OpenRaiser/Intern-Atlas huggingface dataset:
ASNN: Learning to Suggest Neural Architectures from Performance Distributions
Venue: arXiv cs.LG 2025
Year: 2025

The architecture of a neural network (NN) plays a critical role in determining its performance. However, there is no general closed-form function that maps between network structure and accuracy, making the process of architecture design largely heuristic or search-based. In this study, we propose the Architecture Suggesting Neural Network (ASNN), a model designed to learn the relationship between NN architecture and its test accuracy, and to suggest improved architectures accordingly. To train ASNN, we constructed datasets using TensorFlow-based models with varying numbers of layers and nodes. Experimental results were collected for both 2-layer and 3-layer architectures across a grid of configurations, each evaluated with 10 repeated trials to account for stochasticity. Accuracy values were treated as inputs, and architectural parameters as outputs. The trained ASNN was then used iteratively to predict architectures that yield higher performance. In both 2-layer and 3-layer cases, ASNN successfully suggested architectures that outperformed the best results found in the original training data. Repeated prediction and retraining cycles led to the discovery of architectures with improved mean test accuracies, demonstrating the model's capacity to generalize the performance-structure relationship. These results suggest that ASNN provides an efficient alternative to random search for architecture optimization, and offers a promising approach toward automating neural network design. "Parts of the manuscript, including text editing and expression refinement, were supported by OpenAI's ChatGPT. All content was reviewed and verified by the authors."
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding
Venue: arXiv cs.CL 2025
Year: 2025

Session history is a common way of recording user interacting behaviors throughout a browsing activity with multiple products. For example, if an user clicks a product webpage and then leaves, it might because there are certain features that don't satisfy the user, which serve as an important indicator of on-the-spot user preferences. However, all prior works fail to capture and model customer intention effectively because insufficient information exploitation and only apparent information like descriptions and titles are used. There is also a lack of data and corresponding benchmark for explicitly modeling intention in E-commerce product purchase sessions. To address these issues, we introduce the concept of an intention tree and propose a dataset curation pipeline. Together, we construct a sibling multimodal benchmark, SessionIntentBench, that evaluates L(V)LMs' capability on understanding inter-session intention shift with four subtasks. With 1,952,177 intention entries, 1,132,145 session intention trajectories, and 13,003,664 available tasks mined using 10,905 sessions, we provide a scalable way to exploit the existing session data for customer intention understanding. We conduct human annotations to collect ground-truth label for a subset of collected data to form an evaluation gold set. Extensive experiments on the annotated data further confirm that current L(V)LMs fail to capture and utilize the intention across the complex session setting. Further analysis show injecting intention enhances LLMs' performances.
Efficient and Fault-Tolerant Memristive Neural Networks with In-Situ Training
Venue: arXiv cs.ET 2025
Year: 2025

Neuromorphic architectures, which incorporate parallel and in-memory processing, are crucial for accelerating artificial neural network (ANN) computations. This work presents a novel memristor-based multi-layer neural network (memristive MLNN) architecture and an efficient in-situ training algorithm. The proposed design performs matrix-vector multiplications, outer products, and weight updates in constant time $\mathcal{O}(1)$, leveraging the inherent parallelism of memristive crossbars. Each synapse is realized using a single memristor, eliminating the need for transistors, and offering enhanced area and energy efficiency. The architecture is evaluated through LTspice simulations on the IRIS, NASA Asteroid, and Breast Cancer Wisconsin datasets, achieving classification accuracies of 98.22\%, 90.43\%, and 98.59\%, respectively. Robustness is assessed by introducing stuck-at-conducting-state faults in randomly selected memristors. The effects of nonlinearity in memristor conductance and a 10\% device variation are also analyzed. The simulation results establish that the network's performance is not affected significantly by faulty memristors, non-linearity, and device variation.
StepFun-Prover Preview: Let's Think and Verify Step by Step
Venue: arXiv cs.AI 2025
Year: 2025

We present StepFun-Prover Preview, a large language model designed for formal theorem proving through tool-integrated reasoning. Using a reinforcement learning pipeline that incorporates tool-based interactions, StepFun-Prover can achieve strong performance in generating Lean 4 proofs with minimal sampling. Our approach enables the model to emulate human-like problem-solving strategies by iteratively refining proofs based on real-time environment feedback. On the miniF2F-test benchmark, StepFun-Prover achieves a pass@1 success rate of $70.0\%$. Beyond advancing benchmark performance, we introduce an end-to-end training framework for developing tool-integrated reasoning models, offering a promising direction for automated theorem proving and Math AI assistant.
Technical Indicator Networks (TINs): An Interpretable Neural Architecture Modernizing Classic al Technical Analysis for Adaptive Algorithmic Trading
Venue: arXiv cs.LG 2025
Year: 2025

Deep neural networks (DNNs) have transformed fields such as computer vision and natural language processing by employing architectures aligned with domain-specific structural patterns. In algorithmic trading, however, there remains a lack of architectures that directly incorporate the logic of traditional technical indicators. This study introduces Technical Indicator Networks (TINs), a structured neural design that reformulates rule-based financial heuristics into trainable and interpretable modules. The architecture preserves the core mathematical definitions of conventional indicators while extending them to multidimensional data and supporting optimization through diverse learning paradigms, including reinforcement learning. Analytical transformations such as averaging, clipping, and ratio computation are expressed as vectorized layer operators, enabling transparent network construction and principled initialization. This formulation retains the clarity and interpretability of classical strategies while allowing adaptive adjustment and data-driven refinement. As a proof of concept, the framework is validated on the Dow Jones Industrial Average constituents using a Moving Average Convergence Divergence (MACD) TIN. Empirical results demonstrate improved risk-adjusted performance relative to traditional indicator-based strategies. Overall, the findings suggest that TINs provide a generalizable foundation for interpretable, adaptive, and extensible learning architectures in structured decision-making domains and indicate substantial commercial potential for upgrading trading platforms with cross-market visibility and enhanced decision-support capabilities.
Strategic Motivators for Ethical AI System Development: An Empirical and Holistic Model
Venue: arXiv cs.SE 2025
Year: 2025

Artificial Intelligence (AI) presents transformative opportunities for industries and society, but its responsible development is essential to prevent unintended consequences. Ethically sound AI systems demand strategic planning, strong governance, and an understanding of the key drivers that promote responsible practices. This study aims to identify and prioritize the motivators that drive the ethical development of AI systems. A Multivocal Literature Review (MLR) and a questionnaire-based survey were conducted to capture current practices in ethical AI. We applied Interpretive Structure Modeling (ISM) to explore the relationships between motivator categories, followed by MICMAC analysis to classify them by their driving and dependence power. Fuzzy TOPSIS was used to rank these motivators by importance. Twenty key motivators were identified and grouped into eight categories: Human Resource, Knowledge Integration, Coordination, Project Administration, Standards, Technology Factor, Stakeholders, and Strategy & Matrices. ISM results showed that 'Human Resource' and 'Coordination' heavily influence other factors. MICMAC analysis placed categories like Human Resource (CA1), Coordination (CA3), Stakeholders (CA7), and Strategy & Matrices (CA8) in the independent cluster, indicating high driving but low dependence power. Fuzzy TOPSIS ranked motivators such as promoting team diversity, establishing AI governance bodies, appointing oversight leaders, and ensuring data privacy as most critical. To support ethical AI adoption, organizations should align their strategies with these motivators and integrate them into their policies, governance models, and development frameworks.
L-MCAT: Unpaired Multimodal Transformer with Contrastive Attention for Label-Efficient Satellite Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

We propose the Lightweight Multimodal Contrastive Attention Transformer (L-MCAT), a novel transformer-based framework for label-efficient remote sensing image classification using unpaired multimodal satellite data. L-MCAT introduces two core innovations: (1) Modality-Spectral Adapters (MSA) that compress high-dimensional sensor inputs into a unified embedding space, and (2) Unpaired Multimodal Attention Alignment (U-MAA), a contrastive self-supervised mechanism integrated into the attention layers to align heterogeneous modalities without pixel-level correspondence or labels. L-MCAT achieves 95.4% overall accuracy on the SEN12MS dataset using only 20 labels per class, outperforming state-of-the-art baselines while using 47x fewer parameters and 23x fewer FLOPs than MCTrans. It maintains over 92% accuracy even under 50% spatial misalignment, demonstrating robustness for real-world deployment. The model trains end-to-end in under 5 hours on a single consumer GPU.
What Language(s) Does Aya-23 Think In? How Multilinguality Affects Internal Language Representations
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) excel at multilingual tasks, yet their internal language processing remains poorly understood. We analyze how Aya-23-8B, a decoder-only LLM trained on balanced multilingual data, handles code-mixed, cloze, and translation tasks compared to predominantly monolingual models like Llama 3 and Chinese-LLaMA-2. Using logit lens and neuron specialization analyses, we find: (1) Aya-23 activates typologically related language representations during translation, unlike English-centric models that rely on a single pivot language; (2) code-mixed neuron activation patterns vary with mixing rates and are shaped more by the base language than the mixed-in one; and (3) Aya-23's languagespecific neurons for code-mixed inputs concentrate in final layers, diverging from prior findings on decoder-only models. Neuron overlap analysis further shows that script similarity and typological relations impact processing across model types. These findings reveal how multilingual training shapes LLM internals and inform future cross-lingual transfer research.
SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integration
Venue: arXiv cs.AI 2025
Year: 2025

Scientific research increasingly relies on specialized computational tools, yet effectively utilizing these tools demands substantial domain expertise. While Large Language Models (LLMs) show promise in tool automation, they struggle to seamlessly integrate and orchestrate multiple tools for complex scientific workflows. Here, we present SciToolAgent, an LLM-powered agent that automates hundreds of scientific tools across biology, chemistry, and materials science. At its core, SciToolAgent leverages a scientific tool knowledge graph that enables intelligent tool selection and execution through graph-based retrieval-augmented generation. The agent also incorporates a comprehensive safety-checking module to ensure responsible and ethical tool usage. Extensive evaluations on a curated benchmark demonstrate that SciToolAgent significantly outperforms existing approaches. Case studies in protein engineering, chemical reactivity prediction, chemical synthesis, and metal-organic framework screening further demonstrate SciToolAgent's capability to automate complex scientific workflows, making advanced research tools accessible to both experts and non-experts.
Talking-to-Build: How LLM-Assisted Interface Shapes Player Performance and Experience in Minecraft
Venue: arXiv cs.HC 2025
Year: 2025

With large language models (LLMs) on the rise, in-game interactions are shifting from rigid commands to natural conversations. However, the impacts of LLMs on player performance and game experience remain underexplored. This work explores LLM's role as a co-builder during gameplay, examining its impact on task performance, usability, and player experience. Using Minecraft as a sandbox, we present an LLM-assisted interface that engages players through natural language, aiming to facilitate creativity and simplify complex gaming commands. We conducted a mixed-methods study with 30 participants, comparing LLM-assisted and command-based interfaces across simple and complex game tasks. Quantitative and qualitative analyses reveal that the LLM-assisted interface significantly improves player performance, engagement, and overall game experience. Additionally, task complexity has a notable effect on player performance and experience across both interfaces. Our findings highlight the potential of LLM-assisted interfaces to revolutionize virtual experiences, emphasizing the importance of balancing intuitiveness with predictability, transparency, and user agency in AI-driven, multimodal gaming environments.
SWIFT: A General Sensitive Weight Identification Framework for Fast Sensor-Transfer Pansharpening
Venue: arXiv cs.CV 2025
Year: 2025

Pansharpening aims to fuse high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) images to generate high-resolution multispectral (HRMS) images. Although deep learning-based methods have achieved promising performance, they generally suffer from severe performance degradation when applied to data from unseen sensors. Adapting these models through full-scale retraining or designing more complex architectures is often prohibitively expensive and impractical for real-world deployment. To address this critical challenge, we propose a fast and general-purpose framework for cross-sensor adaptation, SWIFT (Sensitive Weight Identification for Fast Transfer). Specifically, SWIFT employs an unsupervised sampling strategy based on data manifold structures to balance sample selection while mitigating the bias of traditional Farthest Point Sampling, efficiently selecting only 3\% of the most informative samples from the target domain. This subset is then used to probe a source-domain pre-trained model by analyzing the gradient behavior of its parameters, allowing for the quick identification and subsequent update of only the weight subset most sensitive to the domain shift. As a plug-and-play framework, SWIFT can be applied to various existing pansharpening models. Extensive experiments demonstrate that SWIFT reduces the adaptation time from hours to approximately one minute on a single NVIDIA RTX 4090 GPU. The adapted models not only substantially outperform direct-transfer baselines but also achieve performance competitive with, and in some cases superior to, full retraining, establishing a new state-of-the-art on cross-sensor pansharpening tasks for the WorldView-2 and QuickBird datasets.
A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies
Venue: arXiv cs.DC 2025
Year: 2025

Scientific and data science applications are becoming increasingly complex, with growing computational and memory demands. Modern high performance computing (HPC) systems provide high parallelism and heterogeneity across nodes, devices, and cores. To achieve good performance, effective scheduling and load balancing techniques are essential. Parallel programming frameworks such as OpenMP now offer a variety of advanced scheduling algorithms to support diverse applications and platforms. This creates an instance of the scheduling algorithm selection problem, which involves identifying the most suitable algorithm for a given combination of workload and system characteristics. In this work, we explore learning-based approaches for selecting scheduling algorithms in OpenMP. We propose and evaluate expert-based and reinforcement learning (RL)-based methods, and conduct a detailed performance analysis across six applications and three systems. Our results show that RL methods are capable of learning high-performing scheduling decisions, although they require significant exploration, with the choice of reward function playing a key role. Expert-based methods, in contrast, rely on prior knowledge and involve less exploration, though they may not always identify the optimal algorithm for a specific application-system pair. By combining expert knowledge with RL-based learning, we achieve improved performance and greater adaptability. Overall, this work demonstrates that dynamic selection of scheduling algorithms during execution is both viable and beneficial for OpenMP applications. The approach can also be extended to MPI-based programs, enabling optimization of scheduling decisions across multiple levels of parallelism.
Computational Advantages of Multi-Grade Deep Learning: Convergence Analysis and Performance Insights
Venue: arXiv cs.LG 2025
Year: 2025

Multi-grade deep learning (MGDL) has been shown to significantly outperform the standard single-grade deep learning (SGDL) across various applications. This work aims to investigate the computational advantages of MGDL focusing on its performance in image regression, denoising, and deblurring tasks, and comparing it to SGDL. We establish convergence results for the gradient descent (GD) method applied to these models and provide mathematical insights into MGDL's improved performance. In particular, we demonstrate that MGDL is more robust to the choice of learning rate under GD than SGDL. Furthermore, we analyze the eigenvalue distributions of the Jacobian matrices associated with the iterative schemes arising from the GD iterations, offering an explanation for MGDL's enhanced training stability.
Deterministic Almost-Linear-Time Gomory-Hu Trees
Venue: arXiv cs.DS 2025
Year: 2025

Given an $m$-edge, undirected, weighted graph $G=(V,E,w)$, a Gomory-Hu tree $T$ (Gomory and Hu, 1961) is a tree over the vertex set $V$ such that all-pairs mincuts in $G$ are preserved exactly in $T$. In this article, we give the first almost-optimal $m^{1+o(1)}$-time deterministic algorithm for constructing a Gomory-Hu tree. Prior to our work, the best deterministic algorithm for this problem dated back to the original algorithm of Gomory and Hu that runs in $nm^{1+o(1)}$ time (using current maxflow algorithms). In fact, this is the first almost-linear time deterministic algorithm for even simpler problems, such as finding the $k$-edge-connected components of a graph. Our new result hinges on two separate and novel components that each introduce a distinct set of de-randomization tools of independent interest: - a deterministic reduction from the all-pairs mincuts problem to the single-souce mincuts problem incurring only subpolynomial overhead, and - a deterministic almost-linear time algorithm for the single-source mincuts problem.
Joint Fiber and Free Space Optical Infrastructure Planning for Hybrid Integrated Access and Backhaul Networks
Venue: arXiv cs.NI 2025
Year: 2025

Integrated access and backhaul (IAB) is one of the promising techniques for 5G networks and beyond (6G), in which the same node/hardware is used to provide both backhaul and cellular services in a multi-hop architecture. Due to the sensitivity of the backhaul links with high rate/reliability demands, proper network planning is needed to ensure the IAB network performs with the desired performance levels. In this paper, we study the effect of infrastructure planning and optimization on the coverage of IAB networks. We concentrate on the cases where the fiber connectivity to the nodes is constrained due to cost. Thereby, we study the performance gains and energy efficiency in the presence of free-space optical (FSO) communication links. Our results indicate hybrid fiber/FSO deployments offer substantial cost savings compared to fully fibered networks, suggesting a beneficial trade-off for strategic link deployment while improving the service coverage probability. As we show, with proper network planning, the service coverage, energy efficiency, and cost efficiency can be improved.
ModalFormer: Multimodal Transformer for Low-Light Image Enhancement
Venue: arXiv cs.CV 2025
Year: 2025

Low-light image enhancement (LLIE) is a fundamental yet challenging task due to the presence of noise, loss of detail, and poor contrast in images captured under insufficient lighting conditions. Recent methods often rely solely on pixel-level transformations of RGB images, neglecting the rich contextual information available from multiple visual modalities. In this paper, we present ModalFormer, the first large-scale multimodal framework for LLIE that fully exploits nine auxiliary modalities to achieve state-of-the-art performance. Our model comprises two main components: a Cross-modal Transformer (CM-T) designed to restore corrupted images while seamlessly integrating multimodal information, and multiple auxiliary subnetworks dedicated to multimodal feature reconstruction. Central to the CM-T is our novel Cross-modal Multi-headed Self-Attention mechanism (CM-MSA), which effectively fuses RGB data with modality-specific features--including deep feature embeddings, segmentation information, geometric cues, and color information--to generate information-rich hybrid attention maps. Extensive experiments on multiple benchmark datasets demonstrate ModalFormer's state-of-the-art performance in LLIE. Pre-trained models and results are made available at https://github.com/albrateanu/ModalFormer.
VESPA: Towards un(Human)supervised Open-World Pointcloud Labeling for Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Data collection for autonomous driving is rapidly accelerating, but manual annotation, especially for 3D labels, remains a major bottleneck due to its high cost and labor intensity. Autolabeling has emerged as a scalable alternative, allowing the generation of labels for point clouds with minimal human intervention. While LiDAR-based autolabeling methods leverage geometric information, they struggle with inherent limitations of lidar data, such as sparsity, occlusions, and incomplete object observations. Furthermore, these methods typically operate in a class-agnostic manner, offering limited semantic granularity. To address these challenges, we introduce VESPA, a multimodal autolabeling pipeline that fuses the geometric precision of LiDAR with the semantic richness of camera images. Our approach leverages vision-language models (VLMs) to enable open-vocabulary object labeling and to refine detection quality directly in the point cloud domain. VESPA supports the discovery of novel categories and produces high-quality 3D pseudolabels without requiring ground-truth annotations or HD maps. On Nuscenes dataset, VESPA achieves an AP of 52.95% for object discovery and up to 46.54% for multiclass object detection, demonstrating strong performance in scalable 3D scene understanding. Code will be available upon acceptance.