pwc-archive/papers-with-abstracts huggingface dataset:
Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeek
Authors: Xueyang Li, Jiahao Li, Yu Song, Yunzhong Lou, Xiangdong Zhou
Date: 2025-05-23 00:00:00

The advent of Computer-Aided Design (CAD) generative modeling will significantly transform the design of industrial products. The recent research endeavor has extended into the realm of Large Language Models (LLMs). In contrast to fine-tuning methods, training-free approaches typically utilize the advanced closed-source LLMs, thereby offering enhanced flexibility and efficiency in the development of AI agents for generating CAD parametric models. However, the substantial cost and limitations of local deployment of the top-tier closed-source LLMs pose challenges in practical applications. The Seek-CAD is the pioneer exploration of locally deployed open-source inference LLM DeepSeek-R1 for CAD parametric model generation with a training-free methodology. This study is the first investigation to incorporate both visual and Chain-of-Thought (CoT) feedback within the self-refinement mechanism for generating CAD models. Specifically, the initial generated parametric CAD model is rendered into a sequence of step-wise perspective images, which are subsequently processed by a Vision Language Model (VLM) alongside the corresponding CoTs derived from DeepSeek-R1 to assess the CAD model generation. Then, the feedback is utilized by DeepSeek-R1 to refine the initial generated model for the next round of generation. Moreover, we present an innovative 3D CAD model dataset structured around the SSR (Sketch, Sketch-based feature, and Refinements) triple design paradigm. This dataset encompasses a wide range of CAD commands, thereby aligning effectively with industrial application requirements and proving suitable for the generation of LLMs. Extensive experiments validate the effectiveness of Seek-CAD under various metrics.
Lung Nodule Segmentation: Exploring Data Efficiency and Advanced Architectures
Authors: Nima Shafiei Rezvani Nezhad, Meysam Mansouri, Saeid Ghayour, Ruhollah Abolhasani MD, Shahla Azizi PhD
Date: 2025-05-26 00:00:00
Proceeding: 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2025 5
Tasks: Lung Nodule Segmentation, Segmentation

This study explores the application of various deep learning models for the segmentation of lung nodules using LIDC- IDRI. Unlike traditional approaches that utilize the full dataset, our work emphasizes the efficacy of training models on a filtered subset of 356 samples. Novel configurations, including attention mechanisms and advanced preprocessing strategies, were em- ployed to optimize segmentation accuracy. Among the models evaluated, the DPLinkNet50 with a Channel Attention Bridge and ResNet backbone demonstrated the highest performance with a Dice score of 0.86 and percision 0.88, significantly outper- forming conventional architectures. This work underscores the potential of leveraging data efficiency and tailored architectures in achieving robust segmentation performance, paving the way for improved computer-aided diagnosis in clinical settings.
PoseBH: Prototypical Multi-Dataset Training Beyond Human Pose Estimation
Authors: Uyoung Jeong, Jonathan Freer, Seungryul Baek, Hyung Jin Chang, Kwang In Kim
Date: 2025-05-23 00:00:00
Proceeding: CVPR 2025 1
Tasks: Pose Estimation

We study multi-dataset training (MDT) for pose estimation, where skeletal heterogeneity presents a unique challenge that existing methods have yet to address. In traditional domains, \eg regression and classification, MDT typically relies on dataset merging or multi-head supervision. However, the diversity of skeleton types and limited cross-dataset supervision complicate integration in pose estimation. To address these challenges, we introduce PoseBH, a new MDT framework that tackles keypoint heterogeneity and limited supervision through two key techniques. First, we propose nonparametric keypoint prototypes that learn within a unified embedding space, enabling seamless integration across skeleton types. Second, we develop a cross-type self-supervision mechanism that aligns keypoint predictions with keypoint embedding prototypes, providing supervision without relying on teacher-student models or additional augmentations. PoseBH substantially improves generalization across whole-body and animal pose datasets, including COCO-WholeBody, AP-10K, and APT-36K, while preserving performance on standard human pose benchmarks (COCO, MPII, and AIC). Furthermore, our learned keypoint embeddings transfer effectively to hand shape estimation (InterHand2.6M) and human body shape estimation (3DPW). The code for PoseBH is available at: https://github.com/uyoung-jeong/PoseBH.
Semantic Correspondence: Unified Benchmarking and a Strong Baseline
Authors: Kaiyan Zhang, Xinghui Li, Jingyi Lu, Kai Han
Date: 2025-05-23 00:00:00
Tasks: Benchmarking, Semantic correspondence, Survey

Establishing semantic correspondence is a challenging task in computer vision, aiming to match keypoints with the same semantic information across different images. Benefiting from the rapid development of deep learning, remarkable progress has been made over the past decade. However, a comprehensive review and analysis of this task remains absent. In this paper, we present the first extensive survey of semantic correspondence methods. We first propose a taxonomy to classify existing methods based on the type of their method designs. These methods are then categorized accordingly, and we provide a detailed analysis of each approach. Furthermore, we aggregate and summarize the results of methods in literature across various benchmarks into a unified comparative table, with detailed configurations to highlight performance variations. Additionally, to provide a detailed understanding on existing methods for semantic matching, we thoroughly conduct controlled experiments to analyse the effectiveness of the components of different methods. Finally, we propose a simple yet effective baseline that achieves state-of-the-art performance on multiple benchmarks, providing a solid foundation for future research in this field. We hope this survey serves as a comprehensive reference and consolidated baseline for future development. Code is publicly available at: https://github.com/Visual-AI/Semantic-Correspondence.
UniTTS: An end-to-end TTS system without decoupling of acoustic and semantic information
Authors: Rui Wang, Qianguo Sun, Tianrong Chen, Zhiyun Zeng, Junlong Wu, Jiaxing Zhang
Date: 2025-05-23 00:00:00
Tasks: Large Language Model, Quantization, text-to-speech, Text to Speech

The emergence of multi-codebook neutral audio codecs such as Residual Vector Quantization (RVQ) and Group Vector Quantization (GVQ) has significantly advanced Large-Language-Model (LLM) based Text-to-Speech (TTS) systems. These codecs are crucial in separating semantic and acoustic information while efficiently harnessing semantic priors. However, since semantic and acoustic information cannot be fully aligned, a significant drawback of these methods when applied to LLM-based TTS is that large language models may have limited access to comprehensive audio information. To address this limitation, we propose DistilCodec and UniTTS, which collectively offer the following advantages: 1) This method can distill a multi-codebook audio codec into a single-codebook audio codec with 32,768 codes while achieving a near 100\% utilization. 2) As DistilCodec does not employ a semantic alignment scheme, a large amount of high-quality unlabeled audio (such as audiobooks with sound effects, songs, etc.) can be incorporated during training, further expanding data diversity and broadening its applicability. 3) Leveraging the comprehensive audio information modeling of DistilCodec, we integrated three key tasks into UniTTS's pre-training framework: audio modality autoregression, text modality autoregression, and speech-text cross-modal autoregression. This allows UniTTS to accept interleaved text and speech/audio prompts while substantially preserving LLM's text capabilities. 4) UniTTS employs a three-stage training process: Pre-Training, Supervised Fine-Tuning (SFT), and Alignment. Source code and model checkpoints are publicly available at https://github.com/IDEA-Emdoor-Lab/UniTTS and https://github.com/IDEA-Emdoor-Lab/DistilCodec.
keepitsimple at SemEval-2025 Task 3: LLM-Uncertainty based Approach for Multilingual Hallucination Span Detection
Authors: Saketh Reddy Vemula, Parameswari Krishnamurthy
Date: 2025-05-23 00:00:00
Tasks: Hallucination, Language Modeling, Language Modelling

Identification of hallucination spans in black-box language model generated text is essential for applications in the real world. A recent attempt at this direction is SemEval-2025 Task 3, Mu-SHROOM-a Multilingual Shared Task on Hallucinations and Related Observable Over-generation Errors. In this work, we present our solution to this problem, which capitalizes on the variability of stochastically-sampled responses in order to identify hallucinated spans. Our hypothesis is that if a language model is certain of a fact, its sampled responses will be uniform, while hallucinated facts will yield different and conflicting results. We measure this divergence through entropy-based analysis, allowing for accurate identification of hallucinated segments. Our method is not dependent on additional training and hence is cost-effective and adaptable. In addition, we conduct extensive hyperparameter tuning and perform error analysis, giving us crucial insights into model behavior.
Mol-MoE: Training Preference-Guided Routers for Molecule Generation
Authors: Diego Calanzone, Pierluca D'Oro, Pierre-Luc Bacon
Date: 2025-02-08 00:00:00
Tasks: Benchmarking, Drug Design, Mixture-of-Experts, Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on single-objective reinforcement learning, limiting their applicability to real-world drug design, where multiple competing properties must be optimized. Traditional multi-objective reinforcement learning (MORL) methods require costly retraining for each new objective combination, making rapid exploration of trade-offs impractical. To overcome these limitations, we introduce Mol-MoE, a mixture-of-experts (MoE) architecture that enables efficient test-time steering of molecule generation without retraining. Central to our approach is a preference-based router training objective that incentivizes the router to combine experts in a way that aligns with user-specified trade-offs. This provides improved flexibility in exploring the chemical property space at test time, facilitating rapid trade-off exploration. Benchmarking against state-of-the-art methods, we show that Mol-MoE achieves superior sample quality and steerability.
Language Models Are Implicitly Continuous
Authors: Samuele Marro, Davide Evangelista, X. Angelo Huang, Emanuele La Malfa, Michele Lombardi, Michael Wooldridge
Date: 2025-04-04 00:00:00
Tasks: Language Modelling

Language is typically modelled with discrete sequences. However, the most successful approaches to language modelling, namely neural networks, are continuous and smooth function approximators. In this work, we show that Transformer-based language models implicitly learn to represent sentences as continuous-time functions defined over a continuous input space. This phenomenon occurs in most state-of-the-art Large Language Models (LLMs), including Llama2, Llama3, Phi3, Gemma, Gemma2, and Mistral, and suggests that LLMs reason about language in ways that fundamentally differ from humans. Our work formally extends Transformers to capture the nuances of time and space continuity in both input and output space. Our results challenge the traditional interpretation of how LLMs understand language, with several linguistic and engineering implications.
Beyond Words: A Latent Memory Approach to Internal Reasoning in LLMs
Authors: José I. Orlicki
Date: 2025-02-28 00:00:00
Tasks: Decoder

Recent advances in large language models (LLMs) have popularized the chain-of-thought (CoT) paradigm, in which models produce explicit reasoning steps in natural language. Although this approach improves interpretability and facilitates external auditing, it may not represent the most computationally efficient method for internal reasoning. In contrast, human cognition relies on implicit mental representations that recall past sensory and episodic information without requiring complete verbalization. In this paper, we propose a framework that integrates implicit mental representations into the internal reasoning processes of LLMs. Preliminary experiments indicate that incorporating an Implicit Memory Module (IMM) into a simple GPT model yields a reduction of between 35% and 57% in final training loss compared to a regular GPT baseline. The addition of an explicit interpretability channel (e.g., a chain-of-thought decoder) is straightforward to implement within this approach. We outline theoretical foundations, propose technical mechanisms to scale the memory module, and discuss how these ideas may lead to more efficient and robust reasoning, with optional future extensions for explicit auditability.
Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
Authors: Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham Kakade, Sitan Chen
Date: 2025-02-10 00:00:00

In recent years, masked diffusion models (MDMs) have emerged as a promising alternative approach for generative modeling over discrete domains. Compared to autoregressive models (ARMs), MDMs trade off complexity at training time with flexibility at inference time. At training time, they must learn to solve an exponentially large number of infilling problems, but at inference time, they can decode tokens in essentially arbitrary order. In this work, we closely examine these two competing effects. On the training front, we theoretically and empirically demonstrate that MDMs indeed train on computationally intractable subproblems compared to their autoregressive counterparts. On the inference front, we show that a suitable strategy for adaptively choosing the token decoding order significantly enhances the capabilities of MDMs, allowing them to sidestep hard subproblems. On logic puzzles like Sudoku, we show that adaptive inference can boost solving accuracy in pretrained MDMs from $<7$% to $\approx 90$%, even outperforming ARMs with $7\times$ as many parameters and that were explicitly trained via teacher forcing to learn the right order of decoding.
Recognition-Synergistic Scene Text Editing
Authors: Zhengyao Fang, Pengyuan Lyu, Jingjing Wu, Chengquan Zhang, Jun Yu, Guangming Lu, Wenjie Pei
Date: 2025-03-11 00:00:00
Proceeding: CVPR 2025 1
Tasks: Scene Text Editing

Scene text editing aims to modify text content within scene images while maintaining style consistency. Traditional methods achieve this by explicitly disentangling style and content from the source image and then fusing the style with the target content, while ensuring content consistency using a pre-trained recognition model. Despite notable progress, these methods suffer from complex pipelines, leading to suboptimal performance in complex scenarios. In this work, we introduce Recognition-Synergistic Scene Text Editing (RS-STE), a novel approach that fully exploits the intrinsic synergy of text recognition for editing. Our model seamlessly integrates text recognition with text editing within a unified framework, and leverages the recognition model's ability to implicitly disentangle style and content while ensuring content consistency. Specifically, our approach employs a multi-modal parallel decoder based on transformer architecture, which predicts both text content and stylized images in parallel. Additionally, our cyclic self-supervised fine-tuning strategy enables effective training on unpaired real-world data without ground truth, enhancing style and content consistency through a twice-cyclic generation process. Built on a relatively simple architecture, RS-STE achieves state-of-the-art performance on both synthetic and real-world benchmarks, and further demonstrates the effectiveness of leveraging the generated hard cases to boost the performance of downstream recognition tasks. Code is available at https://github.com/ZhengyaoFang/RS-STE.
Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph
Authors: Qiaosheng Chen, Kaijia Huang, Xiao Zhou, Weiqing Luo, Yuanning Cui, Gong Cheng
Date: 2025-05-23 00:00:00
Tasks: Benchmarking, Management

The rapid growth of open source machine learning (ML) resources, such as models and datasets, has accelerated IR research. However, existing platforms like Hugging Face do not explicitly utilize structured representations, limiting advanced queries and analyses such as tracing model evolution and recommending relevant datasets. To fill the gap, we construct HuggingKG, the first large-scale knowledge graph built from the Hugging Face community for ML resource management. With 2.6 million nodes and 6.2 million edges, HuggingKG captures domain-specific relations and rich textual attributes. It enables us to further present HuggingBench, a multi-task benchmark with three novel test collections for IR tasks including resource recommendation, classification, and tracing. Our experiments reveal unique characteristics of HuggingKG and the derived tasks. Both resources are publicly available, expected to advance research in open source resource sharing and management.
The Utility of Hyperplane Angle Metric in Detecting Financial Concept Drift
Authors: ZhiPeng
Date: 2025-01-12 00:00:00
Proceeding: Applied Intelligence 2025 1
Tasks: ARC, Drift Detection, Geometric Matching, Time Series, Time Series Analysis

In financial time series analysis, introducing a new metric for concept drift is essential to address the limitations of existing evaluation methods, particularly in terms of speed, interpretability and stability. Performance-based metrics and model-based metrics are the most commonly used to detect concept drift. For example, the error rate, which belongs to performance-based metric, is a frequently used metric that directly reflects the difference between the model's output and the actual results, making it suitable for quick decision-making. Mahalanobis Distance, being a model-based metric, detects concept drift by evaluating deviations in the sample distribution, offering deeper interpretability and stability. Generally speaking, performance-based metrics excel in speed but lack deeper interpretability and stability, while model-based metrics are opposite. To achieve speed, deeper interpretability and stability, we propose a novel metric termed the Angle Between Hyperplanes (ABH), which calculates the angle between earlier and later hyperplanes at two distinct time points through arc-cosine function. This metric quantifies the similarity between two decision boundaries, with the angle reflecting the degree of concept drift detection. In other words, a larger angle indicates a higher probability of detecting concept drift. ABH offers good interpretability, as its angle has a geometric presentation, and it is time-efficient, requiring only the calculation of an arc-cosine function. To validate the effectiveness of the ABH, we integrate it into the Drift Detection Model (DDM) framework, replacing error rate-based metrics to monitor data distribution over time. Empirical studies on synthetic datasets show that ABH achieves approximately a 50% reduction in the Coefficient of Variation (Cv) compared to error rate-based approaches, demonstrating the stability of ABH. On the Shanghai and Shenzhen Stock Exchanges, our model outperforms the recent models. For instance, our model outperforms the Weighted Increment-Decrement Support Vector Machine (WIDSVM), reducing the error rate by 4% and 1%, respectively.
Track Anything Annotate: Video annotation and dataset generation of computer vision models
Authors: Nikita Ivanov, Mark Klimov, Dmitry Glukhikh, Tatiana Chernysheva, Igor Glukhikh
Date: 2025-05-23 00:00:00
Tasks: Dataset Generation

Modern machine learning methods require significant amounts of labelled data, making the preparation process time-consuming and resource-intensive. In this paper, we propose to consider the process of prototyping a tool for annotating and generating training datasets based on video tracking and segmentation. We examine different approaches to solving this problem, from technology selection through to final implementation. The developed prototype significantly accelerates dataset generation compared to manual annotation. All resources are available at https://github.com/lnikioffic/track-anything-annotate
Efficient Visibility Approximation for Game AI using Neural Omnidirectional Distance Fields
Authors: Zhi Ying, Nicholas Edwards, Mikhail Kutuzov
Date: 2024-05-09 00:00:00

Visibility information is critical in game AI applications, but the computational cost of raycasting-based methods poses a challenge for real-time systems. To address this challenge, we propose a novel method that represents a partitioned game scene as neural Omnidirectional Distance Fields (ODFs), allowing scalable and efficient visibility approximation between positions without raycasting. For each position of interest, we map its omnidirectional distance data from the spherical surface onto a UV plane. We then use multi-resolution grids and bilinearly interpolated features to encode directions. This allows us to use a compact multi-layer perceptron (MLP) to reconstruct the high-frequency directional distance data at these positions, ensuring fast inference speed. We demonstrate the effectiveness of our method through offline experiments and in-game evaluation. For in-game evaluation, we conduct a side-by-side comparison with raycasting-based visibility tests in three different scenes. Using a compact MLP (128 neurons and 2 layers), our method achieves an average cold start speedup of 9.35 times and warm start speedup of 4.8 times across these scenes. In addition, unlike the raycasting-based method, whose evaluation time is affected by the characteristics of the scenes, our method's evaluation time remains constant.
FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
Authors: Laines Schmalwasser, Niklas Penzel, Joachim Denzler, Julia Niebling
Date: 2025-05-23 00:00:00

Concepts such as objects, patterns, and shapes are how humans understand the world. Building on this intuition, concept-based explainability methods aim to study representations learned by deep neural networks in relation to human-understandable concepts. Here, Concept Activation Vectors (CAVs) are an important tool and can identify whether a model learned a concept or not. However, the computational cost and time requirements of existing CAV computation pose a significant challenge, particularly in large-scale, high-dimensional architectures. To address this limitation, we introduce FastCAV, a novel approach that accelerates the extraction of CAVs by up to 63.6x (on average 46.4x). We provide a theoretical foundation for our approach and give concrete assumptions under which it is equivalent to established SVM-based methods. Our empirical results demonstrate that CAVs calculated with FastCAV maintain similar performance while being more efficient and stable. In downstream applications, i.e., concept-based explanation methods, we show that FastCAV can act as a replacement leading to equivalent insights. Hence, our approach enables previously infeasible investigations of deep models, which we demonstrate by tracking the evolution of concepts during model training.
a dynamic conditional score model for the log correlation matrix
Authors: Hafner
Date: 2021-10-02 00:00:00
Proceeding: Journal of Econometrics 2021 10
Tasks: Survey

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