Datasets:

youngfish42
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PaperVault

paper_name stringlengths 4 399	paper_url stringlengths 21 343	paper_authors listlengths 0 125	paper_abstract stringlengths 0 43.4k	paper_code stringlengths 1 149	conf stringlengths 6 18
Front Matter	https://www.vldb.org/pvldb/vol20/FrontMatterVol20No1.pdf	[ "Katja Hose", "Matthias Boehm" ]		#	VLDB2026
Replace or Augment: Rethinking the Role of Learned Indexes in Relational Databases	https://www.vldb.org/pvldb/vol20/p1-chen.pdf	[ "Zhipeng Chen", "Meng Li", "Hancheng Wang", "Junhan Lv", "Haipeng Dai", "Siqiang Luo" ]		#	VLDB2026
Evergreen: Efficient Claim Verification for Semantic Aggregates	https://www.vldb.org/pvldb/vol20/p16-lee.pdf	[ "Alexander W. Lee", "Benjamin Han", "Shayak Sen", "Samuel Yeom", "Ugur Çetintemel", "Anupam Datta" ]		#	VLDB2026
Can You Trust the Vectors in Your Vector Database? Black-Hole Attack from Embedding Space Defects	https://www.vldb.org/pvldb/vol20/p31-tang.pdf	[ "Hanxi Li", "Jianan Zhou", "Jiale Lao", "Yibo Wang", "Zhengmao Ye", "Yang Cao", "Junfen Wang", "Mingjie Tang" ]		#	VLDB2026
Rethinking Query Optimization for Multi-Agent Systems	https://www.vldb.org/pvldb/vol20/p46-kaoudi.pdf	[ "Zoi Kaoudi", "Ioana Giurgiu" ]		#	VLDB2026
Prune Early, Intersect Fast: PRISM, a Prefix-based Radix Integer Set with Morphing Nodes	https://www.vldb.org/pvldb/vol20/p53-riedl.pdf	[ "Adrian Riedl", "Thomas Neumann" ]		#	VLDB2026
ParaKplex: A parallel local search algorithm for the maximum K-Plex problem	https://doi.org/10.1016/j.artint.2026.104604	[ "Jieyu Wu", "Rui Sun", "Yiyuan Wang", "Minghao Yin" ]		#	AI2026
The model counting competitions 2021-2023	https://doi.org/10.1016/j.artint.2026.104605	[ "Johannes Klaus Fichte", "Markus Hecher" ]		#	AI2026
Posterior-conditioned multi-agent reinforcement learning for adaptive cyber defense	https://doi.org/10.1016/j.artint.2026.104606	[ "Armita Kazeminajafabadi", "Amirhossein Ravari", "Nathaniel D. Bastian", "Tian Lan", "Mahdi Imani" ]		#	AI2026
Towards efficient federated recommendation with discrete graph convolutional network	https://doi.org/10.1016/j.artint.2026.104607	[ "Yang Li", "Weike Pan", "Qiang Yang", "Zhong Ming" ]		#	AI2026
Evidence-guided learning against noisy and sparse labels on graphs	https://doi.org/10.1016/j.artint.2026.104608	[ "Siyu Yi", "Wei Zhang", "Zhengyang Mao", "Yongdao Zhou", "Ziyue Qiao", "Li Shen", "Dacheng Tao", "Jiancheng Lv", "Wei Ju" ]		#	AI2026
Similarity of planning domain models via answer set programming	https://doi.org/10.1016/j.artint.2026.104620	[ "Lukás Chrpa", "Carmine Dodaro", "Marco Maratea", "Marco Mochi", "Mauro Vallati" ]		#	AI2026
Learning neuro-symbolic convergent term rewriting systems	https://doi.org/10.1016/j.artint.2026.104622	[ "Flavio Petruzzellis", "Alberto Testolin", "Alessandro Sperduti" ]		#	AI2026
Automatic prompt debiasing via contrastive reflection for fairness enhancement in large language models	https://doi.org/10.1016/j.artint.2026.104625	[ "Juncheng Hu", "Yingji Li", "Rui Song", "Kedi Lyu", "Yi Chang" ]		#	AI2026
Theory of mind in action: The instruction inference task in dynamic human-agent collaboration	https://doi.org/10.1016/j.artint.2026.104621	[ "Fardin Saad", "Pradeep K. Murukannaiah", "Munindar P. Singh" ]		#	AI2026
SiameseNet+: Classification of HDLSS data with tensor affinities	https://doi.org/10.1016/j.artint.2026.104623	[ "Hongmin Cai", "Yu Wang", "Fei Qi", "Junyu Li", "Zhuoyao Wang", "Yuhua Qian", "Yiu-Ming Cheung" ]		#	AI2026
ADKF: Adaptive decomposition and KAN-based fusion for long-term time series forecasting	https://doi.org/10.1016/j.artint.2026.104624	[ "Yiguo Zhu", "Xinyang Yuan", "Cong Han", "Peng Cheng" ]		#	AI2026
Multi-subspace meta-learning	https://doi.org/10.1016/j.artint.2026.104626	[ "Weisen Jiang", "Yu Zhang", "James T. Kwok" ]		#	AI2026
P2Mark: Plug-and-play parameter-level watermarking for neural speech generation	https://doi.org/10.1016/j.artint.2026.104627	[ "Yong Ren", "Jiangyan Yi", "Tao Wang", "Jianhua Tao", "Zheng Lian", "Zhengqi Wen", "Chenxing Li", "Ruibo Fu", "Ye Bai", "Xiaohui Zhang" ]		#	AI2026
Capacity alternating-time temporal epistemic logic	https://doi.org/10.1016/j.artint.2026.104628	[ "Gabriel Ballot", "Vadim Malvone", "Jean Leneutre" ]		#	AI2026
Persuading stable matching	https://doi.org/10.1016/j.artint.2026.104629	[ "Jonathan Shaki", "Jiarui Gan", "Sarit Kraus" ]		#	AI2026
Optimal metric distortion for matching on the line	https://doi.org/10.1016/j.artint.2026.104630	[ "Aris Filos-Ratsikas", "Vasilis Gkatzelis", "Mohamad Latifian", "Emma Rewinski", "Alexandros A. Voudouris" ]		#	AI2026
Rising rested bandits: Lower bounds and efficient algorithms	https://doi.org/10.1016/j.artint.2026.104638	[ "Marco Fiandri", "Alberto Maria Metelli", "Francesco Trovò" ]		#	AI2026
Are language models rational? The case of coherence norms and belief revision	https://doi.org/10.1016/j.artint.2026.104639	[ "Thomas Hofweber", "Peter Hase", "Elias Stengel-Eskin", "Mohit Bansal" ]		#	AI2026
Adaptive large language model reasoning for autonomous driving decision-making	https://doi.org/10.1016/j.artint.2026.104640	[ "Haicheng Liao", "Chengyue Wang", "Hanlin Kong", "Zhenning Li", "Zhengbing He", "Zhiyong Cui", "Guohui Zhang", "Cheng-Zhong Xu" ]		#	AI2026
Mitigating data reconstruction attacks in federated learning: Conditions, bounds, and theoretical guarantees	https://doi.org/10.1016/j.artint.2026.104641	[ "Xiaojin Zhang", "Mingcong Xu", "Wei Chen", "Qiang Yang" ]		#	AI2026
Optimal any-angle path planning in static and dynamic environments	https://doi.org/10.1016/j.artint.2026.104642	[ "Yiyuan Zou", "Clark Borst" ]		#	AI2026
ITEACH-Net: Inverted teacher-student search network for emotion recognition in conversation	https://doi.org/10.1016/j.artint.2026.104643	[ "Haiyang Sun", "Zheng Lian", "Chenglong Wang", "Kang Chen", "Licai Sun", "Bin Liu", "Jianhua Tao" ]		#	AI2026
Central description length (CDL) clustering validation index	https://doi.org/10.1016/j.artint.2026.104644	[ "Mahdi Shamsi", "Soosan Beheshti" ]		#	AI2026
Kernel-bounded clustering: Achieving the objective of spectral clustering without eigendecomposition	https://doi.org/10.1016/j.artint.2025.104440	[ "Hang Zhang", "Kai Ming Ting", "Ye Zhu" ]		#	AI2026
A General Theoretical Framework for Learning Smallest Interpretable Models	https://doi.org/10.1016/j.artint.2025.104441	[ "Sebastian Ordyniak", "Giacomo Paesani", "Mateusz Rychlicki", "Stefan Szeider" ]	We develop a general algorithmic framework that allows us to obtain fixed-parameter tractability for computing smallest symbolic models that represent given data. Our framework applies to all ML model types that admit a certain extension property. By establishing this extension property for decision trees, decision set...	#	AI2026
Online POMDP planning with anytime deterministic optimality guarantees	https://doi.org/10.1016/j.artint.2025.104442	[ "Moran Barenboim", "Vadim Indelman" ]	Decision-making under uncertainty is a critical aspect of many practical autonomous systems due to incomplete information. Partially Observable Markov Decision Processes (POMDPs) offer a mathematically principled framework for formulating decision-making problems under such conditions. However, finding an optimal solut...	#	AI2026
Bridging sparse domain semantics via an asymmetric siamese framework with virtual anchor guidance for domain-specific multimodal translation	https://doi.org/10.1016/j.artint.2025.104443	[ "Junjun Guo", "Yifan Liu", "Zhengtao Yu" ]		#	AI2026
Many-objective problems where crossover is provably essential	https://doi.org/10.1016/j.artint.2025.104453	[ "Andre Opris" ]	This article addresses theory in evolutionary many-objective optimization and focuses on the role of crossover operators. The advantages of using crossover are hardly understood and rigorous runtime analyses with crossover are lagging far behind its use in practice, specifically in the case of more than two objectives....	#	AI2026
Natural Language Syntax and First-Order Inference	https://doi.org/10.1016/0004-3702(92)90063-4	[ "David A. McAllester", "Robert Givan" ]		#	AI2026
Cooperation Under Uncertainty in Distributed Expert Systems	https://doi.org/10.1016/0004-3702(92)90064-5	[ "Chengqi Zhang" ]		#	AI2026
Connectionist Learning of Belief Networks	https://doi.org/10.1016/0004-3702(92)90065-6	[ "Radford M. Neal" ]		#	AI2026
Use of the Gibbs Sampler in Expert Systems	https://doi.org/10.1016/0004-3702(92)90066-7	[ "Jeremy York" ]		#	AI2026
A Symbolic Approach to Qualitative Kinematics	https://doi.org/10.1016/0004-3702(92)90025-S	[ "Boi Faltings" ]	Abstract An important problem for mechanism design and analysis is reasoning about the relationship between object shapes and their kinematic function. Such reasoning is difficult because of the unstructured influence of the shapes' metric dimensions. In this paper, we show how a qualitative kinematic analysis can be b...	#	AI2026
Qualitative Superposition	https://doi.org/10.1016/0004-3702(92)90026-T	[ "Enrico W. Coiera" ]		#	AI2026
Characterizing Diagnoses and Systems	https://doi.org/10.1016/0004-3702(92)90027-U	[ "Johan de Kleer", "Alan K. Mackworth", "Raymond Reiter" ]		#	AI2026
On the Complexity of Blocks-World Planning	https://doi.org/10.1016/0004-3702(92)90028-V	[ "Naresh Gupta", "Dana S. Nau" ]		#	AI2026
Reasoning about Model Accuracy	https://doi.org/10.1016/0004-3702(92)90029-W	[ "Daniel S. Weld" ]		#	AI2026
Linear Resolution for Consequence Finding	https://doi.org/10.1016/0004-3702(92)90030-2	[ "Katsumi Inoue" ]		#	AI2026
The Emperor's Real Mind: Review of Roger Penrose'sThe Emperor's New Mind: Concerning Computers, Minds and the Laws of Physics	https://doi.org/10.1016/0004-3702(92)90031-R	[ "Aaron Sloman" ]		#	AI2026
Federated neural nonparametric point processes	https://doi.org/10.1016/j.artint.2025.104454	[ "Hui Chen", "Xuhui Fan", "Hengyu Liu", "Yaqiong Li", "Zhi-Lin Zhao", "Feng Zhou", "Christopher John Quinn", "Longbing Cao" ]	Temporal point processes (TPPs) are effective for modeling event occurrences over time but struggle with sparse and uncertain events in federated systems, where privacy is a major concern. To address this, we propose FedPP , a federated neural nonparametric point process model. FedPP integrates neural embeddings into s...	#	AI2026
Disentangling data distribution for optimal and communication-efficient federated learning	https://doi.org/10.1016/j.artint.2025.104455	[ "Xinyuan Zhao", "Hanlin Gu", "Lixin Fan", "Yuxing Han", "Qiang Yang" ]	Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy. Yet the wide applicability of FL is hindered by entanglement of data distributions across different clients. This paper demonstrates ...	#	AI2026
Human compliance with computational argumentation principles	https://doi.org/10.1016/j.artint.2025.104457	[ "Predrag Teovanovic", "Srdjan Vesic", "Bruno Yun" ]	This paper presents a comprehensive examination of human compliance with normative principles of argumentation across two experimental studies. The first study investigated whether fundamental argumentation principles such as anonymity, independence, void precedence, and maximality align with human reasoning. Additiona...	#	AI2026
OBDDs, SDDs, and circuits of bounded width: Completeness matters	https://doi.org/10.1016/j.artint.2025.104458	[ "Alexis de Colnet", "Sebastian Ordyniak", "Stefan Szeider" ]	Ordered Binary Decision Diagrams (OBDDs) are dynamic data structures with many application areas. The literature suggested that OBDDs of bounded width equate to Boolean circuits of bounded pathwidth. In this paper, we show that this relationship holds only for complete OBDDs. Additionally, we demonstrate that similar l...	#	AI2026
Probabilistically robust counterfactual explanations under model changes	https://doi.org/10.1016/j.artint.2025.104459	[ "Luca Marzari", "Francesco Leofante", "Ferdinando Cicalese", "Alessandro Farinelli" ]	We study the problem of generating robust counterfactual explanations for deep learning models subject to model changes. We focus on plausible model changes altering model parameters and propose a novel framework to reason about the robustness property in this setting. To motivate our solution, we begin by showing for ...	#	AI2026
Multi-objective reinforcement learning for provably incentivising alignment with value systems	https://doi.org/10.1016/j.artint.2025.104460	[ "Manel Rodriguez-Soto", "Roxana Radulescu", "Filippo Bistaffa", "Oriol Ricart", "Arnau Mayoral-Macau", "Maite López-Sánchez", "Juan A. Rodríguez-Aguilar", "Ann Nowé" ]	This paper addresses the problem of ensuring that autonomous learning agents align with multiple moral values. Specifically, we present the theoretical principles and algorithmic tools necessary for creating an environment where we ensure that the agent learns a behaviour aligned with multiple moral values while strivi...	#	AI2026
Is DIBBS a DXBB algorithm?	https://doi.org/10.1016/j.artint.2025.104468	[ "Nathan R. Sturtevant", "Shahaf S. Shperberg", "Ariel Felner" ]		#	AI2026
Using execution logs for improving Pseudo-Boolean propagation	https://doi.org/10.1016/j.artint.2025.104470	[ "Robert Nieuwenhuis", "Albert Oliveras", "Enric Rodríguez-Carbonell", "Rui Zhao" ]	Among all procedures that CDCL-based SAT solvers implement, unit propagation dominates the total running time. Hence, it is not a surprise that large research efforts have been invested on improving it. As a result, the two-watched-literal scheme, enhanced with implementation details boosting its performance, emerged a...	#	AI2026
A structural complexity analysis of synchronous dynamical systems	https://doi.org/10.1016/j.artint.2025.104472	[ "Eduard Eiben", "Robert Ganian", "Thekla Hamm", "Viktoriia Korchemna" ]	Synchronous dynamical systems are well-established models that have been used to capture a range of phenomena in networks, including opinion diffusion, spread of disease and product adoption. We study the three most notable problems in synchronous dynamical systems: whether the system will transition to a target config...	#	AI2026
Automated planning instance generation with neuro-symbolic AI	https://doi.org/10.1016/j.artint.2025.104471	[ "Carlos Núñez-Molina", "Pablo Mesejo", "Juan Fernández-Olivares" ]	• A neuro-symbolic generative model for Automated Planning problems is proposed. • Our method generates valid, diverse and difficult problems, and reduces human effort. • Problem generation is formulated as a Markov Decision Process. • Generative policies are trained with Deep Reinforcement Learning. • Problem constrai...	#	AI2026
LAD2025, A constraint-based solver for the subgraph isomorphism problem	https://doi.org/10.1016/j.artint.2025.104474	[ "Christine Solnon" ]		#	AI2026
Symbolic pattern planning	https://doi.org/10.1016/j.artint.2026.104482	[ "Matteo Cardellini", "Enrico Giunchiglia", "Marco Maratea" ]	In this paper, we propose a novel approach for solving automated planning problems, called Symbolic Pattern Planning. Given a deterministic planning problem Π, we propose to compute a plan by first fixing a pattern –defined as an arbitrary sequence of actions– and then define a formula encoding the state resulting from...	#	AI2026
SHACL validation in the presence of ontologies: Semantics and rewriting techniques	https://doi.org/10.1016/j.artint.2026.104483	[ "Anouk Oudshoorn", "Magdalena Ortiz", "Mantas Simkus" ]	SHACL and OWL are two prominent W3C standards for managing RDF data. These languages share many features, but they have one fundamental difference: OWL, designed for inferring facts from incomplete data, makes the open-world assumption, whereas SHACL is a constraint language that treats the data as complete and must be...	#	AI2026
Settling the score: Portioning with cardinal preferences	https://doi.org/10.1016/j.artint.2026.104487	[ "Edith Elkind", "Matthias Greger", "Patrick Lederer", "Warut Suksompong", "Nicholas Teh" ]	We study a portioning setting in which a public resource such as time or money is to be divided among a given set of candidates, and each agent proposes a division of the resource. We consider two families of aggregation rules for this setting -- those based on coordinate-wise aggregation and those that optimize some n...	#	AI2026
Multi-scale signal modulation for variational graph autoencoders	https://doi.org/10.1016/j.artint.2026.104488	[ "Junwei Cheng", "Chaobo He", "Pengxing Feng", "Weixiong Liu", "Ke Liang" ]		#	AI2026
Multi-excitation projective simulation with a many-body physics-inspired inductive bias	https://doi.org/10.1016/j.artint.2026.104489	[ "Philip A. LeMaitre", "Marius Krumm", "Hans J. Briegel" ]	With the impressive progress of deep learning, applications relying on machine learning are increasingly being integrated into daily life. However, most deep learning models have an opaque, oracle-like nature that makes it difficult to interpret and understand their decisions. This problem led to the development of the...	#	AI2026
Predictable artificial intelligence	https://doi.org/10.1016/j.artint.2026.104491	[ "Lexin Zhou", "Pablo A. M. Casares", "Fernando Martínez-Plumed", "John Burden", "Ryan Burnell", "Lucy Cheke", "Cèsar Ferri", "Alexandru Marcoci", "Behzad Mehrbakhsh", "Yael Moros-Daval", "Seán Sáirséal Ó hÉigeartaigh", "Danaja Rutar", "Wout Schellaert", "Konstantinos Voudouris", "José He...	Many areas of artificial intelligence, and machine learning in particular, aim at being probably correct, i.e., valid on average, rather than pursuing the idealistic goal of being provably valid for all inputs. However, AI systems could still be predictably valid, such as an imperfect robot deliverer for which we can r...	#	AI2026
One mind, many tongues: A deep dive into language-agnostic knowledge neurons in large language models	https://doi.org/10.1016/j.artint.2026.104490	[ "Pengfei Cao", "Yuheng Chen", "Zhuoran Jin", "Yubo Chen", "Kang Liu", "Jun Zhao" ]		#	AI2026
Learning semi-parametric tree models from mixed data	https://doi.org/10.1016/j.artint.2026.104499	[ "Can Zhou", "Nan Li", "Shuai Wang", "Xiaofei Wang", "Jianhua Guo" ]		#	AI2026
Mathematical runtime analysis of a multi-Valued estimation of distribution algorithm	https://doi.org/10.1016/j.artint.2026.104501	[ "Sumit Adak", "Carsten Witt" ]	Estimation of distribution algorithms (EDAs) are powerful optimization techniques that iteratively build probabilistic models based on the best performing solutions, thereby guiding the search process in complex solution landscapes. Classical EDAs handle only binary decision variables, but recent developments have intr...	#	AI2026
Global and local context in short text neural topic model	https://doi.org/10.1016/j.artint.2026.104502	[ "Tung Nguyen", "Linh Ngo Van", "Anh Nguyen Duc", "Sang Dinh Viet" ]		#	AI2026
Proportional justified representation	https://doi.org/10.1016/j.artint.2026.104503	[ "Luis Sánchez-Fernández", "Edith Elkind", "Martin Lackner", "Norberto Fernández García", "Jesús A. Fisteus", "Pablo Basanta-Val", "Piotr Skowron" ]	The goal of multi-winner elections is to choose a fixed-size committee based on voters’ preferences. An important concern in this setting is representation : large groups of voters with cohesive preferences should be adequately represented by the election winners. In an influential paper, Aziz et al. [1] proposed two a...	#	AI2026
A unified theoretical framework for the last-iterate convergence of stochastic adaptive optimization	https://doi.org/10.1016/j.artint.2026.104504	[ "Yuqing Liang", "Dongpo Xu" ]		#	AI2026
Exploring multi-layered networks through random walks: bridging offline optimization and online learning	https://doi.org/10.1016/j.artint.2026.104500	[ "Xiangxiang Dai", "Xutong Liu", "Jinhang Zuo", "Xiaowei Chen", "Wei Chen", "John C. S. Lui" ]	The multi-layered network exploration problem MuLaNE is a signi ! cant challenge derived from various practical applications. In MuLaNE , there are multiple layers of the network, where each node is assigned an importance weight, and each layer is explored through random walks. The objective is to allocate a total rand...	#	AI2026
Efficient constraint generation for stochastic shortest path problems	https://doi.org/10.1016/j.artint.2026.104505	[ "Johannes Schmalz", "Felipe W. Trevizan" ]	Stochastic Shortest Path problems (SSPs) are traditionally solved by computing each state’s cost-to-go by applying Bellman backups. A Bellman backup updates a state’s cost-to-go by iterating through every applicable action, computing the cost-to-go after applying each one, and selecting a minimal action’s cost-to-go. S...	#	AI2026
Domain-independent dynamic programming	https://doi.org/10.1016/j.artint.2026.104506	[ "Ryo Kuroiwa", "J. Christopher Beck" ]	For combinatorial optimization problems, model-based paradigms such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the `holy grail'of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a novel model-based paradigm ba...	#	AI2026
Revisiting the notions of extension and acceptance over incomplete abstract argumentation frameworks	https://doi.org/10.1016/j.artint.2026.104507	[ "Bettina Fazzinga", "Sergio Flesca", "Filippo Furfaro" ]		#	AI2026
An offline risk-aware policy selection method for Bayesian Markov decision processes	https://doi.org/10.1016/j.artint.2026.104519	[ "Giorgio Angelotti", "Nicolas Drougard", "Caroline P. C. Chanel" ]	In Offline Model Learning for Planning and in Offline Reinforcement Learning, the limited data set hinders the estimate of the Value function of the relative Markov Decision Process (MDP). Consequently, the performance of the obtained policy in the real world is bounded and possibly risky, especially when the deploymen...	#	AI2026
Factored planning in partially observable and deterministic multi-agent domains	https://doi.org/10.1016/j.artint.2026.104515	[ "Shashank Shekhar", "Ronen I. Brafman", "Guy Shani" ]	We consider the problem of solving qualitative decentralized partially observable Markov decision processes (QDec-POMDPs) with deterministic actions. QDec-POMDPs model dynamic systems consisting of a collaborative team of agents acting under uncertainty and partial observability, attempting to reach a desirable goal st...	#	AI2026
Neural knowledge graph reasoning with relational digraph	https://doi.org/10.1016/j.artint.2026.104520	[ "Yongqi Zhang", "Haiquan Qiu", "Shuzhi Liu", "Enjun Du", "Quanming Yao" ]		#	AI2026
Prescribed performance-based optimal formation tracking control of nonlinear heterogeneous multi-agent systems via identifier-critic-actor reinforcement learning	https://doi.org/10.1016/j.artint.2026.104521	[ "Boyan Zhu", "Guangdeng Zong", "Ben Niu", "Huanqing Wang", "Xudong Zhao" ]		#	AI2026
Environment promoted invariant information learning for graph out-of-distribution generalization	https://doi.org/10.1016/j.artint.2026.104522	[ "Shuo Wang", "Mingchen Sun", "Qiang Huang", "Ying Wang" ]		#	AI2026
Incentives for early arrival in online cooperative games	https://doi.org/10.1016/j.artint.2026.104523	[ "Dengji Zhao", "Yaoxin Ge", "Yao Zhang", "Zhihao Gavin Tang", "Hu Fu", "Pinyan Lu" ]	Incentives for early arrival (I4EA) was recently proposed for studying online cooperative games. In an online cooperative game, players arrive in an unknown order, and the value increase after each player arrived should be distributed immediately among all the arrived players. Although there is only one arriving order ...	#	AI2026
Interactive graph convolutional filtering	https://doi.org/10.1016/j.artint.2026.104524	[ "Jin Zhang", "Defu Lian", "Hong Xie", "Yawen Li", "Enhong Chen" ]		#	AI2026
Theoretical insights into CycleGAN: Analyzing approximation and estimation errors in unpaired data generation	https://doi.org/10.1016/j.artint.2026.104526	[ "Luwei Sun", "Dongrui Shen", "Han Feng" ]	In this paper, we focus on analyzing the excess risk of the unpaired data generation model, called CycleGAN. Unlike classical GANs, CycleGAN not only transforms data between two unpaired distributions but also ensures the mappings are consistent, which is encouraged by the cycle-consistency term unique to CycleGAN. The...	#	AI2026
Opinion dynamics with median aggregation	https://doi.org/10.1016/j.artint.2026.104527	[ "Petra Berenbrink", "Martin Hoefer", "Dominik Kaaser", "Marten Maack", "Malin Rau", "Lisa Wilhelmi" ]	Understanding the formation and evolution of opinions is of broad interdisciplinary interest. Many classical models for opinion formation focus on the impact of different notions of locality , e.g., locality due to network effects among agents or the role of the proximity of opinions. In practice, however, opinion form...	#	AI2026
Analyzing bullet chats for recommendation intent identification: Dataset and method	https://doi.org/10.1016/j.artint.2026.104528	[ "Yi Zhu", "Qinqin Han", "Yun-Hao Yuan", "Chaowei Zhang", "Jipeng Qiang", "Xindong Wu" ]		#	AI2026
ExPred: Explainable stock movement prediction via hybrid reflection and direct preference hierarchical optimization	https://doi.org/10.1016/j.artint.2026.104539	[ "Shuqi Li", "Heyue Lin", "Xin Liu", "Rui Yan" ]		#	AI2026
Efficient representations for team and imperfect-recall equilibrium computation	https://doi.org/10.1016/j.artint.2026.104529	[ "Luca Carminati", "Brian Hu Zhang", "Federico Cacciamani", "Junkang Li", "Gabriele Farina", "Nicola Gatti", "Tuomas Sandholm" ]	Equilibrium finding in two-player zero-sum games with perfect recall is a well-studied topic that has led to many breakthroughs in computational game theory. This paper aims to generalize such techniques to (timeable) two-player zero-sum games with imperfect recall, or equivalently to two-team zero-sum games. In this s...	#	AI2026
Decision-theoretic planning and cognitive modeling for active cyber deception	https://doi.org/10.1016/j.artint.2026.104540	[ "Aditya Shinde", "Prashant Doshi" ]		#	AI2026
Partial identifiability and misspecification in inverse reinforcement learning	https://doi.org/10.1016/j.artint.2026.104525	[ "Joar Skalse", "Alessandro Abate" ]	The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function R from a policy π . This problem is difficult, for several reasons. First of all, there are typically multiple reward functions which are compatible with a given policy; this means that the reward function is only partially identifiable , and...	#	AI2026
On the convergence of SignSGD under weak first- and second-order gradient Lipschitz	https://doi.org/10.1016/j.artint.2026.104541	[ "Tao Sun", "Xinwang Liu" ]		#	AI2026
Learning gradient-based mixup with extrapolation toward flatter minima for domain generalization	https://doi.org/10.1016/j.artint.2026.104544	[ "Danni Peng", "Sinno Jialin Pan" ]	To address distribution shifts between training and test data, domain generalization (DG) leverages multiple source domains to learn a model that generalizes well to unseen domains. However, existing DG methods often overfit to the source domains, partly due to the limited coverage of the expected region in feature spa...	#	AI2026
Approximate multi-objective search	https://doi.org/10.1016/j.artint.2026.104542	[ "Han Zhang", "Oren Salzman", "T. K. Satish Kumar", "Ariel Felner", "Carlos Hernández Ulloa", "Sven Koenig" ]		#	AI2026
Auction design with ex post ROI constraints	https://doi.org/10.1016/j.artint.2026.104543	[ "Hongtao Lv", "Xiaohui Bei", "Zhenzhe Zheng", "Fan Wu" ]	Motivated by practical constraints in online advertising, we investigate single-parameter auction design for bidders with constraints on their Return On Investment (ROI) -- a targeted minimum ratio between the obtained value and the payment. We focus on ex post ROI constraints, which require the ROI condition to be sat...	#	AI2026
From monotonic graph neural networks to datalog and back: Expressive power and practical applications	https://doi.org/10.1016/j.artint.2026.104545	[ "David Tena Cucala", "Bernardo Cuenca Grau", "Boris Motik", "Egor V. Kostylev" ]	Many tasks over knowledge graphs, such as link prediction, can be conceptualised as a problem of learning a transformation of sets of relational facts. Machine learning models such as graph neural networks (GNNs) can be used to realise this transformation, allowing the transformation to be learned from examples. Howeve...	#	AI2026
An optimal pastification algorithm for LTL[X,F] and LTL[X,G]	https://doi.org/10.1016/j.artint.2026.104553	[ "Alessandro Artale", "Luca Geatti", "Nicola Gigante", "Alessio Mansutti", "Andrea Mazzullo", "Angelo Montanari" ]	We investigate a fragment of Linear Temporal Logic (LTL) comprising the tomorrow (X) and eventually (F) modalities, and present a singly exponential time algorithm for the pastification problem within this fragment. The pastification problem consists of constructing, for a given LTL formula, an equivalent formula that ...	#	AI2026
Utilitarian distortion with predictions	https://doi.org/10.1016/j.artint.2026.104557	[ "Aris Filos-Ratsikas", "Georgios Kalantzis", "Alexandros A. Voudouris" ]		#	AI2026
Exploring the influence of missing data imputation in group fairness metrics	https://doi.org/10.1016/j.artint.2026.104559	[ "Arthur Dantas Mangussi", "Ricardo Cardoso Pereira", "Miriam Seoane Santos", "Ana Carolina Lorena", "Mykola Pechenizkiy", "Pedro Henriques Abreu" ]	Missing data is a common problem in real-world datasets and can be characterized as the lack of information on one or multiple variables in a dataset. The most frequent technique for handling this issue is imputation, which consists in the replacement of the missing values according to a predefined criterion. Since mis...	#	AI2026
Selection hyper-heuristics can automatically adjust the learning period to optimally solve pseudo-Boolean problems	https://doi.org/10.1016/j.artint.2026.104560	[ "Benjamin Doerr", "Pietro S. Oliveto", "John Alasdair Warwicker" ]	The Random Gradient hyper-heuristic was recently shown to be able to learn the optimal neighbourhood size when optimizing the LeadingOnes benchmark via the Randomised Local Search (RLS) meta-heuristic. However, for this to happen, a learning period of a certain length $\tau$ had to be used, differently from classic hyp...	#	AI2026
ATWGNNS: Graph simplified representation and learning of logical formula for premise selection	https://doi.org/10.1016/j.artint.2026.104562	[ "Xingxing He", "Zhongxu Zhao", "Yongqi Lan", "Yingfang Li", "Li Zou", "Luis Martínez", "Tianrui Li" ]		#	AI2026
Sets attacking sets in abstract argumentation - redefining ABA+ semantics via hyper argumentation frameworks	https://doi.org/10.1016/j.artint.2026.104558	[ "Yannis Dimopoulos", "Wolfgang Dvorák", "Anna Rapberger", "Matthias König", "Markus Ulbricht", "Stefan Woltran" ]	Assumption-based argumentation (ABA) is a powerful defeasible reasoning formalism which is based on the interplay of assumptions, their contraries, and inference rules. ABA with preferences ( ABA + ) generalizes the basic model by allowing a qualitative comparison of assumptions. The integration of preferences however ...	#	AI2026
Innovative constraint models for mining frequent and rare association rules using multi-objective optimization	https://doi.org/10.1016/j.artint.2026.104561	[ "Charles Vernerey", "Samir Loudni", "Noureddine Aribi", "Yahia Lebbah" ]	Constraint-based pattern mining is at the core of numerous data mining tasks. Usually, pattern mining problems are handled as constraint-solving tasks. However, in practice, the resulting patterns are often large, and selecting appropriate thresholds for these constraints can be challenging. This paper addresses these ...	#	AI2026
Balancing the spread of two opinions in sparse social networks	https://doi.org/10.1016/j.artint.2026.104563	[ "Dusan Knop", "Simon Schierreich", "Ondrej Suchý" ]	Inspired by the famous Target Set Selection problem, we propose a new discrete model to simultaneously spread two opinions within a social network and perform an initial study of its complexity. Here, we are given a social network, a seed-set of agents for each opinion, two thresholds for each agent, a budget, and a nu...	#	AI2026
A successful strategy for iterated Prisoner's dilemma with any number of channels	https://doi.org/10.1016/j.artint.2026.104572	[ "Zhaoheng Cao", "Juan Shi", "Zhen Wang", "Shuyue Hu", "Chen Chu" ]		#	AI2026

End of preview. Expand in Data Studio

PaperVault Dataset · 论文元数据库

🔎 项目简介 · Overview

PaperVault 是一份持续自动更新的统一论文元数据库，覆盖自然语言处理、计算机视觉、机器学习、数据挖掘、数据库、语音、系统、网络、安全、理论计算机科学、人机交互、计算机图形学与多媒体等方向的顶级会议与期刊。PaperVault is a continuously-updated, unified metadata database of papers from top-tier conferences and journals across NLP, Computer Vision, Machine Learning, Data Mining, Databases, Speech, Systems, Networking, Security, Theory, HCI, Graphics, and Multimedia.

🌐 源仓库 / Source: github.com/youngfish42/PaperVault — Web UI、REST API、采集流水线、Issues / PRs 全部在那里 · Web UI, REST API, crawling pipelines and issues/PRs all live there.

🌐 在线搜索 / Live search: papervault.top — 直接使用 Web 检索界面，无需下载数据 · Use the web search UI directly without downloading the dataset.

🆕 最近更新 · Recent Update

📅 最近更新 · Last updated: 2026-11-12 (Asia/Shanghai)
📊 数据库规模 · Database size: 722,886 篇论文 / 129 个刊物系列 / 667,806 篇含摘要 / 115,424 篇含开源代码（722,886 papers / 129 venue series / 667,806 with abstract / 115,424 with code）

📈 数据看板 · Statistics at a Glance

下列 4 张统计图与 cache.jsonl.gz 同源同步，反映本数据集的最新状态。The four charts below are generated from the same cache.jsonl.gz and always reflect the latest state of this dataset.

Statistics Overview

Papers by Research Field

Annual Paper Collection Trend

Publication Series Word Cloud

📦 数据集内容 · What's in this dataset

路径 Path	子集 Subset	格式 Format	说明 Description
`cache/cache.jsonl.gz`	`papers`（默认 / default）	gzip-compressed JSON Lines (UTF-8)	每行一篇论文 · One paper per line; one JSON object per line
`cache/abstract_backfill_progress.jsonl.gz`	`abstract_backfill_progress`	gzip-compressed JSON Lines (UTF-8)	摘要回填流水线的进度/断点记录，不是论文元数据；仅供工作流恢复使用 · Append-only progress log of the abstract-backfill pipeline (not paper records); used by the workflow to resume between runs

Hugging Face 会自动为 cache.jsonl.gz 生成 Parquet 视图，也可直接用 datasets.load_dataset(...) 读取，无需手动解压。Hugging Face also exposes an auto-generated Parquet view, so datasets.load_dataset(...) works out of the box.

💡 Dataset Viewer 与 datasets.load_dataset("youngfish42/PaperVault") 默认展示/加载的都是 papers 子集（即 cache/cache.jsonl.gz）。如需查看回填进度，请在 Viewer 顶部下拉框切换到 abstract_backfill_progress，或调用 load_dataset("youngfish42/PaperVault", name="abstract_backfill_progress")。The Dataset Viewer and datasets.load_dataset("youngfish42/PaperVault") both default to the papers subset (cache/cache.jsonl.gz). To inspect backfill progress, switch the Viewer's subset dropdown to abstract_backfill_progress or call load_dataset("youngfish42/PaperVault", name="abstract_backfill_progress").

📐 字段 Schema

Field 字段	Type 类型	Notes 说明
`paper_name`	string	论文标题（已归一化）· Normalised paper title
`paper_authors`	list[string]	作者列表，按原始顺序 · Author names in order
`paper_url`	string	论文在原始平台的链接（PDF 或落地页）· Canonical URL on the venue's site (PDF or landing page)
`paper_abstract`	string	摘要；未回填时为空字符串 · Abstract; may be empty when not yet backfilled
`paper_code`	string	从摘要中抽取出的 GitHub 仓库 URL；`"#"` 是「未发现代码链接」的占位符 · GitHub repository URL extracted from the abstract; `"#"` is the sentinel for "no code link discovered"
`conf`	string	会议+年份标识，如 `ACL2024`、`NIPS2023`、`CVPR2025`；去掉末尾四位数字即可得到会议系列。注意 NeurIPS Proceedings 沿用历史命名 `NIPS{year}`。Venue + year identifier (e.g. `ACL2024`, `NIPS2023`, `CVPR2025`). Strip the trailing 4-digit year to recover the venue series. Note that NeurIPS Proceedings entries use the historical name `NIPS{year}`.

缺失字段请按空字符串处理。Treat missing fields as empty strings.

⬇️ 获取方式 · How to download

下面三种方式任选其一即可，无需克隆 GitHub 仓库。Pick any one of the three options below — no GitHub clone is required.

方式 A · Option A — `huggingface_hub`（推荐 / recommended）

from huggingface_hub import hf_hub_download
import gzip, json

path = hf_hub_download(
    repo_id="youngfish42/PaperVault",
    filename="cache/cache.jsonl.gz",
    repo_type="dataset",
)

with gzip.open(path, "rt", encoding="utf-8") as f:
    for line in f:
        record = json.loads(line)
        # 在这里处理一条记录 · do something with the record

方式 B · Option B — `datasets`

from datasets import load_dataset

ds = load_dataset("youngfish42/PaperVault")
print(ds[next(iter(ds))][0])

数据集只有一个默认 split（非 ML 训练集），不要传 split="train"。Single default split — do not pass split="train".

方式 C · Option C — `huggingface-cli` / 直接 HTTPS · Plain HTTPS

huggingface-cli download youngfish42/PaperVault \
    cache/cache.jsonl.gz --repo-type dataset --local-dir ./data

💡 文件压缩后约 120 MB（会随数据持续增长），解压后是 GB 级 JSONL 流，请按行流式读取，不要整体载入内存。The file is ~120 MB compressed (and growing) and decompresses to a multi-GB JSONL stream. Stream it line-by-line rather than loading the whole thing into memory.

🔁 更新节奏 · Update cadence

数据集由三个 GitHub Actions 工作流负责重建并推送到本 Hub 仓库 / The dataset is rebuilt and pushed to this Hub repo by three GitHub Actions workflows：

工作流 Workflow	触发节奏 Schedule	推送的内容 What it pushes
`collect_papers`	每月 15 号 16:00 UTC + 手动触发 · 15th of every month at 16:00 UTC + `workflow_dispatch`	增量抓取新发现的会议/年份组合 · Incremental crawl of newly-discovered conference/year combinations
`backfill_abstracts`	每月 1 号 00:00 UTC + 手动触发 · 1st of every month at 00:00 UTC + `workflow_dispatch`	为已有论文回填 `paper_abstract` · Adds `paper_abstract` for papers that were collected without one
`update_readme`	仅手动触发 (`workflow_dispatch`) · Manual only (`workflow_dispatch`)	默认仅刷新 README 与统计；当输入参数 `mode=force` 时执行全量重建 · Refreshes the README and statistics by default; performs a full rebuild only when invoked with `mode=force`

每次推送都使用 Hugging Face 的 parent_commit 乐观锁机制，避免并发覆盖。Each push uses Hugging Face's parent_commit optimistic-lock mechanism to avoid silently overwriting concurrent updates.

🌐 线上服务 · Live Service

无需下载数据集即可直接体验 Web 检索： No need to download the dataset — try the web search directly:

👉 papervault.top

🔗 关联仓库 · Related repository

如果你需要完整的搜索 Web UI（智能搜索 + Web of Science 风格的高级查询 DSL）、REST API（/api/v1/*）、抓取 / 合并 / 摘要回填流水线源码、收录会议范围、统计仪表盘、项目截图或贡献指南，请前往 GitHub 项目仓库。If you are looking for the full search Web UI (smart search + Web-of-Science-style advanced query DSL), the REST API (/api/v1/*), the crawling / merging / abstract-backfill pipelines source code, conference coverage, statistics dashboards, screenshots or contribution guidelines, please visit the GitHub repository.

👉 github.com/youngfish42/PaperVault

📜 许可证 · License

代码以 GPL-3.0 发布；每条论文记录的著作权仍属于原作者 / 出版方，本数据集仅重新分发公开可获取的元数据与链接。Code is released under GPL-3.0; individual paper records remain the IP of their authors/publishers — this dataset only redistributes publicly available bibliographic metadata and links.