OpenRaiser/Intern-Atlas huggingface dataset:
Metric Representations of Network Data
Venue: arXiv cs.SI 2025
Year: 2025

Networks are structures that encode relationships between pairs of elements or nodes. However, there is no imposed connection between these relationships, i.e., the relationship between two nodes can be independent of every other one in the network, and need not be defined for every possible pair of nodes. This is not true for metric spaces, where the triangle inequality imposes conditions that must be satisfied by triads of distances in the space, and these distances must be defined for every pair of nodes. In this paper, we study how to project networks into q-metric spaces, a generalization of metric spaces that encompasses a larger class of structured representations. In order to do this, we encode as axioms two intuitively desirable properties of the mentioned projections. We show that there is only one way of projecting networks onto q-metric spaces satisfying these axioms. Moreover, for the special case of (regular) metric spaces, this method boils down to computing the shortest path between every node and, for the case of ultrametric spaces, it coincides with single linkage hierarchical clustering. Furthermore, we show that the projection method satisfies two properties of practical relevance: optimality, which enables its utilization for the efficient estimation of combinatorial optimization problems, and nestedness, which entails consistency of the structure induced when projecting onto different q-metric spaces. Finally, we illustrate how metric projections can be used to efficiently search networks aided by metric trees.
Barcodes as Summary of Loss Function Topology
Venue: arXiv cs.LG 2025
Year: 2025

We propose to study neural networks' loss surfaces by methods of topological data analysis. We suggest to apply barcodes of Morse complexes to explore topology of loss surfaces. An algorithm for calculations of the loss function's barcodes of local minima is described. We have conducted experiments for calculating barcodes of local minima for benchmark functions and for loss surfaces of small neural networks. Our experiments confirm our two principal observations for neural networks' loss surfaces. First, the barcodes of local minima are located in a small lower part of the range of values of neural networks' loss function. Secondly, increase of the neural network's depth and width lowers the barcodes of local minima. This has some natural implications for the neural network's learning and for its generalization properties.
Generalized Reputation Computation Ontology and Temporal Graph Architecture
Venue: arXiv cs.SI 2025
Year: 2025

The problem of reliable democratic governance is important for survival of any community, and it will be more critical over time communities with levels of social connectivity in society rapidly increasing with speeds and scales of electronic communication. In order to face such challenge, different sorts of rating and reputation systems are being developed, however reputation gaming and manipulation in such systems appears to be serious problem. We are considering use of advanced reputation system supporting "liquid democracy" principle with generalized design and underlying ontology fitting different sorts of environments such as social networks, financial ecosystems and marketplaces. The suggested system is based on "temporal weighted liquid rank" algorithm employing different sorts of explicit and implicit ratings being exchanged by members of the society. For the purpose, we suggest "incremental reputation" design and graph database used for implementation of the system. Finally, we present evaluation of the system against real social network and financial blockchain data. The entire framework is expected to be the foundation of any multi-agent AI framework, so the evolution of distributed multi-agent AI architecture and dynamics will be based on the organic reputation scores earned by the agents that are part of it.
Incentives for Responsiveness, Instrumental Control and Impact
Venue: arXiv cs.AI 2025
Year: 2025

We introduce three concepts that describe an agent's incentives: response incentives indicate which variables in the environment, such as sensitive demographic information, affect the decision under the optimal policy. Instrumental control incentives indicate whether an agent's policy is chosen to manipulate part of its environment, such as the preferences or instructions of a user. Impact incentives indicate which variables an agent will affect, intentionally or otherwise. For each concept, we establish sound and complete graphical criteria, and discuss general classes of techniques that may be used to produce incentives for safe and fair agent behaviour. Finally, we outline how these notions may be generalised to multi-decision settings. This journal-length paper extends our conference publications "Incentives for Responsiveness, Instrumental Control and Impact" and "Agent Incentives: A Causal Perspective": the material on response incentives and instrumental control incentives is updated, while the work on impact incentives and multi-decision settings is entirely new.
On the Complexity of Destructive Bribery in Approval-Based Multi-winner Voting
Venue: arXiv cs.GT 2025
Year: 2025

A variety of constructive manipulation, control, and bribery problems for approval-based multiwinner voting have been extensively studied recently. However, their destructive counterparts seem to be less explored. This paper investigates the complexity of several destructive bribery problems under five prestigious approval-based multiwinner voting rules -- approval voting, satisfaction approval voting, net-satisfaction approval voting, Chamberlin-Courant approval voting, and proportional approval voting. Broadly, these problems are to determine if a number of given candidates can be excluded from any winning committees by performing a limited number of modification operations. We offer a complete landscape of the complexity of the problems. For NP-hard problems, we study their parameterized complexity with respect to meaningful parameters.
A Novel Kuhnian Ontology for Epistemic Classification of STM Scholarly Articles
Venue: arXiv cs.AI 2025
Year: 2025

Despite rapid gains in scale, research evaluation still relies on opaque, lagging proxies. To serve the scientific community, we pursue transparency: reproducible, auditable epistemic classification useful for funding and policy. Here we formalize KGX3 as a scenario-based model for mapping Kuhnian stages from research papers, prove determinism of the classification pipeline, and define the epistemic manifold that yields paradigm maps. We report validation across recent corpora, operational complexity at global scale, and governance that preserves interpretability while protecting core IP. The system delivers early, actionable signals of drift, crisis, and shift unavailable to citation metrics or citations-anchored NLP. KGX3 is the latest iteration of a deterministic epistemic engine developed since 2019, originating as Soph.io (2020), advanced as iKuhn (2024), and field-tested through Preprint Watch in 2025.
Hierarchical Correlation Clustering and Tree Preserving Embedding
Venue: arXiv cs.LG 2025
Year: 2025

We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters applicable to both positive and negative pairwise dissimilarities. Then, in the following, we study unsupervised representation learning with such hierarchical correlation clustering. For this purpose, we first investigate embedding the respective hierarchy to be used for tree preserving embedding and feature extraction. Thereafter, we study the extension of minimax distance measures to correlation clustering, as another representation learning paradigm. Finally, we demonstrate the performance of our methods on several datasets.
Defending against Backdoor Attack on Deep Neural Networks
Venue: arXiv cs.CR 2025
Year: 2025

Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks. In this paper, we focus on the so-called \textit{backdoor attack}, which injects a backdoor trigger to a small portion of training data (also known as data poisoning) such that the trained DNN induces misclassification while facing examples with this trigger. To be specific, we carefully study the effect of both real and synthetic backdoor attacks on the internal response of vanilla and backdoored DNNs through the lens of Gard-CAM. Moreover, we show that the backdoor attack induces a significant bias in neuron activation in terms of the $\ell_\infty$ norm of an activation map compared to its $\ell_1$ and $\ell_2$ norm. Spurred by our results, we propose the \textit{$\ell_\infty$-based neuron pruning} to remove the backdoor from the backdoored DNN. Experiments show that our method could effectively decrease the attack success rate, and also hold a high classification accuracy for clean images.
R-FORCE: Robust Learning for Random Recurrent Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Random Recurrent Neural Networks (RRNN) are the simplest recurrent networks to model and extract features from sequential data. The simplicity however comes with a price; RRNN are known to be susceptible to diminishing/exploding gradient problem when trained with gradient-descent based optimization. To enhance robustness of RRNN, alternative training approaches have been proposed. Specifically, FORCE learning approach proposed a recursive least squares alternative to train RRNN and was shown to be applicable even for the challenging task of target-learning, where the network is tasked with generating dynamic patterns with no guiding input. While FORCE training indicates that solving target-learning is possible, it appears to be effective only in a specific regime of network dynamics (edge-of-chaos). We thereby investigate whether initialization of RRNN connectivity according to a tailored distribution can guarantee robust FORCE learning. We are able to generate such distribution by inference of four generating principles constraining the spectrum of the network Jacobian to remain in stability region. This initialization along with FORCE learning provides a robust training method, i.e., Robust-FORCE (R-FORCE). We validate R-FORCE performance on various target functions for a wide range of network configurations and compare with alternative methods. Our experiments indicate that R-FORCE facilitates significantly more stable and accurate target-learning for a wide class of RRNN. Such stability becomes critical in modeling multi-dimensional sequences as we demonstrate on modeling time-series of human body joints during physical movements.
Enumerating minimal dominating sets in the (in)comparability graphs of bounded dimension posets
Venue: arXiv cs.DM 2025
Year: 2025

Enumerating minimal transversals in a hypergraph is a notoriously hard problem. It can be reduced to enumerating minimal dominating sets in a graph, in fact even to enumerating minimal dominating sets in an incomparability graph. We provide an output-polynomial time algorithm for incomparability graphs whose underlying posets have bounded dimension. Through a different proof technique, we also provide an output-polynomial algorithm for their complements, i.e., for comparability graphs of bounded dimension posets. Our algorithm for incomparability graphs is based on flashlight search and relies on the geometrical representation of incomparability graphs with bounded dimension, as given by Golumbic et al. in 1983. It runs with polynomial delay and only needs polynomial space. Our algorithm for comparability graphs is based on the flipping method introduced by Golovach et al. in 2015. It performs in incremental-polynomial time and requires exponential space. In addition, we show how to improve the flipping method so that it requires only polynomial space. Since the flipping method is a key tool for the best known algorithms enumerating minimal dominating sets in a number of graph classes, this yields direct improvements on the state of the art.
TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce
Venue: arXiv cs.LG 2025
Year: 2025

Automatic question-answering (QA) systems have boomed during last few years, and commonly used techniques can be roughly categorized into Information Retrieval (IR)-based and generation-based. A key solution to the IR based models is to retrieve the most similar knowledge entries of a given query from a QA knowledge base, and then rerank those knowledge entries with semantic matching models. In this paper, we aim to improve an IR based e-commerce QA system-AliMe with proposed text matching models, including a basic Triple Convolutional Neural Network (TCNN) model and two Attention-based TCNN (ATCNN) models. Experimental results show their effect.
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
Venue: arXiv cs.CR 2025
Year: 2025

Federated learning (FL) is a decentralized model training framework that aims to merge isolated data islands while maintaining data privacy. However, recent studies have revealed that Generative Adversarial Network (GAN) based attacks can be employed in FL to learn the distribution of private datasets and reconstruct recognizable images. In this paper, we exploit defenses against GAN-based attacks in FL and propose a framework, Anti-GAN, to prevent attackers from learning the real distribution of the victim's data. The core idea of Anti-GAN is to manipulate the visual features of private training images to make them indistinguishable to human eyes even restored by attackers. Specifically, Anti-GAN projects the private dataset onto a GAN's generator and combines the generated fake images with the actual images to create the training dataset, which is then used for federated model training. The experimental results demonstrate that Anti-GAN is effective in preventing attackers from learning the distribution of private images while causing minimal harm to the accuracy of the federated model.
Automatic Cross-Domain Transfer Learning for Linear Regression
Venue: arXiv cs.LG 2025
Year: 2025

Transfer learning research attempts to make model induction transferable across different domains. This method assumes that specific information regarding to which domain each instance belongs is known. This paper helps to extend the capability of transfer learning for linear regression problems to situations where the domain information is uncertain or unknown; in fact, the framework can be extended to classification problems. For normal datasets, we assume that some latent domain information is available for transfer learning. The instances in each domain can be inferred by different parameters. We obtain this domain information from the distribution of the regression coefficients corresponding to the explanatory variable $x$ as well as the response variable $y$ based on a Dirichlet process, which is more reasonable. As a result, we transfer not only variable $x$ as usual but also variable $y$, which is challenging since the testing data have no response value. Previous work mainly overcomes the problem via pseudo-labelling based on transductive learning, which introduces serious bias. We provide a novel framework for analysing the problem and considering this general situation: the joint distribution of variable $x$ and variable $y$. Furthermore, our method controls the bias well compared with previous work. We perform linear regression on the new feature space that consists of different latent domains and the target domain, which is from the testing data. The experimental results show that the proposed model performs well on real datasets.
Insignificant Choice Polynomial Time: A Logic Capturing PTIME
Venue: arXiv cs.CC 2025
Year: 2025

In this article choiceless polynomial time (CPT) is extended using non-determini\-stic Abstract State Machines (ASMs), which are restricted by three conditions: (1) choice is restricted to choice among atoms; (2) update sets in a state must be isomorphic; (3) for any two isomorphic update sets on states $S$ and $S^\prime$, respectively, the sets of update sets of the corresponding successor states are isomorphic. The restrictions can be incorporated into the semantics of ASM rules such that update sets are only yielded, if the conditions are satisfied. Furthermore, the conditions can be checked in polynomial time on a simulating Turing machine. Finally, the conditions imply global insignificance, i.e. the final result is independent from the choices. These properties suffice to show that the ASMs restricted this way define a logic capturing PTIME, which we call insignificant choice polynomial time (ICPT)
Impact of Fake Agents on Information Cascades
Venue: arXiv cs.SI 2025
Year: 2025

In online markets, agents often learn from other's actions in addition to their private information. Such observational learning can lead to herding or information cascades in which agents eventually ignore their private information and "follow the crowd". Models for such cascades have been well studied for Bayes-rational agents that arrive sequentially and choose pay-off optimal actions. This paper additionally considers the presence of fake agents that take a fixed action in order to influence subsequent rational agents towards their preferred action. We characterize how the fraction of such fake agents impacts the behavior of rational agents given a fixed quality of private information. Our model results in a Markov chain with a countably infinite state space, for which we give an iterative method to compute an agent's chances of herding and its welfare (expected pay-off). Our main result shows a counter-intuitive phenomenon: there exist infinitely many scenarios where an increase in the fraction of fake agents in fact reduces the chances of their preferred outcome. Moreover, this increase causes a significant improvement in the welfare of every rational agent. Hence, this increase is not only counter-productive for the fake agents but is also beneficial to the rational agents.
A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization
Venue: arXiv cs.LG 2025
Year: 2025

This paper proposes a combined network structure between convolutional neural network (CNN) and long-short term memory (LSTM) quantifier for WiFi fingerprinting indoor localization. In contrast to conventional methods that utilize only spatial data with classification models, our CNN-LSTM network extracts both space and time features of the received channel state information (CSI) from a single router. Furthermore, the proposed network builds a quantification model rather than a limited classification model as in most of the literature work, which enables the estimation of testing points that are not identical to the reference points. We analyze the instability of CSI and demonstrate a mitigation solution using a comprehensive filter and normalization scheme. The localization accuracy is investigated through extensive on-site experiments with several mobile devices including mobile phone (Nexus 5) and laptop (Intel 5300 NIC) on hundreds of testing locations. Using only a single WiFi router, our structure achieves an average localization error of 2.5~m with $\mathrm{80\%}$ of the errors under 4~m, which outperforms the other reported algorithms by approximately $\mathrm{50\%}$ under the same test environment.
CRAUM-Net: Contextual Recursive Attention with Uncertainty Modeling for Salient Object Detection
Venue: arXiv cs.LG 2025
Year: 2025

Salient Object Detection (SOD) plays a crucial role in many computer vision applications, requiring accurate localization and precise boundary delineation of salient regions. In this work, we present a novel framework that integrates multi-scale context aggregation, advanced attention mechanisms, and an uncertainty-aware module for improved SOD performance. Our Adaptive Cross-Scale Context Module effectively fuses features from multiple levels, leveraging Recursive Channel Spatial Attention and Convolutional Block Attention to enhance salient feature representation. We further introduce an edge-aware decoder that incorporates a dedicated Edge Extractor for boundary refinement, complemented by Monte Carlo Dropout to estimate uncertainty in predictions. To train our network robustly, we employ a combination of boundary-sensitive and topology-preserving loss functions, including Boundary IoU, Focal Tversky, and Topological Saliency losses. Evaluation metrics such as uncertainty-calibrated error and Boundary F1 score, along with the standard SOD metrics, demonstrate our method's superior ability to produce accurate and reliable saliency maps. Extensive experiments validate the effectiveness of our approach in capturing fine-grained details while quantifying prediction confidence, advancing the state-of-the-art in salient object detection.
Complexity of Verification and Synthesis of Threshold Automata
Venue: arXiv cs.LO 2025
Year: 2025

Threshold automata are a formalism for modeling and analyzing fault-tolerant distributed algorithms, recently introduced by Konnov, Veith, and Widder, describing protocols executed by a fixed but arbitrary number of processes. We conduct the first systematic study of the complexity of verification and synthesis problems for threshold automata. We prove that the coverability, reachability, safety, and liveness problems are NP-complete, and that the bounded synthesis problem is $Σ_p^2$ complete. A key to our results is a novel characterization of the reachability relation of a threshold automaton as an existential Presburger formula. The characterization also leads to novel verification and synthesis algorithms. We report on an implementation, and provide experimental results.
Escaping a Polygon
Venue: arXiv cs.CG 2025
Year: 2025

Suppose an escaping player ("human") moves continuously at maximum speed $1$ in the interior of a region, while a pursuing player ("zombie") moves continuously at maximum speed $r$ outside the region. For what $r$ can the first player escape the region, that is, reach the boundary a positive distance away from the pursuing player, assuming optimal play by both players? We formalize a model for this infinitesimally alternating 2-player game and prove that it has a unique winner in any locally rectifiable region. Our model thus avoids pathological behaviors (where both players can have "winning strategies") previously identified for pursuit-evasion games such as the Lion and Man problem in certain metric spaces. For some specific regions, including both equilateral triangle and square, we give exact results for the critical speed ratio, above which the pursuing player can win and below which the escaping player can win (and at which the pursuing player can win). For simple polygons, we give a simple formula and polynomial-time algorithm that is guaranteed to give a 10.89898-approximation to the critical speed ratio, and we give a pseudopolynomial-time approximation scheme for approximating the critical speed ratio arbitrarily closely. On the negative side, we prove NP-hardness of the problem for polyhedral domains in 3D, and prove stronger results (PSPACE-hardness and NP-hardness even to approximate) for generalizations to multiple escaping and pursuing players.