pwc-archive/papers-with-abstracts huggingface dataset:
Automated Archival Descriptions with Federated Intelligence of LLMs
Authors: Jinghua Groppe, Andreas Marquet, Annabel Walz, Sven Groppe
Date: 2025-04-08 00:00:00
Tasks: Metadata quality

Enforcing archival standards requires specialized expertise, and manually creating metadata descriptions for archival materials is a tedious and error-prone task. This work aims at exploring the potential of agentic AI and large language models (LLMs) in addressing the challenges of implementing a standardized archival description process. To this end, we introduce an agentic AI-driven system for automated generation of high-quality metadata descriptions of archival materials. We develop a federated optimization approach that unites the intelligence of multiple LLMs to construct optimal archival metadata. We also suggest methods to overcome the challenges associated with using LLMs for consistent metadata generation. To evaluate the feasibility and effectiveness of our techniques, we conducted extensive experiments using a real-world dataset of archival materials, which covers a variety of document types and data formats. The evaluation results demonstrate the feasibility of our techniques and highlight the superior performance of the federated optimization approach compared to single-model solutions in metadata quality and reliability.
Infinite precedence graphs for consistency verification in P-time event graphs
Authors: Davide Zorzenon, Jörg Raisch
Date: 2025-04-07 00:00:00

Precedence constraints are inequalities used to model time dependencies. In 1958, Gallai proved that a finite system of precedence constraints admits solutions if and only if the corresponding precedence graph does not contain positive-weight circuits. We show that this result extends naturally to the case of infinitely many constraints. We then analyze two specific classes of infinite precedence graphs -- $\mathbb{N}$-periodic and ultimately periodic graphs -- and prove that the existence of solutions of their related constraints can be verified in strongly polynomial time. The obtained algorithms find applications in P-time event graphs, which are a subclass of P-time Petri nets able to model production systems under cyclic schedules where tasks need to be performed within given time windows.
A Customized SAT-based Solver for Graph Coloring
Authors: Timo Brand, Daniel Faber, Stephan Held, Petra Mutzel
Date: 2025-04-07 00:00:00

We introduce ZykovColor, a novel SAT-based algorithm to solve the graph coloring problem working on top of an encoding that mimics the Zykov tree. Our method is based on an approach of H\'ebrard and Katsirelos (2020) that employs a propagator to enforce transitivity constraints, incorporate lower bounds for search tree pruning, and enable inferred propagations. We leverage the recently introduced IPASIR-UP interface for CaDiCal to implement these techniques with a SAT solver. Furthermore, we propose new features that take advantage of the underlying SAT solver. These include modifying the integrated decision strategy with vertex domination hints and using incremental bottom-up search that allows to reuse learned clauses from previous calls. Additionally, we integrate a more efficient clique computation to improve the lower bounds during the search. We validate the effectiveness of each new feature through an experimental analysis. ZykovColor outperforms other state-of-the-art graph coloring implementations on the DIMACS benchmark set. Further experiments on random Erd\H{o}s-R\'enyi graphs show that our new approach dominates state-of-the-art SAT-based methods for both very sparse and highly dense graphs.
Flexible Graph Similarity Computation With A Proactive Optimization Strategy
Authors: Zhouyang Liu, Ning Liu, Yixin Chen, Jiezhong He, Dongsheng Li
Date: 2025-04-09 00:00:00
Tasks: Graph Similarity

Graph Edit Distance (GED) offers a principled and flexible measure of graph similarity, as it quantifies the minimum cost needed to transform one graph into another with customizable edit operation costs. Despite recent learning-based efforts to approximate GED via vector space representations, existing methods struggle with adapting to varying operation costs. Furthermore, they suffer from inefficient, reactive mapping refinements due to reliance on isolated node-level distance as guidance. To address these issues, we propose GEN, a novel learning-based approach for flexible GED approximation. GEN addresses the varying costs adaptation by integrating operation costs prior to match establishment, enabling mappings to dynamically adapt to cost variations. Furthermore, GEN introduces a proactive guidance optimization strategy that captures graph-level dependencies between matches, allowing informed matching decisions in a single step without costly iterative refinements. Extensive evaluations on real-world and synthetic datasets demonstrate that GEN achieves up to 37.8% reduction in GED approximation error and 72.7% reduction in inference time compared with state-of-the-art methods, while consistently maintaining robustness under diverse cost settings and graph sizes.
Collaborative Prediction: Tractable Information Aggregation via Agreement
Authors: Natalie Collina, Ira Globus-Harris, Surbhi Goel, Varun Gupta, Aaron Roth, Mirah Shi
Date: 2025-04-08 00:00:00
Tasks: Prediction

We give efficient "collaboration protocols" through which two parties, who observe different features about the same instances, can interact to arrive at predictions that are more accurate than either could have obtained on their own. The parties only need to iteratively share and update their own label predictions-without either party ever having to share the actual features that they observe. Our protocols are efficient reductions to the problem of learning on each party's feature space alone, and so can be used even in settings in which each party's feature space is illegible to the other-which arises in models of human/AI interaction and in multi-modal learning. The communication requirements of our protocols are independent of the dimensionality of the data. In an online adversarial setting we show how to give regret bounds on the predictions that the parties arrive at with respect to a class of benchmark policies defined on the joint feature space of the two parties, despite the fact that neither party has access to this joint feature space. We also give simpler algorithms for the same task in the batch setting in which we assume that there is a fixed but unknown data distribution. We generalize our protocols to a decision theoretic setting with high dimensional outcome spaces, where parties communicate only "best response actions." Our theorems give a computationally and statistically tractable generalization of past work on information aggregation amongst Bayesians who share a common and correct prior, as part of a literature studying "agreement" in the style of Aumann's agreement theorem. Our results require no knowledge of (or even the existence of) a prior distribution and are computationally efficient. Nevertheless we show how to lift our theorems back to this classical Bayesian setting, and in doing so, give new information aggregation theorems for Bayesian agreement.
DDPM Score Matching and Distribution Learning
Authors: Sinho Chewi, Alkis Kalavasis, Anay Mehrotra, Omar Montasser
Date: 2025-04-07 00:00:00
Tasks: Denoising, Density Estimation, Learning Theory, parameter estimation

Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). A key result in this area shows that with accurate score estimates, SGMs can efficiently generate samples from any realistic data distribution (Chen et al., ICLR'23; Lee et al., ALT'23). This distribution learning result, where the learned distribution is implicitly that of the sampler's output, does not explain how score estimation relates to classical tasks of parameter and density estimation. This paper introduces a framework that reduces score estimation to these two tasks, with various implications for statistical and computational learning theory: Parameter Estimation: Koehler et al. (ICLR'23) demonstrate that a score-matching variant is statistically inefficient for the parametric estimation of multimodal densities common in practice. In contrast, we show that under mild conditions, denoising score-matching in DDPMs is asymptotically efficient. Density Estimation: By linking generation to score estimation, we lift existing score estimation guarantees to $(\epsilon,\delta)$-PAC density estimation, i.e., a function approximating the target log-density within $\epsilon$ on all but a $\delta$-fraction of the space. We provide (i) minimax rates for density estimation over H\"older classes and (ii) a quasi-polynomial PAC density estimation algorithm for the classical Gaussian location mixture model, building on and addressing an open problem from Gatmiry et al. (arXiv'24). Lower Bounds for Score Estimation: Our framework offers the first principled method to prove computational lower bounds for score estimation across general distributions. As an application, we establish cryptographic lower bounds for score estimation in general Gaussian mixture models, conceptually recovering Song's (NeurIPS'24) result and advancing his key open problem.
Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond
Authors: Alkis Kalavasis, Anay Mehrotra, Felix Zhou
Date: 2025-04-06 00:00:00
Tasks: Selection bias

We revisit the problem of estimating $k$ linear regressors with self-selection bias in $d$ dimensions with the maximum selection criterion, as introduced by Cherapanamjeri, Daskalakis, Ilyas, and Zampetakis [CDIZ23, STOC'23]. Our main result is a $\operatorname{poly}(d,k,1/\varepsilon) + {k}^{O(k)}$ time algorithm for this problem, which yields an improvement in the running time of the algorithms of [CDIZ23] and [GM24, arXiv]. We achieve this by providing the first local convergence algorithm for self-selection, thus resolving the main open question of [CDIZ23]. To obtain this algorithm, we reduce self-selection to a seemingly unrelated statistical problem called coarsening. Coarsening occurs when one does not observe the exact value of the sample but only some set (a subset of the sample space) that contains the exact value. Inference from coarse samples arises in various real-world applications due to rounding by humans and algorithms, limited precision of instruments, and lag in multi-agent systems. Our reduction to coarsening is intuitive and relies on the geometry of the self-selection problem, which enables us to bypass the limitations of previous analytic approaches. To demonstrate its applicability, we provide a local convergence algorithm for linear regression under another self-selection criterion, which is related to second-price auction data. Further, we give the first polynomial time local convergence algorithm for coarse Gaussian mean estimation given samples generated from a convex partition. Previously, only a sample-efficient algorithm was known due to Fotakis, Kalavasis, Kontonis, and Tzamos [FKKT21, COLT'21].
Binned Group Algebra Factorization for Differentially Private Continual Counting
Authors: Monika Henzinger, Nikita P. Kalinin, Jalaj Upadhyay
Date: 2025-04-06 00:00:00

We study memory-efficient matrix factorization for differentially private counting under continual observation. While recent work by Henzinger and Upadhyay 2024 introduced a factorization method with reduced error based on group algebra, its practicality in streaming settings remains limited by computational constraints. We present new structural properties of the group algebra factorization, enabling the use of a binning technique from Andersson and Pagh (2024). By grouping similar values in rows, the binning method reduces memory usage and running time to $\tilde O(\sqrt{n})$, where $n$ is the length of the input stream, while maintaining a low error. Our work bridges the gap between theoretical improvements in factorization accuracy and practical efficiency in large-scale private learning systems.
Language-Dependent Political Bias in AI: A Study of ChatGPT and Gemini
Authors: Dogus Yuksel, Mehmet Cem Catalbas, Bora Oc
Date: 2025-04-08 00:00:00

As leading examples of large language models, ChatGPT and Gemini claim to provide accurate and unbiased information, emphasizing their commitment to political neutrality and avoidance of personal bias. This research investigates the political tendency of large language models and the existence of differentiation according to the query language. For this purpose, ChatGPT and Gemini were subjected to a political axis test using 14 different languages. The findings of the study suggest that these large language models do exhibit political tendencies, with both models demonstrating liberal and leftist biases. A comparative analysis revealed that Gemini exhibited a more pronounced liberal and left-wing tendency compared to ChatGPT. The study also found that these political biases varied depending on the language used for inquiry. The study delves into the factors that constitute political tendencies and linguistic differentiation, exploring differences in the sources and scope of educational data, structural and grammatical features of languages, cultural and political contexts, and the model's response to linguistic features. From this standpoint, and an ethical perspective, it is proposed that artificial intelligence tools should refrain from asserting a lack of political tendencies and neutrality, instead striving for political neutrality and executing user queries by incorporating these tendencies.
P2Object: Single Point Supervised Object Detection and Instance Segmentation
Authors: Pengfei Chen, Xuehui Yu, Xumeng Han, Kuiran Wang, Guorong Li, Lingxi Xie, Zhenjun Han, Jianbin Jiao
Date: 2025-04-10 00:00:00
Tasks: Instance Segmentation, Multiple Instance Learning, object-detection, Object Detection, Object Recognition, Semantic Segmentation

Object recognition using single-point supervision has attracted increasing attention recently. However, the performance gap compared with fully-supervised algorithms remains large. Previous works generated class-agnostic \textbf{\textit{proposals in an image}} offline and then treated mixed candidates as a single bag, putting a huge burden on multiple instance learning (MIL). In this paper, we introduce Point-to-Box Network (P2BNet), which constructs balanced \textbf{\textit{instance-level proposal bags}} by generating proposals in an anchor-like way and refining the proposals in a coarse-to-fine paradigm. Through further research, we find that the bag of proposals, either at the image level or the instance level, is established on discrete box sampling. This leads the pseudo box estimation into a sub-optimal solution, resulting in the truncation of object boundaries or the excessive inclusion of background. Hence, we conduct a series exploration of discrete-to-continuous optimization, yielding P2BNet++ and Point-to-Mask Network (P2MNet). P2BNet++ conducts an approximately continuous proposal sampling strategy by better utilizing spatial clues. P2MNet further introduces low-level image information to assist in pixel prediction, and a boundary self-prediction is designed to relieve the limitation of the estimated boxes. Benefiting from the continuous object-aware \textbf{\textit{pixel-level perception}}, P2MNet can generate more precise bounding boxes and generalize to segmentation tasks. Our method largely surpasses the previous methods in terms of the mean average precision on COCO, VOC, SBD, and Cityscapes, demonstrating great potential to bridge the performance gap compared with fully supervised tasks.
Fast Globally Optimal and Geometrically Consistent 3D Shape Matching
Authors: Paul Roetzer, Florian Bernard
Date: 2025-04-08 00:00:00

Geometric consistency, i.e. the preservation of neighbourhoods, is a natural and strong prior in 3D shape matching. Geometrically consistent matchings are crucial for many downstream applications, such as texture transfer or statistical shape modelling. Yet, in practice, geometric consistency is often overlooked, or only achieved under severely limiting assumptions (e.g. a good initialisation). In this work, we propose a novel formalism for computing globally optimal and geometrically consistent matchings between 3D shapes which is scalable in practice. Our key idea is to represent the surface of the source shape as a collection of cyclic paths, which are then consistently matched to the target shape. Mathematically, we construct a hyper product graph (between source and target shape), and then cast 3D shape matching as a minimum-cost circulation flow problem in this hyper graph, which yields global geometrically consistent matchings between both shapes. We empirically show that our formalism is efficiently solvable and that it leads to high-quality results.
MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data
Authors: Paul Borne--Pons, Mikolaj Czerkawski, Rosalie Martin, Romain Rouffet
Date: 2025-04-09 00:00:00

Terrain modeling has traditionally relied on procedural techniques, which often require extensive domain expertise and handcrafted rules. In this paper, we present MESA - a novel data-centric alternative by training a diffusion model on global remote sensing data. This approach leverages large-scale geospatial information to generate high-quality terrain samples from text descriptions, showcasing a flexible and scalable solution for terrain generation. The model's capabilities are demonstrated through extensive experiments, highlighting its ability to generate realistic and diverse terrain landscapes. The dataset produced to support this work, the Major TOM Core-DEM extension dataset, is released openly as a comprehensive resource for global terrain data. The results suggest that data-driven models, trained on remote sensing data, can provide a powerful tool for realistic terrain modeling and generation.
SE4Lip: Speech-Lip Encoder for Talking Head Synthesis to Solve Phoneme-Viseme Alignment Ambiguity
Authors: Yihuan Huang, Jiajun Liu, Yanzhen Ren, Wuyang Liu, Juhua Tang
Date: 2025-04-08 00:00:00
Tasks: 3DGS, cross-modal alignment, NeRF

Speech-driven talking head synthesis tasks commonly use general acoustic features (such as HuBERT and DeepSpeech) as guided speech features. However, we discovered that these features suffer from phoneme-viseme alignment ambiguity, which refers to the uncertainty and imprecision in matching phonemes (speech) with visemes (lip). To address this issue, we propose the Speech Encoder for Lip (SE4Lip) to encode lip features from speech directly, aligning speech and lip features in the joint embedding space by a cross-modal alignment framework. The STFT spectrogram with the GRU-based model is designed in SE4Lip to preserve the fine-grained speech features. Experimental results show that SE4Lip achieves state-of-the-art performance in both NeRF and 3DGS rendering models. Its lip sync accuracy improves by 13.7% and 14.2% compared to the best baseline and produces results close to the ground truth videos.
Micro-splatting: Maximizing Isotropic Constraints for Refined Optimization in 3D Gaussian Splatting
Authors: Jee Won Lee, Hansol Lim, SooYeun Yang, Jongseong Choi
Date: 2025-04-08 00:00:00
Tasks: SSIM

Recent advancements in 3D Gaussian Splatting have achieved impressive scalability and real-time rendering for large-scale scenes but often fall short in capturing fine-grained details. Conventional approaches that rely on relatively large covariance parameters tend to produce blurred representations, while directly reducing covariance sizes leads to sparsity. In this work, we introduce Micro-splatting (Maximizing Isotropic Constraints for Refined Optimization in 3D Gaussian Splatting), a novel framework designed to overcome these limitations. Our approach leverages a covariance regularization term to penalize excessively large Gaussians to ensure each splat remains compact and isotropic. This work implements an adaptive densification strategy that dynamically refines regions with high image gradients by lowering the splitting threshold, followed by loss function enhancement. This strategy results in a denser and more detailed gaussian means where needed, without sacrificing rendering efficiency. Quantitative evaluations using metrics such as L1, L2, PSNR, SSIM, and LPIPS, alongside qualitative comparisons demonstrate that our method significantly enhances fine-details in 3D reconstructions.
REWIND: Real-Time Egocentric Whole-Body Motion Diffusion with Exemplar-Based Identity Conditioning
Authors: Jihyun Lee, Weipeng Xu, Alexander Richard, Shih-En Wei, Shunsuke Saito, Shaojie Bai, Te-Li Wang, Minhyuk Sung, Tae-Kyun Kim, Jason Saragih
Date: 2025-04-07 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising, Motion Estimation

We present REWIND (Real-Time Egocentric Whole-Body Motion Diffusion), a one-step diffusion model for real-time, high-fidelity human motion estimation from egocentric image inputs. While an existing method for egocentric whole-body (i.e., body and hands) motion estimation is non-real-time and acausal due to diffusion-based iterative motion refinement to capture correlations between body and hand poses, REWIND operates in a fully causal and real-time manner. To enable real-time inference, we introduce (1) cascaded body-hand denoising diffusion, which effectively models the correlation between egocentric body and hand motions in a fast, feed-forward manner, and (2) diffusion distillation, which enables high-quality motion estimation with a single denoising step. Our denoising diffusion model is based on a modified Transformer architecture, designed to causally model output motions while enhancing generalizability to unseen motion lengths. Additionally, REWIND optionally supports identity-conditioned motion estimation when identity prior is available. To this end, we propose a novel identity conditioning method based on a small set of pose exemplars of the target identity, which further enhances motion estimation quality. Through extensive experiments, we demonstrate that REWIND significantly outperforms the existing baselines both with and without exemplar-based identity conditioning.
PyTopo3D: A Python Framework for 3D SIMP-based Topology Optimization
Authors: Jihoon Kim, Namwoo Kang
Date: 2025-04-08 00:00:00

Three-dimensional topology optimization (TO) is a powerful technique in engineering design, but readily usable, open-source implementations remain limited within the popular Python scientific environment. This paper introduces PyTopo3D, a software framework developed to address this gap. PyTopo3D provides a feature-rich tool for 3D TO by implementing the well-established Solid Isotropic Material with Penalization (SIMP) method and an Optimality Criteria (OC) update scheme, adapted and significantly enhanced from the efficient MATLAB code by Liu and Tovar (2014). While building on proven methodology, PyTopo3D's primary contribution is its integration and extension within Python, leveraging sparse matrix operations, optional parallel solvers, and accelerated KD-Tree sensitivity filtering for performance. Crucially, it incorporates functionalities vital for practical engineering workflows, including the direct import of complex design domains and non-design obstacles via STL files, integrated 3D visualization of the optimization process, and direct STL export of optimized geometries for manufacturing or further analysis. PyTopo3D is presented as an accessible, performance-aware tool and citable reference designed to empower engineers, students, and researchers to more easily utilize 3D TO within their existing Python-based workflows.