pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Global Neural Architecture Search
Authors: Shahid Siddiqui, Christos Kyrkou, Theocharis Theocharides
Date: 2025-02-08 00:00:00
Tasks: Face Recognition, Image Classification, Neural Architecture Search

Neural architecture search (NAS) has shown promise towards automating neural network design for a given task, but it is computationally demanding due to training costs associated with evaluating a large number of architectures to find the optimal one. To speed up NAS, recent works limit the search to network building blocks (modular search) instead of searching the entire architecture (global search), approximate candidates' performance evaluation in lieu of complete training, and use gradient descent rather than naturally suitable discrete optimization approaches. However, modular search does not determine network's macro architecture i.e. depth and width, demanding manual trial and error post-search, hence lacking automation. In this work, we revisit NAS and design a navigable, yet architecturally diverse, macro-micro search space. In addition, to determine relative rankings of candidates, existing methods employ consistent approximations across entire search spaces, whereas different networks may not be fairly comparable under one training protocol. Hence, we propose an architecture-aware approximation with variable training schemes for different networks. Moreover, we develop an efficient search strategy by disjoining macro-micro network design that yields competitive architectures in terms of both accuracy and size. Our proposed framework achieves a new state-of-the-art on EMNIST and KMNIST, while being highly competitive on the CIFAR-10, CIFAR-100, and FashionMNIST datasets and being 2-4x faster than the fastest global search methods. Lastly, we demonstrate the transferability of our framework to real-world computer vision problems by discovering competitive architectures for face recognition applications.
Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification
Authors: Jiachen Li, Xiaojin Gong
Date: 2025-02-10 00:00:00
Tasks: Contrastive Learning, Generalizable Person Re-identification, Person Re-Identification, Representation Learning

Domain-generalizable re-identification (DG Re-ID) aims to train a model on one or more source domains and evaluate its performance on unseen target domains, a task that has attracted growing attention due to its practical relevance. While numerous methods have been proposed, most rely on discriminative or contrastive learning frameworks to learn generalizable feature representations. However, these approaches often fail to mitigate shortcut learning, leading to suboptimal performance. In this work, we propose a novel method called diffusion model-assisted representation learning with a correlation-aware conditioning scheme (DCAC) to enhance DG Re-ID. Our method integrates a discriminative and contrastive Re-ID model with a pre-trained diffusion model through a correlation-aware conditioning scheme. By incorporating ID classification probabilities generated from the Re-ID model with a set of learnable ID-wise prompts, the conditioning scheme injects dark knowledge that captures ID correlations to guide the diffusion process. Simultaneously, feedback from the diffusion model is back-propagated through the conditioning scheme to the Re-ID model, effectively improving the generalization capability of Re-ID features. Extensive experiments on both single-source and multi-source DG Re-ID tasks demonstrate that our method achieves state-of-the-art performance. Comprehensive ablation studies further validate the effectiveness of the proposed approach, providing insights into its robustness. Codes will be available at https://github.com/RikoLi/DCAC.
Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video Environments
Authors: Sankalp Nagaonkar, Augustya Sharma, Ashish Choithani, Ashutosh Trivedi
Date: 2025-02-10 00:00:00
Tasks: Benchmarking, Optical Character Recognition, Optical Character Recognition (OCR)

This paper introduces an open-source benchmark for evaluating Vision-Language Models (VLMs) on Optical Character Recognition (OCR) tasks in dynamic video environments. We present a curated dataset containing 1,477 manually annotated frames spanning diverse domains, including code editors, news broadcasts, YouTube videos, and advertisements. Three state of the art VLMs - Claude-3, Gemini-1.5, and GPT-4o are benchmarked against traditional OCR systems such as EasyOCR and RapidOCR. Evaluation metrics include Word Error Rate (WER), Character Error Rate (CER), and Accuracy. Our results highlight the strengths and limitations of VLMs in video-based OCR tasks, demonstrating their potential to outperform conventional OCR models in many scenarios. However, challenges such as hallucinations, content security policies, and sensitivity to occluded or stylized text remain. The dataset and benchmarking framework are publicly available to foster further research.
Consistency-Guided Robust Learning for Content-Agnostic Radio Frequency Fingerprinting
Authors: Yu Wang; Guan Gui
Date: 2025-01-29 00:00:00
Proceeding: IEEE Communications Letters 2025 1

Radio Frequency Fingerprinting (RFF) is viewed as a potential strategy to enhance wireless security by utilizing inherent hardware characteristics of transmitters. Recently, Deep Learning (DL)-based RFF methods have been extensively studied and significantly improved identification performance. However, new challenges are introduced, particularly content dependency. This dependency emerges when signals contain unique transmitter identifiers (IDs), such as the ICAO addresses in Automatic Dependent Surveillance-Broadcast (ADS-B) system. In such cases, DL models may prioritize these IDs over the intrinsic hardware fingerprint information, resulting in inflated accuracy. Moreover, as these IDs are vulnerable to tampering, their reliability and robustness are substantially compromised. To overcome this, we propose a novel content-agnostic RFF method that incorporates a consistency-guided robust learning framework. The proposed method employs a masking mechanism to zero out signal segments associated with transmitter IDs and processes both original and masked signals through a shared feature embedding, ensuring minimal content dependency while thoroughly extracting fingerprint information across the entire signal. To enhance its effectiveness, we introduce semantic consistency regularization to align the feature semantics of original and masked signals. Additionally, attention consistency regularization, leveraging class activation mapping, is employed to constrain the attention distribution across the two signal variants. These complementary strategies effectively mitigate the risk of over-reliance on transmitter IDs, ensuring comprehensive extraction of fingerprint information. Simulation results demonstrate robust identification despite transmitter ID tampering, and highlight its content independence. The codes can be downloaded from GitHub1.
Neumann eigenmaps for landmark embedding
Authors: Shashank Sule, Wojciech Czaja
Date: 2025-02-10 00:00:00

We present Neumann eigenmaps (NeuMaps), a novel approach for enhancing the standard diffusion map embedding using landmarks, i.e distinguished samples within the dataset. By interpreting these landmarks as a subgraph of the larger data graph, NeuMaps are obtained via the eigendecomposition of a renormalized Neumann Laplacian. We show that NeuMaps offer two key advantages: (1) they provide a computationally efficient embedding that accurately recovers the diffusion distance associated with the reflecting random walk on the subgraph, and (2) they naturally incorporate the Nystr\"om extension within the diffusion map framework through the discrete Neumann boundary condition. Through examples in digit classification and molecular dynamics, we demonstrate that NeuMaps not only improve upon existing landmark-based embedding methods but also enhance the stability of diffusion map embeddings to the removal of highly significant points.
Oracular Programming: A Modular Foundation for Building LLM-Enabled Software
Authors: Jonathan Laurent, André Platzer
Date: 2025-02-07 00:00:00
Tasks: Navigate

Large Language Models have proved surprisingly effective at solving a wide range of tasks from just a handful of examples. However, their lack of reliability and modularity limits their capacity to tackle large problems that require many steps of reasoning. In response, researchers have proposed advanced pipelines that leverage domain-specific knowledge to chain smaller prompts, provide intermediate feedback and improve performance through search. However, the current complexity of writing, tuning, maintaining and improving such pipelines has limited their sophistication. We propose oracular programming, a foundational paradigm for building LLM-enabled applications that lets domain experts express high-level problem-solving strategies as programs with unresolved choice points. These choice points are resolved at runtime by LLMs, which generalize from user-provided examples of correct and incorrect decisions. An oracular program is composed of three orthogonal components: a strategy that consists in a nondeterministic program with choice points that can be reified into a search tree, a policy that specifies how to navigate this tree with the help of LLM oracles, and a set of demonstrations that describe successful and unsuccessful search tree navigation scenarios across diverse problem instances. Each component is expressed in a dedicated programming language and can be independently improved or substituted. We address the key programming language design challenges of modularly composing oracular programs and enforcing consistency between their components as they evolve.
Delay Optimization of a Federated Learning-based UAV-aided IoT network
Authors: Hossein Mohammadi Firouzjaei, Javad Zeraatkar Moghaddam, Mehrdad Ardebilipour
Date: 2025-02-10 00:00:00
Tasks: Federated Learning

This paper explores the integration of power splitting(PS) simultaneous wireless information and power transfer (SWIPT) architecture and federated learning (FL) in Internet of Things (IoT) networks. The use of SWIPT allows power-constrained devices to simultaneously harvest energy and transmit data, addressing the energy limitations faced by IoT devices. The proposed scenario involves an Unmanned Arial Vehicle (UAV) serving as the base station (BS) and edge server, aggregating weight updates from IoT devices and unicasting aggregated updates to each device. The results demonstrate the feasibility of FL in IoT scenarios, ensuring communication efficiency without depleting device batteries.
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era
Authors: Elizaveta Semenova, Alisa Sheinkman, Timothy James Hitge, Siobhan Mackenzie Hall, Jon Cockayne
Date: 2025-02-10 00:00:00
Tasks: Model Selection, Transfer Learning

Surrogate models are widely used to approximate complex systems across science and engineering to reduce computational costs. Despite their widespread adoption, the field lacks standardisation across key stages of the modelling pipeline, including data sampling, model selection, evaluation, and downstream analysis. This fragmentation limits reproducibility and cross-domain utility -- a challenge further exacerbated by the rapid proliferation of AI-driven surrogate models. We argue for the urgent need to establish a structured reporting standard, the Surrogate Model Reporting Specification (SMRS), that systematically captures essential design and evaluation choices while remaining agnostic to implementation specifics. By promoting a standardised yet flexible framework, we aim to improve the reliability of surrogate modelling, foster interdisciplinary knowledge transfer, and, as a result, accelerate scientific progress in the AI era.
On Fairness of Unified Multimodal Large Language Model for Image Generation
Authors: Ming Liu, Hao Chen, Jindong Wang, LiWen Wang, Bhiksha Raj Ramakrishnan, Wensheng Zhang
Date: 2025-02-05 00:00:00
Tasks: Fairness, Image Generation, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model

Unified multimodal large language models (U-MLLMs) have demonstrated impressive performance in visual understanding and generation in an end-to-end pipeline. Compared with generation-only models (e.g., Stable Diffusion), U-MLLMs may raise new questions about bias in their outputs, which can be affected by their unified capabilities. This gap is particularly concerning given the under-explored risk of propagating harmful stereotypes. In this paper, we benchmark the latest U-MLLMs and find that most exhibit significant demographic biases, such as gender and race bias. To better understand and mitigate this issue, we propose a locate-then-fix strategy, where we audit and show how the individual model component is affected by bias. Our analysis shows that bias originates primarily from the language model. More interestingly, we observe a "partial alignment" phenomenon in U-MLLMs, where understanding bias appears minimal, but generation bias remains substantial. Thus, we propose a novel balanced preference model to balance the demographic distribution with synthetic data. Experiments demonstrate that our approach reduces demographic bias while preserving semantic fidelity. We hope our findings underscore the need for more holistic interpretation and debiasing strategies of U-MLLMs in the future.
SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering
Authors: Xuehang Guo, Xingyao Wang, Yangyi Chen, Sha Li, Chi Han, Manling Li, Heng Ji
Date: 2025-02-10 00:00:00
Tasks: Large Language Model

Software engineering (SE) is increasingly collaborative, with developers working together on shared complex codebases. Effective collaboration in shared environments requires participants -- whether humans or AI agents -- to stay on the same page as their environment evolves. When a collaborator's understanding diverges from the current state -- what we term the out-of-sync challenge -- the collaborator's actions may fail, leading to integration issues. In this work, we introduce SyncMind, a framework that systematically defines the out-of-sync problem faced by large language model (LLM) agents in collaborative software engineering (CSE). Based on SyncMind, we create SyncBench, a benchmark featuring 24,332 instances of agent out-of-sync scenarios in real-world CSE derived from 21 popular GitHub repositories with executable verification tests. Experiments on SyncBench uncover critical insights into existing LLM agents' capabilities and limitations. Besides substantial performance gaps among agents (from Llama-3.1 agent <= 3.33% to Claude-3.5-Sonnet >= 28.18%), their consistently low collaboration willingness (<= 4.86%) suggests fundamental limitations of existing LLM in CSE. However, when collaboration occurs, it positively correlates with out-of-sync recovery success. Minimal performance differences in agents' resource-aware out-of-sync recoveries further reveal their significant lack of resource awareness and adaptability, shedding light on future resource-efficient collaborative systems. Code and data are openly available on our project website: https://xhguo7.github.io/SyncMind/.
LLM-Supported Natural Language to Bash Translation
Authors: Finnian Westenfelder, Erik Hemberg, Miguel Tulla, Stephen Moskal, Una-May O'Reilly, Silviu Chiricescu
Date: 2025-02-07 00:00:00
Tasks: In-Context Learning, Translation

The Bourne-Again Shell (Bash) command-line interface for Linux systems has complex syntax and requires extensive specialized knowledge. Using the natural language to Bash command (NL2SH) translation capabilities of large language models (LLMs) for command composition circumvents these issues. However, the NL2SH performance of LLMs is difficult to assess due to inaccurate test data and unreliable heuristics for determining the functional equivalence of Bash commands. We present a manually verified test dataset of 600 instruction-command pairs and a training dataset of 40,939 pairs, increasing the size of previous datasets by 441% and 135%, respectively. Further, we present a novel functional equivalence heuristic that combines command execution with LLM evaluation of command outputs. Our heuristic can determine the functional equivalence of two Bash commands with 95% confidence, a 16% increase over previous heuristics. Evaluation of popular LLMs using our test dataset and heuristic demonstrates that parsing, in-context learning, in-weight learning, and constrained decoding can improve NL2SH accuracy by up to 32%. Our findings emphasize the importance of dataset quality, execution-based evaluation and translation method for advancing NL2SH translation. Our code is available at https://github.com/westenfelder/NL2SH
Grounding Creativity in Physics: A Brief Survey of Physical Priors in AIGC
Authors: Siwei Meng, Yawei Luo, Ping Liu
Date: 2025-02-10 00:00:00
Tasks: 3D Generation, NeRF, Physical Simulations, Survey

Recent advancements in AI-generated content have significantly improved the realism of 3D and 4D generation. However, most existing methods prioritize appearance consistency while neglecting underlying physical principles, leading to artifacts such as unrealistic deformations, unstable dynamics, and implausible objects interactions. Incorporating physics priors into generative models has become a crucial research direction to enhance structural integrity and motion realism. This survey provides a review of physics-aware generative methods, systematically analyzing how physical constraints are integrated into 3D and 4D generation. First, we examine recent works in incorporating physical priors into static and dynamic 3D generation, categorizing methods based on representation types, including vision-based, NeRF-based, and Gaussian Splatting-based approaches. Second, we explore emerging techniques in 4D generation, focusing on methods that model temporal dynamics with physical simulations. Finally, we conduct a comparative analysis of major methods, highlighting their strengths, limitations, and suitability for different materials and motion dynamics. By presenting an in-depth analysis of physics-grounded AIGC, this survey aims to bridge the gap between generative models and physical realism, providing insights that inspire future research in physically consistent content generation.
MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management
Authors: Fengchen Gu, Angelos Stefanidis, Ángel García-Fernández, Jionglong Su, Huakang Li
Date: 2025-02-11 00:00:00
Tasks: Deep Reinforcement Learning, Management

Deep reinforcement learning (DRL) has been applied in financial portfolio management to improve returns in changing market conditions. However, unlike most fields where DRL is widely used, the stock market is more volatile and dynamic as it is affected by several factors such as global events and investor sentiment. Therefore, it remains a challenge to construct a DRL-based portfolio management framework with strong return capability, stable training, and generalization ability. This study introduces a new framework utilizing the Memory Instance Gated Transformer (MIGT) for effective portfolio management. By incorporating a novel Gated Instance Attention module, which combines a transformer variant, instance normalization, and a Lite Gate Unit, our approach aims to maximize investment returns while ensuring the learning process's stability and reducing outlier impacts. Tested on the Dow Jones Industrial Average 30, our framework's performance is evaluated against fifteen other strategies using key financial metrics like the cumulative return and risk-return ratios (Sharpe, Sortino, and Omega ratios). The results highlight MIGT's advantage, showcasing at least a 9.75% improvement in cumulative returns and a minimum 2.36% increase in risk-return ratios over competing strategies, marking a significant advancement in DRL for portfolio management.
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction
Authors: Jaewan Lee, ChangYoung Park, Hongjun Yang, Sungbin Lim, Sehui Han
Date: 2025-02-06 00:00:00
Tasks: Band Gap, Prediction, Property Prediction

Recent advancements in AI have revolutionized property prediction in materials science and accelerating material discovery. Graph neural networks (GNNs) stand out due to their ability to represent crystal structures as graphs, effectively capturing local interactions and delivering superior predictions. However, these methods often lose critical global information, such as crystal systems and repetitive unit connectivity. To address this, we propose CAST, a cross-attention-based multimodal fusion model that integrates graph and text modalities to preserve essential material information. CAST combines node- and token-level features using cross-attention mechanisms, surpassing previous approaches reliant on material-level embeddings like graph mean-pooling or [CLS] tokens. A masked node prediction pretraining strategy further enhances atomic-level information integration. Our method achieved up to 22.9\% improvement in property prediction across four crystal properties including band gap compared to methods like CrysMMNet and MultiMat. Pretraining was key to aligning node and text embeddings, with attention maps confirming its effectiveness in capturing relationships between nodes and tokens. This study highlights the potential of multimodal learning in materials science, paving the way for more robust predictive models that incorporate both local and global information.
Memory Is Not the Bottleneck: Cost-Efficient Continual Learning via Weight Space Consolidation
Authors: Dongkyu Cho, Taesup Moon, Rumi Chunara, Kyunghyun Cho, Sungmin Cha
Date: 2025-02-11 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, GPU, Incremental Learning

Continual learning (CL) has traditionally emphasized minimizing exemplar memory usage, assuming that memory is the primary bottleneck. However, in modern computing environments-particularly those involving large foundation models-memory is inexpensive and abundant, while GPU time constitutes the main cost. This paper re-examines CL under a more realistic setting with sufficient exemplar memory, where the system can retain a representative portion of past data. We find that, under this regime, stability improves due to reduced forgetting, but plasticity diminishes as the model becomes biased toward prior tasks and struggles to adapt to new ones. Notably, even simple baselines like naive replay can match or exceed the performance of state-of-the-art methods at a fraction of the computational cost. Building on this insight, we propose a lightweight yet effective method called Weight Space Consolidation, which directly operates in the model's weight space via two core mechanisms: (1) rank-based parameter resets to recover plasticity, and (2) weight averaging to enhance stability. Our approach outperforms strong baselines across class-incremental learning with image classifiers and continual instruction tuning with large language models, while requiring only one-third to one-fourth of the training cost. These findings challenge long-standing CL assumptions and establish a new, cost-efficient baseline for real-world continual learning systems where exemplar memory is no longer the limiting factor.