OpenRaiser/Intern-Atlas huggingface dataset:
Democratizing GraphRAG: Linear, CPU-Only Graph Retrieval for Multi-Hop QA
Venue: arXiv cs.IR 2025
Year: 2025

GraphRAG systems improve multi-hop retrieval by modeling structure, but many approaches rely on expensive LLM-based graph construction and GPU-heavy inference. We present SPRIG (Seeded Propagation for Retrieval In Graphs), a CPU-only, linear-time, token-free GraphRAG pipeline that replaces LLM graph building with lightweight NER-driven co-occurrence graphs and uses Personalized PageRank (PPR) for 28% with negligible Recall@10 changes. The results characterize when CPU-friendly graph retrieval helps multi-hop recall and when strong lexical hybrids (RRF) are sufficient, outlining a realistic path to democratizing GraphRAG without token costs or GPU requirements.
A Polynomial time Algorithm for 3SAT
Venue: arXiv cs.DS 2026
Year: 2026

By creating some new concepts and methods: checking tree, long unit path, direct contradiction unit pair, indirect contradiction unit pair, additional contradiction unit pair, 2-unit layer and 3-unit layer, redundant units, and destroying parallel pairs , we successfully transform solving a 3SAT problem to solving 2SAT problems in polynomial time. Thus we proved that NP=P.
An O(m^9) ternary minimum-cost network flow LP model of the Assignment Problem polytope with applications to hard combinatorial optimization problems
Venue: arXiv cs.DS 2026
Year: 2026

In this paper, we present a new network flow linear programming (LP) model of the standard Assignment Problem (AP) polytope. The model is not meant to be competitive with the existing standard, two-dimensional abstraction of the AP with respect to solution procedures, as it is very-large-scale, with a variable space of dimension m^9, where m is the number of assignments. However, it allows for hard combinatorial optimization problems (COPs) to be solved as "strict" linear programs. Because the size complexity of the model is O(m^9), it affirms "P=NP." Conditions which can be used to assess the validity (or guide the formulations) of other models are developed. Illustrative applications to hard COPs are provided for the Quadratic Assignment (QAP) and Traveling Salesman (TSP) problems. Issues pertaining to the extended formulations "barriers" for the LP modeling of hard COPs are not discussed because the developments in the paper are focused on the AP polytope only, and also because the applicability/non-applicability of those "barriers" in the context of the modeling framework used is thoroughly addressed in a separate paper*. Specific reasons why applications of the proposed modeling approach in variable spaces of dimension less than m^9 may not yield integral LP models are discussed (in an appendix), along with an illustrative numerical example. *: Diaby, M., M. Karwan, and L. Sun [2024]. On modeling NP-Complete problems as polynomial-sized linear programs: Escaping/Side-stepping the "barriers." Available at: arXiv:2304.07716 [cc.CC].
Parametric equations for temporal style assertions
Venue: arXiv cs.LO 2026
Year: 2026

Temporal logic provided an appealing approach to specifying properties of operating systems and other "reactive" software by allowing propositions to be qualified by "when" they must be true. This paper shows how to get the same effect, with a finer control over specification and a compositional notion of state, using ordinary working mathematics, without the weight of formal logic, by using sequential functions which are an alternate representation of Moore type state machines.
Network Slicing in 5G Mobile Communication Architecture, Profit Modeling, and Challenges
Venue: arXiv cs.NI 2026
Year: 2026

Efficient flexibility and higher system scalability call for enhanced network performance, better energy consumption, lower infrastructure cost, and effective resource utilization. To accomplish this, an architectural optimization and reconstruction of existing cellular network is required. Network slicing is considered to be one of the key enablers and an architectural answer of communication system of 2020 and beyond. Traditional mobile operators provide all types of services to various kinds of customers through a single network, however, with the deployment of network slicing operators are now able to divide entire network into different slices each with its own configuration and specific Quality of Service (QoS) requirements. In a slice-based network, each slice will be considered as a separate logical network. In this way, the infrastructure utilization and resource allocation will be much more energy and cost efficient in comparison to traditional network. In this paper, we provided a comprehensive discussion on concept and system architecture of network slicing with particular focus on its business aspect and profit modeling. We throughly discussed two different dimensions of profit modeling, so called Own-Slice Implementation and Resource Leasing for Outsourced Slices. We further addressed open research directions and existing challenges with the purpose of motivating new advances and adding realistic solutions to this emerging technology.
Warped Hypertime Representations for Long-term Autonomy of Mobile Robots
Venue: arXiv cs.RO 2026
Year: 2026

This paper presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modelling long-term, pseudo-periodic variations caused by human activities. Unlike previous approaches, the proposed method does not treat time and space separately, and its continuous nature respects both the temporal and spatial continuity of the modeled phenomena. The method extends the given spatial model with a set of wrapped dimensions that represent the periodicities of observed changes. By performing clustering over this extended representation, we obtain a model that allows us to predict future states of both discrete and continuous spatial representations. We apply the proposed algorithm to several long-term datasets and show that the method enables a robot to predict future states of representations with different dimensions. The experiments further show that the method achieves more accurate predictions than the previous state of the art.
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
Venue: arXiv cs.AI 2026
Year: 2026

In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a large diversity of goals, aiming to discover what is controllable in their environments, and what is not. Because some goals might prove easy and some impossible, agents must actively select which goal to practice at any moment, to maximize their overall mastery on the set of learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a modular Universal Value Function Approximator with hindsight learning to achieve a diversity of goals of different kinds within a unique policy and 2) an automated curriculum learning mechanism that biases the attention of the agent towards goals maximizing the absolute learning progress. Agents focus sequentially on goals of increasing complexity, and focus back on goals that are being forgotten. Experiments conducted in a new modular-goal robotic environment show the resulting developmental self-organization of a learning curriculum, and demonstrate properties of robustness to distracting goals, forgetting and changes in body properties.
The self-concordant perceptron is efficient on a sub-family feasibility instances
Venue: arXiv cs.DS 2026
Year: 2026

Strict linear feasibility or linear separation is usually tackled using efficient approximation/stochastic algorithms (that may even run in sub-linear times in expectation). However, today state of the art for solving exactly/deterministically such instances is to cast them as a linear programming instances. Inversely, this paper introduces a self-concordant perceptron algorithm which tackles directly strict linear feasibility with interior point paradigm. This algorithm has the same worse times complexity than state of the art linear programming algorithms but it complexity can be characterized more precisely eventually proving that it binary complexity is low on a sub-family of linear feasibility.
NFV Platform Design: A Survey
Venue: arXiv cs.NI 2026
Year: 2026

Due to the intrinsically inefficient service provisioning in traditional networks, Network Function Virtualization (NFV) keeps gaining attention from both industry and academia. By replacing the purpose-built, expensive, proprietary network equipment with software network functions consolidated on commodity hardware, NFV envisions a shift towards a more agile and open service provisioning paradigm with much lower capital expenditure (CapEx) and operational expenditure (OpEx). Nonetheless, just like any complex system, NFV platforms commonly consist of abounding software and hardware components and usually incorporate disparate design choices based on distinct motivations or use cases. This broad collection of convoluted alternatives makes it extremely arduous for network operators to make proper choices. Although numerous efforts have been devoted to investigating different aspects of NFV, none of them specifically focused on NFV platforms or attempted to explore the design space. In this paper, we present a comprehensive survey on NFV platform design. Our study solely targets existing NFV platform implementations. We begin with an architectural view of the standard reference NFV platform and present our taxonomy of existing NFV platforms based on the principal purpose of design. Then we thoroughly explore the design space and elaborate on the implementation choices each platform opts for. We believe that our study gives a detailed guideline for network operators or service providers to choose or implement the most appropriate NFV platforms based on their respective requirements. Note that this report serves as a complementary document for a published IEEE TNSM paper [1]. We will periodically update this document to include the newly proposed NFV platforms and design choices.
Formal Power Series on Algebraic Cryptanalysis
Venue: arXiv cs.SC 2026
Year: 2026

In the complexity estimation for an attack that reduces a cryptosystem to solving a system of polynomial equations, the degree of regularity and an upper bound of the first fall degree are often used in cryptanalysis. While the degree of regularity can be easily computed using a univariate formal power series under the semi-regularity assumption, determining an upper bound of the first fall degree requires investigating the concrete syzygies of an input system. In this paper, we investigate an upper bound of the first fall degree for a polynomial system over a sufficiently large field. In this case, we prove that the first fall degree of a non-semi-regular system is bounded above by the degree of regularity, and that the first fall degree of a multi-graded polynomial system is bounded above by a certain value determined from a multivariate formal power series. Moreover, we provide a theoretical assumption for computing the first fall degree of a polynomial system over a sufficiently large field.
Loss Barcode: A Topological Measure of Escapability in Loss Landscapes
Venue: arXiv cs.LG 2026
Year: 2026

Neural network training is commonly based on SGD. However, the understanding of SGD's ability to converge to good local minima, given the non-convex nature of loss functions and the intricate geometric characteristics of loss landscapes, remains limited. In this paper, we apply topological data analysis methods to loss landscapes to gain insights into the learning process and generalization properties of deep neural networks. We use the loss function topology to relate the local behavior of gradient descent trajectories with the global properties of the loss surface. For this purpose, we define the neural network's Topological Obstructions score ("TO-score") with the help of robust topological invariants, barcodes of the loss function, which quantify the escapability of local minima for gradient-based optimization. Our two principal observations are: 1) the loss barcode of the neural network decreases with increasing depth and width, therefore the topological obstructions to learning diminish; 2) in certain situations there is a connection between the length of minima segments in the loss barcode and the minima's generalization errors. Our statements are based on extensive experiments with fully connected, convolutional, and transformer architectures and several datasets including MNIST, FMNIST, CIFAR10, CIFAR100, SVHN, and multilingual OSCAR text dataset.
Efficient Binary Decision Diagram Manipulation in External Memory
Venue: arXiv cs.DS 2026
Year: 2026

We follow up on the idea of Lars Arge to rephrase the Reduce and Apply procedures of Binary Decision Diagrams (BDDs) as iterative I/O-efficient algorithms. We identify multiple avenues to simplify and improve the performance of his proposed algorithms. Furthermore, we extend the technique to other common BDD operations, many of which are not derivable using Apply operations alone, and we provide asymptotic improvements for the procedures that can be derived using Apply. These algorithms are implemented in a new BDD package, named Adiar. We see very promising results when comparing the performance of Adiar with conventional BDD packages that use recursive depth-first algorithms. For instances larger than 8.2 GiB, our algorithms, in parts using the disk, are 1.47 to 3.69 times slower compared to CUDD and Sylvan, exclusively using main memory. Yet, our proposed techniques are able to obtain this performance at a fraction of the main memory needed by conventional BDD packages to function. Furthermore, with Adiar we are able to manipulate BDDs that outgrow main memory and so surpass the limits of other BDD packages.
Mutation-based Evaluation of Cryptographic API Misuse Detectors
Venue: arXiv cs.CR 2026
Year: 2026

The correct use of cryptography is central to ensuring data security in modern software systems. Hence, several academic and commercial static analysis tools have been developed for detecting and mitigating crypto-API misuse. While developers are optimistically adopting these crypto-API misuse detectors (or crypto-detectors) in their software development cycles, this momentum must be accompanied by a rigorous understanding of their effectiveness at finding crypto-API misuse in practice. This paper describes the MASC framework, which enables a systematic and data-driven evaluation of crypto-detectors using mutation testing. We ground MASC in a comprehensive view of the problem space by developing a data-driven taxonomy of existing crypto-API misuse, containing 107 misuse cases organized among nine semantic clusters. We develop 19 generalizable usage-based mutation operators and three mutation scopes that can expressively instantiate thousands of compilable variants of the misuse cases for thoroughly evaluating crypto-detectors. Using MASC, in a previous study, we evaluated nine major crypto-detectors and discovered 19 unique, undocumented flaws that severely impact the ability of crypto-detectors to discover misuses in practice. This paper substantially extends our MASC framework and offers updated evaluation of the crypto-detectors in our 2022 study, in addition to 5 more, major crypto-detectors. Through this work, we find 6 new, undocumented flaws, and demonstrate that these flaws affect the crypto-detectors regardless of their origin; open-source community, industry, and/or research. We conclude with a discussion on the diverse perspectives that influence the design of crypto-detectors and future directions towards building security-focused crypto-detectors by design.
Learning with Subset Stacking
Venue: arXiv cs.LG 2026
Year: 2026

We propose a new regression algorithm that learns from a set of input-output pairs. Our algorithm is designed for populations where the relation between the input variables and the output variable exhibits a heterogeneous behavior across the predictor space. The algorithm starts with generating subsets that are concentrated around random points in the input space. This is followed by training a local predictor for each subset. Those predictors are then combined in a novel way to yield an overall predictor. We call this algorithm "LEarning with Subset Stacking" or LESS, due to its resemblance to the method of stacking regressors. We offer bagging and boosting variants of LESS and test against the state-of-the-art methods on several datasets. Our comparison shows that LESS is highly competitive.
Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)
Venue: arXiv cs.CR 2026
Year: 2026

In this work, we present a novel matrix-encoding method that is particularly convenient for neural networks to make predictions in a privacy-preserving manner using homomorphic encryption. Based on this encoding method, we implement a convolutional neural network for handwritten image classification over encryption. For two matrices $A$ and $B$ to perform homomorphic multiplication, the main idea behind it, in a simple version, is to encrypt matrix $A$ and the transpose of matrix $B$ into two ciphertexts respectively. With additional operations, the homomorphic matrix multiplication can be calculated over encrypted matrices efficiently. For the convolution operation, we in advance span each convolution kernel to a matrix space of the same size as the input image so as to generate several ciphertexts, each of which is later used together with the ciphertext encrypting input images for calculating some of the final convolution results. We accumulate all these intermediate results and thus complete the convolution operation. In a public cloud with 40 vCPUs, our convolutional neural network implementation on the MNIST testing dataset takes $\sim$ 287 seconds to compute ten likelihoods of 32 encrypted images of size $28 \times 28$ simultaneously. The data owner only needs to upload one ciphertext ($\sim 19.8$ MB) encrypting these 32 images to the public cloud.
Differential privacy for symmetric log-concave mechanisms
Venue: arXiv cs.CR 2026
Year: 2026

Adding random noise to database query results is an important tool for achieving privacy. A challenge is to minimize this noise while still meeting privacy requirements. Recently, a sufficient and necessary condition for $(ε, δ)$-differential privacy for Gaussian noise was published. This condition allows the computation of the minimum privacy-preserving scale for this distribution. We extend this work and provide a sufficient and necessary condition for $(ε, δ)$-differential privacy for all symmetric and log-concave noise densities. Our results allow fine-grained tailoring of the noise distribution to the dimensionality of the query result. We demonstrate that this can yield significantly lower mean squared errors than those incurred by the currently used Laplace and Gaussian mechanisms for the same $ε$ and $δ$.
Network Topology Optimization via Deep Reinforcement Learning
Venue: arXiv cs.NI 2026
Year: 2026

Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the combinatorial nature of network topology, it is extremely difficult to obtain an optimal solution, especially since topology planning in networks also often comes with management-specific constraints. As a result, local optimization with hand-tuned heuristic methods from human experts is often adopted in practice. Yet, heuristic methods cannot cover the global topology design space while taking into account constraints, and cannot guarantee to find good solutions. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm for graph searching, called DRL-GS, for network topology optimization. DRL-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL agent to conduct a topology search. DRL-GS can efficiently search over relatively large topology space and output topology with satisfactory performance. We conduct a case study based on a real-world network scenario, and our experimental results demonstrate the superior performance of DRL-GS in terms of both efficiency and performance.
EqVIO: An Equivariant Filter for Visual Inertial Odometry
Venue: arXiv cs.RO 2026
Year: 2026

Visual-Inertial Odometry (VIO) is the problem of estimating a robot's trajectory by combining information from an inertial measurement unit (IMU) and a camera, and is of great interest to the robotics community. This paper develops a novel Lie group symmetry for the VIO problem and applies the recently proposed equivariant filter. The proposed symmetry is compatible with the invariance of the VIO reference frame, leading to improved filter consistency. The bias-free IMU dynamics are group-affine, ensuring that filter linearisation errors depend only on the bias estimation error and measurement noise. Furthermore, visual measurements are equivariant with respect to the symmetry, enabling the application of the higher-order equivariant output approximation to reduce approximation error in the filter update equation. As a result, the equivariant filter (EqF) based on this Lie group is a consistent estimator for VIO with lower linearisation error in the propagation of state dynamics and a higher order equivariant output approximation than standard formulations. Experimental results on the popular EuRoC and UZH FPV datasets demonstrate that the proposed system outperforms other state-of-the-art VIO algorithms in terms of both speed and accuracy.
Discovering the Representation Bottleneck of Graph Neural Networks
Venue: arXiv cs.LG 2026
Year: 2026

Graph neural networks (GNNs) rely mainly on the message-passing paradigm to propagate node features and build interactions, and different graph learning problems require different ranges of node interactions. In this work, we explore the capacity of GNNs to capture node interactions under contexts of different complexities. We discover that GNNs usually fail to capture the most informative kinds of interaction styles for diverse graph learning tasks, and thus name this phenomenon GNNs' representation bottleneck. As a response, we demonstrate that the inductive bias introduced by existing graph construction mechanisms can result in this representation bottleneck, \emph{i.e.}, preventing GNNs from learning interactions of the most appropriate complexity. To address that limitation, we propose a novel graph rewiring approach based on interaction patterns learned by GNNs to dynamically adjust each node's receptive fields. Extensive experiments on both real-world and synthetic datasets prove the effectiveness of our algorithm in alleviating the representation bottleneck and its superiority in enhancing the performance of GNNs over state-of-the-art graph rewiring baselines.
Towards Unifying Perceptual Reasoning and Logical Reasoning
Venue: arXiv cs.AI 2026
Year: 2026

An increasing number of scientific experiments support the view of perception as Bayesian inference, which is rooted in Helmholtz's view of perception as unconscious inference. Recent study of logic presents a view of logical reasoning as Bayesian inference. In this paper, we give a simple probabilistic model that is applicable to both perceptual reasoning and logical reasoning. We show that the model unifies the two essential processes common in perceptual and logical systems: on the one hand, the process by which perceptual and logical knowledge is derived from another knowledge, and on the other hand, the process by which such knowledge is derived from data. We fully characterise the model in terms of logical consequence relations.