pwc-archive/papers-with-abstracts huggingface dataset:
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
Authors: Hayat Ullah, Muhammad Ali Shafique, Abbas Khan, Arslan Munir
Date: 2025-07-16 00:00:00
Tasks: Benchmarking, Knowledge Distillation, Spatio-temporal Action Recognition, Temporal Action Localization, Video Focal Modulation, Video Recognition

The landscape of video recognition has evolved significantly, shifting from traditional Convolutional Neural Networks (CNNs) to Transformer-based architectures for improved accuracy. While 3D CNNs have been effective at capturing spatiotemporal dynamics, recent Transformer models leverage self-attention to model long-range spatial and temporal dependencies. Despite achieving state-of-the-art performance on major benchmarks, Transformers remain computationally expensive, particularly with dense video data. To address this, we propose a lightweight Video Focal Modulation Network, DVFL-Net, which distills spatiotemporal knowledge from a large pre-trained teacher into a compact nano student model, enabling efficient on-device deployment. DVFL-Net utilizes knowledge distillation and spatial-temporal feature modulation to significantly reduce computation while preserving high recognition performance. We employ forward Kullback-Leibler (KL) divergence alongside spatio-temporal focal modulation to effectively transfer both local and global context from the Video-FocalNet Base (teacher) to the proposed VFL-Net (student). We evaluate DVFL-Net on UCF50, UCF101, HMDB51, SSV2, and Kinetics-400, benchmarking it against recent state-of-the-art methods in Human Action Recognition (HAR). Additionally, we conduct a detailed ablation study analyzing the impact of forward KL divergence. The results confirm the superiority of DVFL-Net in achieving an optimal balance between performance and efficiency, demonstrating lower memory usage, reduced GFLOPs, and strong accuracy, making it a practical solution for real-time HAR applications.
Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design
Authors: Xingyu Su, Xiner Li, Masatoshi Uehara, Sunwoo Kim, Yulai Zhao, Gabriele Scalia, Ehsan Hajiramezanali, Tommaso Biancalani, Degui Zhi, Shuiwang Ji
Date: 2025-07-01 00:00:00

We address the problem of fine-tuning diffusion models for reward-guided generation in biomolecular design. While diffusion models have proven highly effective in modeling complex, high-dimensional data distributions, real-world applications often demand more than high-fidelity generation, requiring optimization with respect to potentially non-differentiable reward functions such as physics-based simulation or rewards based on scientific knowledge. Although RL methods have been explored to fine-tune diffusion models for such objectives, they often suffer from instability, low sample efficiency, and mode collapse due to their on-policy nature. In this work, we propose an iterative distillation-based fine-tuning framework that enables diffusion models to optimize for arbitrary reward functions. Our method casts the problem as policy distillation: it collects off-policy data during the roll-in phase, simulates reward-based soft-optimal policies during roll-out, and updates the model by minimizing the KL divergence between the simulated soft-optimal policy and the current model policy. Our off-policy formulation, combined with KL divergence minimization, enhances training stability and sample efficiency compared to existing RL-based methods. Empirical results demonstrate the effectiveness and superior reward optimization of our approach across diverse tasks in protein, small molecule, and regulatory DNA design.
Domain-Adaptive Small Language Models for Structured Tax Code Prediction
Authors: Souvik Nath, Sumit Wadhwa, Luiz Perez
Date: 2025-07-15 00:00:00
Tasks: Decoder, Small Language Model

Every day, multinational firms process thousands of transactions, each of which must adhere to tax regulations that vary by jurisdiction and are often nuanced. The determination of product and service tax codes, such as HSN or SAC is a major use case in Tax compliance. An accurate determination of such codes is imperative to avoid any tax penalties. This paper proposes a domain-adaptive small language model (SLM) with an encoder-decoder architecture for the enhanced prediction of product and service tax codes. In this approach, we address the problem of predicting hierarchical tax code sequences using unstructured product and services data. We employ an SLM based upon encoder-decoder architecture as this enables sequential generation of tax codes to capture the hierarchical dependencies present within the tax codes. Our experiments demonstrate that encoder-decoder SLMs can be successfully applied to the sequential prediction of structured tax codes, a domain that remains comparatively unexplored in current NLP research. In this paper, we demonstrate the superior performance of the domain-adaptive encoder-decoder SLMs over flat classifiers when applied to the Harmonized System of Nomenclature (HSN), and achieve superior results compared to decoder-only and encoder-only architectures for structured sequence generation tasks. This approach can also be scaled to other government-mandated tax commodity codes, such as United Nations Standard Products and Services Codes (UNSPSC), or Brazil's Nomenclatura Comum do Mercosul (NCM).
AdaMuon: Adaptive Muon Optimizer
Authors: Chongjie Si, Debing Zhang, Wei Shen
Date: 2025-07-15 00:00:00

We propose AdaMuon, an adaptive learning-rate framework built upon the recently validated Muon optimizer, which has demonstrated substantial efficiency gains over AdamW in large-scale model training. AdaMuon augments Muon with two mutually dependent modules: (1) a per-parameter second-moment modulation that captures orthogonal gradient updates to ensure update-level adaptivity, and (2) a RMS-aligned rescaling that regulates the overall update magnitude by aligning it with the intrinsic structure of the parameter space. Empirical results on multiple model scales and learning-rate regimes confirm that AdaMuon consistently outperforms the original Muon, delivering higher acceleration in convergence while maintaining training stability. Our method introduces no additional tuning burden and can be seamlessly integrated into existing Muon training pipelines.
Learning What Matters: Probabilistic Task Selection via Mutual Information for Model Finetuning
Authors: Prateek Chanda, Saral Sureka, Parth Pratim Chatterjee, KrishnaTeja Killamsetty, Nikhil Shivakumar Nayak, Ganesh Ramakrishnan
Date: 2025-07-16 00:00:00
Tasks: Diversity, MMLU

The performance of finetuned large language models (LLMs) hinges critically on the composition of the training mixture. However, selecting an optimal blend of task datasets remains a largely manual, heuristic driven process, with practitioners often relying on uniform or size based sampling strategies. We introduce TASKPGM, a principled and scalable framework for mixture optimization that selects continuous task proportions by minimizing an energy function over a Markov Random Field (MRF). Task relationships are modeled using behavioral divergences such as Jensen Shannon Divergence and Pointwise Mutual Information computed from the predictive distributions of single task finetuned models. Our method yields a closed form solution under simplex constraints and provably balances representativeness and diversity among tasks. We provide theoretical guarantees, including weak submodularity for budgeted variants, and demonstrate consistent empirical improvements on Llama 2 and Mistral across evaluation suites such as MMLU and BIGBench. Beyond performance, TASKPGM offers interpretable insights into task influence and mixture composition, making it a powerful tool for efficient and robust LLM finetuning.
Kodezi Chronos: A Debugging-First Language Model for Repository-Scale, Memory-Driven Code Understanding
Authors: Ishraq Khan, Assad Chowdary, Sharoz Haseeb, Urvish Patel
Date: 2025-07-14 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling, Retrieval

Large Language Models (LLMs) have advanced code generation and software automation, but are fundamentally constrained by limited inference-time context and lack of explicit code structure reasoning. We introduce Kodezi Chronos, a next-generation architecture for autonomous code understanding, debugging, and maintenance, designed to operate across ultra-long contexts comprising entire codebases, histories, and documentation, all without fixed window limits. Kodezi Chronos leverages a multi-level embedding memory engine, combining vector and graph-based indexing with continuous code-aware retrieval. This enables efficient and accurate reasoning over millions of lines of code, supporting repository-scale comprehension, multi-file refactoring, and real-time self-healing actions. Our evaluation introduces a novel Multi Random Retrieval benchmark, specifically tailored to the software engineering domain. Unlike classical retrieval benchmarks, this method requires the model to resolve arbitrarily distant and obfuscated associations across code artifacts, simulating realistic tasks such as variable tracing, dependency migration, and semantic bug localization. Chronos outperforms prior LLMs and code models, demonstrating a 23% improvement in real-world bug detection and reducing debugging cycles by up to 40% compared to traditional sequence-based approaches. By natively interfacing with IDEs and CI/CD workflows, Chronos enables seamless, autonomous software maintenance, elevating code reliability and productivity while reducing manual effort. These results mark a critical advance toward self-sustaining, continuously optimized software ecosystems.
A Lightweight U-Net Model for Accurate Skin Lesion Segmentation
Authors: Fallah H. Najjar, Karrar A. Kadhim, Farhan Mohamed, Mohd Shafry Mohd Rahim, Asniyani Nur Haidar Abdullah
Date: 2025-04-12 00:00:00
Proceeding: Iraqi Journal for Computer Science and Mathematics 2025 4
Tasks: Lesion Classification, Lesion Segmentation, Sensitivity, Skin Lesion Classification, Skin Lesion Segmentation

In this paper, a new lightweight U-Net deep learning-based neural network designed for the segmentation of skin lesions is proposed. Segmentation of skin lesions is the most critical step in computer-aided dermatology diagnosis for the early detection of melanoma and other diseases. However, we address the difficulty related to the precise definition of the lesion margins with an eye on the computation cost. We have demonstrated the state-of-the-art performance of DeepSkinSeg in most metrics on dermoscopic images using the PH2 and Human Against Machine (HAM10000) datasets. The metrics of the DeepSkinSeg model were robustness measured as the Intersection over Union (IoU) at 91.49, Dice coefficient at 95.56, precision at 97.97, sensitivity at 96.84, and accuracy at 96.71 for the PH2 dataset. Other standard generalization capabilities for the HAM10000 dataset could be an IoU of 92.97, a Dice coefficient of 96.36, precision at 97.64, sensitivity at 95.10, and an accuracy of 94.59. DeepSkinSeg has a very efficient inference because the model itself is lightweight, proving to be very helpful for real-time dermatological analysis. This work further advanced the computer-aided diagnosis in the task of skin lesion classification, guaranteeing even more promising clinical applications.
Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
Authors: Jialong Mai, Xiaofen Xing, Yawei Li, Zhipeng Li, Jingyuan Xing, Xiangmin Xu
Date: 2025-07-11 00:00:00
Tasks: 4k, Emotion Recognition, Emotion Recognition in Conversation, Large Language Model, Sentence, Speech Emotion Recognition

Recent research has focused on applying speech large language model (SLLM) to improve speech emotion recognition (SER). However, the inherently high frame rate in speech modality severely limits the signal processing and understanding capabilities of SLLM. For example, a SLLM with a 4K context window can only process 80 seconds of audio at 50Hz feature sampling rate before reaching its capacity limit. Input token compression methods used in SLLM overlook the continuity and inertia of emotions across multiple conversation turns. This paper proposes a Dynamic Parameter Memory (DPM) mechanism with contextual semantics and sentence-level emotion encoding, enabling processing of unlimited-length audio with limited context windows in SLLM. Specifically, DPM progressively encodes sentence-level information and emotions into a temporary LoRA module during inference to effectively "memorize" the contextual information. We trained an emotion SLLM as a backbone and incorporated our DPM into inference for emotion recognition in conversation (ERC). Experimental results on the IEMOCAP dataset show that DPM significantly improves the emotion recognition capabilities of SLLM when processing long audio sequences, achieving state-of-the-art performance.
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
Authors: Binxiong Li, Yuefei Wang, Xu Xiang, Xue Li, Binyu Zhao, Heyang Gao, Qinyu Zhao, Xi Yu
Date: 2025-07-18 00:00:00
Tasks: Clustering, Deep Clustering, Graph Clustering, News Classification

In recent years, models based on Graph Convolutional Networks (GCN) have made significant strides in the field of graph data analysis. However, challenges such as over-smoothing and over-compression remain when handling large-scale and complex graph datasets, leading to a decline in clustering quality. Although the Graph Transformer architecture has mitigated some of these issues, its performance is still limited when processing heterogeneous graph data. To address these challenges, this study proposes a novel deep clustering framework that comprising GCN, Autoencoder (AE), and Graph Transformer, termed the Tri-Learn Graph Fusion Network (Tri-GFN). This framework enhances the differentiation and consistency of global and local information through a unique tri-learning mechanism and feature fusion enhancement strategy. The framework integrates GCN, AE, and Graph Transformer modules. These components are meticulously fused by a triple-channel enhancement module, which maximizes the use of both node attributes and topological structures, ensuring robust clustering representation. The tri-learning mechanism allows mutual learning among these modules, while the feature fusion strategy enables the model to capture complex relationships, yielding highly discriminative representations for graph clustering. It surpasses many state-of-the-art methods, achieving an accuracy improvement of approximately 0.87% on the ACM dataset, 14.14 % on the Reuters dataset, and 7.58 % on the USPS dataset. Due to its outstanding performance on the Reuters dataset, Tri-GFN can be applied to automatic news classification, topic retrieval, and related fields.
DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits
Authors: Garapati Keerthana, Manik Gupta
Date: 2025-07-18 00:00:00
Tasks: Large Language Model

Progress notes are among the most clinically meaningful artifacts in an Electronic Health Record (EHR), offering temporally grounded insights into a patient's evolving condition, treatments, and care decisions. Despite their importance, they are severely underrepresented in large-scale EHR datasets. For instance, in the widely used Medical Information Mart for Intensive Care III (MIMIC-III) dataset, only about $8.56\%$ of hospital visits include progress notes, leaving gaps in longitudinal patient narratives. In contrast, the dataset contains a diverse array of other note types, each capturing different aspects of care. We present DENSE (Documenting Evolving Progress Notes from Scattered Evidence), a system designed to align with clinical documentation workflows by simulating how physicians reference past encounters while drafting progress notes. The system introduces a fine-grained note categorization and a temporal alignment mechanism that organizes heterogeneous notes across visits into structured, chronological inputs. At its core, DENSE leverages a clinically informed retrieval strategy to identify temporally and semantically relevant content from both current and prior visits. This retrieved evidence is used to prompt a large language model (LLM) to generate clinically coherent and temporally aware progress notes. We evaluate DENSE on a curated cohort of patients with multiple visits and complete progress note documentation. The generated notes demonstrate strong longitudinal fidelity, achieving a temporal alignment ratio of $1.089$, surpassing the continuity observed in original notes. By restoring narrative coherence across fragmented documentation, our system supports improved downstream tasks such as summarization, predictive modeling, and clinical decision support, offering a scalable solution for LLM-driven note synthesis in real-world healthcare settings.
Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations
Authors: Yong Feng, XiaoLei Zhang, Shijin Feng, Yong Zhao, Yihan Chen
Date: 2025-07-18 00:00:00
Tasks: Crack Segmentation, Deep Learning, image-classification, Image Classification

Tunnel lining crack is a crucial indicator of tunnels' safety status. Aiming to classify and segment tunnel cracks with enhanced accuracy and efficiency, this study proposes a two-step deep learning-based method. An automatic tunnel image classification model is developed using the DenseNet-169 in the first step. The proposed crack segmentation model in the second step is based on the DeepLabV3+, whose internal logic is evaluated via a score-weighted visual explanation technique. Proposed method combines tunnel image classification and segmentation together, so that the selected images containing cracks from the first step are segmented in the second step to improve the detection accuracy and efficiency. The superior performances of the two-step method are validated by experiments. The results show that the accuracy and frames per second (FPS) of the tunnel crack classification model are 92.23% and 39.80, respectively, which are higher than other convolutional neural networks (CNN) based and Transformer based models. Also, the intersection over union (IoU) and F1 score of the tunnel crack segmentation model are 57.01% and 67.44%, respectively, outperforming other state-of-the-art models. Moreover, the provided visual explanations in this study are conducive to understanding the "black box" of deep learning-based models. The developed two-stage deep learning-based method integrating visual explanations provides a basis for fast and accurate quantitative assessment of tunnel health status.
Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion
Authors: Baoshen Guo, Zhiqing Hong, Junyi Li, Shenhao Wang, Jinhua Zhao
Date: 2025-07-08 00:00:00
Tasks: Denoising, Privacy Preserving

Urban mobility data has significant connections with economic growth and plays an essential role in various smart-city applications. However, due to privacy concerns and substantial data collection costs, fine-grained human mobility trajectories are difficult to become publicly available on a large scale. A promising solution to address this issue is trajectory synthesizing. However, existing works often ignore the inherent structural complexity of trajectories, unable to handle complicated high-dimensional distributions and generate realistic fine-grained trajectories. In this paper, we propose Cardiff, a coarse-to-fine Cascaded hybrid diffusion-based trajectory synthesizing framework for fine-grained and privacy-preserving mobility generation. By leveraging the hierarchical nature of urban mobility, Cardiff decomposes the generation process into two distinct levels, i.e., discrete road segment-level and continuous fine-grained GPS-level: (i) In the segment-level, to reduce computational costs and redundancy in raw trajectories, we first encode the discrete road segments into low-dimensional latent embeddings and design a diffusion transformer-based latent denoising network for segment-level trajectory synthesis. (ii) Taking the first stage of generation as conditions, we then design a fine-grained GPS-level conditional denoising network with a noise augmentation mechanism to achieve robust and high-fidelity generation. Additionally, the Cardiff framework not only progressively generates high-fidelity trajectories through cascaded denoising but also flexibly enables a tunable balance between privacy preservation and utility. Experimental results on three large real-world trajectory datasets demonstrate that our method outperforms state-of-the-art baselines in various metrics.
Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion
Authors: Zizhao Zhang, Tianxiang Zhao, Yu Sun, Liping Sun, Jichuan Kang
Date: 2025-07-18 00:00:00
Tasks: Decision Making, Fault Diagnosis

To address the challenges posed by cascading reactions caused by component failures in autonomous cargo ships (ACS) and the uncertainties in emergency decision-making, this paper proposes a novel hybrid feature fusion framework for constructing a graph-structured dataset of failure modes. By employing an improved cuckoo search algorithm (HN-CSA), the literature retrieval efficiency is significantly enhanced, achieving improvements of 7.1% and 3.4% compared to the NSGA-II and CSA search algorithms, respectively. A hierarchical feature fusion framework is constructed, using Word2Vec encoding to encode subsystem/component features, BERT-KPCA to process failure modes/reasons, and Sentence-BERT to quantify the semantic association between failure impact and emergency decision-making. The dataset covers 12 systems, 1,262 failure modes, and 6,150 propagation paths. Validation results show that the GATE-GNN model achieves a classification accuracy of 0.735, comparable to existing benchmarks. Additionally, a silhouette coefficient of 0.641 indicates that the features are highly distinguishable. In the label prediction results, the Shore-based Meteorological Service System achieved an F1 score of 0.93, demonstrating high prediction accuracy. This paper not only provides a solid foundation for failure analysis in autonomous cargo ships but also offers reliable support for fault diagnosis, risk assessment, and intelligent decision-making systems. The link to the dataset is https://github.com/wojiufukele/Graph-Structured-about-CSA.
CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models
Authors: Quang-Binh Nguyen, Minh Luu, Quang Nguyen, Anh Tran, Khoi Nguyen
Date: 2025-07-18 00:00:00
Tasks: Disentanglement

Disentangling content and style from a single image, known as content-style decomposition (CSD), enables recontextualization of extracted content and stylization of extracted styles, offering greater creative flexibility in visual synthesis. While recent personalization methods have explored the decomposition of explicit content style, they remain tailored for diffusion models. Meanwhile, Visual Autoregressive Modeling (VAR) has emerged as a promising alternative with a next-scale prediction paradigm, achieving performance comparable to that of diffusion models. In this paper, we explore VAR as a generative framework for CSD, leveraging its scale-wise generation process for improved disentanglement. To this end, we propose CSD-VAR, a novel method that introduces three key innovations: (1) a scale-aware alternating optimization strategy that aligns content and style representation with their respective scales to enhance separation, (2) an SVD-based rectification method to mitigate content leakage into style representations, and (3) an Augmented Key-Value (K-V) memory enhancing content identity preservation. To benchmark this task, we introduce CSD-100, a dataset specifically designed for content-style decomposition, featuring diverse subjects rendered in various artistic styles. Experiments demonstrate that CSD-VAR outperforms prior approaches, achieving superior content preservation and stylization fidelity.