pwc-archive/papers-with-abstracts huggingface dataset:
Will Pre-Training Ever End? A First Step Toward Next-Generation Foundation MLLMs via Self-Improving Systematic Cognition
Authors: Xiaoying Zhang, Da Peng, YiPeng Zhang, Zonghao Guo, Chengyue Wu, Chi Chen, Wei Ke, Helen Meng, Maosong Sun
Date: 2025-03-16 00:00:00
Tasks: Caption Generation, Image Captioning, Multimodal Reasoning, Self-Learning

Recent progress in (multimodal) large language models ((M)LLMs) has shifted focus from pre-training to inference-time compute scaling and post-training optimization, driven by concerns over limited high-quality real-world data. However, these strategies alone are insufficient for advancing model capabilities. We hypothesize that effective model improvement requires a strong synergy among pre-training, inference-time compute scaling, and post-training optimization. In this paper, we validate this hypothesis in the context of multimodal pre-training for foundation MLLM construction. We introduce Self-Improving cognition (SIcog), a self-learning framework for constructing next-generation foundation MLLMs by imparting multimodal knowledge and enhancing their systematic cognitive capabilities through multimodal pre-training with self-generated data. Specifically, we introduce Chain-of-Description, a step-by-step visual understanding method to improve comprehensive perception, and integrate structured chain-of-thought (CoT) reasoning to support in-depth multimodal reasoning. SIcog first equips a base model with systematic perception and reasoning using minimal external supervision. The enhanced model then generates candidate image captions and CoT-style reasoning responses for unlabeled images and image-question pairs across diverse tasks, which are curated through a self-consistency mechanism. These curated samples are subsequently used for large-scale multimodal pre-training, completing a self-learning cycle that strengthens the model's cognitive foundation. Extensive experiments demonstrate that SIcog produces next-generation foundation MLLMs with substantially improved multimodal cognition, outperforming prevailing pre-training approaches. These findings empirically establish SIcog as a promising framework for realizing a complete self-improving paradigm.
Speculative Decoding for Verilog: Speed and Quality, All in One
Authors: Changran Xu, Yi Liu, Yunhao Zhou, Shan Huang, Ningyi Xu, Qiang Xu
Date: 2025-03-18 00:00:00
Tasks: All, Code Generation

The rapid advancement of large language models (LLMs) has revolutionized code generation tasks across various programming languages. However, the unique characteristics of programming languages, particularly those like Verilog with specific syntax and lower representation in training datasets, pose significant challenges for conventional tokenization and decoding approaches. In this paper, we introduce a novel application of speculative decoding for Verilog code generation, showing that it can improve both inference speed and output quality, effectively achieving speed and quality all in one. Unlike standard LLM tokenization schemes, which often fragment meaningful code structures, our approach aligns decoding stops with syntactically significant tokens, making it easier for models to learn the token distribution. This refinement addresses inherent tokenization issues and enhances the model's ability to capture Verilog's logical constructs more effectively. Our experimental results show that our method achieves up to a 5.05x speedup in Verilog code generation and increases pass@10 functional accuracy on RTLLM by up to 17.19% compared to conventional training strategies. These findings highlight speculative decoding as a promising approach to bridge the quality gap in code generation for specialized programming languages.
EAGLE: Contextual Point Cloud Generation via Adaptive Continuous Normalizing Flow with Self-Attention
Authors: Linhao Wang, Qichang Zhang, Yifan Yang, Hao Wang
Date: 2025-03-05 00:00:00
Tasks: Point Cloud Generation

As 3D point clouds become the prevailing shape representation in computer vision, how to generate high-resolution point clouds has become a pressing issue. Flow-based generative models can effectively perform point cloud generation tasks. However, traditional CNN-based flow architectures rely only on local information to extract features, making it difficult to capture global contextual information. Inspired by the wide adoption of Transformers, we explored the complementary roles of self-attention mechanisms in Transformers, CNN, and continuous normalizing flows. To this end, we propose a probabilistic model via adaptive normalizing flows and self-attention. Our idea leverages self-attention mechanisms to capture global contextual information. We also propose adaptive continuous normalizing flows by introducing adaptive bias correction mechanism. Combined with normalization, the mechanism dynamically handles different input contexts and mitigates potential bias-shift issues from standard initialization. Experimental results demonstrate that EAGLE achieves competitive performance in point cloud generation.
Learning local neighborhoods of non-Gaussian graphical models: A measure transport approach
Authors: Sarah Liaw, Rebecca Morrison, Youssef Marzouk, Ricardo Baptista
Date: 2025-03-18 00:00:00

Identifying the Markov properties or conditional independencies of a collection of random variables is a fundamental task in statistics for modeling and inference. Existing approaches often learn the structure of a probabilistic graphical model, which encodes these dependencies, by assuming that the variables follow a distribution with a simple parametric form. Moreover, the computational cost of many algorithms scales poorly for high-dimensional distributions, as they need to estimate all the edges in the graph simultaneously. In this work, we propose a scalable algorithm to infer the conditional independence relationships of each variable by exploiting the local Markov property. The proposed method, named Localized Sparsity Identification for Non-Gaussian Distributions (L-SING), estimates the graph by using flexible classes of transport maps to represent the conditional distribution for each variable. We show that L-SING includes existing approaches, such as neighborhood selection with Lasso, as a special case. We demonstrate the effectiveness of our algorithm in both Gaussian and non-Gaussian settings by comparing it to existing methods. Lastly, we show the scalability of the proposed approach by applying it to high-dimensional non-Gaussian examples, including a biological dataset with more than 150 variables.
Cooperative Deterministic Learning-Based Formation Control for a Group of Nonlinear Mechanical Systems Under Complete Uncertainty
Authors: Maryam Norouzi, Mingxi Zhou, Chengzhi Yuan
Date: 2025-03-17 00:00:00

In this work we address the formation control problem for a group of nonlinear mechanical systems with complete uncertain dynamics under a virtual leader-following framework. We propose a novel cooperative deterministic learning-based adaptive formation control algorithm. This algorithm is designed by utilizing artificial neural networks to simultaneously achieve formation tracking control and locally-accurate identification/learning of the nonlinear uncertain dynamics of the considered group of mechanical systems. To demonstrate the practicality and verify the effectiveness of the proposed results, numerical simulations have been conducted.
Evaluating the Application of SOLID Principles in Modern AI Framework Architectures
Authors: Jonesh Shrestha
Date: 2025-03-18 00:00:00
Tasks: Philosophy

This research evaluates the extent to which modern AI frameworks, specifically TensorFlow and scikit-learn, adhere to the SOLID design principles - Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion. Analyzing the frameworks architectural documentation and design philosophies, this research investigates architectural trade-offs when balancing software engineering best practices with AI-specific needs. I examined each frameworks documentation, source code, and architectural components to evaluate their adherence to these principles. The results show that both frameworks adopt certain aspects of SOLID design principles but make intentional trade-offs to address performance, scalability, and the experimental nature of AI development. TensorFlow focuses on performance and scalability, sometimes sacrificing strict adherence to principles like Single Responsibility and Interface Segregation. While scikit-learns design philosophy aligns more closely with SOLID principles through consistent interfaces and composition principles, sticking closer to SOLID guidelines but with occasional deviations for performance optimizations and scalability. This research discovered that applying SOLID principles in AI frameworks depends on context, as performance, scalability, and flexibility often require deviations from traditional software engineering principles. This research contributes to understanding how domain-specific constraints influence architectural decisions in modern AI frameworks and how these frameworks strategically adapted design choices to effectively balance these contradicting requirements.
A Revisit to the Decoder for Camouflaged Object Detection
Authors: Seung Woo Ko, Joopyo Hong, Suyoung Kim, Seungjai Bang, Sungzoon Cho, Nojun Kwak, Hyung-Sin Kim, Joonseok Lee
Date: 2025-03-18 00:00:00
Tasks: Decoder, object-detection, Object Detection, Segmentation

Camouflaged object detection (COD) aims to generate a fine-grained segmentation map of camouflaged objects hidden in their background. Due to the hidden nature of camouflaged objects, it is essential for the decoder to be tailored to effectively extract proper features of camouflaged objects and extra-carefully generate their complex boundaries. In this paper, we propose a novel architecture that augments the prevalent decoding strategy in COD with Enrich Decoder and Retouch Decoder, which help to generate a fine-grained segmentation map. Specifically, the Enrich Decoder amplifies the channels of features that are important for COD using channel-wise attention. Retouch Decoder further refines the segmentation maps by spatially attending to important pixels, such as the boundary regions. With extensive experiments, we demonstrate that ENTO shows superior performance using various encoders, with the two novel components playing their unique roles that are mutually complementary.
Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided Learning
Authors: Hao Chen, Julian Najera, Dagmawit Geresu, Meenal Datta, Cody Smith, Scott Howard
Date: 2025-03-17 00:00:00
Tasks: Denoising

Multimodal and multi-information microscopy techniques such as Fluorescence Lifetime Imaging Microscopy (FLIM) extend the informational channels beyond intensity-based fluorescence microscopy but suffer from reduced image quality due to complex noise patterns. For FLIM, the intrinsic relationship between intensity and lifetime information means noise in each channel is a multivariate function across channels without necessarily sharing structural features. Based on this, we present a novel Zero-Shot Denoising Framework with an Intensity-Guided Learning approach. Our correlation-preserving strategy maintains important biological information that might be lost when channels are processed independently. Our framework implements separate processing paths for each channel and utilizes a pre-trained intensity denoising prior to guide the refinement of lifetime components across multiple channels. Through experiments on real-world FLIM-acquired biological samples, we show that our approach outperforms existing methods in both noise reduction and lifetime preservation, thereby enabling more reliable extraction of physiological and molecular information.
Unlocking Learning Potentials: The Transformative Effect of Generative AI in Education Across Grade Levels
Authors: Meijuan Xie, Liling Luo
Date: 2025-03-15 00:00:00

The advent of generative artificial intelligence (GAI) has brought about a notable surge in the field of education. The use of GAI to support learning is becoming increasingly prevalent among students. However, the manner and extent of its utilisation vary considerably from one individual to another. And researches about student's utilisation and perceptions of GAI remains relatively scarce. To gain insight into the issue, this paper proposed a hybrid-survey method to examine the impact of GAI on students across four different grades in six key areas (LIPSAL): learning interest, independent learning, problem solving, self-confidence, appropriate use, and learning enjoyment. Firstly, through questionnaire, we found that among LIPSAL, GAI has the greatest impact on the concept of appropriate use, the lowest level of learning interest and self-confidence. Secondly, a comparison of four grades revealed that the high and low factors of LIPSAL exhibited grade-related variation, and college students exhibited a higher level than high school students across LIPSAL. Thirdly, through interview, the students demonstrated a comprehensive understanding of the application of GAI. We found that students have a positive attitude towards GAI and are very willing to use it, which is why GAI has grown so rapidly in popularity. They also told us prospects and challenges in using GAI. In the future, as GAI matures technologically, it will have an greater impact on students. These findings may help better understand usage by different students and inform future research in digital education.
Functional classification of metabolic networks
Authors: Jorge Reyes, Jörn Dunkel
Date: 2025-03-18 00:00:00
Tasks: Classification

Chemical reaction networks underpin biological and physical phenomena across scales, from microbial interactions to planetary atmosphere dynamics. Bacterial communities exhibit complex competitive interactions for resources, human organs and tissues demonstrate specialized biochemical functions, and planetary atmospheres are capable of displaying diverse organic and inorganic chemical processes. Despite their complexities, comparing these networks methodically remains a challenge due to the vast underlying degrees of freedom. In biological systems, comparative genomics has been pivotal in tracing evolutionary trajectories and classifying organisms via DNA sequences. However, purely genomic classifications often fail to capture functional roles within ecological systems. Metabolic changes driven by nutrient availability highlight the need for classification schemes that integrate metabolic information. Here we introduce and apply a computational framework for a classification scheme of organisms that compares matrix representations of chemical reaction networks using the Grassmann distance, corresponding to measuring distances between the fundamental subspaces of stoichiometric matrices. Applying this framework to human gut microbiome data confirms that metabolic distances are distinct from phylogenetic distances, underscoring the limitations of genetic information in metabolic classification. Importantly, our analysis of metabolic distances reveals functional groups of organisms enriched or depleted in specific metabolic processes and shows robustness to metabolically silent genetic perturbations. The generalizability of metabolic Grassmann distances is illustrated by application to chemical reaction networks in human tissue and planetary atmospheres, highlighting its potential for advancing functional comparisons across diverse chemical reaction systems.
GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching
Authors: Feng Qiao, Zhexiao Xiong, Eric Xing, Nathan Jacobs
Date: 2025-03-17 00:00:00
Tasks: Autonomous Driving, Image Generation, Stereo Matching

Stereo images are fundamental to numerous applications, including extended reality (XR) devices, autonomous driving, and robotics. Unfortunately, acquiring high-quality stereo images remains challenging due to the precise calibration requirements of dual-camera setups and the complexity of obtaining accurate, dense disparity maps. Existing stereo image generation methods typically focus on either visual quality for viewing or geometric accuracy for matching, but not both. We introduce GenStereo, a diffusion-based approach, to bridge this gap. The method includes two primary innovations (1) conditioning the diffusion process on a disparity-aware coordinate embedding and a warped input image, allowing for more precise stereo alignment than previous methods, and (2) an adaptive fusion mechanism that intelligently combines the diffusion-generated image with a warped image, improving both realism and disparity consistency. Through extensive training on 11 diverse stereo datasets, GenStereo demonstrates strong generalization ability. GenStereo achieves state-of-the-art performance in both stereo image generation and unsupervised stereo matching tasks. Our framework eliminates the need for complex hardware setups while enabling high-quality stereo image generation, making it valuable for both real-world applications and unsupervised learning scenarios. Project page is available at https://qjizhi.github.io/genstereo
LLMs' Leaning in European Elections
Authors: Federico Ricciuti
Date: 2025-03-16 00:00:00

Many studies suggest that LLMs have left wing leans. The article extends the US presidential election analysis made in previous works, where multiple LLMs were asked to vote between Joe Biden and Donald Trump in a virtual election, and the results showed a clear lean of LLMs toward Joe Biden. This article considers natural follow-up questions that could arise from that experiment, such as: what is the extent of this phenomenon? Is it generalizable to multiple virtual elections in other countries? The article considers virtual elections in ten european countries: Germany, France, Italy, Spain, Poland, Romania, Netherlands, Belgium, Czech Republic, and Sweden, and with four different LLMs: gpt4o, claude 3.5 sonnet, mistral-large, and gemini-2.0-flash.
Room Impulse Response Estimation through Optimal Mass Transport Barycenters
Authors: Rumeshika Pallewela, Yuyang Liu, Filip Elvander
Date: 2025-03-18 00:00:00
Tasks: Speech Enhancement

In this work, we consider the problem of jointly estimating a set of room impulse responses (RIRs) corresponding to closely spaced microphones. The accurate estimation of RIRs is crucial in acoustic applications such as speech enhancement, noise cancellation, and auralization. However, real-world constraints such as short excitation signals, low signal-to-noise ratios, and poor spectral excitation, often render the estimation problem ill-posed. In this paper, we address these challenges by means of optimal mass transport (OMT) regularization. In particular, we propose to use an OMT barycenter, or generalized mean, as a mechanism for information sharing between the microphones. This allows us to quantify and exploit similarities in the delay-structures between the different microphones without having to impose rigid assumptions on the room acoustics. The resulting estimator is formulated in terms of the solution to a convex optimization problem which can be implemented using standard solvers. In numerical examples, we demonstrate the potential of the proposed method in addressing otherwise ill-conditioned estimation scenarios.
GraphTEN: Graph Enhanced Texture Encoding Network
Authors: Bo Peng, Jintao Chen, Mufeng Yao, Chenhao Zhang, Jianghui Zhang, Mingmin Chi, Jiang Tao
Date: 2025-03-18 00:00:00

Texture recognition is a fundamental problem in computer vision and pattern recognition. Recent progress leverages feature aggregation into discriminative descriptions based on convolutional neural networks (CNNs). However, modeling non-local context relations through visual primitives remains challenging due to the variability and randomness of texture primitives in spatial distributions. In this paper, we propose a graph-enhanced texture encoding network (GraphTEN) designed to capture both local and global features of texture primitives. GraphTEN models global associations through fully connected graphs and captures cross-scale dependencies of texture primitives via bipartite graphs. Additionally, we introduce a patch encoding module that utilizes a codebook to achieve an orderless representation of texture by encoding multi-scale patch features into a unified feature space. The proposed GraphTEN achieves superior performance compared to state-of-the-art methods across five publicly available datasets.
Public Sector Efficiency in Delivering Social Services and Its Impact on Human Development: A Comparative Study of Healthcare and Education Spending in India, Pakistan, and Bangladesh
Authors: Tuhin G. M. Al Mamun, Rahim Md. Abdur, Md. Sharif Hassan, Mohammad Bin Amin, Judit Oláh
Date: 2025-03-15 00:00:00

The research investigates the effects of public spending on health and education in shaping the human development in south Asian three countries: India, Pakistan and Bangladesh. The study uses the VAR (Vector Auto regression) model to estimate the effects on government spending on these sectors to evaluate the human development. The findings state that there are different degrees of impact in these three countries. In Bangladesh and India, health spending increases the human development in short term. On the other hand education spending shows the significance on the HDI.Moreover, the study also highlights that there are different levels of effectiveness of government spending across these three countries. In order to maximize the human development an optimum country specific strategies should be adopted.
Inducing Causal Structure for Interpretable Neural Networks Applied to Glucose Prediction for T1DM Patients
Authors: Ana Esponera, Giovanni Cinnà
Date: 2025-03-18 00:00:00
Tasks: counterfactual

Causal abstraction techniques such as Interchange Intervention Training (IIT) have been proposed to infuse neural network with expert knowledge encoded in causal models, but their application to real-world problems remains limited. This article explores the application of IIT in predicting blood glucose levels in Type 1 Diabetes Mellitus (T1DM) patients. The study utilizes an acyclic version of the simglucose simulator approved by the FDA to train a Multi-Layer Perceptron (MLP) model, employing IIT to impose causal relationships. Results show that the model trained with IIT effectively abstracted the causal structure and outperformed the standard one in terms of predictive performance across different prediction horizons (PHs) post-meal. Furthermore, the breakdown of the counterfactual loss can be leveraged to explain which part of the causal mechanism are more or less effectively captured by the model. These preliminary results suggest the potential of IIT in enhancing predictive models in healthcare by effectively complying with expert knowledge.