OpenRaiser/Intern-Atlas huggingface dataset:
BitROM: Weight Reload-Free CiROM Architecture Towards Billion-Parameter 1.58-bit LLM Inference
Venue: arXiv cs.AR 2025
Year: 2025

Compute-in-Read-Only-Memory (CiROM) accelerators offer outstanding energy efficiency for CNNs by eliminating runtime weight updates. However, their scalability to Large Language Models (LLMs) is fundamentally constrained by their vast parameter sizes. Notably, LLaMA-7B - the smallest model in LLaMA series - demands more than 1,000 cm2 of silicon area even in advanced CMOS nodes. This paper presents BitROM, the first CiROM-based accelerator that overcomes this limitation through co-design with BitNet's 1.58-bit quantization model, enabling practical and efficient LLM inference at the edge. BitROM introduces three key innovations: 1) a novel Bidirectional ROM Array that stores two ternary weights per transistor; 2) a Tri-Mode Local Accumulator optimized for ternary-weight computations; and 3) an integrated Decode-Refresh (DR) eDRAM that supports on-die KV-cache management, significantly reducing external memory access during decoding. In addition, BitROM integrates LoRA-based adapters to enable efficient transfer learning across various downstream tasks. Evaluated in 65nm CMOS, BitROM achieves 20.8 TOPS/W and a bit density of 4,967 kB/mm2 - offering a 10x improvement in area efficiency over prior digital CiROM designs. Moreover, the DR eDRAM contributes to a 43.6% reduction in external DRAM access, further enhancing deployment efficiency for LLMs in edge applications.
A 410GFLOP/s, 64 RISC-V Cores, 204.8GBps Shared-Memory Cluster in 12nm FinFET with Systolic Execution Support for Efficient B5G/6G AI-Enhanced O-RAN
Venue: arXiv cs.DC 2025
Year: 2025

We present HeartStream, a 64-RV-core shared-L1-memory cluster (410 GFLOP/s peak performance and 204.8 GBps L1 bandwidth) for energy-efficient AI-enhanced O-RAN. The cores and cluster architecture are customized for baseband processing, supporting complex (16-bit real&imaginary) instructions: multiply&accumulate, division&square-root, SIMD instructions, and hardware-managed systolic queues, improving up to 1.89x the energy efficiency of key baseband kernels. At 800MHz@0.8V, HeartStream delivers up to 243GFLOP/s on complex-valued wireless workloads. Furthermore, the cores also support efficient AI processing on received data at up to 72 GOP/s. HeartStream is fully compatible with base station power and processing latency limits: it achieves leading-edge software-defined PUSCH efficiency (49.6GFLOP/s/W) and consumes just 0.68W (645MHz@0.65V), within the 4 ms end-to-end constraint for B5G/6G uplink.
Solving contact problems using Fiber Monte Carlo
Venue: arXiv cs.CE 2025
Year: 2025

Computational modeling of contact is fundamental to many engineering applications, yet accurately and efficiently solving complex contact problems remains challenging. In this work, we propose a new contact algorithm that computes contact forces by taking the gradient of an energy function of the contact volume (overlap) with respect to the geometry descriptors. While elegant in concept, evaluating this gradient is non-trivial due to the arbitrary geometry of the contact region. Inspired by the recently proposed Fiber Monte Carlo (FMC) method, we develop an algorithm that accurately computes contact forces based on the overlap volume between bodies with complex geometries. Our computational framework operates independently of mesh conformity, eliminating the need for master-slave identification and projection iterations, thus handling arbitrary discretizations. Moreover, by removing explicit complementarity constraints, the method retains a simple structure that can be easily incorporated into existing numerical solvers, such as the finite element method. In this paper, numerical examples cover a wide range of contact scenarios, from classical small-deformation static contact to complex large-deformation dynamic contact in both two- and three-dimensional settings with nonlinear material behavior. These cases include Hertzian contact for small-deformation verification; contact between wedge- and cone-shaped bodies to assess pressure and displacement predictions at non-smooth boundaries; contact involving Neo-Hookean hyperelastic materials for evaluating nonlinear responses under finite deformation; and dynamic collision cases to examine transient behavior.
UOPSL: Unpaired OCT Predilection Sites Learning for Fundus Image Diagnosis Augmentation
Venue: arXiv cs.CV 2025
Year: 2025

Significant advancements in AI-driven multimodal medical image diagnosis have led to substantial improvements in ophthalmic disease identification in recent years. However, acquiring paired multimodal ophthalmic images remains prohibitively expensive. While fundus photography is simple and cost-effective, the limited availability of OCT data and inherent modality imbalance hinder further progress. Conventional approaches that rely solely on fundus or textual features often fail to capture fine-grained spatial information, as each imaging modality provides distinct cues about lesion predilection sites. In this study, we propose a novel unpaired multimodal framework \UOPSL that utilizes extensive OCT-derived spatial priors to dynamically identify predilection sites, enhancing fundus image-based disease recognition. Our approach bridges unpaired fundus and OCTs via extended disease text descriptions. Initially, we employ contrastive learning on a large corpus of unpaired OCT and fundus images while simultaneously learning the predilection sites matrix in the OCT latent space. Through extensive optimization, this matrix captures lesion localization patterns within the OCT feature space. During the fine-tuning or inference phase of the downstream classification task based solely on fundus images, where paired OCT data is unavailable, we eliminate OCT input and utilize the predilection sites matrix to assist in fundus image classification learning. Extensive experiments conducted on 9 diverse datasets across 28 critical categories demonstrate that our framework outperforms existing benchmarks.
LADB: Latent Aligned Diffusion Bridges for Semi-Supervised Domain Translation
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models excel at generating high-quality outputs but face challenges in data-scarce domains, where exhaustive retraining or costly paired data are often required. To address these limitations, we propose Latent Aligned Diffusion Bridges (LADB), a semi-supervised framework for sample-to-sample translation that effectively bridges domain gaps using partially paired data. By aligning source and target distributions within a shared latent space, LADB seamlessly integrates pretrained source-domain diffusion models with a target-domain Latent Aligned Diffusion Model (LADM), trained on partially paired latent representations. This approach enables deterministic domain mapping without the need for full supervision. Compared to unpaired methods, which often lack controllability, and fully paired approaches that require large, domain-specific datasets, LADB strikes a balance between fidelity and diversity by leveraging a mixture of paired and unpaired latent-target couplings. Our experimental results demonstrate superior performance in depth-to-image translation under partial supervision. Furthermore, we extend LADB to handle multi-source translation (from depth maps and segmentation masks) and multi-target translation in a class-conditioned style transfer task, showcasing its versatility in handling diverse and heterogeneous use cases. Ultimately, we present LADB as a scalable and versatile solution for real-world domain translation, particularly in scenarios where data annotation is costly or incomplete.
Echo Chambers and Information Brokers on Truth Social: A Study of Network Dynamics and Political Discourse
Venue: arXiv cs.SI 2025
Year: 2025

This study examines the structural dynamics of Truth Social, a politically aligned social media platform, during two major political events: the U.S. Supreme Court's overturning of Roe v. Wade and the FBI's search of Mar-a-Lago. Using a large-scale dataset of user interactions based on re-truths (platform-native reposts), we analyze how the network evolves in relation to fragmentation, polarization, and user influence. Our findings reveal a segmented and ideologically homogenous structure dominated by a small number of central figures. Political events prompt temporary consolidation around shared narratives, followed by rapid returns to fragmented, echo-chambered clusters. Centrality metrics highlight the disproportionate role of key influencers, particularly @realDonaldTrump, in shaping visibility and directing discourse. These results contribute to research on alternative platforms, political communication, and online network behavior, demonstrating how infrastructure and community dynamics together reinforce ideological boundaries and limit cross-cutting engagement.
Perfectly-Private Analog Secure Aggregation in Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model. To address the risk of exposing sensitive data through local models, secure aggregation via secure multiparty computation has been proposed to enhance privacy. At the same time, perfect privacy can only be achieved by a uniform distribution of the masked local models to be aggregated. This raises a problem when working with real valued data, as there is no measure on the reals that is invariant under the masking operation, and hence information leakage is bound to occur. Shifting the data to a finite field circumvents this problem, but as a downside runs into an inherent accuracy complexity tradeoff issue due to fixed point modular arithmetic as opposed to floating point numbers that can simultaneously handle numbers of varying magnitudes. In this paper, a novel secure parameter aggregation method is proposed that employs the torus rather than a finite field. This approach guarantees perfect privacy for each party's data by utilizing the uniform distribution on the torus, while avoiding accuracy losses. Experimental results show that the new protocol performs similarly to the model without secure aggregation while maintaining perfect privacy. Compared to the finite field secure aggregation, the torus-based protocol can in some cases significantly outperform it in terms of model accuracy and cosine similarity, hence making it a safer choice.
A Use Case Lens on Digital Cultural Heritage
Venue: arXiv cs.DL 2025
Year: 2025

This article proposes a novel methodological approach for developing use cases for CH e-infrastuctures documented using Jupyter Notebooks (JNs), enabling transparency and reproducibility. We also address the present problem of use cases that are not consistently documented to cover all key aspects that are derived from the use case literature review outside of CH field to define a useful use case. Purpose. Our primary objective is to explore the practices around creating and analysing use cases related to digital cultural heritage. Our review of the literature showed a substantial deviation in the depth and coverage of use cases and revealed the need for a more robust and consistent approach to creating use cases in a digital heritage context. We developed a framework to develop use cases to support the ongoing efforts to expand the use of eInfrastructures in the digital heritage domain as a first step. Design/methodology/approach. Our research design combines desk research of existing literature and analysing examples of use cases documented in projects. We examine the challenges and inconsistencies in the current practice of use case production in digital heritage. Finally, we synthesize a systematic process to generate use cases which is illustrated by five example use cases within the context. Our work impacts directly such infrastructures and communities as the International GLAM Labs Community, AI for Libraries, Archives, and Museums (AI4LAM) and Time Machine Organisation. This work advances the use of data research infrastructures within communities of researchers, scholars, students, GLAM (Galleries, Libraries, Archives, and Museums) institutions, and Cultural Heritage and Cultural and Creative Industries (CCIs).
Compressing CNN models for resource-constrained systems by channel and layer pruning
Venue: arXiv cs.LG 2025
Year: 2025

Convolutional Neural Networks (CNNs) have achieved significant breakthroughs in various fields. However, these advancements have led to a substantial increase in the complexity and size of these networks. This poses a challenge when deploying large and complex networks on edge devices. Consequently, model compression has emerged as a research field aimed at reducing the size and complexity of CNNs. One prominent technique in model compression is model pruning. This paper will present a new technique of pruning that combines both channel and layer pruning in what is called a "hybrid pruning framework". Inspired by EfficientNet, a renowned CNN architecture known for scaling up networks from both channel and layer perspectives, this hybrid approach applies the same principles but in reverse, where it scales down the network through pruning. Experiments on the hybrid approach demonstrated a notable decrease in the overall complexity of the model, with only a minimal reduction in accuracy compared to the baseline model. This complexity reduction translates into reduced latency when deploying the pruned models on an NVIDIA JETSON TX2 embedded AI device.
Data-driven generative simulation of SDEs using diffusion models
Venue: arXiv cs.LG 2025
Year: 2025

This paper introduces a new approach to generating sample paths of unknown stochastic differential equations (SDEs) using diffusion models, a class of generative AI models commonly employed in image and video applications. Unlike the traditional Monte Carlo methods for simulating SDEs, which require explicit specifications of the drift and diffusion coefficients, our method takes a model-free, data-driven approach. Given a finite set of sample paths from an SDE, we utilize conditional diffusion models to generate new, synthetic paths of the same SDE. To demonstrate the effectiveness of our approach, we conduct a simulation experiment to compare our method with alternative benchmark ones including neural SDEs. Furthermore, in an empirical study we leverage these synthetically generated sample paths to enhance the performance of reinforcement learning algorithms for continuous-time mean-variance portfolio selection, hinting promising applications of diffusion models in financial analysis and decision-making.
Membrane: A Cryptographic Access Control System for Data Lakes
Venue: arXiv cs.CR 2025
Year: 2025

Organizations use data lakes to store and analyze sensitive data. But hackers may compromise data lake storage to bypass access controls and access sensitive data. To address this, we propose Membrane, a system that (1) cryptographically enforces data-dependent access control views over a data lake, (2) without restricting the analytical queries data scientists can run. We observe that data lakes, unlike DBMSes, disaggregate computation and storage into separate trust domains, making at-rest encryption sufficient to defend against remote attackers targeting data lake storage, even when running analytical queries in plaintext. This leads to a new system design for Membrane that combines encryption at rest with SQL-aware encryption. Using block ciphers, a fast symmetric-key primitive with hardware acceleration in CPUs, we develop a new SQL-aware encryption protocol well-suited to at-rest encryption. Membrane adds overhead only at the start of an interactive session due to decrypting views, delaying the first query result by up to $\approx 20\times$; subsequent queries process decrypted data in plaintext, resulting in low amortized overhead.
Parallel, Asymptotically Optimal Algorithms for Moving Target Traveling Salesman Problems
Venue: arXiv cs.RO 2025
Year: 2025

The Moving Target Traveling Salesman Problem (MT-TSP) seeks an agent trajectory that intercepts several moving targets, within a particular time window for each target. In the presence of generic nonlinear target trajectories or kinematic constraints on the agent, no prior algorithm guarantees convergence to an optimal MT-TSP solution. Therefore, we introduce the Iterated Random Generalized (IRG) TSP framework. The key idea behind IRG is to alternate between randomly sampling a set of agent configuration-time points, corresponding to interceptions of targets, and finding a sequence of interception points by solving a generalized TSP (GTSP). This alternation enables asymptotic convergence to the optimum. We introduce two parallel algorithms within the IRG framework. The first algorithm, IRG-PGLNS, solves GTSPs using PGLNS, our parallelized extension of the state-of-the-art solver GLNS. The second algorithm, Parallel Communicating GTSPs (PCG), solves GTSPs corresponding to several sets of points simultaneously. We present numerical results for three variants of the MT-TSP: one where intercepting a target only requires coming within a particular distance, another where the agent is a variable-speed Dubins car, and a third where the agent is a redundant robot arm. We show that IRG-PGLNS and PCG both converge faster than a baseline based on prior work.
An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Images
Venue: arXiv cs.CV 2025
Year: 2025

Background: Arsenicosis is a serious public health concern in South and Southeast Asia, primarily caused by long-term consumption of arsenic-contaminated water. Its early cutaneous manifestations are clinically significant but often underdiagnosed, particularly in rural areas with limited access to dermatologists. Automated, image-based diagnostic solutions can support early detection and timely interventions. Methods: In this study, we propose an end-to-end framework for arsenicosis diagnosis using mobile phone-captured skin images. A dataset comprising 20 classes and over 11000 images of arsenic-induced and other dermatological conditions was curated. Multiple deep learning architectures, including convolutional neural networks (CNNs) and Transformer-based models, were benchmarked for arsenicosis detection. Model interpretability was integrated via LIME and Grad-CAM, while deployment feasibility was demonstrated through a web-based diagnostic tool. Results: Transformer-based models significantly outperformed CNNs, with the Swin Transformer achieving the best results (86\\% accuracy). LIME and Grad-CAM visualizations confirmed that the models attended to lesion-relevant regions, increasing clinical transparency and aiding in error analysis. The framework also demonstrated strong performance on external validation samples, confirming its ability to generalize beyond the curated dataset. Conclusion: The proposed framework demonstrates the potential of deep learning for non-invasive, accessible, and explainable diagnosis of arsenicosis from mobile-acquired images. By enabling reliable image-based screening, it can serve as a practical diagnostic aid in rural and resource-limited communities, where access to dermatologists is scarce, thereby supporting early detection and timely intervention.
Quantifying Accuracy of an Event-Based Star Tracker via Earth's Rotation
Venue: arXiv cs.CV 2025
Year: 2025

Event-based cameras (EBCs) are a promising new technology for star tracking-based attitude determination, but prior studies have struggled to determine accurate ground truth for real data. We analyze the accuracy of an EBC star tracking system utilizing the Earth's motion as the ground truth for comparison. The Earth rotates in a regular way with very small irregularities which are measured to the level of milli-arcseconds. By keeping an event camera static and pointing it through a ground-based telescope at the night sky, we create a system where the only camera motion in the celestial reference frame is that induced by the Earth's rotation. The resulting event stream is processed to generate estimates of orientation which we compare to the International Earth Rotation and Reference System (IERS) measured orientation of the Earth. The event camera system is able to achieve a root mean squared across error of 18.47 arcseconds and an about error of 78.84 arcseconds. Combined with the other benefits of event cameras over framing sensors (reduced computation due to sparser data streams, higher dynamic range, lower energy consumption, faster update rates), this level of accuracy suggests the utility of event cameras for low-cost and low-latency star tracking. We provide all code and data used to generate our results: https://gitlab.kitware.com/nest-public/telescope_accuracy_quantification.
A Survey of Reinforcement Learning for Large Reasoning Models
Venue: arXiv cs.CL 2025
Year: 2025

In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontier of LLM capabilities, particularly in addressing complex logical tasks such as mathematics and coding. As a result, RL has emerged as a foundational methodology for transforming LLMs into LRMs. With the rapid progress of the field, further scaling of RL for LRMs now faces foundational challenges not only in computational resources but also in algorithm design, training data, and infrastructure. To this end, it is timely to revisit the development of this domain, reassess its trajectory, and explore strategies to enhance the scalability of RL toward Artificial SuperIntelligence (ASI). In particular, we examine research applying RL to LLMs and LRMs for reasoning abilities, especially since the release of DeepSeek-R1, including foundational components, core problems, training resources, and downstream applications, to identify future opportunities and directions for this rapidly evolving area. We hope this review will promote future research on RL for broader reasoning models. Github: https://github.com/TsinghuaC3I/Awesome-RL-for-LRMs
Deep opacity and AI: A threat to XAI and to privacy protection mechanisms
Venue: arXiv cs.CY 2025
Year: 2025

It is known that big data analytics and AI pose a threat to privacy, and that some of this is due to some kind of "black box problem" in AI. I explain how this becomes a problem in the context of justification for judgments and actions. Furthermore, I suggest distinguishing three kinds of opacity: 1) the subjects do not know what the system does ("shallow opacity"), 2) the analysts do not know what the system does ("standard black box opacity"), or 3) the analysts cannot possibly know what the system might do ("deep opacity"). If the agents, data subjects as well as analytics experts, operate under opacity, then these agents cannot provide justifications for judgments that are necessary to protect privacy, e.g., they cannot give "informed consent", or guarantee "anonymity". It follows from these points that agents in big data analytics and AI often cannot make the judgments needed to protect privacy. So I conclude that big data analytics makes the privacy problems worse and the remedies less effective. As a positive note, I provide a brief outlook on technical ways to handle this situation.
Improved Receiver Chain Performance via Error Location Inference
Venue: arXiv cs.IT 2025
Year: 2025

Modern spacecraft communication systems rely on concatenated error correction schemes, typically combining convolutional and Reed-Solomon (RS) codes. This paper presents a decoder-side method that uses a machine learning model to estimate the likelihood of byte-level corruption in received data frames. These estimates are used to mark erasures prior to RS decoding, enhancing its correction capacity without requiring changes to spacecraft hardware or encoding standards. The approach enables improved data recovery under degraded signal conditions at a gain of 0.3 decibels.