pwc-archive/papers-with-abstracts huggingface dataset:
Lightweight and Effective Preference Construction in PIBT for Large-Scale Multi-Agent Pathfinding
Authors: Keisuke Okumura, Hiroki Nagai
Date: 2025-05-19 00:00:00

PIBT is a computationally lightweight algorithm that can be applied to a variety of multi-agent pathfinding (MAPF) problems, generating the next collision-free locations of agents given another. Because of its simplicity and scalability, it is becoming a popular underlying scheme for recent large-scale MAPF methods involving several hundreds or thousands of agents. Vanilla PIBT makes agents behave greedily towards their assigned goals, while agents typically have multiple best actions, since the graph shortest path is not always unique. Consequently, tiebreaking about how to choose between these actions significantly affects resulting solutions. This paper studies two simple yet effective techniques for tiebreaking in PIBT, without compromising its computational advantage. The first technique allows an agent to intelligently dodge another, taking into account whether each action will hinder the progress of the next timestep. The second technique is to learn, through multiple PIBT runs, how an action causes regret in others and to use this information to minimise regret collectively. Our empirical results demonstrate that these techniques can reduce the solution cost of one-shot MAPF and improve the throughput of lifelong MAPF. For instance, in densely populated one-shot cases, the combined use of these tiebreaks achieves improvements of around 10-20% in sum-of-costs, without significantly compromising the speed of a PIBT-based planner.
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection
Authors: Tiankai Yang, Junjun Liu, Wingchun Siu, Jiahang Wang, Zhuangzhuang Qian, Chanjuan Song, Cheng Cheng, Xiyang Hu, Yue Zhao
Date: 2025-05-19 00:00:00
Tasks: Anomaly Detection, Code Generation, Diversity, Fraud Detection, Model Selection

Anomaly detection (AD) is essential in areas such as fraud detection, network monitoring, and scientific research. However, the diversity of data modalities and the increasing number of specialized AD libraries pose challenges for non-expert users who lack in-depth library-specific knowledge and advanced programming skills. To tackle this, we present AD-AGENT, an LLM-driven multi-agent framework that turns natural-language instructions into fully executable AD pipelines. AD-AGENT coordinates specialized agents for intent parsing, data preparation, library and model selection, documentation mining, and iterative code generation and debugging. Using a shared short-term workspace and a long-term cache, the agents integrate popular AD libraries like PyOD, PyGOD, and TSLib into a unified workflow. Experiments demonstrate that AD-AGENT produces reliable scripts and recommends competitive models across libraries. The system is open-sourced to support further research and practical applications in AD.
Learning Robust Spectral Dynamics for Temporal Domain Generalization
Authors: En Yu, Jie Lu, Xiaoyu Yang, Guangquan Zhang, Zhen Fang
Date: 2025-05-19 00:00:00
Tasks: Domain Generalization

Modern machine learning models struggle to maintain performance in dynamic environments where temporal distribution shifts, \emph{i.e., concept drift}, are prevalent. Temporal Domain Generalization (TDG) seeks to enable model generalization across evolving domains, yet existing approaches typically assume smooth incremental changes, struggling with complex real-world drifts involving long-term structure (incremental evolution/periodicity) and local uncertainties. To overcome these limitations, we introduce FreKoo, which tackles these challenges via a novel frequency-domain analysis of parameter trajectories. It leverages the Fourier transform to disentangle parameter evolution into distinct spectral bands. Specifically, low-frequency component with dominant dynamics are learned and extrapolated using the Koopman operator, robustly capturing diverse drift patterns including both incremental and periodicity. Simultaneously, potentially disruptive high-frequency variations are smoothed via targeted temporal regularization, preventing overfitting to transient noise and domain uncertainties. In addition, this dual spectral strategy is rigorously grounded through theoretical analysis, providing stability guarantees for the Koopman prediction, a principled Bayesian justification for the high-frequency regularization, and culminating in a multiscale generalization bound connecting spectral dynamics to improved generalization. Extensive experiments demonstrate FreKoo's significant superiority over SOTA TDG approaches, particularly excelling in real-world streaming scenarios with complex drifts and uncertainties.
A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics
Authors: Takeshi Kojima, Yaonan Zhu, Yusuke Iwasawa, Toshinori Kitamura, Gang Yan, Shu Morikuni, Ryosuke Takanami, Alfredo Solano, Tatsuya Matsushima, Akiko Murakami, Yutaka Matsuo
Date: 2025-05-19 00:00:00
Tasks: Survey

Recent Foundation Model-enabled robotics (FMRs) display greatly improved general-purpose skills, enabling more adaptable automation than conventional robotics. Their ability to handle diverse tasks thus creates new opportunities to replace human labor. However, unlike general foundation models, FMRs interact with the physical world, where their actions directly affect the safety of humans and surrounding objects, requiring careful deployment and control. Based on this proposition, our survey comprehensively summarizes robot control approaches to mitigate physical risks by covering all the lifespan of FMRs ranging from pre-deployment to post-accident stage. Specifically, we broadly divide the timeline into the following three phases: (1) pre-deployment phase, (2) pre-incident phase, and (3) post-incident phase. Throughout this survey, we find that there is much room to study (i) pre-incident risk mitigation strategies, (ii) research that assumes physical interaction with humans, and (iii) essential issues of foundation models themselves. We hope that this survey will be a milestone in providing a high-resolution analysis of the physical risks of FMRs and their control, contributing to the realization of a good human-robot relationship.
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification
Authors: Lucas M. Dorneles, Luan Fonseca Garcia, Joel Luís Carbonera
Date: 2025-05-19 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification

Neural networks have become increasingly popular in the last few years as an effective tool for the task of image classification due to the impressive performance they have achieved on this task. In image classification tasks, it is common to use data augmentation strategies to increase the robustness of trained networks to changes in the input images and to avoid overfitting. Although data augmentation is a widely adopted technique, the literature lacks a body of research analyzing the effects data augmentation methods have on the patterns learned by neural network models working on complex datasets. The primary objective of this work is to propose a methodology and set of metrics that may allow a quantitative approach to analyzing the effects of data augmentation in convolutional networks applied to image classification. An important tool used in the proposed approach lies in the concept of class activation maps for said models, which allow us to identify and measure the importance these models assign to each individual pixel in an image when executing the classification task. From these maps, we may then extract metrics over the similarities and differences between maps generated by these models trained on a given dataset with different data augmentation strategies. Experiments made using this methodology suggest that the effects of these data augmentation techniques not only can be analyzed in this way but also allow us to identify different impact profiles over the trained models.
AdaDim: Dimensionality Adaptation for SSL Representational Dynamics
Authors: Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib
Date: 2025-05-18 00:00:00
Tasks: Self-Supervised Learning

A key factor in effective Self-Supervised learning (SSL) is preventing dimensional collapse, which is where higher-dimensional representation spaces span a lower-dimensional subspace. Therefore, SSL optimization strategies involve guiding a model to produce representations ($R$) with a higher dimensionality. Dimensionality is either optimized through a dimension-contrastive approach that encourages feature decorrelation or through a sample-contrastive method that promotes a uniform spread of sample representations. Both families of SSL algorithms also utilize a projection head that maps $R$ into a lower-dimensional embedding space $Z$. Recent work has characterized the projection head as a filter of irrelevant features from the SSL objective by reducing mutual information, $I(R;Z)$. Therefore, the current literature's view is that a good SSL representation space should have a high $H(R)$ and a low $I(R;Z)$. However, this view of the problem is lacking in terms of an understanding of the underlying training dynamics that influences both terms, as well as how the values of $H(R)$ and $I(R;Z)$ arrived at the end of training reflect the downstream performance of an SSL model. We address both gaps in the literature by demonstrating that increases in $H(R)$ due to feature decorrelation at the start of training lead to a higher $I(R;Z)$, while increases in $H(R)$ due to samples distributing uniformly in a high-dimensional space at the end of training cause $I(R;Z)$ to plateau or decrease. Furthermore, our analysis shows that the best performing SSL models do not have the highest $H(R)$ nor the lowest $I(R;Z)$, but arrive at an optimal intermediate point for both. We develop a method called AdaDim to exploit these observed training dynamics by adaptively weighting between losses based on feature decorrelation and uniform sample spread.
RealMath: A Continuous Benchmark for Evaluating Language Models on Research-Level Mathematics
Authors: Jie Zhang, Cezara Petrui, Kristina Nikolić, Florian Tramèr
Date: 2025-05-18 00:00:00
Tasks: Mathematical Reasoning

Existing benchmarks for evaluating mathematical reasoning in large language models (LLMs) rely primarily on competition problems, formal proofs, or artificially challenging questions -- failing to capture the nature of mathematics encountered in actual research environments. We introduce RealMath, a novel benchmark derived directly from research papers and mathematical forums that assesses LLMs' abilities on authentic mathematical tasks. Our approach addresses three critical challenges: sourcing diverse research-level content, enabling reliable automated evaluation through verifiable statements, and designing a continually refreshable dataset to mitigate contamination risks. Experimental results across multiple LLMs reveal surprising capabilities in handling research mathematics compared to competition problems, suggesting current models may already serve as valuable assistants for working mathematicians despite limitations on highly challenging problems. The code and dataset for RealMath are publicly available.
Measuring Information Distortion in Hierarchical Ultra long Novel Generation:The Optimal Expansion Ratio
Authors: Hanwen Shen, Ting Ying
Date: 2025-05-18 00:00:00
Tasks: Text Compression

Writing novels with Large Language Models (LLMs) raises a critical question: how much human-authored outline is necessary to generate high-quality million-word novels? While frameworks such as DOME, Plan&Write, and Long Writer have improved stylistic coherence and logical consistency, they primarily target shorter novels (10k--100k words), leaving ultra-long generation largely unexplored. Drawing on insights from recent text compression methods like LLMZip and LLM2Vec, we conduct an information-theoretic analysis that quantifies distortion occurring when LLMs compress and reconstruct ultra-long novels under varying compression-expansion ratios. We introduce a hierarchical two-stage generation pipeline (outline -> detailed outline -> manuscript) and find an optimal outline length that balances information preservation with human effort. Through extensive experimentation with Chinese novels, we establish that a two-stage hierarchical outline approach significantly reduces semantic distortion compared to single-stage methods. Our findings provide empirically-grounded guidance for authors and researchers collaborating with LLMs to create million-word novels.
A Survey of Attacks on Large Language Models
Authors: Wenrui Xu, Keshab K. Parhi
Date: 2025-05-18 00:00:00
Tasks: Autonomous Driving, Financial Analysis, Survey

Large language models (LLMs) and LLM-based agents have been widely deployed in a wide range of applications in the real world, including healthcare diagnostics, financial analysis, customer support, robotics, and autonomous driving, expanding their powerful capability of understanding, reasoning, and generating natural languages. However, the wide deployment of LLM-based applications exposes critical security and reliability risks, such as the potential for malicious misuse, privacy leakage, and service disruption that weaken user trust and undermine societal safety. This paper provides a systematic overview of the details of adversarial attacks targeting both LLMs and LLM-based agents. These attacks are organized into three phases in LLMs: Training-Phase Attacks, Inference-Phase Attacks, and Availability & Integrity Attacks. For each phase, we analyze the details of representative and recently introduced attack methods along with their corresponding defenses. We hope our survey will provide a good tutorial and a comprehensive understanding of LLM security, especially for attacks on LLMs. We desire to raise attention to the risks inherent in widely deployed LLM-based applications and highlight the urgent need for robust mitigation strategies for evolving threats.
mCLM: A Function-Infused and Synthesis-Friendly Modular Chemical Language Model
Authors: Carl Edwards, Chi Han, Gawon Lee, Thao Nguyen, Bowen Jin, Chetan Kumar Prasad, Sara Szymkuć, Bartosz A. Grzybowski, Ying Diao, Jiawei Han, Ge Liu, Hao Peng, Martin D. Burke, Heng Ji
Date: 2025-05-18 00:00:00
Tasks: Language Modeling, Language Modelling

Despite their ability to understand chemical knowledge and accurately generate sequential representations, large language models (LLMs) remain limited in their capacity to propose novel molecules with drug-like properties. In addition, the molecules that LLMs propose can often be challenging to make in the lab. To more effectively enable the discovery of functional small molecules, LLMs need to learn a molecular language. However, LLMs are currently limited by encoding molecules from atoms. In this paper, we argue that just like tokenizing texts into (sub-)word tokens instead of characters, molecules should be decomposed and reassembled at the level of functional building blocks, i.e., parts of molecules that bring unique functions and serve as effective building blocks for real-world automated laboratory synthesis. This motivates us to propose mCLM, a modular Chemical-Language Model tokenizing molecules into building blocks and learning a bilingual language model of both natural language descriptions of functions and molecule building blocks. By reasoning on such functional building blocks, mCLM guarantees to generate efficiently synthesizable molecules thanks to recent progress in block-based chemistry, while also improving the functions of molecules in a principled manner. In experiments on 430 FDA-approved drugs, we find mCLM capable of significantly improving 5 out of 6 chemical functions critical to determining drug potentials. More importantly, mCLM can reason on multiple functions and improve the FDA-rejected drugs (``fallen angels'') over multiple iterations to greatly improve their shortcomings.
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers
Authors: Taniya Kapoor, Abhishek Chandra, Anastasios Stamou, Stephen J Roberts
Date: 2025-05-18 00:00:00
Tasks: Operator learning, Physics-informed machine learning

Real-world systems, from aerospace to railway engineering, are modeled with partial differential equations (PDEs) describing the physics of the system. Estimating robust solutions for such problems is essential. Deep learning-based architectures, such as neural PDE solvers, have recently gained traction as a reliable solution method. The current state of development of these approaches, however, primarily focuses on improving accuracy. The environmental impact of excessive computation, leading to increased carbon emissions, has largely been overlooked. This paper introduces a carbon emission measure for a range of PDE solvers. Our proposed metric, EcoL2, balances model accuracy with emissions across data collection, model training, and deployment. Experiments across both physics-informed machine learning and operator learning architectures demonstrate that the proposed metric presents a holistic assessment of model performance and emission cost. As such solvers grow in scale and deployment, EcoL2 represents a step toward building performant scientific machine learning systems with lower long-term environmental impact.
FreqSelect: Frequency-Aware fMRI-to-Image Reconstruction
Authors: Junliang Ye, Lei Wang, Md Zakir Hossain
Date: 2025-05-18 00:00:00
Tasks: Image Reconstruction

Reconstructing natural images from functional magnetic resonance imaging (fMRI) data remains a core challenge in natural decoding due to the mismatch between the richness of visual stimuli and the noisy, low resolution nature of fMRI signals. While recent two-stage models, combining deep variational autoencoders (VAEs) with diffusion models, have advanced this task, they treat all spatial-frequency components of the input equally. This uniform treatment forces the model to extract meaning features and suppress irrelevant noise simultaneously, limiting its effectiveness. We introduce FreqSelect, a lightweight, adaptive module that selectively filters spatial-frequency bands before encoding. By dynamically emphasizing frequencies that are most predictive of brain activity and suppressing those that are uninformative, FreqSelect acts as a content-aware gate between image features and natural data. It integrates seamlessly into standard very deep VAE-diffusion pipelines and requires no additional supervision. Evaluated on the Natural Scenes dataset, FreqSelect consistently improves reconstruction quality across both low- and high-level metrics. Beyond performance gains, the learned frequency-selection patterns offer interpretable insights into how different visual frequencies are represented in the brain. Our method generalizes across subjects and scenes, and holds promise for extension to other neuroimaging modalities, offering a principled approach to enhancing both decoding accuracy and neuroscientific interpretability.
ProMi: An Efficient Prototype-Mixture Baseline for Few-Shot Segmentation with Bounding-Box Annotations
Authors: Florent Chiaroni, Ali Ayub, Ola Ahmad
Date: 2025-05-18 00:00:00

In robotics applications, few-shot segmentation is crucial because it allows robots to perform complex tasks with minimal training data, facilitating their adaptation to diverse, real-world environments. However, pixel-level annotations of even small amount of images is highly time-consuming and costly. In this paper, we present a novel few-shot binary segmentation method based on bounding-box annotations instead of pixel-level labels. We introduce, ProMi, an efficient prototype-mixture-based method that treats the background class as a mixture of distributions. Our approach is simple, training-free, and effective, accommodating coarse annotations with ease. Compared to existing baselines, ProMi achieves the best results across different datasets with significant gains, demonstrating its effectiveness. Furthermore, we present qualitative experiments tailored to real-world mobile robot tasks, demonstrating the applicability of our approach in such scenarios. Our code: https://github.com/ThalesGroup/promi.
Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets
Authors: Ahmet Bilican, M. Akin Yilmaz, A. Murat Tekalp, R. Gökberk Cinbiş
Date: 2025-05-18 00:00:00
Tasks: Diversity, Image Generation, parameter-efficient fine-tuning, Text to Image Generation, Text-to-Image Generation

Efficiently adapting large foundation models is critical, especially with tight compute and memory budgets. Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA offer limited granularity and effectiveness in few-parameter regimes. We propose Wavelet Fine-Tuning (WaveFT), a novel PEFT method that learns highly sparse updates in the wavelet domain of residual matrices. WaveFT allows precise control of trainable parameters, offering fine-grained capacity adjustment and excelling with remarkably low parameter count, potentially far fewer than LoRA's minimum, ideal for extreme parameter-efficient scenarios. Evaluated on personalized text-to-image generation using Stable Diffusion XL as baseline, WaveFT significantly outperforms LoRA and other PEFT methods, especially at low parameter counts; achieving superior subject fidelity, prompt alignment, and image diversity.
Scalable Strategies for Continual Learning with Replay
Authors: Truman Hickok
Date: 2025-05-18 00:00:00
Tasks: Continual Learning, Task Arithmetic

Future deep learning models will be distinguished by systems that perpetually learn through interaction, imagination, and cooperation, blurring the line between training and inference. This makes continual learning a critical challenge, as methods that efficiently maximize bidirectional transfer across learning trajectories will be essential. Replay is on track to play a foundational role in continual learning, allowing models to directly reconcile new information with past knowledge. In practice, however, replay is quite unscalable, doubling the cost of continual learning when applied naively. Moreover, the continual learning literature has not fully synchronized with the multi-task fine-tuning literature, having not fully integrated highly scalable techniques like model merging and low rank adaptation into a replay-enabled toolset that can produce a unified model in the face of many sequential tasks. In this paper, we begin by applying and analyzing low rank adaptation in a continual learning setting. Next, we introduce consolidation, a phasic approach to replay which leads to up to 55\% less replay samples being needed for a given performance target. Then, we propose sequential merging, an offshoot of task arithmetic which is tailored to the continual learning setting and is shown to work well in combination with replay. Finally, we demonstrate that the developed strategies can operate synergistically, resulting in a highly scalable toolset that outperforms standalone variants.
Towards Budget-Friendly Model-Agnostic Explanation Generation for Large Language Models
Authors: Junhao Liu, Haonan Yu, Xin Zhang
Date: 2025-05-18 00:00:00
Tasks: Explanation Generation

With Large language models (LLMs) becoming increasingly prevalent in various applications, the need for interpreting their predictions has become a critical challenge. As LLMs vary in architecture and some are closed-sourced, model-agnostic techniques show great promise without requiring access to the model's internal parameters. However, existing model-agnostic techniques need to invoke LLMs many times to gain sufficient samples for generating faithful explanations, which leads to high economic costs. In this paper, we show that it is practical to generate faithful explanations for large-scale LLMs by sampling from some budget-friendly models through a series of empirical studies. Moreover, we show that such proxy explanations also perform well on downstream tasks. Our analysis provides a new paradigm of model-agnostic explanation methods for LLMs, by including information from budget-friendly models.