OpenRaiser/Intern-Atlas huggingface dataset:
ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Reliable 3D object perception is essential in autonomous driving. Owing to its sensing capabilities in all weather conditions, 4D radar has recently received much attention. However, compared to LiDAR, 4D radar provides much sparser point cloud. In this paper, we propose a 3D object detection method, termed ZFusion, which fuses 4D radar and vision modality. As the core of ZFusion, our proposed FP-DDCA (Feature Pyramid-Double Deformable Cross Attention) fuser complements the (sparse) radar information and (dense) vision information, effectively. Specifically, with a feature-pyramid structure, the FP-DDCA fuser packs Transformer blocks to interactively fuse multi-modal features at different scales, thus enhancing perception accuracy. In addition, we utilize the Depth-Context-Split view transformation module due to the physical properties of 4D radar. Considering that 4D radar has a much lower cost than LiDAR, ZFusion is an attractive alternative to LiDAR-based methods. In typical traffic scenarios like the VoD (View-of-Delft) dataset, experiments show that with reasonable inference speed, ZFusion achieved the state-of-the-art mAP (mean average precision) in the region of interest, while having competitive mAP in the entire area compared to the baseline methods, which demonstrates performance close to LiDAR and greatly outperforms those camera-only methods.
Semiflows, Home Spaces, and Home States, Applications to the Analysis of Parameterized Petri Nets
Venue: arXiv cs.FL 2025
Year: 2025

After rapidly recalling basic notations relatively to semiflows and Petri nets, we define F, the set of semiflows over Z that we associate with a specific class of invariants. We then focus on F+, the set of semiflows with non-negative coordinates which are important to study the behavior of a Petri net. We recall known behavioral properties attached to semiflows in F+ that we associate with two sets of bounds regarding boundedness then liveness. We recall the notions of home states and home spaces for which we regrouped old and new properties. We introduce a new result on the decidability of liveness under the existence of a home state. The notions of minimality of semiflows and minimality of supports allow us to define generating sets that are particularly critical to develop an effective analysis of invariants and behavioral properties of Petri nets such as boundedness or even liveness. We also recall three known decomposition theorems considering N, Q+, and Q respectively where the decomposition over N is being improved with a necessary and sufficient condition. We use the notion the notion of generating sets to show that extremums linked to the set of bounds mentioned here above, are indeed computable by providing their values. Two related Petri net modeling arithmetic operations (one of which represents an Euclidean division) illustrate how results on semiflows and home spaces can be methodically used in efficiently analyzing the liveness of the parameterized model and underlining the efficiency brought by the combination of these results.
HumanDreamer-X: Photorealistic Single-image Human Avatars Reconstruction via Gaussian Restoration
Venue: arXiv cs.CV 2025
Year: 2025

Single-image human reconstruction is vital for digital human modeling applications but remains an extremely challenging task. Current approaches rely on generative models to synthesize multi-view images for subsequent 3D reconstruction and animation. However, directly generating multiple views from a single human image suffers from geometric inconsistencies, resulting in issues like fragmented or blurred limbs in the reconstructed models. To tackle these limitations, we introduce \textbf{HumanDreamer-X}, a novel framework that integrates multi-view human generation and reconstruction into a unified pipeline, which significantly enhances the geometric consistency and visual fidelity of the reconstructed 3D models. In this framework, 3D Gaussian Splatting serves as an explicit 3D representation to provide initial geometry and appearance priority. Building upon this foundation, \textbf{HumanFixer} is trained to restore 3DGS renderings, which guarantee photorealistic results. Furthermore, we delve into the inherent challenges associated with attention mechanisms in multi-view human generation, and propose an attention modulation strategy that effectively enhances geometric details identity consistency across multi-view. Experimental results demonstrate that our approach markedly improves generation and reconstruction PSNR quality metrics by 16.45% and 12.65%, respectively, achieving a PSNR of up to 25.62 dB, while also showing generalization capabilities on in-the-wild data and applicability to various human reconstruction backbone models.
EnrichIndex: Using LLMs to Enrich Retrieval Indices Offline
Venue: arXiv cs.CL 2025
Year: 2025

Existing information retrieval systems excel in cases where the language of target documents closely matches that of the user query. However, real-world retrieval systems are often required to implicitly reason whether a document is relevant. For example, when retrieving technical texts or tables, their relevance to the user query may be implied through a particular jargon or structure, rather than explicitly expressed in their content. Large language models (LLMs) hold great potential in identifying such implied relevance by leveraging their reasoning skills. Nevertheless, current LLM-augmented retrieval is hindered by high latency and computation cost, as the LLM typically computes the query-document relevance online, for every query anew. To tackle this issue we introduce EnrichIndex, a retrieval approach which instead uses the LLM offline to build semantically-enriched retrieval indices, by performing a single pass over all documents in the retrieval corpus once during ingestion time. Furthermore, the semantically-enriched indices can complement existing online retrieval approaches, boosting the performance of LLM re-rankers. We evaluated EnrichIndex on five retrieval tasks, involving passages and tables, and found that it outperforms strong online LLM-based retrieval systems, with an average improvement of 11.7 points in recall @ 10 and 10.6 points in NDCG @ 10 compared to strong baselines. In terms of online calls to the LLM, it processes 293.3 times fewer tokens which greatly reduces the online latency and cost. Overall, EnrichIndex is an effective way to build better retrieval indices offline by leveraging the strong reasoning skills of LLMs.
APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
Venue: arXiv cs.CL 2025
Year: 2025

Training effective AI agents for multi-turn interactions requires high-quality data that captures realistic human-agent dynamics, yet such data is scarce and expensive to collect manually. We introduce APIGen-MT, a two-phase framework that generates verifiable and diverse multi-turn agent data. In the first phase, our agentic pipeline produces detailed task blueprints with ground-truth actions, leveraging a committee of LLM reviewers and iterative feedback loops. These blueprints are then transformed into complete interaction trajectories through simulated human-agent interplay. We train a family of models -- the xLAM-2-fc-r series with sizes ranging from 1B to 70B parameters. Our models outperform frontier models such as GPT-4o and Claude 3.5 on $τ$-bench and BFCL benchmarks, with the smaller models surpassing their larger counterparts, particularly in multi-turn settings, while maintaining superior consistency across multiple trials. Comprehensive experiments demonstrate that our verified blueprint-to-details approach yields high-quality training data, enabling the development of more reliable, efficient, and capable agents. We open-source 5K synthetic data trajectories and the trained xLAM-2-fc-r models to advance research in AI agents. Models at https://huggingface.co/collections/Salesforce/xlam-2-67ef5be12949d8dcdae354c4; Dataset at https://huggingface.co/datasets/Salesforce/APIGen-MT-5k and Website at https://apigen-mt.github.io
Quantifying the uncertainty of model-based synthetic image quality metrics
Venue: arXiv cs.CV 2025
Year: 2025

The quality of synthetically generated images (e.g. those produced by diffusion models) are often evaluated using information about image contents encoded by pretrained auxiliary models. For example, the Fréchet Inception Distance (FID) uses embeddings from an InceptionV3 model pretrained to classify ImageNet. The effectiveness of this feature embedding model has considerable impact on the trustworthiness of the calculated metric (affecting its suitability in several domains, including medical imaging). Here, uncertainty quantification (UQ) is used to provide a heuristic measure of the trustworthiness of the feature embedding model and an FID-like metric called the Fréchet Autoencoder Distance (FAED). We apply Monte Carlo dropout to a feature embedding model (convolutional autoencoder) to model the uncertainty in its embeddings. The distribution of embeddings for each input are then used to compute a distribution of FAED values. We express uncertainty as the predictive variance of the embeddings as well as the standard deviation of the computed FAED values. We find that their magnitude correlates with the extent to which the inputs are out-of-distribution to the model's training data, providing some validation of its ability to assess the trustworthiness of the FAED.
Improving Interoperability in Scientific Computing via MaRDI Open Interfaces
Venue: arXiv cs.MS 2025
Year: 2025

MaRDI Open Interfaces is a software project aimed at improving reuse and interoperability in Scientific Computing by alleviating the difficulties of crossing boundaries between different programming languages, in which numerical packages are usually implemented, and of switching between multiple implementations of the same mathematical problem. The software consists of a set of formal interface specifications for common Scientific Computing tasks, as well as a set of loosely coupled libraries that facilitate implementing these interfaces or adapting existing implementations for multiple programming languages and handle data marshalling automatically without sacrificing performance, enabling users to use different implementations without significant code efforts. The software has high reuse potential due to aim to solve general numerical problems.
Do Larger Language Models Generalize Better? A Scaling Law for Implicit Reasoning at Pretraining Time
Venue: arXiv cs.AI 2025
Year: 2025

Reasoning is an integral part of many tasks performed by language models (LMs). However, the effects of scaling model sizes and data on reasoning abilities at pretraining time remain understudied. To rigorously investigate this problem, we pretrain LMs from scratch on a synthetic implicit multihop reasoning environment designed to closely replicate the structure and distribution of real-world large-scale knowledge graphs. We then assess the LMs' ability to complete the missing edges in the graph, which requires multi-hop reasoning that can be viewed as a simplification of implicit reasoning during real-world pretraining. Interestingly, we observe that overparameterization can impair the implicit reasoning performance due to excessive memorization. We investigate different factors that affect the loss curve when scaling different components of the knowledge graph, model size, and training steps. To predict the optimal model size for a specific knowledge graph, we find an empirical scaling law that shows optimal-sized LMs can approximately reason over 0.008 bit information per parameter. This work shows counterintuitive effects of model size scaling and provides new insights into the relationship between scaling and reasoning in LLMs.
An Algebraic Geometry Approach to Viewing Graph Solvability
Venue: arXiv cs.CV 2025
Year: 2025

The concept of viewing graph solvability has gained significant interest in the context of structure-from-motion. A viewing graph is a mathematical structure where nodes are associated to cameras and edges represent the epipolar geometry connecting overlapping views. Solvability studies under which conditions the cameras are uniquely determined by the graph. In this paper we propose a novel framework for analyzing solvability problems based on Algebraic Geometry, demonstrating its potential in understanding structure-from-motion graphs and proving a conjecture that was previously proposed.
Falafels: A tool for Estimating Federated Learning Energy Consumption via Discrete Simulation
Venue: arXiv cs.DC 2025
Year: 2025

The growth in computational power and data hungriness of Machine Learning has led to an important shift of research efforts towards the distribution of ML models on multiple machines, leading in even more powerful models. However, there exists many Distributed Artificial Intelligence paradigms and for each of them the platform and algorithm configurations play an important role in terms of training time and energy consumption. Many mathematical models and frameworks can respectively predict and benchmark this energy consumption, nonetheless, the former lacks of realism and extensibility while the latter suffers high run-times and actual power consumption. In this article, we introduce Falafels, an extensible tool that predicts the energy consumption and training time of -but not limited to -Federated Learning systems. It distinguishes itself with its discrete-simulatorbased solution leading to nearly instant run-time and fast development of new algorithms. Furthermore, we show this approach permits the use of an evolutionary algorithm providing the ability to optimize the system configuration for a given machine learning workload.
High-Performance Parallelization of Dijkstra's Algorithm Using MPI and CUDA
Venue: arXiv cs.DC 2025
Year: 2025

This paper investigates the parallelization of Dijkstra's algorithm for computing the shortest paths in large-scale graphs using MPI and CUDA. The primary hypothesis is that by leveraging parallel computing, the computation time can be significantly reduced compared to a serial implementation. To validate this, I implemented three versions of the algorithm: a serial version, an MPI-based parallel version, and a CUDA-based parallel version. Experimental results demonstrate that the MPI implementation achieves over 5x speedup, while the CUDA implementation attains more than 10x improvement relative to the serial benchmark. However, the study also reveals inherent challenges in parallelizing Dijkstra's algorithm, including its sequential logic and significant synchronization overhead. Furthermore, the use of an adjacency matrix as the data structure is examined, highlighting its impact on memory consumption and performance in both dense and sparse graphs.
Semi-supervised learning for marine anomaly detection on board satellites
Venue: arXiv cs.CV 2025
Year: 2025

Aquatic bodies face numerous environmental threats caused by several marine anomalies. Marine debris can devastate habitats and endanger marine life through entanglement, while harmful algal blooms can produce toxins that negatively affect marine ecosystems. Additionally, ships may discharge oil or engage in illegal and overfishing activities, causing further harm. These marine anomalies can be identified by applying trained deep learning (DL) models on multispectral satellite imagery. Furthermore, the detection of other anomalies, such as clouds, could be beneficial in filtering out irrelevant images. However, DL models often require a large volume of labeled data for training, which can be both costly and time-consuming, particularly for marine anomaly detection where expert annotation is needed. A potential solution is the use of semi-supervised learning methods, which can also utilize unlabeled data. In this project, we implement and study the performance of FixMatch for Semantic Segmentation, a semi-supervised algorithm for semantic segmentation. Firstly, we found that semi-supervised models perform best with a high confidence threshold of 0.9 when there is a limited amount of labeled data. Secondly, we compare the performance of semi-supervised models with fully-supervised models under varying amounts of labeled data. Our findings suggest that semi-supervised models outperform fully-supervised models with limited labeled data, while fully-supervised models have a slightly better performance with larger volumes of labeled data. We propose two hypotheses to explain why fully-supervised models surpass semi-supervised ones when a high volume of labeled data is used. All of our experiments were conducted using a U-Net model architecture with a limited number of parameters to ensure compatibility with space-rated hardware.
Scalable heliostat surface predictions from focal spots: Sim-to-Real transfer of inverse Deep Learning Raytracing
Venue: arXiv cs.CV 2025
Year: 2025

Concentrating Solar Power (CSP) plants are a key technology in the transition toward sustainable energy. A critical factor for their safe and efficient operation is the distribution of concentrated solar flux on the receiver. However, flux distributions from individual heliostats are sensitive to surface imperfections. Measuring these surfaces across many heliostats remains impractical in real-world deployments. As a result, control systems often assume idealized heliostat surfaces, leading to suboptimal performance and potential safety risks. To address this, inverse Deep Learning Raytracing (iDLR) has been introduced as a novel method for inferring heliostat surface profiles from target images recorded during standard calibration procedures. In this work, we present the first successful Sim-to-Real transfer of iDLR, enabling accurate surface predictions directly from real-world target images. We evaluate our method on 63 heliostats under real operational conditions. iDLR surface predictions achieve a median mean absolute error (MAE) of 0.17 mm and show good agreement with deflectometry ground truth in 84% of cases. When used in raytracing simulations, it enables flux density predictions with a mean accuracy of 90% compared to deflectometry over our dataset, and outperforms the commonly used ideal heliostat surface assumption by 26%. We tested this approach in a challenging double-extrapolation scenario-involving unseen sun positions and receiver projection-and found that iDLR maintains high predictive accuracy, highlighting its generalization capabilities. Our results demonstrate that iDLR is a scalable, automated, and cost-effective solution for integrating realistic heliostat surface models into digital twins. This opens the door to improved flux control, more precise performance modeling, and ultimately, enhanced efficiency and safety in future CSP plants.
RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm
Venue: arXiv cs.LG 2025
Year: 2025

Post-training Quantization (PTQ) has become a widely used technique for improving inference efficiency of large language models (LLMs). However, existing PTQ methods generally suffer from crucial limitations such as heavy calibration data requirements and inflexible choice of target number of bits. In this paper, we propose RaanA, a unified PTQ framework that overcomes these challenges by introducing two novel components: 1) RaBitQ-H, a variant of a randomized vector quantization method RaBitQ, designed for fast, accurate, and highly efficient quantization; and 2) AllocateBits, an algorithm that optimally allocates bit-widths across layers based on their quantization sensitivity. RaanA achieves competitive performance with state-of-the-art quantization methods while being extremely fast, requiring minimal calibration data, and enabling flexible bit allocation. Extensive experiments demonstrate RaanA's efficacy in balancing efficiency and accuracy. The code is publicly available at https://github.com/FFTYYY/RaanA .
A Hybrid Reinforcement Learning Framework for Hard Latency Constrained Resource Scheduling
Venue: arXiv cs.NI 2025
Year: 2025

In the forthcoming 6G era, extend reality (XR) has been regarded as an emerging application for ultra-reliable and low latency communications (URLLC) with new traffic characteristics and more stringent requirements. In addition to the quasi-periodical traffic in XR, burst traffic with both large frame size and random arrivals in some real world low latency communication scenarios has become the leading cause of network congestion or even collapse, and there still lacks an efficient algorithm for the resource scheduling problem under burst traffic with hard latency constraints. We propose a novel hybrid reinforcement learning framework for resource scheduling with hard latency constraints (HRL-RSHLC), which reuses polices from both old policies learned under other similar environments and domain-knowledge-based (DK) policies constructed using expert knowledge to improve the performance. The joint optimization of the policy reuse probabilities and new policy is formulated as an Markov Decision Problem (MDP), which maximizes the hard-latency constrained effective throughput (HLC-ET) of users. We prove that the proposed HRL-RSHLC can converge to KKT points with an arbitrary initial point. Simulations show that HRL-RSHLC can achieve superior performance with faster convergence speed compared to baseline algorithms.
Artificial Geographically Weighted Neural Network: A Novel Framework for Spatial Analysis with Geographically Weighted Layers
Venue: arXiv cs.LG 2025
Year: 2025

Geographically Weighted Regression (GWR) is a widely recognized technique for modeling spatial heterogeneity. However, it is commonly assumed that the relationships between dependent and independent variables are linear. To overcome this limitation, we propose an Artificial Geographically Weighted Neural Network (AGWNN), a novel framework that integrates geographically weighted techniques with neural networks to capture complex nonlinear spatial relationships. Central to this framework is the Geographically Weighted Layer (GWL), a specialized component designed to encode spatial heterogeneity within the neural network architecture. To rigorously evaluate the performance of AGWNN, we conducted comprehensive experiments using both simulated datasets and real-world case studies. Our results demonstrate that AGWNN significantly outperforms traditional GWR and standard Artificial Neural Networks (ANNs) in terms of model fitting accuracy. Notably, AGWNN excels in modeling intricate nonlinear relationships and effectively identifies complex spatial heterogeneity patterns, offering a robust and versatile tool for advanced spatial analysis.