OpenRaiser/Intern-Atlas huggingface dataset:
ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder
Venue: arXiv cs.LG 2025
Year: 2025

Predicting the distribution of outcomes under hypothetical interventions is crucial across healthcare, economics, and policy-making. However, existing methods often require restrictive assumptions, and are typically limited by the lack of amortization across problem instances. We propose ACTIVA, a transformer-based conditional variational autoencoder (VAE) architecture for amortized causal inference, which estimates interventional distributions directly from observational data without. ACTIVA learns a latent representation conditioned on observational inputs and intervention queries, enabling zero-shot inference by amortizing causal knowledge from diverse training scenarios. We provide theoretical insights showing that ACTIVA predicts interventional distributions as mixtures over observationally equivalent causal models. Empirical evaluations on synthetic and semi-synthetic datasets confirm the effectiveness of our amortized approach and highlight promising directions for future real-world applications.
Scaling Law Phenomena Across Regression Paradigms: Multiple and Kernel Approaches
Venue: arXiv cs.LG 2025
Year: 2025

Recently, Large Language Models (LLMs) have achieved remarkable success. A key factor behind this success is the scaling law observed by OpenAI. Specifically, for models with Transformer architecture, the test loss exhibits a power-law relationship with model size, dataset size, and the amount of computation used in training, demonstrating trends that span more than seven orders of magnitude. This scaling law challenges traditional machine learning wisdom, notably the Oscar Scissors principle, which suggests that an overparametrized algorithm will overfit the training datasets, resulting in poor test performance. Recent research has also identified the scaling law in simpler machine learning contexts, such as linear regression. However, fully explaining the scaling law in large practical models remains an elusive goal. In this work, we advance our understanding by demonstrating that the scaling law phenomenon extends to multiple regression and kernel regression settings, which are significantly more expressive and powerful than linear methods. Our analysis provides deeper insights into the scaling law, potentially enhancing our understanding of LLMs.
Achieving Optimal Sample Complexity for a Broader Class of Signals in Sparse Phase Retrieval
Venue: arXiv cs.IT 2025
Year: 2025

Sparse phase retrieval, which aims to recover a $k$-sparse signal from $m$ phaseless measurements, poses a fundamental question regarding the minimal sample complexity required for success. While the optimal sample complexity is known to be $Ω(k \log n)$, existing algorithms can only achieve it for signals under restrictive structural assumptions. This paper introduces a novel and robust initialization algorithm, termed \ac{gESP}, designed to overcome this limitation. Theoretically, we prove that gESP significantly expands the family of signals that can be recovered with the optimal sample complexity. Our analysis unifies existing results on previously studied signal models and surpasses them by establishing performance bounds for a more general class of signals. Extensive simulations validate our theoretical findings, demonstrating that gESP consistently outperforms state-of-the-art methods across diverse signal types, thereby pushing the boundaries of efficient and optimal sparse phase retrieval.
Flexible Exoskeleton Control Based on Binding Alignment Strategy and Full-arm Coordination Mechanism
Venue: arXiv cs.RO 2025
Year: 2025

In rehabilitation, powered, and teleoperation exoskeletons, connecting the human body to the exoskeleton through binding attachments is a common configuration. However, the uncertainty of the tightness and the donning deviation of the binding attachments will affect the flexibility and comfort of the exoskeletons, especially during high-speed movement. To address this challenge, this paper presents a flexible exoskeleton control approach with binding alignment and full-arm coordination. Firstly, the sources of the force interaction caused by donning offsets are analyzed, based on which the interactive force data is classified into the major, assistant, coordination, and redundant component categories. Then, a binding alignment strategy (BAS) is proposed to reduce the donning disturbances by combining different force data. Furthermore, we propose a full-arm coordination mechanism (FCM) that focuses on two modes of arm movement intent, joint-oriented and target-oriented, to improve the flexible performance of the whole exoskeleton control during high-speed motion. In this method, we propose an algorithm to distinguish the two intentions to resolve the conflict issue of the force component. Finally, a series of experiments covering various aspects of exoskeleton performance (flexibility, adaptability, accuracy, speed, and fatigue) were conducted to demonstrate the benefits of our control framework in our full-arm exoskeleton.
SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graph
Venue: arXiv cs.CL 2025
Year: 2025

Multi-entity question answering (MEQA) poses significant challenges for large language models (LLMs), which often struggle to consolidate scattered information across multiple documents. An example question might be "What is the distribution of IEEE Fellows among various fields of study?", which requires retrieving information from diverse sources e.g., Wikipedia pages. The effectiveness of current retrieval-augmented generation (RAG) methods is limited by the LLMs' capacity to aggregate insights from numerous pages. To address this gap, this paper introduces a structured RAG (SRAG) framework that systematically organizes extracted entities into relational tables (e.g., tabulating entities with schema columns like "name" and "field of study") and then apply table-based reasoning techniques. Our approach decouples retrieval and reasoning, enabling LLMs to focus on structured data analysis rather than raw text aggregation. Extensive experiments on Wikipedia-based multi-entity QA tasks demonstrate that SRAG significantly outperforms state-of-the-art long-context LLMs and RAG solutions, achieving a 29.6% improvement in accuracy. The results underscore the efficacy of structuring unstructured data to enhance LLMs' reasoning capabilities.
From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images
Venue: arXiv cs.CV 2025
Year: 2025

Spatial transcriptomics (ST) measures gene expression at fine-grained spatial resolution, offering insights into tissue molecular landscapes. Previous methods for spatial gene expression prediction typically crop spots of interest from histopathology slide images, and train models to map each spot to a corresponding gene expression profile. However, these methods inherently lose the spatial resolution in gene expression: 1) each spot often contains multiple cells with distinct gene expression profiles; 2) spots are typically defined at fixed spatial resolutions, limiting the ability to predict gene expression at varying scales. To address these limitations, this paper presents PixNet, a dense prediction network capable of predicting spatially resolved gene expression across spots of varying sizes and scales directly from histopathology slide images. Different from previous methods that map individual spots to gene expression values, we generate a spatially dense continuous gene expression map from the histopathology slide image, and aggregate values within spots of interest to predict the gene expression. Our PixNet outperforms state-of-the-art methods on four common ST datasets in multiple spatial scales. The source code will be publicly available.
Bayesian Inverse Problems Meet Flow Matching: Efficient and Flexible Inference via Transformers
Venue: arXiv cs.LG 2025
Year: 2025

The efficient resolution of Bayesian inverse problems remains challenging due to the high computational cost of traditional sampling methods. In this paper, we propose a novel framework that integrates Conditional Flow Matching (CFM) with a transformer-based architecture to enable fast and flexible sampling from complex posterior distributions. The proposed methodology involves the direct learning of conditional probability trajectories from the data, leveraging CFM's ability to bypass iterative simulation and transformers' capacity to process arbitrary numbers of observations. The efficacy of the proposed framework is demonstrated through its application to three problems: a simple nonlinear model, a disease dynamics framework, and a two-dimensional Darcy flow Partial Differential Equation. The primary outcomes demonstrate that the relative errors in parameters recovery are as low as 1.5%, and that the inference time is reduced by up to 2000 times on CPU in comparison with the Monte Carlo Markov Chain. This framework facilitates the expeditious resolution of Bayesian problems through the utilisation of sampling from the learned conditional distribution.
An Empirical Smart Contracts Latency Analysis on Ethereum Blockchain for Trustworthy Inter-Provider Agreements
Venue: arXiv cs.NI 2025
Year: 2025

As 6G networks evolve, inter-provider agreements become crucial for dynamic resource sharing and network slicing across multiple domains, requiring on-demand capacity provisioning while enabling trustworthy interaction among diverse operators. To address these challenges, we propose a blockchain-based Decentralized Application (DApp) on Ethereum that introduces four smart contracts, organized into a Preliminary Agreement Phase and an Enforcement Phase, and measures their gas usage, thereby establishing an open marketplace where service providers can list, lease, and enforce resource sharing. We present an empirical evaluation of how gas price, block size, and transaction count affect transaction processing time on the live Sepolia Ethereum testnet in a realistic setting, focusing on these distinct smart-contract phases with varying computational complexities. We first examine transaction latency as the number of users (batch size) increases, observing median latencies from 12.5 s to 23.9 s in the Preliminary Agreement Phase and 10.9 s to 24.7 s in the Enforcement Phase. Building on these initial measurements, we perform a comprehensive Kruskal-Wallis test (p < 0.001) to compare latency distributions across quintiles of gas price, block size, and transaction count. The post-hoc analyses reveal that high-volume blocks overshadow fee variations when transaction logic is more complex (effect sizes up to 0.43), whereas gas price exerts a stronger influence when the computation is lighter (effect sizes up to 0.36). Overall, 86% of transactions finalize within 30 seconds, underscoring that while designing decentralized applications, there must be a balance between contract complexity and fee strategies. The implementation of this work is publicly accessible online.
AVR: Active Vision-Driven Precise Robot Manipulation with Viewpoint and Focal Length Optimization
Venue: arXiv cs.RO 2025
Year: 2025

Robotic manipulation in complex scenes demands precise perception of task-relevant details, yet fixed or suboptimal viewpoints often impair fine-grained perception and induce occlusions, constraining imitation-learned policies. We present AVR (Active Vision-driven Robotics), a bimanual teleoperation and learning framework that unifies head-tracked viewpoint control (HMD-to-2-DoF gimbal) with motorized optical zoom to keep targets centered at an appropriate scale during data collection and deployment. In simulation, an AVR plugin augments RoboTwin demonstrations by emulating active vision (ROI-conditioned viewpoint change, aspect-ratio-preserving crops with explicit zoom ratios, and super-resolution), yielding 5-17% gains in task success across diverse manipulations. On our real-world platform, AVR improves success on most tasks, with over 25% gains compared to the static-view baseline, and extended studies further demonstrate robustness under occlusion, clutter, and lighting disturbances, as well as generalization to unseen environments and objects. These results pave the way for future robotic precision manipulation methods in the pursuit of human-level dexterity and precision.
CHRONOS: Compensating Hardware Related Overheads with Native Multi Timer Support for Real-Time Operating Systems
Venue: arXiv cs.OS 2025
Year: 2025

The management of timing constraints in a real-time operating system (RTOS) is usually realized through a global tick counter. This counter acts as the foundational time unit for all tasks in the systems. In order to establish a connection between a tick and an amount of elapsed time in the real world, often this tick counter is periodically incremented by a hardware timer. At a fixed interval, this timer generates an interrupt that increments the counter. In an RTOS, jobs can only become ready upon a timer tick. That means, during a tick interrupt, the tick counter will be incremented, jobs will be released, and potentially, a scheduling decision will be conducted to select a new job to be run. As this process naturally uses some processing time, it is beneficial regarding the system utilization to minimize the time spent in tick interrupts. In modern microcontrollers, multiple hardware timers are often available. To utilize multiple timers to reduce the overhead caused by tick interrupts, multiple methods are introduced in this paper. The number of interrupts that are triggered by these timers can then be reduced by mapping tasks to timers in such a manner that the greatest common divisor (GCD) of all task periods in a subset is maximized, and the GCD is adopted as the interrupt interval of the timer. To find an optimal mapping of tasks to timers, an MIQCP-model is presented that minimizes the overall number of tick interrupts that occur in a system, while ensuring a correct task release behavior. The presented methods are implemented in FreeRTOS and evaluated on an embedded system. The evaluation of the methods show, that compared to the baseline implementation in FreeRTOS that uses a single timer with a fixed period, the presented methods can provide a significant reduction in overhead of up to $\approx10\times$ in peak and up to $\approx 6\times$ in average.
Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots
Venue: arXiv cs.RO 2025
Year: 2025

This paper contributes the Aerial Gym Simulator, a highly parallelized, modular framework for simulation and rendering of arbitrary multirotor platforms based on NVIDIA Isaac Gym. Aerial Gym supports the simulation of under-, fully- and over-actuated multirotors offering parallelized geometric controllers, alongside a custom GPU-accelerated rendering framework for ray-casting capable of capturing depth, segmentation and vertex-level annotations from the environment. Multiple examples for key tasks, such as depth-based navigation through reinforcement learning are provided. The comprehensive set of tools developed within the framework makes it a powerful resource for research on learning for control, planning, and navigation using state information as well as exteroceptive sensor observations. Extensive simulation studies are conducted and successful sim2real transfer of trained policies is demonstrated. The Aerial Gym Simulator is open-sourced at: https://github.com/ntnu-arl/aerial_gym_simulator.
Revisiting Locally Differentially Private Protocols: Towards Better Trade-offs in Privacy, Utility, and Attack Resistance
Venue: arXiv cs.CR 2025
Year: 2025

Local Differential Privacy (LDP) offers strong privacy protection, especially in settings in which the server collecting the data is untrusted. However, designing LDP mechanisms that achieve an optimal trade-off between privacy, utility and robustness to adversarial inference attacks remains challenging. In this work, we introduce a general multi-objective optimization framework for refining LDP protocols, enabling the joint optimization of privacy and utility under various adversarial settings. While our framework is flexible to accommodate multiple privacy and security attacks as well as utility metrics, in this paper, we specifically optimize for Attacker Success Rate (ASR) under \emph{data reconstruction attack} as a concrete measure of privacy leakage and Mean Squared Error (MSE) as a measure of utility. More precisely, we systematically revisit these trade-offs by analyzing eight state-of-the-art LDP protocols and proposing refined counterparts that leverage tailored optimization techniques. Experimental results demonstrate that our proposed adaptive mechanisms consistently outperform their non-adaptive counterparts, achieving substantial reductions in ASR while preserving utility, and pushing closer to the ASR-MSE Pareto frontier. By bridging the gap between theoretical guarantees and real-world vulnerabilities, our framework enables modular and context-aware deployment of LDP mechanisms with tunable privacy-utility trade-offs.
On Noncoherent Multiple-Antenna Rayleigh Block-Fading Channels at Finite Blocklength
Venue: arXiv cs.IT 2025
Year: 2025

This paper investigates the maximum coding rate at which data can be transmitted over a noncoherent, multiple-input, multiple-output (MIMO) Rayleigh block-fading channel using an error-correcting code of a given blocklength with a block-error probability not exceeding a given value. A high-SNR normal approximation is derived that becomes accurate as the signal-to-noise ratio (SNR) and the number of coherence intervals over which we code tend to infinity. The obtained normal approximation complements the nonasymptotic bounds that have appeared in the literature, but whose evaluation is computationally demanding. It further lays the theoretical foundation for an analytical analysis of the fundamental tradeoff between diversity, multiplexing, and channel-estimation cost at finite blocklength and finite SNR.
Diversity Covariance-Aware Prompt Learning for Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Prompt tuning can further enhance the performance of visual-language models across various downstream tasks (e.g., few-shot learning), enabling them to better adapt to specific applications and needs. In this paper, we present a Diversity Covariance-Aware framework that learns distributional information from the data to enhance the few-shot ability of the prompt model. First, we propose a covariance-aware method that models the covariance relationships between visual features and uses anisotropic Mahalanobis distance, instead of the suboptimal cosine distance, to measure the similarity between two modalities. We rigorously derive and prove the validity of this modeling process. Then, we propose the diversity-aware method, which learns multiple diverse soft prompts to capture different attributes of categories and aligns them independently with visual modalities. This method achieves multi-centered covariance modeling, leading to more diverse decision boundaries. Extensive experiments on 11 datasets in various tasks demonstrate the effectiveness of our method.
Unmasking Implicit Bias: Evaluating Persona-Prompted LLM Responses in Power-Disparate Social Scenarios
Venue: arXiv cs.CY 2025
Year: 2025

Large language models (LLMs) have demonstrated remarkable capabilities in simulating human behaviour and social intelligence. However, they risk perpetuating societal biases, especially when demographic information is involved. We introduce a novel framework using cosine distance to measure semantic shifts in responses and an LLM-judged Preference Win Rate (WR) to assess how demographic prompts affect response quality across power-disparate social scenarios. Evaluating five LLMs over 100 diverse social scenarios and nine demographic axes, our findings suggest a "default persona" bias toward middle-aged, able-bodied, native-born, Caucasian, atheistic males with centrist views. Moreover, interactions involving specific demographics are associated with lower-quality responses. Lastly, the presence of power disparities increases variability in response semantics and quality across demographic groups, suggesting that implicit biases may be heightened under power-imbalanced conditions. These insights expose the demographic biases inherent in LLMs and offer potential paths toward future bias mitigation efforts in LLMs.
None of the Above, Less of the Right: Parallel Patterns between Humans and LLMs on Multi-Choice Questions Answering
Venue: arXiv cs.CY 2025
Year: 2025

Multiple-choice exam questions with "None of the above" (NA) options have been extensively studied in educational testing, in which existing research suggests that they better assess true knowledge. However, their impact on Large Language Models (LLMs) evaluation remains underexplored. Through systematic experiments with 28 LLMs on the MMLU benchmark, we examine how NA options affect model performance and confidence calibration. Our analysis reveals that NA options, when used as the correct answer, lead to a consistent 30-50\% performance drop across models regardless of scale--suggesting that LLMs lack the meta-cognitive ability to systematically evaluate and reject all given options when none are correct. This degradation shows strong domain dependence, with minimal impact on mathematical reasoning (14.6\% drop) but severe effects on tasks requiring uncertainty handling like business ethics (48.1\% drop). Our results highlight important implications for benchmark design and raise questions about LLMs' ability to handle uncertainty in real-world applications.
A Reconfigurable Stream-Based FPGA Accelerator for Bayesian Confidence Propagation Neural Networks
Venue: arXiv cs.AR 2025
Year: 2025

Brain-inspired algorithms are attractive and emerging alternatives to classical deep learning methods for use in various machine learning applications. Brain-inspired systems can feature local learning rules, both unsupervised/semi-supervised learning and different types of plasticity (structural/synaptic), allowing them to potentially be faster and more energy-efficient than traditional machine learning alternatives. Among the more salient brain-inspired algorithms are Bayesian Confidence Propagation Neural Networks (BCPNNs). BCPNN is an important tool for both machine learning and computational neuroscience research, and recent work shows that BCPNN can reach state-of-the-art performance in tasks such as learning and memory recall compared to other models. Unfortunately, BCPNN is primarily executed on slow general-purpose processors (CPUs) or power-hungry graphics processing units (GPUs), reducing the applicability of using BCPNN in (among others) Edge systems. In this work, we design a custom stream-based accelerator for BCPNN using Field-Programmable Gate Arrays (FPGA) using Xilinx Vitis High-Level Synthesis (HLS) flow. Furthermore, we model our accelerator's performance using first principles, and we empirically show that our proposed accelerator is between 1.3x - 5.3x faster than an Nvidia A100 GPU while at the same time consuming between 2.62x - 3.19x less power and 5.8x - 16.5x less energy without any degradation in performance.