OpenRaiser/Intern-Atlas huggingface dataset:
Dominance or Fair Play in Social Networks? A Model of Influencer Popularity Dynamic
Venue: arXiv cs.SI 2025
Year: 2025

This paper presents a data-driven mean-field approach to model the popularity dynamics of users seeking public attention, i.e., influencers. We propose a novel analytical model that integrates individual activity patterns, expertise in producing viral content, exogenous events, and the platform's role in visibility enhancement, ultimately determining each influencer's success. We analytically derive sufficient conditions for system ergodicity, enabling predictions of popularity distributions. A sensitivity analysis explores various system configurations, highlighting conditions favoring either dominance or fair play among influencers. Our findings offer valuable insights into the potential evolution of social networks towards more equitable or biased influence ecosystems.
Movable-Antenna-Enhanced Physical-Layer Service Integration: Performance Analysis and Optimization
Venue: arXiv cs.IT 2025
Year: 2025

Movable antennas (MAs) have drawn increasing attention in wireless communications due to their capability to create favorable channel conditions via local movement within a confined region. In this letter, we investigate its application in physical-layer service integration (PHY-SI), where a multi-MA base station (BS) simultaneously transmits both confidential and multicast messages to two users. The multicast message is intended for both users, while the confidential message is intended only for one user and must remain perfectly secure from the other. Our goal is to jointly optimize the secrecy and multicast beamforming, as well as the MAs' positions at the BS to maximize the secrecy rate for one user while satisfying the multicast rate requirement for both users. To gain insights, we first conduct performance analysis of this MA-enhanced PHY-SI system in two special cases, revealing its unique characteristics compared to conventional PHY-SI with fixed-position antennas (FPAs). To address the secrecy rate maximization problem, we propose a two-layer optimization framework that integrates the semidefinite relaxation (SDR) technique and a discrete sampling algorithm. Numerical results demonstrate that MAs can greatly enhance the achievable secrecy rate region for PHY-SI compared to FPAs.
Direction Estimation of Sound Sources Using Microphone Arrays and Signal Strength
Venue: arXiv cs.SD 2025
Year: 2025

Sound-tracking refers to the process of determining the direction from which a sound originates, making it a fundamental component of sound source localization. This capability is essential in a variety of applications, including security systems, acoustic monitoring, and speaker tracking, where accurately identifying the direction of a sound source enables real-time responses, efficient resource allocation, and improved situational awareness. While sound-tracking is closely related to localization, it specifically focuses on identifying the direction of the sound source rather than estimating its exact position in space. Despite its utility, sound-tracking systems face several challenges, such as maintaining directional accuracy and precision, along with the need for sophisticated hardware configurations and complex signal processing algorithms. This paper presents a sound-tracking method using three electret microphones. We estimate the direction of a sound source using a lightweight method that analyzes signals from three strategically placed microphones. By comparing the average power of the received signals, the system infers the most probable direction of the sound. The results indicate that the power level from each microphone effectively determines the sound source direction. Our system employs a straightforward and cost-effective hardware design, ensuring simplicity and affordability in implementation. It achieves a localization error of less than 6 degrees and a precision of 98%. Additionally, its effortless integration with various systems makes it versatile and adaptable. Consequently, this technique presents a robust and reliable solution for sound-tracking and localization, with potential applications spanning diverse domains such as security systems, smart homes, and acoustic monitoring.
REAL: Benchmarking Abilities of Large Language Models for Housing Transactions and Services
Venue: arXiv cs.AI 2025
Year: 2025

The development of large language models (LLMs) has greatly promoted the progress of chatbot in multiple fields. There is an urgent need to evaluate whether LLMs can play the role of agent in housing transactions and services as well as humans. We present Real Estate Agent Large Language Model Evaluation (REAL), the first evaluation suite designed to assess the abilities of LLMs in the field of housing transactions and services. REAL comprises 5,316 high-quality evaluation entries across 4 topics: memory, comprehension, reasoning and hallucination. All these entries are organized as 14 categories to assess whether LLMs have the knowledge and ability in housing transactions and services scenario. Additionally, the REAL is used to evaluate the performance of most advanced LLMs. The experiment results indicate that LLMs still have significant room for improvement to be applied in the real estate field.
Reinforcement Learning-based Feature Generation Algorithm for Scientific Data
Venue: arXiv cs.LG 2025
Year: 2025

Feature generation (FG) aims to enhance the prediction potential of original data by constructing high-order feature combinations and removing redundant features. It is a key preprocessing step for tabular scientific data to improve downstream machine-learning model performance. Traditional methods face the following two challenges when dealing with the feature generation of scientific data: First, the effective construction of high-order feature combinations in scientific data necessitates profound and extensive domain-specific expertise. Secondly, as the order of feature combinations increases, the search space expands exponentially, imposing prohibitive human labor consumption. Advancements in the Data-Centric Artificial Intelligence (DCAI) paradigm have opened novel avenues for automating feature generation processes. Inspired by that, this paper revisits the conventional feature generation workflow and proposes the Multi-agent Feature Generation (MAFG) framework. Specifically, in the iterative exploration stage, multi-agents will construct mathematical transformation equations collaboratively, synthesize and identify feature combinations ex-hibiting high information content, and leverage a reinforcement learning mechanism to evolve their strategies. Upon completing the exploration phase, MAFG integrates the large language models (LLMs) to interpreta-tively evaluate the generated features of each significant model performance breakthrough. Experimental results and case studies consistently demonstrate that the MAFG framework effectively automates the feature generation process and significantly enhances various downstream scientific data mining tasks.
On characterization and existence of constrained correlated equilibria in Markov games
Venue: arXiv cs.GT 2025
Year: 2025

Markov games with coupling constraints provide a natural framework to study constrained decision-making involving self-interested agents, where the feasibility of an individual agent's strategy depends on the joint strategies of the others. Such games arise in numerous real-world applications involving safety requirements and budget caps, for example, in environmental management, electricity markets, and transportation systems. While correlated equilibria have emerged as an important solution concept in unconstrained settings due to their computational tractability and amenability to learning, their constrained counterparts remain less explored. In this paper, we study constrained correlated equilibria-feasible policies where any unilateral modifications are either unprofitable or infeasible. We first characterize the constrained correlated equilibrium showing that different sets of modifications result in an equivalent notion, a result which may enable efficient learning algorithms. We then address existence conditions. In particular, we show that a strong Slater-type condition is necessary in games with playerwise coupling constraints, but can be significantly weakened when all players share common coupling constraints. Under this relaxed condition, we prove the existence of a constrained correlated equilibrium.
Near-Field Codebook-Based 3D Spherical Channel Estimation for UCA XL-MIMO Systems
Venue: arXiv cs.IT 2025
Year: 2025

Extremely large-scale multiple input multiple output (XL-MIMO), a key technology for 6G communications, faces challenges in near-field channel estimation due to spherical wavefronts and the need for three-dimensional (3D) spatial characterization, particularly with uniform circular arrays (UCAs). This letter proposes a spherical-domain simultaneous orthogonal matching pursuit (S-SOMP) based scheme tailored for near-field 3D channel estimation in UCA-equipped XL-MIMO systems. We establish a sparse channel representation based on the near-field spherical wave model. Then, a novel spherical-domain transform matrix codebook is designed via joint discrete sampling of distance, azimuth, and elevation parameters, leveraging analytical approximations to ensure low correlation between steering vectors. This structured codebook enables accurate sparse signal recovery using the S-SOMP algorithm for efficient joint estimation of channel path gains, spatial angles, and distances. Simulation results demonstrate significant channel estimation accuracy improvements compared to existing benchmarks.
Decoupled Relative Learning Rate Schedules
Venue: arXiv cs.LG 2025
Year: 2025

In this work, we introduce a novel approach for optimizing LLM training by adjusting learning rates across weights of different components in Transformer models. Traditional methods often apply a uniform learning rate across all network layers, potentially overlooking the unique dynamics of each part. Remarkably, our introduced relative learning rates, RLRS, method accelerates the training process by up to $23\%$, particularly in complex models such as Mixture of Experts (MoE). Hyperparameters of RLRS can be efficiently tuned on smaller models and then effectively reused on models up to $27\times$ larger. This simple and effective method results in a substantial reduction in training time and computational resources, offering a practical and scalable solution for optimizing large-scale neural networks.
Affine Frequency Division Multiplexing Over Wideband Doubly-Dispersive Channels With Time-Scaling Effects
Venue: arXiv cs.PF 2025
Year: 2025

The recently proposed affine frequency division multiplexing (AFDM) modulation has been considered as a promising technology for narrowband doubly-dispersive channels. However, the time-scaling effects, i.e., pulse widening and pulse shortening phenomena, in extreme wideband doubly-dispersive channels have not been considered in the literatures. In this paper, we investigate such wideband transmission and develop an efficient transmission structure with chirp-periodic prefix (CPP) and chirp-periodic suffix (CPS) for AFDM system. We derive the input-output relationship of AFDM system under time-scaled wideband doubly-dispersive channels and demonstrate the sparsity in discrete affine Fourier (DAF) domain equivalent channels. We further optimize the AFDM chirp parameters to accommodate the time-scaling characteristics in wideband doubly-dispersive channels and verify the superiority of the derived chirp parameters by pairwise error probability (PEP) analysis. We also develop an efficient cross domain distributed orthogonal approximate message passing (CD-D-OAMP) algorithm for AFDM symbol detection and analyze its corresponding state evolution. By analyzing the detection complexity of CD-D-OAMP detector and evaluating the error performance of AFDM systems based on simulations, we demonstrate that the AFDM system with our optimized chirp parameters outperforms the existing competitive modulation schemes in time-scaled wideband doubly-dispersive channels. Moreover, our proposed CD-D-OAMP detector can achieve the desirable trade-off between the complexity and performance, while supporting parallel computing to significantly reduce the computational latency.
H2HTalk: Evaluating Large Language Models as Emotional Companion
Venue: arXiv cs.CL 2025
Year: 2025

As digital emotional support needs grow, Large Language Model companions offer promising authentic, always-available empathy, though rigorous evaluation lags behind model advancement. We present Heart-to-Heart Talk (H2HTalk), a benchmark assessing companions across personality development and empathetic interaction, balancing emotional intelligence with linguistic fluency. H2HTalk features 4,650 curated scenarios spanning dialogue, recollection, and itinerary planning that mirror real-world support conversations, substantially exceeding previous datasets in scale and diversity. We incorporate a Secure Attachment Persona (SAP) module implementing attachment-theory principles for safer interactions. Benchmarking 50 LLMs with our unified protocol reveals that long-horizon planning and memory retention remain key challenges, with models struggling when user needs are implicit or evolve mid-conversation. H2HTalk establishes the first comprehensive benchmark for emotionally intelligent companions. We release all materials to advance development of LLMs capable of providing meaningful and safe psychological support.
An Efficient Deep Learning Framework for Brain Stroke Diagnosis Using Computed Tomography Images
Venue: arXiv cs.CV 2025
Year: 2025

Brain stroke is a leading cause of mortality and long-term disability worldwide, underscoring the need for precise and rapid prediction techniques. Computed Tomography (CT) scan is considered one of the most effective methods for diagnosing brain strokes. Most stroke classification techniques use a single slice-level prediction mechanism, requiring radiologists to manually select the most critical CT slice from the original CT volume. Although clinical evaluations are often used in traditional diagnostic procedures, machine learning (ML) has opened up new avenues for improving stroke diagnosis. To supplement traditional diagnostic techniques, this study investigates machine learning models for early brain stroke prediction using CT scan images. This research proposes a novel machine learning approach to brain stroke detection, focusing on optimizing classification performance with pre-trained deep learning models and advanced optimization strategies. Pre-trained models, including DenseNet201, InceptionV3, MobileNetV2, ResNet50, and Xception, are used for feature extraction. Feature engineering techniques, including BFO, PCA, and LDA, further enhance model performance. These features are then classified using machine learning algorithms, including SVC, RF, XGB, DT, LR, KNN, and GNB. Our experiments demonstrate that the combination of MobileNetV2, LDA, and SVC achieved the highest classification accuracy of 97.93%, significantly outperforming other model-optimizer-classifier combinations. The results underline the effectiveness of integrating lightweight pre-trained models with robust optimization and classification techniques for brain stroke diagnosis.
Heterogeneous participation and allocation skews: when is choice "worth it"?
Venue: arXiv cs.CY 2025
Year: 2025

A core ethos of the Economics and Computation (EconCS) community is that people have complex private preferences and information of which the central planner is unaware, but which an appropriately designed mechanism can uncover to improve collective decisionmaking. This ethos underlies the community's largest deployed success stories, from stable matching systems to participatory budgeting. I ask: is this choice and information aggregation ``worth it''? In particular, I discuss how such systems induce \textit{heterogeneous participation}: those already relatively advantaged are, empirically, more able to pay time costs and navigate administrative burdens imposed by the mechanisms. I draw on three case studies, including my own work -- complex democratic mechanisms, resident crowdsourcing, and school matching. I end with lessons for practice and research, challenging the community to help reduce participation heterogeneity and design and deploy mechanisms that meet a ``best of both worlds'' north star: \textit{use preferences and information from those who choose to participate, but provide a ``sufficient'' quality of service to those who do not.}
Multi-Hop Reasoning for Question Answering with Hyperbolic Representations
Venue: arXiv cs.CL 2025
Year: 2025

Hyperbolic representations are effective in modeling knowledge graph data which is prevalently used to facilitate multi-hop reasoning. However, a rigorous and detailed comparison of the two spaces for this task is lacking. In this paper, through a simple integration of hyperbolic representations with an encoder-decoder model, we perform a controlled and comprehensive set of experiments to compare the capacity of hyperbolic space versus Euclidean space in multi-hop reasoning. Our results show that the former consistently outperforms the latter across a diverse set of datasets. In addition, through an ablation study, we show that a learnable curvature initialized with the delta hyperbolicity of the utilized data yields superior results to random initializations. Furthermore, our findings suggest that hyperbolic representations can be significantly more advantageous when the datasets exhibit a more hierarchical structure.
EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledge
Venue: arXiv cs.CL 2025
Year: 2025

Knowledge Graphs (KGs) are structured knowledge repositories containing entities and relations between them. In this paper, we investigate the problem of automatically updating KGs over time with respect to the evolution of knowledge in unstructured textual sources. This problem requires identifying a wide range of update operations based on the state of an existing KG at a specific point in time. This contrasts with traditional information extraction pipelines, which extract knowledge from text independently of the current state of a KG. To address this challenge, we propose a method for lifelong construction of a dataset consisting of Wikidata KG snapshots over time and Wikipedia passages paired with the corresponding edit operations that they induce in a particular KG snapshot. The resulting dataset comprises 376K Wikipedia passages aligned with a total of 1.25M KG edits over 10 different snapshots of Wikidata from 2019 to 2025. Our experimental results highlight challenges in updating KG snapshots based on emerging textual knowledge, positioning the dataset as a valuable benchmark for future research. We will publicly release our dataset and model implementations.
Towards Automatic Error Recovery in Parsing Expression
Venue: arXiv cs.PL 2025
Year: 2025

Error recovery is an essential feature for a parser that should be plugged in Integrated Development Environments (IDEs), which must build Abstract Syntax Trees (ASTs) even for syntactically invalid programs in order to offer features such as automated refactoring and code completion. Parsing Expressions Grammars (PEGs) are a formalism that naturally describes recursive top-down parsers using a restricted form of backtracking. Labeled failures are a conservative extension of PEGs that adds an error reporting mechanism for PEG parsers, and these labels can also be associated with recovery expressions to also be an error recovery mechanism. These expressions can use the full expressivity of PEGs to recover from syntactic errors. Manually annotating a large grammar with labels and recovery expressions can be difficult. In this work, we present an algorithm that automatically annotates a PEG with labels, and builds their corresponding recovery expressions. We evaluate this algorithm by adding error recovery to the parser of the Titan programming language. The results shown that with a small amount of manual intervention our algorithm can be used to produce error recovering parsers for PEGs where most of the alternatives are disjoint.
MLASDO: a software tool to detect and explain clinical and omics inconsistencies applied to the Parkinson's Progression Markers Initiative cohort
Venue: arXiv cs.LG 2025
Year: 2025

Inconsistencies between clinical and omics data may arise within medical cohorts. The identification, annotation and explanation of anomalous omics-based patients or individuals may become crucial to better reshape the disease, e.g., by detecting early onsets signaled by the omics and undetectable from observable symptoms. Here, we developed MLASDO (Machine Learning based Anomalous Sample Detection on Omics), a new method and software tool to identify, characterize and automatically describe anomalous samples based on omics data. Its workflow is based on three steps: (1) classification of healthy and cases individuals using a support vector machine algorithm; (2) detection of anomalous samples within groups; (3) explanation of anomalous individuals based on clinical data and expert knowledge. We showcase MLASDO using transcriptomics data of 317 healthy controls (HC) and 465 Parkinson's disease (PD) cases from the Parkinson's Progression Markers Initiative. In this cohort, MLASDO detected 15 anomalous HC with a PD-like transcriptomic signature and PD-like clinical features, including a lower proportion of CD4/CD8 naive T-cells and CD4 memory T-cells compared to HC (P<3.5*10^-3). MLASDO also identified 22 anomalous PD cases with a transcriptomic signature more similar to that of HC and some clinical features more similar to HC, including a lower proportion of mature neutrophils compared to PD cases (P<6*10^-3). In summary, MLASDO is a powerful tool that can help the clinician to detect and explain anomalous HC and cases of interest to be followed up. MLASDO is an open-source R package available at: https://github.com/JoseAdrian3/MLASDO.
Re-Emergent Misalignment: How Narrow Fine-Tuning Erodes Safety Alignment in LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Recent work has shown that fine-tuning large language models (LLMs) on code with security vulnerabilities can result in misaligned and unsafe behaviors across broad domains. These results prompted concerns about the emergence of harmful behaviors from narrow domain fine-tuning. In this paper, we contextualize these findings by analyzing how such narrow adaptation impacts the internal mechanisms and behavioral manifestations of LLMs. Through a series of experiments covering output probability distributions, loss and gradient vector geometry, layer-wise activation dynamics, and activation space dimensions, we find that behaviors attributed to "emergent misalignment" may be better interpreted as an erosion of prior alignment. We show that fine tuning on insecure code induces internal changes that oppose alignment. Further, we identify a shared latent dimension in the model's activation space that governs alignment behavior. We show that this space is activated by insecure code and by misaligned responses more generally, revealing how narrow fine-tuning can degrade general safety behavior by interfering with shared internal mechanisms. Our findings offer a mechanistic interpretation for previously observed misalignment phenomena, and highlights the fragility of alignment in LLMs. The results underscore the need for more robust fine-tuning strategies that preserve intended behavior across domains.