OpenRaiser/Intern-Atlas huggingface dataset:
Ice-Filling: Near-Optimal Channel Estimation for Dense Array Systems
Venue: arXiv cs.IT 2025
Year: 2025

By deploying a large number of antennas with sub-half-wavelength spacing in a compact space, dense array systems (DASs) can fully unleash the multiplexing and diversity gains of limited apertures. To acquire these gains, accurate channel state information acquisition is necessary but challenging due to the large antenna numbers. To overcome this obstacle, this paper reveals that designing the observation matrix to exploit the high spatial correlation of DAS channels is crucial for realizing near-optimal Bayesian channel estimation. Specifically, we prove that the observation matrix design for channel estimation is equivalent to a time-domain duality of point-to-point multiple-input multiple-output precoding, except for the change in the total power constraint on the precoding matrix to the pilot-wise discrete power constraint on the observation matrix. Inspired by Bayesian regression, a novel ice-filling algorithm is proposed to design amplitude-and-phase controllable observation matrices, and a majorization-minimization algorithm is proposed to address the phase-only controllable case. Particularly, we prove that the ice-filling algorithm can be interpreted as a ``quantized" water-filling algorithm, wherein the latter's continuous power-allocation process is converted into the former's discrete pilot-assignment process. To support the near-optimality of the proposed designs, we provide comprehensive analyses on the achievable mean square errors and their asymptotic expressions. Finally, numerical results confirm that our proposed designs achieve the near-optimal channel estimation performance and outperform existing approaches significantly.
Formation-Controlled Dimensionality Reduction
Venue: arXiv cs.LG 2025
Year: 2025

Dimensionality reduction represents the process of generating a low dimensional representation of high dimensional data. Motivated by the formation control of mobile agents, we propose a nonlinear dynamical system for dimensionality reduction. The system consists of two parts; the control of neighbor points, addressing local structures, and the control of remote points, accounting for global structures.We also include a brief mathematical analysis of the model and its numerical procedure. Numerical experiments are performed on both synthetic and real datasets and comparisons with existing models demonstrate the soundness and effectiveness of the proposed model.
Achieving Tight $O(4^k)$ Runtime Bounds on Jump$_k$ by Proving that Genetic Algorithms Evolve Near-Maximal Population Diversity
Venue: arXiv cs.NE 2025
Year: 2025

The JUMP$_k$ benchmark was the first problem for which crossover was proven to give a speed-up over mutation-only evolutionary algorithms. Jansen and Wegener (2002) proved an upper bound of $O(\text{poly}(n) + 4^k/p_c)$ for the ($μ$+1) Genetic Algorithm ($(μ+1)$ GA), but only for unrealistically small crossover probabilities $p_c$. To this date, it remains an open problem to prove similar upper bounds for realistic $p_c$; the best known runtime bound, in terms of function evaluations, for $p_c = Ω(1)$ is $O((n/χ)^{k-1})$, $χ$ a positive constant. We provide a novel approach and analyse the evolution of the population diversity, measured as sum of pairwise Hamming distances, for a variant of the $(μ+1)$ GA on JUMP$_k$. The $(μ+1)$-$λ_c$-GA creates one offspring in each generation either by applying mutation to one parent or by applying crossover $λ_c$ times to the same two parents (followed by mutation), to amplify the probability of creating an accepted offspring in generations with crossover. We show that population diversity in the $(μ+1)$-$λ_c$-GA converges to an equilibrium of near-perfect diversity. This yields an improved time bound of $O(μn \log(μ) + 4^k)$ function evaluations for a range of $k$ under the mild assumptions $p_c = O(1/k)$ and $μ\in Ω(kn)$. For all constant $k$, the restriction is satisfied for some $p_c = Ω(1)$ and it implies that the expected runtime for all constant $k$ and an appropriate $μ= Θ(kn)$ is bounded by $O(n^2 \log n)$, irrespective of $k$. For larger $k$, the expected time of the $(μ+1)$-$λ_c$-GA is $Θ(4^k)$, which is tight for a large class of unbiased black-box algorithms and faster than the original $(μ+1)$ GA by a factor of $Ω(1/p_c)$. We also show that our analysis can be extended to other unitation functions such as JUMP$_{k, δ}$ and HURDLE.
RIP Twitter API: A eulogy to its vast research contributions
Venue: arXiv cs.CY 2025
Year: 2025

Since 2006, Twitter's APIs have been rich sources of data for researchers studying social phenomena such as misinformation, public communication, crisis response, and political behavior. However, in 2023, Twitter began heavily restricting data access, dismantling its academic access program, and setting the Enterprise API price at $42,000 per month. Lacking funds to pay this fee, academics are scrambling to continue their research. This study systematically tabulates the number of studies, citations, publication dates, disciplines, and major topics of research using Twitter data between 2006 and 2024. While we cannot know exactly what will be lost now that Twitter data is cost-prohibitive, we can illustrate its research value during the years it was available. A search of eight databases found that between 2006 and 2024, a total of 33,306 studies were published in 8,914 venues, with 610,738 citations across 16 disciplines. Major disciplines include social science, engineering, data science, and public health. Major topics include information dissemination, tweet credibility, research methodologies, event detection, and human behavior. Twitter-based studies increased by a median of 25% annually from 2006 to 2023, but following Twitter's decision to charge for data, the number of studies dropped by 13%. Much of the 2024 research likely used data collected before the API shutdown, suggesting further decline ahead. This trend highlights a growing loss of empirical insight and access to real-time, public communication-raising concerns about the long-term consequences for studying society, technology, and global events in an era increasingly connected by social media.
Offline Reinforcement Learning with Domain-Unlabeled Data
Venue: arXiv cs.LG 2025
Year: 2025

Offline reinforcement learning (RL) is vital in areas where active data collection is expensive or infeasible, such as robotics or healthcare. In the real world, offline datasets often involve multiple domains that share the same state and action spaces but have distinct dynamics, and only a small fraction of samples are clearly labeled as belonging to the target domain we are interested in. For example, in robotics, precise system identification may only have been performed for part of the deployments. To address this challenge, we consider Positive-Unlabeled Offline RL (PUORL), a novel offline RL setting in which we have a small amount of labeled target-domain data and a large amount of domain-unlabeled data from multiple domains, including the target domain. For PUORL, we propose a plug-and-play approach that leverages positive-unlabeled (PU) learning to train a domain classifier. The classifier then extracts target-domain samples from the domain-unlabeled data, augmenting the scarce target-domain data. Empirical results on a modified version of the D4RL benchmark demonstrate the effectiveness of our method: even when only 1 to 3 percent of the dataset is domain-labeled, our approach accurately identifies target-domain samples and achieves high performance, even under substantial dynamics shift. Our plug-and-play algorithm seamlessly integrates PU learning with existing offline RL pipelines, enabling effective multi-domain data utilization in scenarios where comprehensive domain labeling is prohibitive.
sEMG-Based Joint Angle Estimation via Hierarchical Spiking Attentional Feature Decomposition Network
Venue: arXiv cs.HC 2025
Year: 2025

Surface electromyography (sEMG) has demonstrated significant potential in simultaneous and proportional control (SPC). However, existing algorithms for predicting joint angles based onsEMGoften suffer fromhigh inference costs or are limited to specific subjects rather than multi-subject scenarios. To address these challenges, we introduced a hierarchical Spiking Attentional Feature Decomposition Network (SAFE-Net). This network initially compresses sEMG signals into neural spiking forms using a Spiking Sparse Attention Encoder (SSAE). Subsequently, the compressed features are decomposed into kinematic and biological features through a Spiking Attentional Feature Decomposition (SAFD) module. Finally, the kinematic and biological features are used to predict joint angles and identify subject identities, respectively. Our validation on two datasets and comparison with two existing methods, Informer and Spikformer, demonstrate that SSAE achieves significant power consumption savings of 39.1% and 37.5% respectively over them in terms of inference costs. Furthermore, SAFE-Net surpasses Informer and Spikformer in recognition accuracy on both datasets. This study underscores the potential of SAFE-Net to advance the field of SPC in lower limb rehabilitation exoskeleton robots.
ST-LoRA: Low-rank Adaptation for Spatio-Temporal Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Spatio-temporal forecasting is essential for understanding future dynamics within real-world systems by leveraging historical data from multiple locations. Existing methods often prioritize the development of intricate neural networks to capture the complex dependencies of the data. These methods neglect node-level heterogeneity and face over-parameterization when attempting to model node-specific characteristics. In this paper, we present a novel low-rank adaptation framework for existing spatio-temporal prediction models, termed \model, which alleviates the aforementioned problems through node-level adjustments. Specifically, we introduce the node-adaptive low-rank layer and node-specific predictor, capturing the complex functional characteristics of nodes while maintaining computational efficiency. Extensive experiments on multiple real-world datasets demonstrate that our method consistently achieves superior performance across various forecasting models with minimal computational overhead, improving performance by 7% with only 1% additional parameter cost. The source code is available at https://github.com/RWLinno/ST-LoRA.
Byzantine Reliable Broadcast with Low Communication and Time Complexity
Venue: arXiv cs.DC 2025
Year: 2025

Byzantine reliable broadcast is a fundamental problem in distributed computing, which has been studied extensively over the past decades. State-of-the-art algorithms are predominantly based on the approach to share encoded fragments of the broadcast message, yielding an asymptotically optimal communication complexity when the message size exceeds the network size, a condition frequently encountered in practice. However, algorithms following the standard coding approach incur an overhead factor of at least 3, which can already be a burden for bandwidth-constrained applications. Minimizing this overhead is an important objective with immediate benefits to protocols that use a reliable broadcast routine as a building block. This paper introduces a novel mechanism to lower the communication and computational complexity. Two algorithms are presented that employ this mechanism to reliably broadcast messages in an asynchronous network where less than a third of all nodes are Byzantine. The first algorithm reduces the overhead factor to 2 and has a time complexity of 3 if the sender is honest, whereas the second algorithm attains an optimal time complexity of 2 with the same overhead factor in the absence of equivocation. Moreover, an optimization for real-world implementations is proposed, reducing the overhead factor to 3/2 under normal operation. Lastly, a lower bound is proved that an overhead factor lower than 3/2 cannot be achieved for a relevant class of reliable broadcast algorithms.
Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI
Venue: arXiv cs.LG 2025
Year: 2025

Generative AI is becoming increasingly prevalent in creative fields, sparking urgent debates over how current copyright laws can keep pace with technological innovation. Recent controversies of AI models generating near-replicas of copyrighted material highlight the need to adapt current legal frameworks and develop technical methods to mitigate copyright infringement risks. This task requires understanding the intersection between computational concepts such as large-scale data scraping and probabilistic content generation, legal definitions of originality and fair use, and economic impacts on IP rights holders. However, most existing research on copyright in AI takes a purely computer science or law-based approach, leaving a gap in coordinating these approaches that only multidisciplinary efforts can effectively address. To bridge this gap, our survey adopts a comprehensive approach synthesizing insights from law, policy, economics, and computer science. It begins by discussing the foundational goals and considerations that should be applied to copyright in generative AI, followed by methods for detecting and assessing potential violations in AI system outputs. Next, it explores various regulatory options influenced by legal, policy, and economic frameworks to manage and mitigate copyright concerns associated with generative AI and reconcile the interests of IP rights holders with that of generative AI producers. The discussion then introduces techniques to safeguard individual creative works from unauthorized replication, such as watermarking and cryptographic protections. Finally, it describes advanced training strategies designed to prevent AI models from reproducing protected content. In doing so, we highlight key opportunities for action and offer actionable strategies that creators, developers, and policymakers can use in navigating the evolving copyright landscape.
Learning-Based Joint Antenna Selection and Precoding Design for Cell-Free MIMO Networks
Venue: arXiv cs.IT 2025
Year: 2025

This paper considers a downlink cell-free multiple-input multiple-output (MIMO) network in which multiple multi-antenna access points (APs) serve multiple users via coherent joint transmission. In order to reduce the energy consumption by radio frequency components, each AP selects a subset of antennas for downlink data transmission after estimating the channel state information (CSI). We aim to maximize the sum spectral efficiency by jointly optimizing the antenna selection and precoding design. To alleviate the fronthaul overhead and enable real-time network operation, we propose a distributed scalable machine learning algorithm. In particular, at each AP, we deploy a convolutional neural network (CNN) for antenna selection and a graph neural network (GNN) for precoding design. Different from conventional centralized solutions that require a large amount of CSI and signaling exchange among the APs, the proposed distributed machine learning algorithm takes only locally estimated CSI as input. With well-trained learning models, it is shown that the proposed algorithm significantly outperforms the distributed baseline schemes and achieves a sum spectral efficiency comparable to its centralized counterpart.
JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

The proliferation of large language models (LLMs) has underscored concerns regarding their security vulnerabilities, notably against jailbreak attacks, where adversaries design jailbreak prompts to circumvent safety mechanisms for potential misuse. Addressing these concerns necessitates a comprehensive analysis of jailbreak prompts to evaluate LLMs' defensive capabilities and identify potential weaknesses. However, the complexity of evaluating jailbreak performance and understanding prompt characteristics makes this analysis laborious. We collaborate with domain experts to characterize problems and propose an LLM-assisted framework to streamline the analysis process. It provides automatic jailbreak assessment to facilitate performance evaluation and support analysis of components and keywords in prompts. Based on the framework, we design JailbreakLens, a visual analysis system that enables users to explore the jailbreak performance against the target model, conduct multi-level analysis of prompt characteristics, and refine prompt instances to verify findings. Through a case study, technical evaluations, and expert interviews, we demonstrate our system's effectiveness in helping users evaluate model security and identify model weaknesses.
Improved Paraphrase Generation via Controllable Latent Diffusion
Venue: arXiv cs.CL 2025
Year: 2025

Paraphrase generation strives to generate high-quality and diverse expressions of a given text, a domain where diffusion models excel. Though SOTA diffusion generation reconciles generation quality and diversity, textual diffusion suffers from a truncation issue that hinders efficiency and quality control. In this work, we propose \textit{L}atent \textit{D}iffusion \textit{P}araphraser~(LDP), a novel paraphrase generation by modeling a controllable diffusion process given a learned latent space. LDP achieves superior generation efficiency compared to its diffusion counterparts. It can facilitate only input segments to ensure paraphrase semantics, improving the results without external features. Experiments show that LDP better reconciles paraphrase generation quality and diversity than baselines. Further analysis shows that our method is also helpful to other similar text generations and domain adaptations
3D Gaussian Splatting as Markov Chain Monte Carlo
Venue: arXiv cs.CV 2025
Year: 2025

While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which can lead to poor-quality renderings, and reliance on a good initialization. In this work, we rethink the set of 3D Gaussians as a random sample drawn from an underlying probability distribution describing the physical representation of the scene-in other words, Markov Chain Monte Carlo (MCMC) samples. Under this view, we show that the 3D Gaussian updates can be converted as Stochastic Gradient Langevin Dynamics (SGLD) updates by simply introducing noise. We then rewrite the densification and pruning strategies in 3D Gaussian Splatting as simply a deterministic state transition of MCMC samples, removing these heuristics from the framework. To do so, we revise the 'cloning' of Gaussians into a relocalization scheme that approximately preserves sample probability. To encourage efficient use of Gaussians, we introduce a regularizer that promotes the removal of unused Gaussians. On various standard evaluation scenes, we show that our method provides improved rendering quality, easy control over the number of Gaussians, and robustness to initialization.
Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems. Previous methods assume the answer to such a question can be found either in a single table or multiple tables identified through question decomposition or rewriting. However, neither of these approaches is sufficient, as many questions require retrieving multiple tables and joining them through a join plan that cannot be discerned from the user query itself. If the join plan is not considered in the retrieval stage, the subsequent steps of reasoning and answering based on those retrieved tables are likely to be incorrect. To address this problem, we introduce a method that uncovers useful join relations for any query and database during table retrieval. We use a novel re-ranking method formulated as a mixed-integer program that considers not only table-query relevance but also table-table relevance that requires inferring join relationships. Our method outperforms the state-of-the-art approaches for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up to 5.4% in accuracy.
ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval augmented generation (RAG) is frequently used to mitigate hallucinations and provide up-to-date knowledge for large language models (LLMs). However, given that document retrieval is an imprecise task and sometimes results in erroneous or even harmful content being presented in context, this raises the question of how LLMs handle retrieved information: If the provided content is incorrect, does the model know to ignore it, or does it recapitulate the error? Conversely, when the model's initial response is incorrect, does it always know to use the retrieved information to correct itself, or does it insist on its wrong prior response? To answer this, we curate a dataset of over 1200 questions across six domains (e.g., drug dosages, Olympic records, locations) along with content relevant to answering each question. We further apply precise perturbations to the answers in the content that range from subtle to blatant errors. We benchmark six top-performing LLMs, including GPT-4o, on this dataset and find that LLMs are susceptible to adopting incorrect retrieved content, overriding their own correct prior knowledge over 60% of the time. However, the more unrealistic the retrieved content is (i.e. more deviated from truth), the less likely the model is to adopt it. Also, the less confident a model is in its initial response (via measuring token probabilities), the more likely it is to adopt the information in the retrieved content. We exploit this finding and demonstrate simple methods for improving model accuracy where there is conflicting retrieved content. Our results highlight a difficult task and benchmark for LLMs -- namely, their ability to correctly discern when it is wrong in light of correct retrieved content and to reject cases when the provided content is incorrect.
Accelerating Particle-in-Cell Monte Carlo Simulations with MPI, OpenMP/OpenACC and Asynchronous Multi-GPU Programming
Venue: arXiv cs.DC 2025
Year: 2025

As fusion energy devices advance, plasma simulations are crucial for reactor design. Our work extends BIT1 hybrid parallelization by integrating MPI with OpenMP and OpenACC, focusing on asynchronous multi-GPU programming. Results show significant performance gains: 16 MPI ranks plus OpenMP threads reduced runtime by 53% on a petascale EuroHPC supercomputer, while OpenACC multicore achieved a 58% reduction. At 64 MPI ranks, OpenACC outperformed OpenMP, improving the particle mover function by 24%. On MareNostrum 5, OpenACC async(n) delivered strong performance, but OpenMP asynchronous multi-GPU approach proved more effective at extreme scaling, maintaining efficiency up to 400 GPUs. Speedup and parallel efficiency (PE) studies revealed OpenMP asynchronous multi-GPU achieving 8.77x speedup (54.81% PE), surpassing OpenACC (8.14x speedup, 50.87% PE). While PE declined at high node counts due to communication overhead, asynchronous execution mitigated scalability bottlenecks. OpenMP nowait and depend clauses improved GPU performance via efficient data transfer and task management. Using NVIDIA Nsight tools, we confirmed BIT1 efficiency for large-scale plasma simulations. OpenMP asynchronous multi-GPU implementation delivered exceptional performance in portability, high throughput, and GPU utilization, positioning BIT1 for exascale supercomputing and advancing fusion energy research. MareNostrum 5 brings us closer to achieving exascale performance.