OpenRaiser/Intern-Atlas huggingface dataset:
Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) have facilitated the generation of high-quality, cost-effective synthetic data for developing downstream models and conducting statistical analyses in various domains. However, the increased reliance on synthetic data may pose potential negative impacts. Numerous studies have demonstrated that LLM-generated synthetic data can perpetuate and even amplify societal biases and stereotypes, and produce erroneous outputs known as ``hallucinations'' that deviate from factual knowledge. In this paper, we aim to audit artifacts, such as classifiers, generators, or statistical plots, to identify those trained on or derived from synthetic data and raise user awareness, thereby reducing unexpected consequences and risks in downstream applications. To this end, we take the first step to introduce synthetic artifact auditing to assess whether a given artifact is derived from LLM-generated synthetic data. We then propose an auditing framework with three methods including metric-based auditing, tuning-based auditing, and classification-based auditing. These methods operate without requiring the artifact owner to disclose proprietary training details. We evaluate our auditing framework on three text classification tasks, two text summarization tasks, and two data visualization tasks across three training scenarios. Our evaluation demonstrates the effectiveness of all proposed auditing methods across all these tasks. For instance, black-box metric-based auditing can achieve an average accuracy of $0.868 \pm 0.071$ for auditing classifiers and $0.880 \pm 0.052$ for auditing generators using only 200 random queries across three scenarios. We hope our research will enhance model transparency and regulatory compliance, ensuring the ethical and responsible use of synthetic data.
Generalization of Medical Large Language Models through Cross-Domain Weak Supervision
Venue: arXiv cs.CL 2025
Year: 2025

The advancement of large language models (LLMs) has opened new frontiers in natural language processing, particularly in specialized domains like healthcare. In this paper, we propose the Incremental Curriculum-Based Fine-Tuning (ICFT) framework to enhance the generative capabilities of medical large language models (MLLMs). ICFT combines curriculum-based learning, dual-stage memory coordination, and parameter-efficient fine-tuning to enable a progressive transition from general linguistic knowledge to strong domain-specific expertise. Experimental results across diverse medical NLP tasks, including question answering, preference classification, and response generation, demonstrate that ICFT consistently outperforms state-of-the-art baselines, achieving improvements in both accuracy and efficiency. Further analysis reveals the framework's ability to generalize to unseen data, reduce errors, and deliver diverse, contextually relevant medical responses. These findings establish ICFT as a robust and scalable solution for adapting LLMs to the medical domain, offering practical benefits for real-world healthcare applications.
Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) have showcased remarkable capabilities across various domains. Accompanying the evolving capabilities and expanding deployment scenarios of LLMs, their deployment challenges escalate due to their sheer scale and the advanced yet complex activation designs prevalent in notable model series, such as Llama, Gemma, Mistral. These challenges have become particularly pronounced in resource-constrained deployment scenarios, where mitigating inference bottlenecks is imperative. Among various recent efforts, activation approximation has emerged as a promising avenue for pursuing inference efficiency, sometimes considered indispensable in applications such as private inference. Despite achieving substantial speedups with minimal impact on utility, even appearing sound and practical for real-world deployment, the safety implications of activation approximations remain unclear. In this work, we fill this critical gap in LLM safety by conducting the first systematic safety evaluation of activation approximations. Our safety vetting spans seven state-of-the-art techniques across three popular categories (activation polynomialization, activation sparsification, and activation quantization), revealing consistent safety degradation across ten safety-aligned LLMs. To overcome the hurdle of devising a unified defense accounting for diverse activation approximation methods, we perform an in-depth analysis of their shared error patterns and uncover three key findings. We propose QuadA, a novel safety enhancement method tailored to mitigate the safety compromises introduced by activation approximations. Extensive experiments and ablation studies corroborate QuadA's effectiveness in enhancing the safety capabilities of LLMs after activation approximations.
STAF: Sinusoidal Trainable Activation Functions for Implicit Neural Representation
Venue: arXiv cs.CV 2025
Year: 2025

Implicit Neural Representations (INRs) have emerged as a powerful framework for modeling continuous signals. The spectral bias of ReLU-based networks is a well-established limitation, restricting their ability to capture fine-grained details in target signals. While previous works have attempted to mitigate this issue through frequency-based encodings or architectural modifications, these approaches often introduce additional complexity and do not fully address the underlying challenge of learning high-frequency components efficiently. We introduce Sinusoidal Trainable Activation Functions (STAF), designed to directly tackle this limitation by enabling networks to adaptively learn and represent complex signals with higher precision and efficiency. STAF inherently modulates its frequency components, allowing for self-adaptive spectral learning. This capability significantly improves convergence speed and expressivity, making STAF highly effective for both signal representations and inverse problems. Through extensive evaluations across a range of tasks, including signal representation (shape, image, audio) and inverse problems (super-resolution, denoising), as well as neural radiance fields (NeRF), we demonstrate that STAF consistently outperforms state-of-the-art methods in accuracy and reconstruction fidelity. These results establish STAF as a robust solution to spectral bias and the capacity--convergence tradeoff, with broad applicability in computer vision and graphics. Our codebase is publicly accessible at https://github.com/AlirezaMorsali/STAF.
FedHPD: Heterogeneous Federated Reinforcement Learning via Policy Distillation
Venue: arXiv cs.LG 2025
Year: 2025

Federated Reinforcement Learning (FedRL) improves sample efficiency while preserving privacy; however, most existing studies assume homogeneous agents, limiting its applicability in real-world scenarios. This paper investigates FedRL in black-box settings with heterogeneous agents, where each agent employs distinct policy networks and training configurations without disclosing their internal details. Knowledge Distillation (KD) is a promising method for facilitating knowledge sharing among heterogeneous models, but it faces challenges related to the scarcity of public datasets and limitations in knowledge representation when applied to FedRL. To address these challenges, we propose Federated Heterogeneous Policy Distillation (FedHPD), which solves the problem of heterogeneous FedRL by utilizing action probability distributions as a medium for knowledge sharing. We provide a theoretical analysis of FedHPD's convergence under standard assumptions. Extensive experiments corroborate that FedHPD shows significant improvements across various reinforcement learning benchmark tasks, further validating our theoretical findings. Moreover, additional experiments demonstrate that FedHPD operates effectively without the need for an elaborate selection of public datasets.
Worth Their Weight: Randomized and Regularized Block Kaczmarz Algorithms without Preprocessing
Venue: arXiv cs.LG 2025
Year: 2025

Due to the ever growing amounts of data leveraged for machine learning and scientific computing, it is increasingly important to develop algorithms that sample only a small portion of the data at a time. In the case of linear least-squares, the randomized block Kaczmarz method (RBK) is an appealing example of such an algorithm, but its convergence is only understood under sampling distributions that require potentially prohibitively expensive preprocessing steps. To address this limitation, we analyze RBK when the data is sampled uniformly, showing that its iterates converge in a Monte Carlo sense to a $\textit{weighted}$ least-squares solution. Unfortunately, for general problems the bias of the weighted least-squares solution and the variance of the iterates can become arbitrarily large. We show that these quantities can be rigorously controlled by incorporating regularization into the RBK iterations, yielding the regularized algorithm ReBlocK. Numerical experiments including examples arising from natural gradient optimization demonstrate that ReBlocK can outperform both RBK and minibatch stochastic gradient descent for inconsistent problems with rapidly decaying singular values.
The Beatbots: A Musician-Informed Multi-Robot Percussion Quartet
Venue: arXiv cs.RO 2025
Year: 2025

Artistic creation is often seen as a uniquely human endeavor, yet robots bring distinct advantages to music-making, such as precise tempo control, unpredictable rhythmic complexities, and the ability to coordinate intricate human and robot performances. While many robotic music systems aim to mimic human musicianship, our work emphasizes the unique strengths of robots, resulting in a novel multi-robot performance instrument called the Beatbots, capable of producing music that is challenging for humans to replicate using current methods. The Beatbots were designed using an ``informed prototyping'' process, incorporating feedback from three musicians throughout development. We evaluated the Beatbots through a live public performance, surveying participants (N=28) to understand how they perceived and interacted with the robotic performance. Results show that participants valued the playfulness of the experience, the aesthetics of the robot system, and the unconventional robot-generated music. Expert musicians and non-expert roboticists demonstrated especially positive mindset shifts during the performance, although participants across all demographics had favorable responses. We propose design principles to guide the development of future robotic music systems and identify key robotic music affordances that our musician consultants considered particularly important for robotic music performance.
TxnSails: Achieving Serializable Transaction Scheduling with Self-Adaptive Isolation Level Selection
Venue: arXiv cs.DB 2025
Year: 2025

Achieving the serializable isolation level, regarded as the gold standard for transaction processing, is costly. Recent studies reveal that adjusting specific query patterns within a workload can still achieve serializability even at lower isolation levels. Nevertheless, these studies typically overlook the trade-off between the performance advantages of lower isolation levels and the overhead required to maintain serializability, potentially leading to suboptimal isolation level choices that fail to maximize performance. In this paper, we present TxnSails, a middle-tier solution designed to achieve serializable scheduling with self-adaptive isolation level selection. First, TxnSails incorporates a unified concurrency control algorithm that achieves serializability at lower isolation levels with minimal additional overhead. Second, TxnSails employs a deep learning method to characterize the trade-off between the performance benefits and overhead associated with lower isolation levels, thus predicting the optimal isolation level. Finally, TxnSails implements a cross-isolation validation mechanism to ensure serializability during real-time isolation level transitions. Extensive experiments demonstrate that TxnSails outperforms state-of-the-art solutions by up to 26.7x and PostgreSQL's serializable isolation level by up to 4.8x.
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs
Venue: arXiv cs.CL 2025
Year: 2025

The recent success of specialized Large Language Models (LLMs) in domains such as mathematical reasoning and coding has led to growing interest in methods for merging these expert LLMs into a unified Mixture-of-Experts (MoE) model, with the goal of enhancing performance in each domain while retaining effectiveness on general tasks. However, the effective merging of expert models remains an open challenge, especially for models with highly divergent weight parameters or different architectures. State-of-the-art MoE merging methods only work with homogeneous model architectures and rely on simple unweighted averaging to merge expert layers, which does not address parameter interference and requires extensive fine-tuning of the merged MoE to restore performance. To address these limitations, this paper introduces new MoE merging techniques, including strategies to mitigate parameter interference, routing heuristics to reduce the need for MoE fine-tuning, and a novel method for merging experts with different architectures. Extensive experiments across multiple domains demonstrate the effectiveness of our proposed methods, reducing fine-tuning costs, improving performance over state-of-the-art methods, and expanding the applicability of MoE merging.
Robust Trajectory Generation and Control for Quadrotor Motion Planning with Field-of-View Control Barrier Certification
Venue: arXiv cs.RO 2025
Year: 2025

Many approaches to multi-robot coordination are susceptible to failure due to communication loss and uncertainty in estimation. We present a real-time communication-free distributed navigation algorithm certified by control barrier functions, that models and controls the onboard sensing behavior to keep neighbors in the limited field of view for position estimation. The approach is robust to temporary tracking loss and directly synthesizes control to stabilize visual contact through control Lyapunov-barrier functions. The main contributions of this paper are a continuous-time robust trajectory generation and control method certified by control barrier functions for distributed multi-robot systems and a discrete optimization procedure, namely, MPC-CBF, to approximate the certified controller. In addition, we propose a linear surrogate of high-order control barrier function constraints and use sequential quadratic programming to solve MPC-CBF efficiently.
Geoinformatics-Guided Machine Learning for Power Plant Classification
Venue: arXiv cs.LG 2025
Year: 2025

This paper proposes an approach in the area of Knowledge-Guided Machine Learning (KGML) via a novel integrated framework comprising CNN (Convolutional Neural Networks) and ViT (Vision Transformers) along with GIS (Geographic Information Systems) to enhance power plant classification in the context of energy management. Knowledge from geoinformatics derived through Spatial Masks (SM) in GIS is infused into an architecture of CNN and ViT, in this proposed KGML approach. It is found to provide much better performance compared to the baseline of CNN and ViT only in the classification of multiple types of power plants from real satellite imagery, hence emphasizing the vital role of the geoinformatics-guided approach. This work makes a contribution to the main theme of KGML that can be beneficial in many AI systems today. It makes broader impacts on AI in Smart Cities, and Environmental Computing.
Multiphysics Continuous Shape Optimization of the TAP Reactor Components
Venue: arXiv cs.CE 2025
Year: 2025

The Transatomic Power (TAP) reactor has an unusual design for a molten salt reactor technology, building upon the foundation laid by the Molten Salt Reactor Experiment (MSRE). This design introduces three key modifications to enhance efficiency and compactness: a revised fuel salt composition, an alternative moderator material, and moderator pins surrounded by the molten salt fuel. Unlike traditional solid-fueled reactors that rely on excess positive reactivity at the beginning of life, the TAP concept employs a dynamic approach. The core's design, featuring a cylindrical geometry with square assemblies of moderator rods surrounded by flowing fuel salt, provides flexibility in adjusting the moderator-to-fuel ratio during operation - using movable moderator rods - further adding criticality control capability in addition to the control rods system. Shape optimization of the core can play a crucial role in enhancing performance and efficiency. By applying multiphysics continuous shape optimization techniques to key components, such as the unit cells of the TAP reactor or its moderator assemblies, we can fine-tune the reactor's geometry to achieve optimal performance in key physics like neutronics and thermal hydraulics. We explore this aspect using the optimization module in the Multiphysics Object Oriented Simulation Environment (MOOSE) framework which allows for multiphysics continuous shape optimization. The results reported here illustrate the benefits of applying continuous shape optimization in the design of nuclear reactor components and can help in extending the TAP reactor's performance.
Parallel Coding for Orthogonal Delay-Doppler Division Multiplexing
Venue: arXiv cs.IT 2025
Year: 2025

This paper proposes a novel parallel coding transmission strategy and an iterative detection and decoding receiver signal processing technique for orthogonal delay-Doppler division multiplexing (ODDM) modulation. Specifically, the proposed approach employs a parallel channel encoding (PCE) scheme that consists of multiple short-length codewords for each delay-Doppler multicarrier (DDMC) symbol. Building upon such a PCE transmission framework, we then introduce an iterative detection and decoding algorithm incorporating a successive decoding feedback (SDF) technique, which enables instant information exchange between the detector and decoder for each DDMC symbol. To characterize the error performance of the proposed scheme, we perform density evolution analysis considering the finite blocklength effects. Our analysis results, coupled with extensive simulations, demonstrate that the proposed PCE scheme with the SDF algorithm not only showcases a better overall performance but also requires much less decoding complexity to implement, compared to the conventional benchmark scheme that relies on a single long channel code for coding the entire ODDM frame.
Federated Linear Dueling Bandits
Venue: arXiv cs.LG 2025
Year: 2025

Contextual linear dueling bandits have recently garnered significant attention due to their widespread applications in important domains such as recommender systems and large language models. Classical dueling bandit algorithms are typically only applicable to a single agent. However, many applications of dueling bandits involve multiple agents who wish to collaborate for improved performance yet are unwilling to share their data. This motivates us to draw inspirations from federated learning, which involves multiple agents aiming to collaboratively train their neural networks via gradient descent (GD) without sharing their raw data. Previous works have developed federated linear bandit algorithms which rely on closed-form updates of the bandit parameters (e.g., the linear function parameters) to achieve collaboration. However, in linear dueling bandits, the linear function parameters lack a closed-form expression and their estimation requires minimizing a loss function. This renders these previous methods inapplicable. In this work, we overcome this challenge through an innovative and principled combination of online gradient descent (OGD, for minimizing the loss function to estimate the linear function parameters) and federated learning, hence introducing our federated linear dueling bandit with OGD (FLDB-OGD) algorithm. Through rigorous theoretical analysis, we prove that FLDB-OGD enjoys a sub-linear upper bound on its cumulative regret and demonstrate a theoretical trade-off between regret and communication complexity. We conduct empirical experiments to demonstrate the effectiveness of FLDB-OGD and reveal valuable insights, such as the benefit of a larger number of agents, the regret-communication trade-off, among others.
Advanced Architectures Integrated with Agentic AI for Next-Generation Wireless Networks
Venue: arXiv cs.NI 2025
Year: 2025

This paper investigates a range of cutting-edge technologies and architectural innovations aimed at simplifying network operations, reducing operational expenditure (OpEx), and enabling the deployment of new service models. The focus is on (i) Proposing novel, more efficient 6G architectures, with both Control and User planes enabling the seamless expansion of services, while addressing long-term 6G network evolution. (ii) Exploring advanced techniques for constrained artificial intelligence (AI) operations, particularly the design of AI agents for real-time learning, optimizing energy consumption, and the allocation of computational resources. (iii) Identifying technologies and architectures that support the orchestration of backend services using serverless computing models across multiple domains, particularly for vertical industries. (iv) Introducing optically-based, ultra-high-speed, low-latency network architectures, with fast optical switching and real-time control, replacing conventional electronic switching to reduce power consumption by an order of magnitude.
SatFlow: Generative model based framework for producing High Resolution Gap Free Remote Sensing Imagery
Venue: arXiv cs.CV 2025
Year: 2025

Frequent, high-resolution remote sensing imagery is crucial for agricultural and environmental monitoring. Satellites from the Landsat collection offer detailed imagery at 30m resolution but with lower temporal frequency, whereas missions like MODIS and VIIRS provide daily coverage at coarser resolutions. Clouds and cloud shadows contaminate about 55\% of the optical remote sensing observations, posing additional challenges. To address these challenges, we present SatFlow, a generative model-based framework that fuses low-resolution MODIS imagery and Landsat observations to produce frequent, high-resolution, gap-free surface reflectance imagery. Our model, trained via Conditional Flow Matching, demonstrates better performance in generating imagery with preserved structural and spectral integrity. Cloud imputation is treated as an image inpainting task, where the model reconstructs cloud-contaminated pixels and fills gaps caused by scan lines during inference by leveraging the learned generative processes. Experimental results demonstrate the capability of our approach in reliably imputing cloud-covered regions. This capability is crucial for downstream applications such as crop phenology tracking, environmental change detection etc.,