OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Agent Contracts
Venue: arXiv cs.GT 2026
Year: 2026

We study a natural combinatorial single-principal multi-agent contract design problem, in which a principal motivates a team of agents to exert effort toward a given task. At the heart of our model is a reward function, which maps the agent efforts to an expected reward of the principal. We seek to design computationally efficient algorithms for finding optimal (or near-optimal) linear contracts for reward functions that belong to the complement-free hierarchy. Our first main result gives constant-factor approximation algorithms for submodular and XOS reward functions, with value oracles for submodular reward functions and value and demand oracles for XOS reward functions. It relies on an unconventional use of ``prices'' and (approximate) demand queries for selecting the set of agents that the principal should contract with, and exploits a novel scaling property of XOS functions and their marginals, which may be of independent interest. As our second main result, we show that constant approximation is the best we can get for submodular reward functions, even with both value and demand oracles. For the larger class of subadditive reward functions, we establish an $Ω(\sqrt{n})$ impossibility for settings with $n$ agents. A striking feature of this impossibility is that it applies to subadditive functions that are constant-factor close to submodular. This rapid degradation presents a surprising departure from previous literature, e.g., on combinatorial auctions, where approximation guarantees tend to deteriorate more
DMH-HARQ: Reliable and Open Latency-Constrained Wireless Transport Network
Venue: arXiv cs.IT 2026
Year: 2026

The extreme requirements for high reliability and low latency in the upcoming Sixth Generation (6G) wireless networks are challenging the design of multi-hop wireless transport networks. Inspired by the advent of the virtualization concept in the wireless networks design and openness paradigm as fostered by the Open-Radio Access Network (O-RAN) Alliance, we target a revolutionary resource allocation scheme to improve the overall transmission efficiency. In this paper, we investigate the problem of automatic repeat request (ARQ) in multi-hop decode-and-forward (DF) relaying in the finite blocklength (FBL) regime, and propose a dynamic scheme of multi-hop hybrid ARQ (HARQ), which maximizes the end-to-end (E2E) communication reliability in the wireless transport network. We also propose an integer dynamic programming (DP) algorithm to efficiently solve the optimal Dynamic Multi-Hop HARQ (DMH-HARQ) strategy. Constrained within a certain time frame to accomplish E2E transmission, our proposed approach is proven to outperform the conventional listening-based cooperative ARQ, as well as any static HARQ strategy, regarding the E2E reliability. It is applicable without dependence on special delay constraint, and is particularly competitive for long-distance transport network with many hops.
Explainability of Text Processing and Retrieval Methods: A Survey
Venue: arXiv cs.IR 2026
Year: 2026

Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.
Multilayer Horizontal Visibility Graphs for Multivariate Time Series Analysis
Venue: arXiv cs.SI 2026
Year: 2026

Multivariate time series analysis is a vital but challenging task, with multidisciplinary applicability, tackling the characterization of multiple interconnected variables over time and their dependencies. Traditional methodologies often adapt univariate approaches or rely on assumptions specific to certain domains or problems, presenting limitations. A recent promising alternative is to map multivariate time series into high-level network structures such as multiplex networks, with past work relying on connecting successive time series components with interconnections between contemporary timestamps. In this work, we first define a novel cross-horizontal visibility mapping between lagged timestamps of different time series and then introduce the concept of multilayer horizontal visibility graphs. This allows describing cross-dimension dependencies via inter-layer edges, leveraging the entire structure of multilayer networks. To this end, a novel parameter-free topological measure is proposed and common measures are extended for the multilayer setting. Our approach is general and applicable to any kind of multivariate time series data. We provide an extensive experimental evaluation with both synthetic and real-world datasets. We first explore the proposed methodology and the data properties highlighted by each measure, showing that inter-layer edges based on cross-horizontal visibility preserve more information than previous mappings, while also complementing the information captured by commonly used intra-layer edges. We then illustrate the applicability and validity of our approach in multivariate time series mining tasks, showcasing its potential for enhanced data analysis and insights.
Improving Fairness with Ensemble Combination: Margin-Dependent Bounds
Venue: arXiv cs.LG 2026
Year: 2026

The concern about hidden discrimination in machine learning models is growing, as their widespread real-world applications increasingly impact human lives. Various techniques, including commonly used group fairness measures and several fairness-aware ensemble-based methods, have been developed to enhance fairness. However, existing fairness measures typically focus on only one aspect -- either group or individual fairness, and the compatibility difficulty among these measures indicates a possibility of remaining biases even when one of them is satisfied. Moreover, existing mechanisms to boost fairness usually present empirical results to show validity, yet few of them discuss whether fairness can be boosted with certain theoretical guarantees. To address these issues, we propose a fairness quality measure named `discriminative risk' by only perturbing protected attributes in instances, to express both individual and group fairness aspects. Furthermore, we investigate its properties and establish the first- and second-order oracle bounds and their relaxations, which show that fairness is possibly improved via ensemble combination with margin-dependent bounds. The analysis is suitable for both binary and multi-class classification. A few ensemble pruning methods are also proposed to utilise our proposed measure and obtain both accurate and fair sub-ensembles; comprehensive experiments are conducted to evaluate the effectiveness of the proposed fairness measure and pruning methods.
On the Global Convergence of Risk-Averse Natural Policy Gradient Methods with Expected Conditional Risk Measures
Venue: arXiv cs.LG 2026
Year: 2026

Risk-sensitive reinforcement learning (RL) has become a popular tool for controlling the risk of uncertain outcomes and ensuring reliable performance in highly stochastic sequential decision-making problems. While it has been shown that policy gradient methods can find globally optimal policies in the risk-neutral setting, it remains unclear if the risk-averse variants enjoy the same global convergence guarantees. In this paper, we consider a class of dynamic time-consistent risk measures, named Expected Conditional Risk Measures (ECRMs), and derive natural policy gradient (NPG) updates for ECRMs-based RL problems. We provide global optimality and iteration complexity of the proposed risk-averse NPG algorithm with softmax parameterization and entropy regularization under both exact and inexact policy evaluation. Furthermore, we test our risk-averse NPG algorithm on a stochastic Cliffwalk environment to demonstrate the efficacy of our method.
Understanding the Issues, Their Causes and Solutions in Microservices Systems: An Empirical Study
Venue: arXiv cs.SE 2026
Year: 2026

Many small to large organizations have adopted the Microservices Architecture (MSA) style to develop and deliver their core businesses. Despite the popularity of MSA in the software industry, there is a limited evidence-based and thorough understanding of the types of issues (e.g., errors, faults, failures, and bugs) that microservices system developers experience, the causes of the issues, and the solutions as potential fixing strategies to address the issues. To ameliorate this gap, we conducted a mixed-methods empirical study that collected data from 2,641 issues from the issue tracking systems of 15 open-source microservices systems on GitHub, 15 interviews, and an online survey completed by 150 practitioners from 42 countries across 6 continents. Our analysis led to comprehensive taxonomies for the issues, causes, and solutions. The findings of this study informthat Technical Debt, Continuous Integration and Delivery, Exception Handling, Service Execution and Communication, and Security are the most dominant issues in microservices systems. Furthermore, General Programming Errors, Missing Features and Artifacts, and Invalid Configuration and Communication are the main causes behind the issues. Finally, we found 177 types of solutions that can be applied to fix the identified issues. Based on our study results, we propose a future research framework that outlines key problem dimensions and actionable study strategies to support the engineering of emergent and next-generation microservices systems.
Hardness of monadic second-order formulae over succinct graphs
Venue: arXiv cs.CC 2026
Year: 2026

Our main result is a succinct counterpoint to Courcelle's meta-theorem as follows: every cw-nontrivial monadic second-order (MSO) property is either NP-hard or coNP-hard over graphs given by succinct representations. Succint representations are Boolean circuits computing the adjacency relation. Cw-nontrivial properties are those which have infinitely many models and infinitely many countermodels with bounded cliquewidth. Moreover, we explore what happens when the cw-nontriviality condition is dropped and show that, under a reasonable complexity assumption, the previous dichotomy fails, even for questions expressible in first-order logic.
Critical Relaxed-Stable Matchings with Ties in the Many-to-Many Setting
Venue: arXiv cs.DS 2026
Year: 2026

We study the many-to-many bipartite matching problem in the presence of preferences where ties, as well as lower quotas, may appear on both sides of the bipartition. The input is a bipartite graph $G=(A \cup B, E)$, where each vertex in $A \cup B$ has a positive upper quota and a non-negative lower quota denoting the maximum and minimum number of vertices that can be assigned to it from its neighborhood. Additionally, each vertex specifies a preference ordering, possibly containing ties, over its neighbors. A \textit{critical} matching is a matching which fulfills vertex lower quotas to the maximum possible extent. We seek to compute a matching that is critical as well as optimal with respect to the preferences of vertices. Stability, a well-accepted notion of optimality in the presence of two-sided preferences, is generalized to weak-stability in the presence of ties. However, a matching that is critical as well as weakly stable may not exist. Popularity is another well-investigated notion of optimality for the two-sided preference model; however, in the presence of ties (even without lower quotas), a popular matching may not exist. We, therefore, consider the notion of relaxed stability, which was introduced and studied by Krishnaa, Limaye, Nasre, and Nimbhorkar~(JoCO 2023). We show that a matching that is critical as well as relaxed-stable always exists, although computing a maximum-size relaxed-stable matching turns out to be NP-hard. Our main contribution achieves a $\frac{2}{3}$-approximation to the maximum cardinality critical relaxed-stable matching in polynomial time.
A Computational Analysis of Strategic Nominations: Modeling Equilibrium and Complexity in Organizational Elections
Venue: arXiv cs.GT 2026
Year: 2026

We study organizational elections in which each group nominates one candidate and receives as payoff its members expected utility under a probabilistic winning rule. We empirically justify a standard monotonicity assumption by simulating two- and three-group elections, finding that a candidates aggregate voter utility correlates monotonically with win probability. For three or more groups, we show that pure-strategy Nash equilibria (PSNE) may fail to exist even under egoistic preferences, and that deciding PSNE existence is NP-complete in a succinct (general form) representation. For cross-monotone winning-probability functions, we give simple sufficient conditions for PSNE existence and an FPT algorithm to compute one, parameterized by the number of irresolute groups and nominating depth. Finally, for crossmonotone, order-preserving winning-probability functions, we bound the price of anarchy of egoistic games by the number of groups.
(Un)fair devices: Moving beyond AI accuracy in personal sensing
Venue: arXiv cs.CY 2026
Year: 2026

Personal devices are omnipresent in our lives, seamlessly monitoring our activities, from smart rings tracking sleep patterns to smartwatches keeping an eye on missed heartbeats. The rich data streams from such devices fuel advanced Artificial Intelligence (AI) applications. Instead of solely relying on direct sensor measurements, these applications are increasingly leveraging Machine Learning (ML) model estimates to derive insights. But are these estimates biased or not? This literature review delivers compelling evidence about the impact of hidden biases that creep into ML models deployed on personal devices. We discuss critical bias issues drawn from prior work such as racial bias in pulse oximeters, weight bias in optical heart rate sensors, and sex bias in audio-based diagnostics. In response to these challenges, we advocate for a shift from prioritizing performance-oriented evaluations of personal devices to adopting assessments grounded in a human-centered approach. To facilitate this transition, we provide guidelines for the design, development, evaluation, and use of unbiased AI in personal devices, recognizing their potential impact on improving our health, lifestyle, and productivity -- more than any other technology.
A Survey of Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.
An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies
Venue: arXiv cs.AI 2026
Year: 2026

Inconsistency handling is an important issue in knowledge management. Especially in ontology engineering, logical inconsistencies may occur during ontology construction. A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology. However, previous studies on selecting maximum consistent subsets have rarely considered the semantics of the axioms, which may result in irrational inference. In this paper, we propose a novel approach to reasoning with inconsistent ontologies in description logics based on the embeddings of axioms. We first give a method for turning axioms into distributed semantic vectors to compute the semantic connections between the axioms. We then define an embedding-based method for selecting the maximum consistent subsets and use it to define an inconsistency-tolerant inference relation. We show the rationality of our inference relation by considering some logical properties. Finally, we conduct experiments on several ontologies to evaluate the reasoning power of our inference relation. The experimental results show that our embedding-based method can outperform existing inconsistency-tolerant reasoning methods based on maximal consistent subsets.
Regulatory Markets: The Future of AI Governance
Venue: arXiv cs.AI 2026
Year: 2026

Appropriately regulating artificial intelligence is an increasingly urgent and widespread policy challenge. We identify two primary, competing problem. First is a technical deficit: Legislatures and regulatory face significant challenges in rapidly translating conventional command-and-control legal requirements into technical requirements. Second is a democratic deficit: Over-reliance on industry to provide technical standards fails to ensure that the many values-based decisions that must be made to shape AI development and deployment are made by democratically accountable public, not private, actors. We propose a solution: regulatory markets, in which governments require the targets of regulation to purchase regulatory services from a government-licensed private regulator. This approach to AI regulation could overcome the limitations of both command-and-control regulation and excessive delegation to industry. Regulatory markets could enable governments to establish policy priorities for the regulation of AI while relying on market forces and industry R&D efforts to pioneer the technical methods of regulation that best achieve policymakers' stated objectives.
An Exploratory Study of Bug-Introducing Changes: Exploring Relationships in Bug-Introducing Changes Towards Causal Understanding
Venue: arXiv cs.SE 2026
Year: 2026

Context: Many studies consider the relation between individual aspects of the software engineering process and bug-introduction, e.g., software testing and code review. These studies typically only identify correlations between their set of variables without accounting for interactions with external variables, such as confounding factors. Objective: Within this study, we provide a broad empirical view on practices of software development and their relation to bug-introducing changes \rev{to enable} future work on causal relations between those aspects. Method: We consider the bugs, the type of change that introduced the bug, aspects of the build process, code review, software tests, and any other discussion related to the bug that we can identify. We use a qualitative approach that first describes variables of the development process and then groups the variables based on their relations. From these groups, we deduce how their (pairwise) interactions affect bug-introducing changes. Results: We found multiple relevant relations within the development process of bug-introducing changes. Logical groups of variables and their relations provide a framework for discovering areas of interest regarding intermediate effects in the process and confounders towards bug-introduction. Conclusion: Software engineering practices applied during the development of bug-introducing changes are interdependent. This work lays the foundation to understand why bugs are introduced using causal modeling, discovery, and inference.
CNN-based IoT Device Identification: A Comparative Study on Payload vs. Fingerprint
Venue: arXiv cs.CR 2026
Year: 2026

The proliferation of the Internet of Things (IoT) has introduced a massive influx of devices into the market, bringing with them significant security vulnerabilities. In this diverse ecosystem, robust IoT device identification is a critical preventive measure for network security and vulnerability management. This study proposes a deep learning-based method to identify IoT devices using the Aalto dataset. We employ Convolutional Neural Networks (CNN) to classify devices by converting network packet payloads into pseudo-images. Furthermore, we compare the performance of this payload-based approach against a feature-based fingerprinting method. Our results indicate that while the fingerprint-based method is significantly faster (approximately 10x), the payload-based image classification achieves comparable accuracy, highlighting the trade-offs between computational efficiency and data granularity in IoT security.
FedHB: Hierarchical Bayesian Federated Learning
Venue: arXiv cs.LG 2026
Year: 2026

We propose a novel hierarchical Bayesian approach to Federated Learning (FL), where our model reasonably describes the generative process of clients' local data via hierarchical Bayesian modeling: constituting random variables of local models for clients that are governed by a higher-level global variate. Interestingly, the variational inference in our Bayesian model leads to an optimisation problem whose block-coordinate descent solution becomes a distributed algorithm that is separable over clients and allows them not to reveal their own private data at all, thus fully compatible with FL. We also highlight that our block-coordinate algorithm has particular forms that subsume the well-known FL algorithms including Fed-Avg and Fed-Prox as special cases. Beyond introducing novel modeling and derivations, we also offer convergence analysis showing that our block-coordinate FL algorithm converges to an (local) optimum of the objective at the rate of $O(1/\sqrt{t})$, the same rate as regular (centralised) SGD, as well as the generalisation error analysis where we prove that the test error of our model on unseen data is guaranteed to vanish as we increase the training data size, thus asymptotically optimal.