OpenRaiser/Intern-Atlas huggingface dataset:
PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology
Venue: arXiv cs.LG 2025
Year: 2025

Balancing performance and interpretability in multivariate time series classification is a significant challenge due to data complexity and high dimensionality. This paper introduces PHeatPruner, a method integrating persistent homology and sheaf theory to address these challenges. Persistent homology facilitates the pruning of up to 45% of the applied variables while maintaining or enhancing the accuracy of models such as Random Forest, CatBoost, XGBoost, and LightGBM, all without depending on posterior probabilities or supervised optimization algorithms. Concurrently, sheaf theory contributes explanatory vectors that provide deeper insights into the data's structural nuances. The approach was validated using the UEA Archive and a mastitis detection dataset for dairy cows. The results demonstrate that PHeatPruner effectively preserves model accuracy. Furthermore, our results highlight PHeatPruner's key features, i.e. simplifying complex data and offering actionable insights without increasing processing time or complexity. This method bridges the gap between complexity reduction and interpretability, suggesting promising applications in various fields.
Explicit Constructions for Rack-Aware Minimum Storage Partially Cooperative Regenerating Codes
Venue: arXiv cs.IT 2025
Year: 2025

The rack-aware storage model improves repair efficiency by exploiting locality within racks to minimize cross-rack traffic in a distributed storage system. While the partially cooperative repair model presents a solution for multiple node failures that reduces the need to exchange data with all other host racks (defined as racks containing failed nodes), thus enhancing system flexibility. In this paper, we focus on rack-aware minimum storage partially cooperative regenerating (MSPCR) codes for repairing multiple node failures. We first derive the lower bound on the repair bandwidth for rack-aware MSPCR codes using extremal combinatorics, and then explicitly construct the first class of (asymptotically) optimal repair schemes for rack-aware MSPCR codes with a sub-packetization level of $(\bar{s}+\bar{h}-δ)\bar{s}^{\bar{n}}$, which is smaller than that of the known rack-aware minimum-storage cooperative regenerating (MSCR) codes when $δ\geq 2$. By utilizing the grouping technique, we explicitly construct the second class of (asymptotically) optimal repair schemes for rack-aware MSPCR codes with a sub-packetization level of $2^{\bar{n}}$. In particular, when $δ=1$, our second codes reduce to rack-aware MSCR codes, while achieving an $(\bar{h}+1)$-fold reduction in sub-packetization level compared to the known rack-aware MSCR codes.
Optimal Control of Sensor-Induced Illusions on Robotic Agents
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a novel problem of creating and regulating localization and navigation illusions considering two agents: a receiver and a producer. A receiver is moving on a plane localizing itself using the intensity of signals from three known towers observed at its position. Based on this position estimate, it follows a simple policy to reach its goal. The key idea is that a producer alters the signal intensities to alter the position estimate of the receiver while ensuring it reaches a different destination with the belief that it reached its goal. We provide a precise mathematical formulation of this problem and show that it allows standard techniques from control theory to be applied to generate localization and navigation illusions that result in a desired receiver behavior.
Achievable Rates and Error Probability Bounds of Frequency-based Channels of Unlimited Input Resolution
Venue: arXiv cs.IT 2025
Year: 2025

We consider a molecular channel, in which messages are encoded to the frequency of objects in a pool, and whose output during reading time is a noisy version of the input frequencies, as obtained by sampling with replacement from the pool. Motivated by recent DNA storage techniques, we focus on the regime in which the input resolution is unlimited. We propose two error probability bounds for this channel; the first bound is based on random coding analysis of the error probability of the maximum likelihood decoder and the second bound is derived by code expurgation techniques. We deduce an achievable bound on the capacity of this channel, and compare it to both the achievable bounds under limited input resolution, as well as to a converse bound.
On constrained intersection representations of graphs and digraphs
Venue: arXiv cs.DM 2025
Year: 2025

We study the problem of determining optimal directed intersection representations of DAGs in a model introduced by Kostochka, Liu, Machado, and Milenkovic [ISIT2019]: vertices are assigned color sets so that there is an arc from a vertex $u$ to a vertex $v$ if and only if their color sets have nonempty intersection and $v$ gets assigned strictly more colors than $u$, and the goal is to minimize the total number of colors. We show that the problem is polynomially solvable in the class of triangle-free and Hamiltonian DAGs and also disclose the relationship of this problem with several other models of intersection representations of graphs and digraphs.
Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant
Venue: arXiv cs.CL 2025
Year: 2025

In recent years, multi-agent frameworks powered by large language models (LLMs) have advanced rapidly. Despite this progress, there is still a notable absence of benchmark datasets specifically tailored to evaluate their performance. To bridge this gap, we introduce Auto-SLURP, a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks in the context of intelligent personal assistants. Auto-SLURP extends the original SLURP dataset -- initially developed for natural language understanding tasks -- by relabeling the data and integrating simulated servers and external services. This enhancement enables a comprehensive end-to-end evaluation pipeline, covering language understanding, task execution, and response generation. Our experiments demonstrate that Auto-SLURP presents a significant challenge for current state-of-the-art frameworks, highlighting that truly reliable and intelligent multi-agent personal assistants remain a work in progress. The dataset and related code are available at https://github.com/lorashen/Auto-SLURP/.
An Empirical Study of Evaluating Long-form Question Answering
Venue: arXiv cs.IR 2025
Year: 2025

\Ac{LFQA} aims to generate lengthy answers to complex questions. This scenario presents great flexibility as well as significant challenges for evaluation. Most evaluations rely on deterministic metrics that depend on string or n-gram matching, while the reliability of large language model-based evaluations for long-form answers remains relatively unexplored. We address this gap by conducting an in-depth study of long-form answer evaluation with the following research questions: (i) To what extent do existing automatic evaluation metrics serve as a substitute for human evaluations? (ii) What are the limitations of existing evaluation metrics compared to human evaluations? (iii) How can the effectiveness and robustness of existing evaluation methods be improved? We collect 5,236 factoid and non-factoid long-form answers generated by different large language models and conduct a human evaluation on 2,079 of them, focusing on correctness and informativeness. Subsequently, we investigated the performance of automatic evaluation metrics by evaluating these answers, analyzing the consistency between these metrics and human evaluations. We find that the style, length of the answers, and the category of questions can bias the automatic evaluation metrics. However, fine-grained evaluation helps mitigate this issue on some metrics. Our findings have important implications for the use of large language models for evaluating long-form question answering. All code and datasets are available at https://github.com/bugtig6351/lfqa_evaluation.
Subexponential and Parameterized Mixing Times of Glauber Dynamics on Independent Sets
Venue: arXiv cs.DS 2025
Year: 2025

Given a graph $G$, the hard-core model defines a probability distribution over its independent sets, assigning to each set of size $k$ a probability of $\frac{λ^k}{Z}$, where $λ>0$ is a parameter known as the \emph{fugacity} and $Z$ is a normalization constant. The Glauber dynamics is a simple Markov chain that converges to this distribution and enables efficient sampling. Its \emph{mixing time}, the number of steps needed to approach the stationary distribution, has been widely studied across various graph classes, with most previous work emphasizing the dichotomy between polynomial and exponential mixing times, with a particular focus on sparse classes of graphs. Inspired by the modern fine-grained approach to computational complexity, we investigate subexponential mixing times of the Glauber dynamics on geometric intersection graphs, such as disk graphs. We further study parameterized mixing times with respect to two structural parameters that can remain small even in dense graphs: the tree-independence number and the path-independence number. Building on a result of Dyer, Greenhill, and Müller, we show that Glauber dynamics mixes in polynomial time on graphs of bounded path-independence number, and in quasi-polynomial time when the tree-independence number is bounded. Moreover, we prove that both bounds are tight via a conductance argument, thereby resolving a question raised in their work. This work provides a simple and efficient algorithm for sampling from the hard-core model. Unlike classical approaches that rely explicitly on geometric representations or on constructing decompositions such as tree decompositions or separator trees, our analysis only requires their existence to establish mixing time bounds-these structures are not used directly by the algorithm itself.
Efficiency, Expressivity, and Extensibility in a Close-to-Metal NPU Programming Interface
Venue: arXiv cs.SE 2025
Year: 2025

Accelerators such as neural processing units (NPUs) deliver an enticing balance of performance and efficiency compared to general purpose compute architectures. However, effectively leveraging accelerator capabilities is not always simple: low-level programming toolkits may require substantial developer effort while high-level programming toolkits may abstract critical optimization features. This work aims to increase efficiency of designers using IRON, a toolkit for close-to-metal NPU performance engineers. We provide an updated programmer interface to IRON containing new and refined programming constructs. The new interface includes extensible features for placement and data transformation. These contributions are evaluated in terms of 1) efficiency, with analysis showing ~26% average reduction in lines of code and decreases in Halstead metrics for a variety of designs; 2) expressivity, demonstrating the new interface supports the wide range of features and patterns already supported by IRON; and 3) extensibility, illustrating the new tooling for placement and tiling can be extended to accommodate common use-cases.
A Novel Taxonomy and Classification Scheme for Code Smell Interactions
Venue: arXiv cs.SE 2025
Year: 2025

Code smells are indicators of potential design flaws in source code and do not appear alone but in combination with other smells, creating complex interactions. While existing literature classifies these smell interactions into collocated, coupled, and inter-smell relations, however, to the best of our knowledge, no research has used the existing knowledge of code smells and (or) their relationships with other code smells in the detection of code smells. This gap highlights the need for deeper investigation into how code smells interact with each other and assist in their detection. This would improve the overall comprehension of code smells and how they interact more effectively. This study presents a novel taxonomy and a proposed classification scheme for the possible code smell interactions considering a specific programming language as a domain. This paper has dealt with one scenario called Inter smell detection within the domain. The experiments have been carried out using several popular machine learning (ML) models. Results primarily show the presence of code smell interactions namely Inter-smell Detection within domain. These results are compatible with the available facts in the literature suggesting a promising direction for future research in code smell detection.
Boundaried Kernelization
Venue: arXiv cs.DS 2025
Year: 2025

The notion of a (polynomial) kernelization from parameterized complexity is a well-studied model for efficient preprocessing for hard computational problems. By now, it is quite well understood which parameterized problems do or (conditionally) do not admit a polynomial kernelization. Unfortunately, polynomial kernelizations seem to require strong restrictions on the global structure of inputs. To avoid this restriction, we propose a model for efficient local preprocessing that is aimed at local structure in inputs. Our notion, dubbed boundaried kernelization, is inspired by protrusions and protrusion replacement, which are tools in meta-kernelization [Bodlaender et al. J'ACM 2016]. Unlike previous work, we study the preprocessing of suitable boundaried graphs in their own right, in significantly more general settings, and aiming for polynomial rather than exponential bounds. We establish polynomial boundaried kernelizations for a number of problems, while unconditionally ruling out such results for others. We also show that boundaried kernelization can be a tool for regular kernelization by using it to obtain an improved kernelization for Vertex Cover parameterized by the vertex-deletion distance to a graph of bounded treedepth.
Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues
Venue: arXiv cs.CL 2025
Year: 2025

The ability to generate explanations that are understood by explainees is the quintessence of explainable artificial intelligence. Since understanding depends on the explainee's background and needs, recent research focused on co-constructive explanation dialogues, where an explainer continuously monitors the explainee's understanding and adapts their explanations dynamically. We investigate the ability of large language models (LLMs) to engage as explainers in co-constructive explanation dialogues. In particular, we present a user study in which explainees interact with an LLM in two settings, one of which involves the LLM being instructed to explain a topic co-constructively. We evaluate the explainees' understanding before and after the dialogue, as well as their perception of the LLMs' co-constructive behavior. Our results suggest that LLMs show some co-constructive behaviors, such as asking verification questions, that foster the explainees' engagement and can improve understanding of a topic. However, their ability to effectively monitor the current understanding and scaffold the explanations accordingly remains limited.
Dynamic QoS Prediction via a Non-Negative Tensor Snowflake Factorization
Venue: arXiv cs.LG 2025
Year: 2025

Dynamic quality of service (QoS) data exhibit rich temporal patterns in user-service interactions, which are crucial for a comprehensive understanding of user behavior and service conditions in Web service. As the number of users and services increases, there is a large amount of unobserved QoS data, which significantly affects users'choice of services. To predict unobserved QoS data, we propose a Non-negative Snowflake Factorization of tensors model. This method designs a snowflake core tensor to enhance the model's learning capability. Additionally, it employs a single latent factor-based, nonnegative multiplication update on tensor (SLF-NMUT) for parameter learning. Empirical results demonstrate that the proposed model more accurately learns dynamic user-service interaction patterns, thereby yielding improved predictions for missing QoS data.
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations
Venue: arXiv cs.LG 2025
Year: 2025

Advances in neural operators have introduced discretization invariant surrogate models for PDEs on general geometries, yet many approaches struggle to encode local geometric structure and variable domains efficiently. We introduce enf2enf, a neural field approach for predicting steady-state PDEs with geometric variability. Our method encodes geometries into latent features anchored at specific spatial locations, preserving locality throughout the network. These local representations are combined with global parameters and decoded to continuous physical fields, enabling effective modeling of complex shape variations. Experiments on aerodynamic and structural benchmarks demonstrate competitive or superior performance compared to graph-based, neural operator, and recent neural field methods, with real-time inference and efficient scaling to high-resolution meshes.
ECG Identity Authentication in Open-set with Multi-model Pretraining and Self-constraint Center & Irrelevant Sample Repulsion Learning
Venue: arXiv cs.CR 2025
Year: 2025

Electrocardiogram (ECG) signal exhibits inherent uniqueness, making it a promising biometric modality for identity authentication. As a result, ECG authentication has gained increasing attention in recent years. However, most existing methods focus primarily on improving authentication accuracy within closed-set settings, with limited research addressing the challenges posed by open-set scenarios. In real-world applications, identity authentication systems often encounter a substantial amount of unseen data, leading to potential security vulnerabilities and performance degradation. To address this issue, we propose a robust ECG identity authentication system that maintains high performance even in open-set settings. Firstly, we employ a multi-modal pretraining framework, where ECG signals are paired with textual reports derived from their corresponding fiducial features to enhance the representational capacity of the signal encoder. During fine-tuning, we introduce Self-constraint Center Learning and Irrelevant Sample Repulsion Learning to constrain the feature distribution, ensuring that the encoded representations exhibit clear decision boundaries for classification. Our method achieves 99.83% authentication accuracy and maintains a False Accept Rate as low as 5.39% in the presence of open-set samples. Furthermore, across various open-set ratios, our method demonstrates exceptional stability, maintaining an Open-set Classification Rate above 95%.
Exploring the Potential of Latent Embeddings for Sea Ice Characterization using ICESat-2 Data
Venue: arXiv cs.LG 2025
Year: 2025

The Ice, Cloud, and Elevation Satellite-2 (ICESat-2) provides high-resolution measurements of sea ice height. Recent studies have developed machine learning methods on ICESat-2 data, primarily focusing on surface type classification. However, the heavy reliance on manually collected labels requires significant time and effort for supervised learning, as it involves cross-referencing track measurements with overlapping background optical imagery. Additionally, the coincidence of ICESat-2 tracks with background images is relatively rare due to the different overpass patterns and atmospheric conditions. To address these limitations, this study explores the potential of unsupervised autoencoder on unlabeled data to derive latent embeddings. We develop autoencoder models based on Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to reconstruct topographic sequences from ICESat-2 and derive embeddings. We then apply Uniform Manifold Approximation and Projection (UMAP) to reduce dimensions and visualize the embeddings. Our results show that embeddings from autoencoders preserve the overall structure but generate relatively more compact clusters compared to the original ICESat-2 data, indicating the potential of embeddings to lessen the number of required labels samples.
Proof-of-TBI -- Fine-Tuned Vision Language Model Consortium and OpenAI-o3 Reasoning LLM-Based Medical Diagnosis Support System for Mild Traumatic Brain Injury (TBI) Prediction
Venue: arXiv cs.AI 2025
Year: 2025

Mild Traumatic Brain Injury (TBI) detection presents significant challenges due to the subtle and often ambiguous presentation of symptoms in medical imaging, making accurate diagnosis a complex task. To address these challenges, we propose Proof-of-TBI, a medical diagnosis support system that integrates multiple fine-tuned vision-language models with the OpenAI-o3 reasoning large language model (LLM). Our approach fine-tunes multiple vision-language models using a labeled dataset of TBI MRI scans, training them to diagnose TBI symptoms effectively. The predictions from these models are aggregated through a consensus-based decision-making process. The system evaluates the predictions from all fine-tuned vision language models using the OpenAI-o3 reasoning LLM, a model that has demonstrated remarkable reasoning performance, to produce the most accurate final diagnosis. The LLM Agents orchestrates interactions between the vision-language models and the reasoning LLM, managing the final decision-making process with transparency, reliability, and automation. This end-to-end decision-making workflow combines the vision-language model consortium with the OpenAI-o3 reasoning LLM, enabled by custom prompt engineering by the LLM agents. The prototype for the proposed platform was developed in collaboration with the U.S. Army Medical Research team in Newport News, Virginia, incorporating five fine-tuned vision-language models. The results demonstrate the transformative potential of combining fine-tuned vision-language model inputs with the OpenAI-o3 reasoning LLM to create a robust, secure, and highly accurate diagnostic system for mild TBI prediction. To the best of our knowledge, this research represents the first application of fine-tuned vision-language models integrated with a reasoning LLM for TBI prediction tasks.
GeoFINDR: Practical Approach to Verify Cloud Instances Geolocation in Multicloud
Venue: arXiv cs.NI 2025
Year: 2025

In multicloud environments, where legal obligations, technical constraints and economic interests are at stake, it is of interest to stakeholders to be able to locate cloud data or the cloud instance where data are decrypted for processing, making it particularly vulnerable. This paper proposes an original and practical delay-based approach, called GeoFINDR, to locate a cloud instance, e.g. a Virtual Machine (VM), over the Internet, based on RIPE Atlas landmarks. First, the assumed threat model and assumptions are more realistic than in existing solutions, e.g. VM-scale localization in multicloud environments, a Cloud Service Provider (CSP) lying about the VM's location. Second, the originality of the approach lies in four original ideas: (1) geolocalization is performed from the VM, (2) a Greedy algorithm selects a first set LM_A of distributed audit landmarks in the vicinity of the declared area, (3) a sectorization algorithm identifies a set LM_S of other landmarks with distance delay behavior similar to that of the VM to estimate the sector of the VM, and (4) the estimated location of the VM is calculated as the barycenter position of the LM_S landmarks. An open source tool is published on GitHub and experiments show that localization accuracy can be as high as 22.1km, under unfavorable conditions where the CSP lies about the location of the VM.