenxuan Yu, Ying Fang, Wenbo Chu, Keqiang Li

Addressing the urgent need for high-performance real-time low-light image enhancement on resource-constrained edge devices in low-illumination scenarios such as nighttime and tunnels, this paper presents UltraFast-LiNET, an ultra-lightweight network designed for extreme efficiency. The proposed network introduces Dynamic Shift Convolution, DSConv, a highly compact operation with only 12 learnable parameters. By using DSConv to construct a Multi-Scale Shift Residual Block, MSRB, UltraFast-LiNET effectively enlarges the receptive field while maintaining an exceptionally small parameter count. To alleviate gradient instability during the training of lightweight models, we further propose a multi-level gradient-aware loss function that strengthens supervision signals across different feature levels. UltraFast-LiNET supports flexible parameter configurations, enabling the model size to be reduced to as few as 36 learnable parameters. Remarkably, with only 180 learnable parameters, UltraFast-LiNET achieves a PSNR of 19.81 dB on the LOL dataset, surpassing the current state-of-the-art by 0.08 dB. Experimental results demonstrate that the proposed method achieves an effective balance between millisecond-level real-time processing and superior enhancement quality under extreme computational constraints. These results highlight its chip-level computational efficiency and broad prospects for edge-side deployment.

arXiv

Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings

Christian Grashei, Christian Brechenmacher, Rao Muhammad Umer, Jingsong Liu, Carsten Marr, Peter J. Schüffler, Ewa Szczurek

Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exceed a billion parameters and produce high-dimensional embeddings, thus limiting their applicability for research or clinical use when computing resources are tight. Here, we introduce Pathryoshka, a multi-teacher distillation framework inspired by RADIO distillation and Matryoshka Representation Learning to reduce pathology FM sizes while allowing for adaptable embedding dimensions. We evaluate our framework with a distilled model on ten public pathology benchmarks with varying downstream tasks. Compared to its much larger teachers, Pathryoshka reduces the model size by 86-92% at on-par performance. It outperforms state-of-the-art single-teacher distillation models of comparable size by a median margin of 7.0 in accuracy. By enabling efficient local deployment without sacrificing accuracy or representational richness, Pathryoshka democratizes access to state-of-the-art pathology FMs for the broader research and clinical community.

arXiv

EvDiff: Event-Based Video Reconstruction using One-Step Diffusion Models

Weilun Li, Lei Sun, Ruixi Gao, Qi Jiang, Yuqin Ma, kaiwei wang, Ming-Hsuan Yang, Luc Van Gool, Danda Pani Paudel

As neuromorphic sensors, event cameras asynchronously record changes in brightness as streams of sparse events with the advantages of high temporal resolution and high dynamic range. Reconstructing intensity images from events is a highly ill-posed task due to the inherent ambiguity of absolute brightness. Early methods generally follow an end-to-end regression paradigm, directly mapping events to intensity frames in a deterministic manner. While effective to some extent, these approaches often yield perceptually inferior results and struggle to scale up in model capacity and training data. In this work, we propose EvDiff, an event-based diffusion model that follows a surrogate training framework to produce high-quality videos. To reduce the high computational cost of high-frame-rate video generation, we design an event-based diffusion model that performs only a single forward diffusion step, equipped with a temporally consistent EvEncoder. Furthermore, our novel Surrogate Training Framework eliminates the dependence on paired event-image datasets, allowing the model to leverage large-scale image datasets for higher capacity. The proposed EvDiff is capable of generating high-quality colorful videos solely from monochromatic event streams. Experiments on real-world datasets demonstrate that our method strikes a sweet spot between fidelity and realism, outperforming existing approaches on both pixel-level and perceptual metrics. The code will be released publicly.

arXiv

SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control

Zhengyi Luo, Ye Yuan, Tingwu Wang, Chenran Li, Fernando Castañeda, Sirui Chen, Zi-Ang Cao, Jiefeng Li, David Minor, Qingwei Ben, Jinhyung Park, David Sami, Zi Wang, Xingye Da, Runyu Ding, Cyrus Hogg, Lina Song, Edy Lim, Eugene Jeong, Tairan He, Haoru Xue, Wenli Xiao, Simon Yuen, Jan Kautz, Yan Chang, Umar Iqbal, Linxi "Jim" Fan, Yuke Zhu

Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for humanoid control. Current neural controllers for humanoids remain modest in size, target a limited set of behaviors, and are trained on a handful of GPUs. We show that scaling model capacity, data, and compute yields a generalist humanoid controller capable of natural, robust whole-body movements. We position motion tracking as a scalable task for humanoid control, leveraging dense supervision from diverse motion-capture data to acquire human motion priors without manual reward engineering. We build a foundation model for motion tracking by scaling along three axes: network size (1.2M to 42M parameters), dataset volume (100M+ frames from 700 hours of motion capture), and compute (21k GPU hours). Beyond demonstrating the benefits of scale, we further show downstream utility through a real-time kinematic planner that bridges motion tracking to tasks such as navigation, enabling natural and interactive control, as well as a unified token space that supports virtual reality (VR) teleoperation and vision-language-action (VLA) models with a single policy. Through this interface, we demonstrate autonomous VLA-driven whole-body loco-manipulation requiring coordinated hand and foot placement. Scaling motion tracking exhibits favorable properties: performance improves steadily with compute and data diversity, and learned policies generalize to unseen motions, establishing motion tracking at scale as a practical foundation for humanoid control.

arXiv

Delving into Cascaded Instability: A Lipschitz Continuity View on Image Restoration and Object Detection Synergy

Qing Zhao, Weijian Deng, Pengxu Wei, ZiYi Dong, Hannan Lu, Xiangyang Ji, Liang Lin

To improve detection robustness in adverse conditions (e.g., haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the detector. However, the functional mismatch between restoration and detection networks can introduce instability and hinder effective integration -- an issue that remains underexplored. We revisit this limitation through the lens of Lipschitz continuity, analyzing the functional differences between restoration and detection networks in both the input space and the parameter space. Our analysis shows that restoration networks perform smooth, continuous transformations, while object detectors operate with discontinuous decision boundaries, making them highly sensitive to minor perturbations. This mismatch introduces instability in traditional cascade frameworks, where even imperceptible noise from restoration is amplified during detection, disrupting gradient flow and hindering optimization. To address this, we propose Lipschitz-regularized object detection (LROD), a simple yet effective framework that integrates image restoration directly into the detector's feature learning, harmonizing the Lipschitz continuity of both tasks during training. We implement this framework as Lipschitz-regularized YOLO (LR-YOLO), extending seamlessly to existing YOLO detectors. Extensive experiments on haze and low-light benchmarks demonstrate that LR-YOLO consistently improves detection stability, optimization smoothness, and overall accuracy.

arXiv

Exploring Instruction Data Quality for Explainable Image Quality Assessment

Yunhao Li, Sijing Wu, Jun Jia, Kang Fu, Qi Jia, Yucheng Zhu, Wei Sun, Guangtao Zhai

In recent years, with the rapid development of large multimodal models (LMMs), explainable image quality assessment (IQA) has attracted increasing attention, aiming to understand the perceptual quality problems of images. Existing studies typically construct large-scale instruction tuning datasets to enhance the quality perception capabilities of LMMs, following the data scaling law. However, as the fundamental capabilities of LMMs continue to improve, existing instruction tuning datasets may contain redundant and less challenging samples, resulting in substantial computational costs. In this paper, we investigate whether data quantity remains the dominant factor in LMM instruction tuning for explainable IQA. Based on a strong pre-trained LMM, we observe that randomly selecting a properly sized subset of training data can outperform full-data fine-tuning, indicating substantial redundancy in existing instruction tuning datasets. Motivated by this observation, we propose Q-Selector, a clustering-based data selection framework consisting of three stages: hierarchical LMM-based clustering feature extraction, cluster quota allocation through density and transferability, and SVD-based cluster sampling strategy. Specifically, Q-Selector extracts multi-layer features from the vision encoder, text encoder, and large language model components of LMMs as clustering features. It then determines the sampling quota for each cluster based on inter-cluster transferability and intra-cluster density. Finally, Q-Selector employs a Singular Value Decomposition (SVD)-based sampling strategy to select high-quality instruction data. Experimental results demonstrate that Q-Selector achieves 102.1% and 103.7% of the performance of full-data fine-tuning using only 10% of the training data on explainable IQA and image aesthetics assessment tasks, respectively.

arXiv

Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift

Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari, Vasile Palade, Vincent Vigneron

The Segment Anything Model (SAM) exhibits strong zero-shot performance on natural images but suffers from domain shift and overconfidence when applied to medical volumes. We propose \textbf{CalSAM}, a lightweight adaptation framework that (i) reduces encoder sensitivity to domain shift via a \emph{Feature Fisher Information Penalty} (FIP) computed on 3D feature maps and (ii) penalizes overconfident voxel-wise errors through a \emph{Confidence Misalignment Penalty} (CMP). The combined loss, \(\mathcal{L}_{\mathrm{CalSAM}}\) fine-tunes only the mask decoder while keeping SAM's encoders frozen. On cross-center and scanner-shift evaluations, CalSAM substantially improves accuracy and calibration: e.g., on the BraTS scanner split (Siemens$\to$GE) CalSAM shows a $+7.4\%$ relative improvement in $\mathrm{DSC}$ (80.1\% vs.\ 74.6\%), a $-26.9\%$ reduction in $\mathrm{HD95}$ (4.6 mm vs.\ 6.3 mm), and a $-39.5\%$ reduction in $\mathrm{ECE}$ (5.2\% vs.\ 8.6\%). On ATLAS-C (motion corruptions), CalSAM achieves a $+5.3\%$ relative improvement in $\mathrm{DSC}$ (75.9\%) and a $-32.6\%$ reduction in $\mathrm{ECE}$ (5.8\%). Ablations show FIP and CMP contribute complementary gains ($p<0.01$), and the Fisher penalty incurs a modest $\sim$15\% training-time overhead. CalSAM therefore delivers improved domain generalization and better-calibrated uncertainty estimates for brain MRI segmentation, while retaining the computational benefits of freezing SAM's encoder.

arXiv

Assessing the Effectiveness of Deep Embeddings for Tree Species Classification in the Dutch Forest Inventory

Takayuki Ishikawa, Carmelo Bonannella, Bas J. W. Lerink, Marc Rußwurm

National Forest Inventory (NFI) serves as the primary source of forest information, however, maintaining these inventories requires labor-intensive on-site campaigns by forestry experts to identify and document tree species. Embeddings from deep pre-trained remote sensing models offer new opportunities to update NFIs more frequently and at larger scales. This work systematically investigates how deep embeddings improve tree species classification accuracy in the Netherlands with few annotated data. We evaluate this question on three embedding models: Presto, Alpha Earth, and TESSERA, using three tree species datasets of varying difficulty. Data-wise, we compare the available embeddings from Alpha Earth and TESSERA with dynamically calculated embeddings from a pre-trained Presto model, for which we extracted time series from Sentinel-1 , Sentinel-2, and weather data, along with elevation data downloaded from Google Earth Engine. Our results demonstrate that publicly available remote sensing time series deep embeddings outperform the current state-of-the-art hand crafted features in NFI species classification in the Netherlands, yielding performance gains of roughly 7 to 9 percentage points in overall accuracy and up to 17 points in macro-F1 on the NFI datasets at the reference plot level. This indicates that classic hand defined features are too simple for this task and highlights the potential of using deep embeddings for data-limited applications such as NFI classification. Country-scale maps built from the pre-computed embeddings reach accuracy consistent with the plot-level results and with an independent comparison against the NFI. By leveraging openly available satellite data and deep embeddings from pre-trained models, this approach consistently improves classification accuracy compared to traditional methods and can effectively complement existing forest inventory processes.

arXiv

Generalizable Operating Room Expert with Multimodal Enhancement

Peiqi He, Zhenhao Zhang, Yixiang Zhang, Jiaxin Liu, Xiongjun Zhao, Shaoliang Peng

Precise spatial modeling in the operating room (OR) is essential for intraoperative awareness, hazard avoidance, and surgical decision-making. Although existing approaches exploit multimodal data to learn spatial relationships, many depend on sensing modalities that are difficult to deploy in real clinical environments and remain limited in explicit 3D reasoning under constrained sensing conditions. Meanwhile, models trained primarily on readily available 2D data often fail to capture the fine-grained geometric and semantic structure of complex OR scenes. To address these limitations, we introduce \textbf{OR-Expert}, a large vision-language framework for 3D spatial reasoning with RGB-only inference. OR-Expert internally derives depth, panoptic segmentation, and point-cloud cues from RGB images and encodes them as structured spatial representations. Its Spatial-Enhanced Feature Fusion Block aligns these pseudo-modalities with RGB and textual features in a shared token space, enabling joint semantic, geometric, and language reasoning. The unified end-to-end MLLM therefore supports detailed spatial understanding without requiring external depth, segmentation, or point-cloud sensors, or additional expert annotations at inference time. Experiments on multiple operating-room benchmarks demonstrate that OR-Expert achieves state-of-the-art performance and generalizes effectively to unseen surgical scenes and downstream spatial reasoning tasks.

arXiv

Shortest-Path Decomposition for Foliage-Robust 3D Tree Modeling and Above-Ground Biomass Estimation from Point Clouds

Di Wang, Shi Li

Estimating above-ground biomass (AGB) from terrestrial laser scanning (TLS) via quantitative structural models (QSMs) is highly accurate under leaf-off conditions, yet major QSM paradigms degrade sharply when foliage is present. The standard remedy, leaf-wood separation, introduces its own errors and complexity. We present topology-driven foliage suppression that replaces explicit leaf--wood classification inside the reconstruction pipeline. On the shortest-path tree of a point-cloud graph, the number of root-to-node paths traversing a node equals the size of its rooted subtree, so path traversal frequency is an exact topological descriptor of branching hierarchy. Three decompositions of the path structure, plus an adaptive frequency threshold at the elbow of frequency growth along each branch, recover the branching hierarchy from a single Dijkstra run. We evaluate it on 90 trees under exclusively leaf-on conditions across tropical TLS and temperate UAV laser scanning (ULS) platforms. On dense tropical TLS it achieves a mean absolute percentage deviation (MAPD) of 17.2% with RMSE 2.37~Mg (RMSE% 34.8%) and an R^2 of 0.956, outperforming the best separation-assisted TreeQSM pipeline (21.2%). Structural analysis confirms that foliage introduces 2.6~cm of mean geometric error (3.8~cm RMS) and preserves the hierarchical volume distribution. On low-density ULS data, MAPD reaches 6.4% with field-measured diameter at breast height (DBH) and 32.3% with DBH inferred allometrically from tree height. All AGB experiments use identical parameter settings. Directly recovering branching hierarchy from shortest-path structure removes a barrier to operational QSM-based forest inventory under leaf-on conditions.

arXiv

Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets for Vision

Ahmet Bilican, M. Akın Yılmaz, A. Murat Tekalp, R. Gökberk Cinbiş

Efficiently adapting large pretrained models is critical under tight compute and memory budgets. While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA achieve efficiency through low-rank updates, their discrete rank constraint limits fine-grained parameter control and confines adaptations to low-dimensional subspaces. We propose Wavelet Fine-Tuning (WaveFT), which learns sparse updates in the wavelet domain of weight matrices, enabling fine-grained control over trainable parameters well below LoRA's minimum rank. Wavelet bases provide semi-local receptive fields that aggregate spatially coherent gradients, offering better coverage than direct weight sparsity (SHiRA) without the destructive interference of global Fourier bases (FourierFT). We provide theoretical analysis showing: (i) sparse methods achieve high-rank updates, avoiding LoRA's subspace bottleneck and enabling higher representational capacity, and (ii) a gradient coverage framework explaining when WaveFT is preferable. We perform experiments across text-to-image generation, image classification, and language understanding. WaveFT demonstrates state-of-the-art results among PEFT methods for vision tasks, where wavelets effectively capture sparse gradient structure through improved coverage, while performing comparably on NLP tasks. WaveFT has officially been included in the Hugging Face PEFT library (huggingface.co/docs/peft/en/package_reference/waveft).

arXiv

Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration

David Rivas-Villar, Álvaro S. Hervella, José Rouco, Jorge Novo

Current color fundus image registration approaches are limited, among other things, by the lack of labeled data, which is even more significant in the medical domain, motivating the use of unsupervised learning. Therefore, in this work, we develop a novel unsupervised descriptor learning method that does not rely on keypoint detection. This enables the resulting descriptor network to be agnostic to the keypoint detector used during the registration inference. To validate this approach, we perform an extensive and comprehensive comparison on the reference public retinal image registration dataset. Additionally, we test our method with multiple keypoint detectors of varied nature, even proposing some novel ones. Our results demonstrate that the proposed approach offers accurate registration, not incurring in any performance loss versus supervised methods. Additionally, it demonstrates accurate performance regardless of the keypoint detector used. Thus, this work represents a notable step towards leveraging unsupervised learning in the medical domain.

arXiv

Unsupervised Deep Learning-based Keypoint Localization Estimating Descriptor Matching Performance

David Rivas-Villar, Álvaro S. Hervella, José Rouco, Jorge Novo

Retinal image registration, particularly for color fundus images, is a challenging yet essential task with diverse clinical applications. Existing registration methods for color fundus images typically rely on keypoints and descriptors for alignment; however, a significant limitation is their reliance on labeled data, which is particularly scarce in the medical domain. In this work, we present a novel unsupervised registration pipeline that entirely eliminates the need for labeled data. Our approach is based on the principle that locations with distinctive descriptors constitute reliable keypoints. This fully inverts the conventional state-of-the-art approach, conditioning the detector on the descriptor rather than the opposite. First, we propose an innovative descriptor learning method that operates without keypoint detection or any labels, generating descriptors for arbitrary locations in retinal images. Next, we introduce a novel, label-free keypoint detector network which works by estimating descriptor performance directly from the input image. We validate our method through a comprehensive evaluation on four hold-out datasets, demonstrating that our unsupervised descriptor outperforms state-of-the-art supervised descriptors and that our unsupervised detector significantly outperforms existing unsupervised detection methods. Finally, our full registration pipeline achieves performance comparable to the leading supervised methods, while not employing any labeled data. Additionally, the label-free nature and design of our method enable direct adaptation to other domains and modalities.

arXiv

SelfDRSC++: Self-Supervised Dual Reversed Rolling Shutter Correction via Video Interpolation

Wei Shang, Dongwei Ren, Wanying Zhang, Qilong Wang, Pengfei Zhu, Wangmeng Zuo

Modern consumer cameras often use rolling shutter, capturing scenes row-by-row and causing distortion in dynamic scenes. Existing correction methods rely on supervised learning with high-frame-rate global shutter images as ground truth. We propose SelfDRSC++, a self-supervised framework for RS distortion correction {from simultaneously captured top-to-bottom and bottom-to-top RS images}. A lightweight network with a bidirectional correlation matching block jointly optimizes optical flows and corrected RS features, improving performance with fewer parameters. A self-supervised strategy enforces {a physically constrained RS--GS--RS cycle} between input and reconstructed dual reversed RS images. RS reconstruction is formulated as a specialized video frame interpolation task, enabling feasible one-stage training. Extensive experiments on synthetic and real-world data show that SelfDRSC++ achieves competitive quantitative performance, improves perceptual quality, and produces high-frame-rate GS sequences with better temporal consistency.

arXiv

Interpretable ODE-style Generative Diffusion Model via Force Field Construction

Weiyang Jin, Yongpei Zhu, Yuxi Peng

For a considerable time, researchers have focused on developing a method that establishes a deep connection between the generative diffusion model and mathematical physics. Despite previous efforts, progress has been limited to the pursuit of a single specialized method. In order to advance the interpretability of diffusion models and explore new research directions, it is essential to establish a unified ODE-style generative diffusion model. Such a model should draw inspiration from physical models and possess a clear geometric meaning. This paper aims to identify various physical models that are suitable for constructing ODE-style generative diffusion models accurately from a mathematical perspective. We then summarize these models into a unified method. Additionally, we perform a case study where we use the theoretical model identified by our method to develop a range of new diffusion model methods, and conduct experiments. Our experiments on CIFAR-10 demonstrate the effectiveness of our approach. We have constructed a computational framework that attains highly proficient results with regards to image generation speed, alongside an additional model that demonstrates exceptional performance in both Inception score and FID score. These results underscore the significance of our method in advancing the field of diffusion models.

arXiv

When Do LLMs Actually Help? Evaluating LLMs as Data Quality Annotators

Praphulla Lal Shrestha

LLMs have been increasingly used to catch data quality issues automatically, but we know very little about how consistent these judgments actually are. This study tests an LLM on two e-commerce data quality tasks, entity matching and brand mislabeling, against rule based baselines and human verified ground truth, under both zero-shot and few-shot prompting. On entity matching while using the Abt Buy benchmark (2,194 labeled pairs), a simple rule based baseline (F1=0.950) performed about as well as LLM zero shot prompting (F1=0.948). Moreover, a few-shot prompt revision that looked effective on a small validation sample reduced full-scale performance to F1=0.914. This showed that small sample prompt evaluation can be misleading. On brand mislabeling detection, using 500 Amazon product listings with synthetically injected labeling errors, the LLM clearly outperformed a naive rule based baseline (F1=0.833 vs 0.721), because it could draw on background knowledge of brand product relationships that a simple rule could not access. Testing consistency across repeated runs (200 pairs, 5 runs at temperature 0.7) showed the model agreeing with itself 99.7% of the time on average, with 99% of pairs giving identical answers across all 5 runs. Using majority voting across these runs only improved F1 by 0.005, at 5 times the inference cost. These results suggest that the value of using an LLM over traditional methods depends heavily on the task. LLMs offer little advantage when strong lexical signals already exist, but a clear advantage when the task requires background knowledge, all while remaining highly consistent across repeated queries.

arXiv

Class Imbalance and Batch Effects in LLM-Based Screening for Systematic Reviews

Gilberto Sussumu Hida, Danilo Monteiro Ribeiro, Clayton Suguio Hida

This study analyses LLMs in imbalanced binary classification, using study screening in systematic reviews as the application domain. An experiment was conducted in five reviews, comparing individual and batch processing, with and without prevalence metadata. The results indicate a limited influence of the prevalence metadata, with no evidence that it improves performance. In contrast, batch processing produced larger behavioral changes that varied according to the prevalence of the class. The aggregate and item-level analyses did not always coincide. Therefore, batch processing should be evaluated not only in terms of cost, but also in relation to its effects on decision-making behavior.

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