pwc-archive/papers-with-abstracts huggingface dataset:
Towards Calibration Enhanced Network by Inverse Adversarial Attack
Authors: Yupeng Cheng, Zi Pong Lim, Sarthak Ketanbhai Modi, Yon Shin Teo, Yushi Cao, Shang-Wei Lin
Date: 2025-04-08 00:00:00
Tasks: Adversarial Attack, Optical Character Recognition, Optical Character Recognition (OCR)

Test automation has become increasingly important as the complexity of both design and content in Human Machine Interface (HMI) software continues to grow. Current standard practice uses Optical Character Recognition (OCR) techniques to automatically extract textual information from HMI screens for validation. At present, one of the key challenges faced during the automation of HMI screen validation is the noise handling for the OCR models. In this paper, we propose to utilize adversarial training techniques to enhance OCR models in HMI testing scenarios. More specifically, we design a new adversarial attack objective for OCR models to discover the decision boundaries in the context of HMI testing. We then adopt adversarial training to optimize the decision boundaries towards a more robust and accurate OCR model. In addition, we also built an HMI screen dataset based on real-world requirements and applied multiple types of perturbation onto the clean HMI dataset to provide a more complete coverage for the potential scenarios. We conduct experiments to demonstrate how using adversarial training techniques yields more robust OCR models against various kinds of noises, while still maintaining high OCR model accuracy. Further experiments even demonstrate that the adversarial training models exhibit a certain degree of robustness against perturbations from other patterns.
Multi-Object Grounding via Hierarchical Contrastive Siamese Transformers
Authors: Chengyi Du, Keyan Jin
Date: 2025-04-14 00:00:00
Tasks: Object, Object Localization

Multi-object grounding in 3D scenes involves localizing multiple objects based on natural language input. While previous work has primarily focused on single-object grounding, real-world scenarios often demand the localization of several objects. To tackle this challenge, we propose Hierarchical Contrastive Siamese Transformers (H-COST), which employs a Hierarchical Processing strategy to progressively refine object localization, enhancing the understanding of complex language instructions. Additionally, we introduce a Contrastive Siamese Transformer framework, where two networks with the identical structure are used: one auxiliary network processes robust object relations from ground-truth labels to guide and enhance the second network, the reference network, which operates on segmented point-cloud data. This contrastive mechanism strengthens the model' s semantic understanding and significantly enhances its ability to process complex point-cloud data. Our approach outperforms previous state-of-the-art methods by 9.5% on challenging multi-object grounding benchmarks.
LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models
Authors: Parshin Shojaee, Ngoc-Hieu Nguyen, Kazem Meidani, Amir Barati Farimani, Khoa D Doan, Chandan K Reddy
Date: 2025-04-14 00:00:00
Tasks: Equation Discovery, Memorization

Scientific equation discovery is a fundamental task in the history of scientific progress, enabling the derivation of laws governing natural phenomena. Recently, Large Language Models (LLMs) have gained interest for this task due to their potential to leverage embedded scientific knowledge for hypothesis generation. However, evaluating the true discovery capabilities of these methods remains challenging, as existing benchmarks often rely on common equations that are susceptible to memorization by LLMs, leading to inflated performance metrics that do not reflect discovery. In this paper, we introduce LLM-SRBench, a comprehensive benchmark with 239 challenging problems across four scientific domains specifically designed to evaluate LLM-based scientific equation discovery methods while preventing trivial memorization. Our benchmark comprises two main categories: LSR-Transform, which transforms common physical models into less common mathematical representations to test reasoning beyond memorized forms, and LSR-Synth, which introduces synthetic, discovery-driven problems requiring data-driven reasoning. Through extensive evaluation of several state-of-the-art methods, using both open and closed LLMs, we find that the best-performing system so far achieves only 31.5% symbolic accuracy. These findings highlight the challenges of scientific equation discovery, positioning LLM-SRBench as a valuable resource for future research.
REPA-E: Unlocking VAE for End-to-End Tuning of Latent Diffusion Transformers
Authors: Xingjian Leng, Jaskirat Singh, Yunzhong Hou, Zhenchang Xing, Saining Xie, Liang Zheng
Date: 2025-04-15 00:00:00
Tasks: Image Generation

In this paper we tackle a fundamental question: "Can we train latent diffusion models together with the variational auto-encoder (VAE) tokenizer in an end-to-end manner?" Traditional deep-learning wisdom dictates that end-to-end training is often preferable when possible. However, for latent diffusion transformers, it is observed that end-to-end training both VAE and diffusion-model using standard diffusion-loss is ineffective, even causing a degradation in final performance. We show that while diffusion loss is ineffective, end-to-end training can be unlocked through the representation-alignment (REPA) loss -- allowing both VAE and diffusion model to be jointly tuned during the training process. Despite its simplicity, the proposed training recipe (REPA-E) shows remarkable performance; speeding up diffusion model training by over 17x and 45x over REPA and vanilla training recipes, respectively. Interestingly, we observe that end-to-end tuning with REPA-E also improves the VAE itself; leading to improved latent space structure and downstream generation performance. In terms of final performance, our approach sets a new state-of-the-art; achieving FID of 1.26 and 1.83 with and without classifier-free guidance on ImageNet 256 x 256. Code is available at https://end2end-diffusion.github.io/.
Dynamik: Syntactically-Driven Dynamic Font Sizing for Emphasis of Key Information
Authors: Naoto Nishida, Yoshio Ishiguro, Jun Rekiomto, Naomi Yamashita
Date: 2025-04-13 00:00:00
Tasks: Sentence

In today's globalized world, there are increasing opportunities for individuals to communicate using a common non-native language (lingua franca). Non-native speakers often have opportunities to listen to foreign languages, but may not comprehend them as fully as native speakers do. To aid real-time comprehension, live transcription of subtitles is frequently used in everyday life (e.g., during Zoom conversations, watching YouTube videos, or on social networking sites). However, simultaneously reading subtitles while listening can increase cognitive load. In this study, we propose Dynamik, a system that reduces cognitive load during reading by decreasing the size of less important words and enlarging important ones, thereby enhancing sentence contrast. Our results indicate that Dynamik can reduce certain aspects of cognitive load, specifically, participants' perceived performance and effort among individuals with low proficiency in English, as well as enhance the users' sense of comprehension, especially among people with low English ability. We further discuss our methods' applicability to other languages and potential improvements and further research directions.
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation
Authors: Hanghui Guo, Jia Zhu, Shimin Di, Weijie Shi, Zhangze Chen, Jiajie Xu
Date: 2025-04-14 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Dynamic Retrieval-augmented Generation (RAG) has shown great success in mitigating hallucinations in large language models (LLMs) during generation. However, existing dynamic RAG methods face significant limitations in two key aspects: 1) Lack of an effective mechanism to control retrieval triggers, and 2) Lack of effective scrutiny of retrieval content. To address these limitations, we propose an innovative dynamic RAG method, DioR (Adaptive Cognitive Detection and Contextual Retrieval Optimization), which consists of two main components: adaptive cognitive detection and contextual retrieval optimization, specifically designed to determine when retrieval is needed and what to retrieve for LLMs is useful. Experimental results demonstrate that DioR achieves superior performance on all tasks, demonstrating the effectiveness of our work.
RouterKT: Mixture-of-Experts for Knowledge Tracing
Authors: Han Liao, Shuaishuai Zu
Date: 2025-04-11 00:00:00
Tasks: Knowledge Tracing, Mixture-of-Experts

Knowledge Tracing (KT) is a fundamental task in Intelligent Tutoring Systems (ITS), which aims to model the dynamic knowledge states of students based on their interaction histories. However, existing KT models often rely on a global forgetting decay mechanism for capturing learning patterns, assuming that students' performance is predominantly influenced by their most recent interactions. Such approaches fail to account for the diverse and complex learning patterns arising from individual differences and varying learning stages. To address this limitation, we propose RouterKT, a novel Mixture-of-Experts (MoE) architecture designed to capture heterogeneous learning patterns by enabling experts to specialize in different patterns without any handcrafted learning pattern bias such as forgetting decay. Specifically, RouterKT introduces a \textbf{person-wise routing mechanism} to effectively model individual-specific learning behaviors and employs \textbf{multi-heads as experts} to enhance the modeling of complex and diverse patterns. Comprehensive experiments on ten benchmark datasets demonstrate that RouterKT exhibits significant flexibility and improves the performance of various KT backbone models, with a maximum average AUC improvement of 3.29\% across different backbones and datasets, outperforming other state-of-the-art models. Moreover, RouterKT demonstrates consistently superior inference efficiency compared to existing approaches based on handcrafted learning pattern bias, highlighting its usability for real-world educational applications. The source code is available at https://github.com/ringotc/RouterKT.git.
ESCT3D: Efficient and Selectively Controllable Text-Driven 3D Content Generation with Gaussian Splatting
Authors: Huiqi Wu, Jianbo Mei, Yingjie Huang, Yining Xu, Jingjiao You, Yilong Liu, Li Yao
Date: 2025-04-14 00:00:00
Tasks: 3D Generation, Text to 3D

In recent years, significant advancements have been made in text-driven 3D content generation. However, several challenges remain. In practical applications, users often provide extremely simple text inputs while expecting high-quality 3D content. Generating optimal results from such minimal text is a difficult task due to the strong dependency of text-to-3D models on the quality of input prompts. Moreover, the generation process exhibits high variability, making it difficult to control. Consequently, multiple iterations are typically required to produce content that meets user expectations, reducing generation efficiency. To address this issue, we propose GPT-4V for self-optimization, which significantly enhances the efficiency of generating satisfactory content in a single attempt. Furthermore, the controllability of text-to-3D generation methods has not been fully explored. Our approach enables users to not only provide textual descriptions but also specify additional conditions, such as style, edges, scribbles, poses, or combinations of multiple conditions, allowing for more precise control over the generated 3D content. Additionally, during training, we effectively integrate multi-view information, including multi-view depth, masks, features, and images, to address the common Janus problem in 3D content generation. Extensive experiments demonstrate that our method achieves robust generalization, facilitating the efficient and controllable generation of high-quality 3D content.
HBS -- Hardware Build System: A Tcl-based, minimal common abstraction approach for build system for hardware designs
Authors: Michał Kruszewski
Date: 2025-04-13 00:00:00
Tasks: Graph Generation

Build systems become an indispensable part of the software implementation and deployment process. New programming languages are released with the build system integrated into the language tools, for example, Go, Rust, or Zig. However, in the hardware description domain, no official build systems have been released with the predominant Hardware Description Languages (HDL) such as VHDL or SystemVerilog. Moreover, hardware design projects are often multilanguage. The paper proposes a new build system for the hardware description domain. The system is called the Hardware Build System (HBS). The main goals of the system include simplicity, readability, a minimal number of dependencies, and ease of integration with the existing Electronic Design Automation (EDA) tools. The system proposes a novel, minimal common abstraction approach, whose particular implications are described in the article. All the core functionalities are implemented in Tcl. Only the EDA tool's independent features, such as dependency graph generation, are implemented in a Python wrapper.
Evolving Financial Trading Strategies with Vectorial Genetic Programming
Authors: Rui Menoita, Sara Silva
Date: 2025-04-07 00:00:00

Establishing profitable trading strategies in financial markets is a challenging task. While traditional methods like technical analysis have long served as foundational tools for traders to recognize and act upon market patterns, the evolving landscape has called for more advanced techniques. We explore the use of Vectorial Genetic Programming (VGP) for this task, introducing two new variants of VGP, one that allows operations with complex numbers and another that implements a strongly-typed version of VGP. We evaluate the different variants on three financial instruments, with datasets spanning more than seven years. Despite the inherent difficulty of this task, it was possible to evolve profitable trading strategies. A comparative analysis of the three VGP variants and standard GP revealed that standard GP is always among the worst whereas strongly-typed VGP is always among the best.
Question Tokens Deserve More Attention: Enhancing Large Language Models without Training through Step-by-Step Reading and Question Attention Recalibration
Authors: Feijiang Han, Licheng Guo, Hengtao Cui, Zhiyuan Lyu
Date: 2025-04-13 00:00:00
Tasks: GSM8K

Large Language Models (LLMs) often struggle with tasks that require a deep understanding of complex questions, especially when faced with long-range dependencies or multi-step reasoning. This work investigates the limitations of current LLMs in question comprehension and identifies three insights: (1) repeating question tokens improves comprehension by increasing attention to question regions, (2) increased backward dependencies negatively affect performance due to unidirectional attentional constraints, and (3) recalibrating attentional mechanisms to prioritize question-relevant regions improves performance. Based on these findings, we first propose a family of prompt-based strategies - Step-by-Step Reading (SSR), SSR+, and SSR++ - that guide LLMs to incrementally process question tokens and align their reasoning with the input structure. These methods significantly improve performance, with SSR++ achieving state-of-the-art results on several benchmarks: 96.66% on GSM8K, 94.61% on ASDiv, and 76.28% on AQuA. Second, we introduce a training-free attention recalibration mechanism that dynamically adjusts attention distributions during inference to emphasize question-relevant regions. This method improves the accuracy of LLaMA 3.1-8B on AQuA by 5.17% without changing model parameters or input prompts. Taken together, our results highlight the importance of structured prompt design and attention optimization in improving LLM comprehension, providing lightweight yet effective tools for improving performance in various NLP tasks.
DTFSal: Audio-Visual Dynamic Token Fusion for Video Saliency Prediction
Authors: Kiana Hooshanfar, Alireza Hosseini, Ahmad Kalhor, Babak Nadjar Araabi
Date: 2025-04-14 00:00:00
Tasks: Computational Efficiency, Saliency Prediction, Video Saliency Prediction

Audio-visual saliency prediction aims to mimic human visual attention by identifying salient regions in videos through the integration of both visual and auditory information. Although visual-only approaches have significantly advanced, effectively incorporating auditory cues remains challenging due to complex spatio-temporal interactions and high computational demands. To address these challenges, we propose Dynamic Token Fusion Saliency (DFTSal), a novel audio-visual saliency prediction framework designed to balance accuracy with computational efficiency. Our approach features a multi-scale visual encoder equipped with two novel modules: the Learnable Token Enhancement Block (LTEB), which adaptively weights tokens to emphasize crucial saliency cues, and the Dynamic Learnable Token Fusion Block (DLTFB), which employs a shifting operation to reorganize and merge features, effectively capturing long-range dependencies and detailed spatial information. In parallel, an audio branch processes raw audio signals to extract meaningful auditory features. Both visual and audio features are integrated using our Adaptive Multimodal Fusion Block (AMFB), which employs local, global, and adaptive fusion streams for precise cross-modal fusion. The resulting fused features are processed by a hierarchical multi-decoder structure, producing accurate saliency maps. Extensive evaluations on six audio-visual benchmarks demonstrate that DFTSal achieves SOTA performance while maintaining computational efficiency.
Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations
Authors: Zhehao Dong, Zhen Lu, Yue Yang
Date: 2025-04-13 00:00:00
Tasks: Computational Efficiency, Language Modeling, Language Modelling, Large Language Model

Configuring computational fluid dynamics (CFD) simulations typically demands extensive domain expertise, limiting broader access. Although large language models (LLMs) have advanced scientific computing, their use in automating CFD workflows is underdeveloped. We introduce a novel approach centered on domain-specific LLM adaptation. By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM, our custom dataset of 28716 natural language-to-OpenFOAM configuration pairs with chain-of-thought (CoT) annotations, we enable direct translation from natural language descriptions to executable CFD setups. A multi-agent framework orchestrates the process, autonomously verifying inputs, generating configurations, running simulations, and correcting errors. Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance, achieving 88.7% solution accuracy and 82.6% first-attempt success rate. This significantly outperforms larger general-purpose models like Qwen2.5-72B-Instruct, DeepSeek-R1, and Llama3.3-70B-Instruct, while also requiring fewer correction iterations and maintaining high computational efficiency. The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows. Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.
Uncertainty Guided Refinement for Fine-Grained Salient Object Detection
Authors: Yao Yuan, Pan Gao, Qun Dai, Jie Qin, Wei Xiang
Date: 2025-04-13 00:00:00
Tasks: object-detection, Object Detection, Saliency Prediction, Salient Object Detection

Recently, salient object detection (SOD) methods have achieved impressive performance. However, salient regions predicted by existing methods usually contain unsaturated regions and shadows, which limits the model for reliable fine-grained predictions. To address this, we introduce the uncertainty guidance learning approach to SOD, intended to enhance the model's perception of uncertain regions. Specifically, we design a novel Uncertainty Guided Refinement Attention Network (UGRAN), which incorporates three important components, i.e., the Multilevel Interaction Attention (MIA) module, the Scale Spatial-Consistent Attention (SSCA) module, and the Uncertainty Refinement Attention (URA) module. Unlike conventional methods dedicated to enhancing features, the proposed MIA facilitates the interaction and perception of multilevel features, leveraging the complementary characteristics among multilevel features. Then, through the proposed SSCA, the salient information across diverse scales within the aggregated features can be integrated more comprehensively and integrally. In the subsequent steps, we utilize the uncertainty map generated from the saliency prediction map to enhance the model's perception capability of uncertain regions, generating a highly-saturated fine-grained saliency prediction map. Additionally, we devise an adaptive dynamic partition (ADP) mechanism to minimize the computational overhead of the URA module and improve the utilization of uncertainty guidance. Experiments on seven benchmark datasets demonstrate the superiority of the proposed UGRAN over the state-of-the-art methodologies. Codes will be released at https://github.com/I2-Multimedia-Lab/UGRAN.
ColonScopeX: Leveraging Explainable Expert Systems with Multimodal Data for Improved Early Diagnosis of Colorectal Cancer
Authors: Natalia Sikora, Robert L. Manschke, Alethea M. Tang, Peter Dunstan, Dean A. Harris, Su Yang
Date: 2025-04-09 00:00:00

Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths and the third most prevalent malignant tumour worldwide. Early detection of CRC remains problematic due to its non-specific and often embarrassing symptoms, which patients frequently overlook or hesitate to report to clinicians. Crucially, the stage at which CRC is diagnosed significantly impacts survivability, with a survival rate of 80-95\% for Stage I and a stark decline to 10\% for Stage IV. Unfortunately, in the UK, only 14.4\% of cases are diagnosed at the earliest stage (Stage I). In this study, we propose ColonScopeX, a machine learning framework utilizing explainable AI (XAI) methodologies to enhance the early detection of CRC and pre-cancerous lesions. Our approach employs a multimodal model that integrates signals from blood sample measurements, processed using the Savitzky-Golay algorithm for fingerprint smoothing, alongside comprehensive patient metadata, including medication history, comorbidities, age, weight, and BMI. By leveraging XAI techniques, we aim to render the model's decision-making process transparent and interpretable, thereby fostering greater trust and understanding in its predictions. The proposed framework could be utilised as a triage tool or a screening tool of the general population. This research highlights the potential of combining diverse patient data sources and explainable machine learning to tackle critical challenges in medical diagnostics.