OpenRaiser/Intern-Atlas huggingface dataset:
An Empirical Validation of Open Source Repository Stability Metrics
Venue: arXiv cs.SE 2025
Year: 2025

Over the past few decades, open source software has been continuously integrated into software supply chains worldwide, drastically increasing reliance and dependence. Because of the role this software plays, it is important to understand ways to measure and promote its stability and potential for sustainability. Recent work proposed the use of control theory to understand repository stability and evaluate repositories' ability to return to equilibrium after a disturbance such as the introduction of a new feature request, a spike in bug reports, or even the influx or departure of contributors. This approach leverages commit frequency patterns, issue resolution rate, pull request merge rate, and community activity engagement to provide a Composite Stability Index (CSI). While this framework has theoretical foundations, there is no empirical validation of the CSI in practice. In this paper, we present the first empirical validation of the proposed CSI by experimenting with 100 highly ranked GitHub repositories. Our results suggest that (1) sampling weekly commit frequency pattern instead of daily is a more feasible measure of commit frequency stability across repositories and (2) improved statistical inferences (swapping mean with median), particularly with ascertaining resolution and review times in issues and pull request, improves the overall issue and pull request stability index. Drawing on our empirical dataset, we also derive data-driven half-width parameters that better align stability scores with real project behavior. These findings both confirm the viability of a control-theoretic lens on open-source health and provide concrete, evidence-backed applications for real-world project monitoring tools.
MaRGen: Multi-Agent LLM Approach for Self-Directed Market Research and Analysis
Venue: arXiv cs.CL 2025
Year: 2025

We present an autonomous framework that leverages Large Language Models (LLMs) to automate end-to-end business analysis and market report generation. At its core, the system employs specialized agents - Researcher, Reviewer, Writer, and Retriever - that collaborate to analyze data and produce comprehensive reports. These agents learn from real professional consultants' presentation materials at Amazon through in-context learning to replicate professional analytical methodologies. The framework executes a multi-step process: querying databases, analyzing data, generating insights, creating visualizations, and composing market reports. We also introduce a novel LLM-based evaluation system for assessing report quality, which shows alignment with expert human evaluations. Building on these evaluations, we implement an iterative improvement mechanism that optimizes report quality through automated review cycles. Experimental results show that report quality can be improved by both automated review cycles and consultants' unstructured knowledge. In experimental validation, our framework generates detailed 6-page reports in 7 minutes at a cost of approximately \$1. Our work could be an important step to automatically create affordable market insights.
Deterministic Fault-Tolerant Local Load Balancing and its Applications against Adaptive Adversaries
Venue: arXiv cs.DC 2025
Year: 2025

Load balancing is among the basic primitives in distributed computing. In this paper, we consider this problem when executed locally on a network with nodes prone to failures. We show that there exist lightweight network topologies that are immune to message delivery failures incurred by (at most) a constant fraction of all nodes. More precisely, we design a novel deterministic fault-tolerant local load balancing (LLB) algorithm, which, similarly to their classical counterparts working in fault-free networks, has a relatively simple structure and guarantees exponentially fast convergence to the average value despite crash and omission failures. As the second part of our contribution, we show three applications of the newly developed fault-tolerant local load balancing protocol. We give a randomized consensus algorithm, working against $t < n / 3$ crash failures, that improves over the best-known consensus solution by Hajiaghayi et al. with respect to communication complexity, yet with an arguable simpler technique of combining a randomly and locally selected virtual communication graph with a deterministic fault-tolerant local load balancing on this graph. We also give a new solution for consensus for networks with omission failures. Our solution works against $t < \frac{n}{C\log{n} (\log\log n)^2}$ omissions, for some constant $C$, is nearly optimal in terms of time complexity, but most notably -- it has communication complexity $O((t^2 + n)\text{ polylog } {n})$, matching, within a polylogarithmic factor, the lower bound by Abraham et. al. with respect to both terms depending on $t$ and $n$. Ours is the first algorithm in the literature that is simultaneously nearly optimal, in terms of $n,t$, with respect to both complexity measures, against the adaptive omission-causing adversary.
ReMu: Reconstructing Multi-layer 3D Clothed Human from Image Layers
Venue: arXiv cs.GR 2025
Year: 2025

The reconstruction of multi-layer 3D garments typically requires expensive multi-view capture setups and specialized 3D editing efforts. To support the creation of life-like clothed human avatars, we introduce ReMu for reconstructing multi-layer clothed humans in a new setup, Image Layers, which captures a subject wearing different layers of clothing with a single RGB camera. To reconstruct physically plausible multi-layer 3D garments, a unified 3D representation is necessary to model these garments in a layered manner. Thus, we first reconstruct and align each garment layer in a shared coordinate system defined by the canonical body pose. Afterwards, we introduce a collision-aware optimization process to address interpenetration and further refine the garment boundaries leveraging implicit neural fields. It is worth noting that our method is template-free and category-agnostic, which enables the reconstruction of 3D garments in diverse clothing styles. Through our experiments, we show that our method reconstructs nearly penetration-free 3D clothed humans and achieves competitive performance compared to category-specific methods. Project page: https://eth-ait.github.io/ReMu/
Open-Attribute Recognition for Person Retrieval: Finding People Through Distinctive and Novel Attributes
Venue: arXiv cs.CV 2025
Year: 2025

Pedestrian Attribute Recognition (PAR) plays a crucial role in various vision tasks such as person retrieval and identification. Most existing attribute-based retrieval methods operate under the closed-set assumption that all attribute classes are consistently available during both training and inference. However, this assumption limits their applicability in real-world scenarios where novel attributes may emerge. Moreover, predefined attributes in benchmark datasets are often generic and shared across individuals, making them less discriminative for retrieving the target person. To address these challenges, we propose the Open-Attribute Recognition for Person Retrieval (OAPR) task, which aims to retrieve individuals based on attribute cues, regardless of whether those attributes were seen during training. To support this task, we introduce a novel framework designed to learn generalizable body part representations that cover a broad range of attribute categories. Furthermore, we reconstruct four widely used datasets for open-attribute recognition. Comprehensive experiments on these datasets demonstrate the necessity of the OAPR task and the effectiveness of our framework. The source code and pre-trained models will be publicly available upon publication.
Quenched large deviations for Monte Carlo integration with Coulomb gases
Venue: arXiv cs.LG 2025
Year: 2025

Gibbs measures, such as Coulomb gases, are popular in modelling systems of interacting particles. Recently, we proposed to use Gibbs measures as randomized numerical integration algorithms with respect to a target measure $π$ on $\mathbb R^d$, following the heuristics that repulsiveness between particles should help reduce integration errors. A major issue in this approach is to tune the interaction kernel and confining potential of the Gibbs measure, so that the equilibrium measure of the system is the target distribution $π$. Doing so usually requires another Monte Carlo approximation of the \emph{potential}, i.e. the integral of the interaction kernel with respect to $π$. Using the methodology of large deviations from Garcia--Zelada (2019), we show that a random approximation of the potential preserves the fast large deviation principle that guarantees the proposed integration algorithm to outperform independent or Markov quadratures. For non-singular interaction kernels, we make minimal assumptions on this random approximation, which can be the result of a computationally cheap Monte Carlo preprocessing. For the Coulomb interaction kernel, we need the approximation to be based on another Gibbs measure, and we prove in passing a control on the uniform convergence of the approximation of the potential.
Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search
Venue: arXiv cs.DB 2025
Year: 2025

Hybrid search, the integration of lexical and semantic retrieval, has become a cornerstone of modern information retrieval systems, driven by demanding applications like Retrieval-Augmented Generation (RAG). The architectural design space for these systems is vast and complex, yet a systematic understanding of the trade-offs among their core components -- retrieval paradigms, combination schemes, and re-ranking methods -- is lacking. To address this, and informed by our experience building the Infinity open-source database, we present the first experimental analysis of advanced hybrid search architectures. Our framework integrates four retrieval paradigms -- Full-Text Search (FTS), Sparse Vector Search (SVS), Dense Vector Search (DVS), and Tensor Search (TenS) -- and evaluates their combinations and re-ranking strategies across 11 real-world datasets. Our results reveal three key findings: (1) A "weakest link" phenomenon, where a weak path can substantially degrade overall accuracy, highlighting the need for path-wise quality assessment before fusion. (2) A data-driven map of performance trade-offs, demonstrating that optimal configurations depend heavily on resource constraints and data characteristics, precluding a one-size-fits-all solution. (3) The identification of Tensor-based Re-ranking Fusion (TRF) as a high-efficacy alternative to mainstream fusion methods, offering the semantic power of tensor search at a fraction of the computational and memory cost. Our findings offer concrete guidelines for designing adaptive, scalable hybrid search systems and identify key directions for future research.
Artificial Intelligence and Misinformation in Art: Can Vision Language Models Judge the Hand or the Machine Behind the Canvas?
Venue: arXiv cs.CY 2025
Year: 2025

The attribution of artworks in general and of paintings in particular has always been an issue in art. The advent of powerful artificial intelligence models that can generate and analyze images creates new challenges for painting attribution. On the one hand, AI models can create images that mimic the style of a painter, which can be incorrectly attributed, for example, by other AI models. On the other hand, AI models may not be able to correctly identify the artist for real paintings, inducing users to incorrectly attribute paintings. In this paper, both problems are experimentally studied using state-of-the-art AI models for image generation and analysis on a large dataset with close to 40,000 paintings from 128 artists. The results show that vision language models have limited capabilities to: 1) perform canvas attribution and 2) to identify AI generated images. As users increasingly rely on queries to AI models to get information, these results show the need to improve the capabilities of VLMs to reliably perform artist attribution and detection of AI generated images to prevent the spread of incorrect information.
Cryptocurrency Price Forecasting Using Machine Learning: Building Intelligent Financial Prediction Models
Venue: arXiv cs.LG 2025
Year: 2025

Cryptocurrency markets are experiencing rapid growth, but this expansion comes with significant challenges, particularly in predicting cryptocurrency prices for traders in the U.S. In this study, we explore how deep learning and machine learning models can be used to forecast the closing prices of the XRP/USDT trading pair. While many existing cryptocurrency prediction models focus solely on price and volume patterns, they often overlook market liquidity, a crucial factor in price predictability. To address this, we introduce two important liquidity proxy metrics: the Volume-To-Volatility Ratio (VVR) and the Volume-Weighted Average Price (VWAP). These metrics provide a clearer understanding of market stability and liquidity, ultimately enhancing the accuracy of our price predictions. We developed four machine learning models, Linear Regression, Random Forest, XGBoost, and LSTM neural networks, using historical data without incorporating the liquidity proxy metrics, and evaluated their performance. We then retrained the models, including the liquidity proxy metrics, and reassessed their performance. In both cases (with and without the liquidity proxies), the LSTM model consistently outperformed the others. These results underscore the importance of considering market liquidity when predicting cryptocurrency closing prices. Therefore, incorporating these liquidity metrics is essential for more accurate forecasting models. Our findings offer valuable insights for traders and developers seeking to create smarter and more risk-aware strategies in the U.S. digital assets market.
Hyperspectral Image Recovery Constrained by Multi-Granularity Non-Local Self-Similarity Priors
Venue: arXiv cs.CV 2025
Year: 2025

Hyperspectral image (HSI) recovery, as an upstream image processing task, holds significant importance for downstream tasks such as classification, segmentation, and detection. In recent years, HSI recovery methods based on non-local prior representations have demonstrated outstanding performance. However, these methods employ a fixed-format factor to represent the non-local self-similarity tensor groups, making them unable to adapt to diverse missing scenarios. To address this issue, we introduce the concept of granularity in tensor decomposition for the first time and propose an HSI recovery model constrained by multi-granularity non-local self-similarity priors. Specifically, the proposed model alternately performs coarse-grained decomposition and fine-grained decomposition on the non-local self-similarity tensor groups. Among them, the coarse-grained decomposition builds upon Tucker tensor decomposition, which extracts global structural information of the image by performing singular value shrinkage on the mode-unfolded matrices. The fine-grained decomposition employs the FCTN decomposition, capturing local detail information through modeling pairwise correlations among factor tensors. This architectural approach achieves a unified representation of global, local, and non-local priors for HSIs. Experimental results demonstrate that the model has strong applicability and exhibits outstanding recovery effects in various types of missing scenes such as pixels and stripes.
Nakamoto Consensus from Multiple Resources
Venue: arXiv cs.CR 2025
Year: 2025

The blocks in the Bitcoin blockchain record the amount of work W that went into creating them through proofs of work. When honest parties control a majority of the work, consensus is achieved by picking the chain with the highest recorded weight. Resources other than work have been considered to secure such longest-chain blockchains. In Chia, blocks record the amount of space S (via a proof of space) and sequential computational steps V (via a VDF). In this paper, we ask what weight functions Γ(S,V,W) (that assign a weight to a block as a function of the recorded space, speed, and work) are secure in the sense that whenever the weight of the resources controlled by honest parties is larger than the weight of adversarial parties, the blockchain is secure against private double-spending attacks. We completely classify such functions in an idealized "continuous" model: Γ(S,V,W) is secure against private double-spending attacks if and only if it is homogeneous of degree one in the timed resources V and W, i.e., αΓ(S,V,W)=Γ(S,αV, αW). This includes Bitcoin rule Γ(S,V,W)=W and Chia rule Γ(S,V,W) = SV. In a more realistic model where blocks are created at discrete time-points, one additionally needs some mild assumptions on the dependency on S (basically, the weight should not grow too much if S is slightly increased, say linear as in Chia). Our classification is more general and allows various instantiations of the same resource. It provides a powerful tool for designing new longest-chain blockchains. E.g., consider combining different PoWs to counter centralization, say the Bitcoin PoW W_1 and a memory-hard PoW W_2. Previous work suggested to use W_1+W_2 as weight. Our results show that using {\sqrt}(W_1){\cdot}{\sqrt}(W_2), {\min}{W_1,W_2} are also secure, and we argue that in practice these are much better choices.
HT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokens
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning has achieved remarkable success in modeling sequential data, including event sequences, temporal point processes, and irregular time series. Recently, transformers have largely replaced recurrent networks in these tasks. However, transformers often underperform RNNs in classification tasks where the objective is to predict future targets. The reason behind this performance gap remains largely unexplored. In this paper, we identify a key limitation of transformers: the absence of a single state vector that provides a compact and effective representation of the entire sequence. Additionally, we show that contrastive pretraining of embedding vectors fails to capture local context, which is crucial for accurate prediction. To address these challenges, we introduce history tokens, a novel concept that facilitates the accumulation of historical information during next-token prediction pretraining. Our approach significantly improves transformer-based models, achieving impressive results in finance, e-commerce, and healthcare tasks. The code is publicly available on GitHub.
PESTO: Real-Time Pitch Estimation with Self-supervised Transposition-equivariant Objective
Venue: arXiv cs.SD 2025
Year: 2025

In this paper, we introduce PESTO, a self-supervised learning approach for single-pitch estimation using a Siamese architecture. Our model processes individual frames of a Variable-$Q$ Transform (VQT) and predicts pitch distributions. The neural network is designed to be equivariant to translations, notably thanks to a Toeplitz fully-connected layer. In addition, we construct pitch-shifted pairs by translating and cropping the VQT frames and train our model with a novel class-based transposition-equivariant objective, eliminating the need for annotated data. Thanks to this architecture and training objective, our model achieves remarkable performances while being very lightweight ($130$k parameters). Evaluations on music and speech datasets (MIR-1K, MDB-stem-synth, and PTDB) demonstrate that PESTO not only outperforms self-supervised baselines but also competes with supervised methods, exhibiting superior cross-dataset generalization. Finally, we enhance PESTO's practical utility by developing a streamable VQT implementation using cached convolutions. Combined with our model's low latency (less than 10 ms) and minimal parameter count, this makes PESTO particularly suitable for real-time applications.
WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning
Venue: arXiv cs.AI 2025
Year: 2025

Planning collision-free paths for a large group of agents is a challenging problem with numerous real-world applications. While recent advances in Multi-Agent Path Finding (MAPF) have shown promising progress, standard MAPF algorithms rely on simplified kinodynamic models, preventing agents from directly following the generated MAPF plan. To bridge this gap, we propose kinodynamic Temporal Plan Graph Planning (kTPG), a multi-agent speed optimization algorithm that efficiently refines a MAPF plan into a kinodynamically feasible plan while accounting for uncertainties and preserving collision-freeness. Building on kTPG, we propose Windowed kTPG (WinkTPG), a MAPF execution framework that incrementally refines MAPF plans using a window-based mechanism, dynamically incorporating agent information during execution to reduce uncertainty. Experiments show that WinkTPG can generate speed profiles for up to 1,000 agents in 1 second and improves solution quality by up to 51.7% over existing MAPF execution methods.
Frequency-Constrained Learning for Long-Term Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Many real-world time series exhibit strong periodic structures arising from physical laws, human routines, or seasonal cycles. However, modern deep forecasting models often fail to capture these recurring patterns due to spectral bias and a lack of frequency-aware inductive priors. Motivated by this gap, we propose a simple yet effective method that enhances long-term forecasting by explicitly modeling periodicity through spectral initialization and frequency-constrained optimization. Specifically, we extract dominant low-frequency components via Fast Fourier Transform (FFT)-guided coordinate descent, initialize sinusoidal embeddings with these components, and employ a two-speed learning schedule to preserve meaningful frequency structure during training. Our approach is model-agnostic and integrates seamlessly into existing Transformer-based architectures. Extensive experiments across diverse real-world benchmarks demonstrate consistent performance gains--particularly at long horizons--highlighting the benefits of injecting spectral priors into deep temporal models for robust and interpretable long-range forecasting. Moreover, on synthetic data, our method accurately recovers ground-truth frequencies, further validating its interpretability and effectiveness in capturing latent periodic patterns.