{"date":"2026-04-13","schema_version":1,"captured_at":"2026-08-25T23:37:02.127Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"2026 Hungarian parliamentary election","views":1025494,"rank":3,"extract":"Parliamentary elections were held in Hungary on 12 April 2026 to elect all 199 members of the National Assembly. It was the 10th parliamentary election and the highest-turnout election since Hungary's transition to democracy in 1990. 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While existing DFML methods typically generate synthetic data from these models to perform meta-learning, a comprehensive analysis of DFML's robustness-particularly its failure modes and vulnerability to potential attacks-remai","authors":["Hu, Zixuan","Wei, Yongxian","Shen, Li","Wang, Zhenyi","Wu, Baoyuan","Yuan, Chun"],"url":"https://arxiv.org/abs/2311.14756","arxiv_id":"2311.14756","published":"2026-04-13"},{"title":"Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy","abstract":"The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic to maximize vehicle speed and throughput. This paper explores advisory autonomy, in which real-time driving advisories are issued to the human drivers, thus achieving near-term performance of automated vehicles. Due to the complexity of traffic systems, recent studies of coordinating","authors":["Cho, Jung-Hoon","Li, Sirui","Kim, Jeongyun","Wu, Cathy"],"url":"https://arxiv.org/abs/2312.09436","arxiv_id":"2312.09436","published":"2026-04-13"},{"title":"Embedding Non-Distortive Cancelable Face Template Generation","abstract":"Biometric authentication systems are crucial for security, but developing them involves various complexities, including privacy, security, and achieving high accuracy without directly storing pure biometric data in storage. We introduce an innovative image distortion technique that makes facial images unrecognizable to the eye but still identifiable by any custom embedding neural network model. Using the proposed app","authors":["Zakharov, Dmytro","Kuznetsov, Oleksandr","Frontoni, Emanuele","Kryvinska, Natalia"],"url":"https://arxiv.org/abs/2402.02540","arxiv_id":"2402.02540","published":"2026-04-13"},{"title":"Exploring a Behavioral Model of &#34;Positive Friction&#34; in Human-AI Interaction","abstract":"Designing seamless, frictionless user experiences has long been a dominant trend in both applied behavioral science and artificial intelligence (AI), in which the goal of making desirable actions easy and efficient informs efforts to minimize friction in user experiences. However, in some settings, friction can be genuinely beneficial, such as the insertion of deliberate delays to increase reflection, preventing indi","authors":["Chen, Zeya","Schmidt, Ruth"],"url":"https://arxiv.org/abs/2402.09683","arxiv_id":"2402.09683","published":"2026-04-13"},{"title":"Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning","abstract":"Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL). While agent cooperation can be represented by graph structures, prevailing graph learning methods in MARL are limited. They rely solely on one-step observations, neglecting crucial historical experiences, leading to deficient graphs that foster redundant or detrimental information exchanges. Additionally, high computatio","authors":["Duan, Wei","Lu, Jie","Xuan, Junyu"],"url":"https://arxiv.org/abs/2403.19253","arxiv_id":"2403.19253","published":"2026-04-13"},{"title":"Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning","abstract":"Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Existing methods for learning this graph primarily focus on agent-pair relations, neglecting higher-order relationships. While several approaches attempt to extend cooperation modelling to encompass behaviour similarities within groups, they commonly fall short in ","authors":["Duan, Wei","Lu, Jie","Xuan, Junyu"],"url":"https://arxiv.org/abs/2404.10976","arxiv_id":"2404.10976","published":"2026-04-13"},{"title":"Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias","abstract":"Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplify the popularity bias in recommendation models, resulting in more frequent recommendation of hot items and fewer long-tail items, but also include a substantial amount of redundant information that is essentially meaningless for the model&#","authors":["He, Xin","Fan, Wenqi","Wang, Ruobing","Wang, Yili","Wang, Ying","Pan, Shirui"],"url":"https://arxiv.org/abs/2405.16772","arxiv_id":"2405.16772","published":"2026-04-13"},{"title":"Contrastive Feedback Mechanism for Simultaneous Speech Translation","abstract":"Recent advances in simultaneous speech translation (SST) focus on the decision policies that enable the use of offline-trained ST models for simultaneous inference. These decision policies not only control the quality-latency trade-off in SST but also mitigate the impact of unstable predictions on translation quality by delaying translation for more context or discarding these predictions through stable hypothesis de","authors":["Tan, Haotian","Sakti, Sakriani"],"url":"https://arxiv.org/abs/2407.20524","arxiv_id":"2407.20524","published":"2026-04-13"},{"title":"Detection and Characterization of Coordinated Online Behavior: A Survey","abstract":"Coordination is a fundamental aspect of life. The advent of social media has made it integral also to online human interactions, such as those that characterize thriving online communities and social movements. At the same time, coordination is also core to effective disinformation, manipulation, and hate campaigns. This survey collects, categorizes, and critically discusses the body of work produced as a result of t","authors":["Mannocci, Lorenzo","Mazza, Michele","Monreale, Anna","Tesconi, Maurizio","Cresci, Stefano"],"url":"https://arxiv.org/abs/2408.01257","arxiv_id":"2408.01257","published":"2026-04-13"},{"title":"TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis","abstract":"Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to either single-step inference or are constrained to natural language answers. In this work, we introduce TS-Reasoner, a domain-specialized agent designed for multi-step time series inference. By integrating large language model (LLM) reasoning w","authors":["Ye, Wen","Yang, Wei","Cao, Defu","Zhang, Yizhou","Tang, Lumingyuan","Cai, Jie"],"url":"https://arxiv.org/abs/2410.04047","arxiv_id":"2410.04047","published":"2026-04-13"},{"title":"Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture","abstract":"Electrocardiogram (ECG) captures the heart's electrical signals, offering valuable information for diagnosing cardiac conditions. However, the scarcity of labeled data makes it challenging to fully leverage supervised learning in the medical domain. Self-supervised learning (SSL) offers a promising solution, enabling models to learn from unlabeled data and uncover meaningful patterns. In this paper, we show that ","authors":["Kim, Sehun"],"url":"https://arxiv.org/abs/2410.08559","arxiv_id":"2410.08559","published":"2026-04-13"},{"title":"On Divergence Measures for Training GFlowNets","abstract":"Generative Flow Networks (GFlowNets) are amortized inference models designed to sample from unnormalized distributions over composable objects, with applications in generative modeling for tasks in fields such as causal discovery, NLP, and drug discovery. Traditionally, the training procedure for GFlowNets seeks to minimize the expected log-squared difference between a proposal (forward policy) and a target (backward","authors":["da Silva, Tiago","da Silva, Eliezer de Souza","Mesquita, Diego"],"url":"https://arxiv.org/abs/2410.09355","arxiv_id":"2410.09355","published":"2026-04-13"},{"title":"FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening","abstract":"Scalability of Graph Neural Networks (GNNs) remains a significant challenge. To tackle this, methods like coarsening, condensation, and computation trees are used to train on a smaller graph, resulting in faster computation. Nonetheless, prior research has not adequately addressed the computational costs during the inference phase. This paper presents a novel approach to improve the scalability of GNNs by reducing co","authors":["Roy, Shubhajit","Ruparel, Hrriday","Ved, Kishan","Dasgupta, Anirban"],"url":"https://arxiv.org/abs/2410.15001","arxiv_id":"2410.15001","published":"2026-04-13"},{"title":"OmniPrism: Learning Disentangled Visual Concept for Image Generation","abstract":"Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. However, existing methods are typically constrained to single-aspect concept generation or are easily disrupted by irrelevant concepts in multi-aspect concept scenarios, leading to concept confusion and hindering creative generation. To address this, we propose OmniPrism, a visual conce","authors":["Li, Yangyang","Liu, Daqing","Liu, Wu","He, Allen","Liu, Xinchen","Zhang, Yongdong"],"url":"https://arxiv.org/abs/2412.12242","arxiv_id":"2412.12242","published":"2026-04-13"},{"title":"Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges","abstract":"Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely attributed to their English-centric pre-training data. In this paper, we introduce and investigate cross-lingual latent transplantation (XTransplant), a probing framework which aims to further exploit the model's internalized multilingual knowledge during inference and examine its effects ","authors":["Ye, Yangfan","Feng, Xiaocheng","Feng, Xiachong","Qin, Libo","Huang, Yichong","Huang, Lei"],"url":"https://arxiv.org/abs/2412.12686","arxiv_id":"2412.12686","published":"2026-04-13"},{"title":"Automatic Self-supervised Learning for Social Recommendations","abstract":"In recent years, researchers have leveraged social relations to enhance recommendation performance. However, most existing social recommendation methods require carefully designed auxiliary social tasks tailored to specific scenarios, which depend heavily on domain knowledge and expertise. To address this limitation, we propose Automatic Self-supervised Learning for Social Recommendations (AusRec), which integrates m","authors":["He, Xin","Fan, Wenqi","Sun, Mingchen","Wang, Ying","Wang, Xin"],"url":"https://arxiv.org/abs/2412.18735","arxiv_id":"2412.18735","published":"2026-04-13"},{"title":"Graph Defense Diffusion Model","abstract":"Graph Neural Networks (GNNs) are highly vulnerable to adversarial attacks, which can greatly degrade their performance. Existing graph purification methods attempt to address this issue by filtering attacked graphs. However, they struggle to defend effectively against multiple types of adversarial attacks (e.g., targeted attacks and non-targeted attacks) simultaneously due to limited flexibility. Additionally, these ","authors":["He, Xin","Fan, Wenqi","Wang, Yili","Liu, Chengyi","Miao, Rui","Juan, Xin"],"url":"https://arxiv.org/abs/2501.11568","arxiv_id":"2501.11568","published":"2026-04-13"},{"title":"Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space","abstract":"Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which causes all node representations to converge to a single value and become indistinguishable. This issue stems from the inherent limitations of GNNs, which struggle to distinguish the importance of information from different neighborhoods. I","authors":["He, Xin","Wang, Yili","Fan, Wenqi","Shen, Xu","Juan, Xin","Miao, Rui"],"url":"https://arxiv.org/abs/2501.15461","arxiv_id":"2501.15461","published":"2026-04-13"},{"title":"Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity","abstract":"Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conformal prediction framework to hierarchical classification, where prediction sets are commonly restricted to internal nodes of a predefined hierarchy, and propose two computationally efficient inference algorithms. The first algorithm returns i","authors":["Mortier, Thomas","Javanmardi, Alireza","Sale, Yusuf","Hüllermeier, Eyke","Waegeman, Willem"],"url":"https://arxiv.org/abs/2501.19038","arxiv_id":"2501.19038","published":"2026-04-13"},{"title":"Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks","abstract":"Subspace inference for neural networks assumes that a subspace of their parameter space suffices to produce a reliable uncertainty quantification. In this work, we underpin the validity of this assumption by using low rank techniques. We derive an expression for a subspace model to a Bayesian inference scenario based on the Laplace approximation that is, in a certain sense, optimal given a specific dataset. We empiri","authors":["Faller, Josua","Martin, Jörg"],"url":"https://arxiv.org/abs/2502.02345","arxiv_id":"2502.02345","published":"2026-04-13"},{"title":"Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution","abstract":"Pervasive polysemanticity in large language models (LLMs) undermines discrete neuron-concept attribution, posing a significant challenge for model interpretation and control. We systematically analyze both encoder and decoder based LLMs across diverse datasets, and observe that even highly salient neurons for specific semantic concepts consistently exhibit polysemantic behavior. Importantly, we uncover a consistent p","authors":["Haider, Muhammad Umair","Rizwan, Hammad","Sajjad, Hassan","Ju, Peizhong","Siddique, A. B."],"url":"https://arxiv.org/abs/2502.06809","arxiv_id":"2502.06809","published":"2026-04-13"},{"title":"AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society","abstract":"Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models ","authors":["Piao, Jinghua","Yan, Yuwei","Zhang, Jun","Li, Nian","Yan, Junbo","Lan, Xiaochong"],"url":"https://arxiv.org/abs/2502.08691","arxiv_id":"2502.08691","published":"2026-04-13"},{"title":"Deep Spatio-Temporal Neural Network for Air Quality Reanalysis","abstract":"Air quality prediction is key to mitigating health impacts and guiding decisions, yet existing models tend to focus on temporal trends while overlooking spatial generalization. We propose AQ-Net, a spatiotemporal reanalysis model for both observed and unobserved stations in the near future. AQ-Net utilizes the LSTM and multi-head attention for the temporal regression. We also propose a cyclic encoding technique to en","authors":["Kheder, Ammar","Foreback, Benjamin","Wang, Lili","Liu, Zhi-Song","Boy, Michael"],"url":"https://arxiv.org/abs/2502.11941","arxiv_id":"2502.11941","published":"2026-04-13"},{"title":"Reflection of Episodes: Learning to Play Game from Expert and Self Experiences","abstract":"StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience. This framework first obtains key informati","authors":["Xu, Xiaojie","Li, Zongyuan","Lu, Chang","Qi, Runnan","Ni, Yanan","Jiang, Lumin"],"url":"https://arxiv.org/abs/2502.13388","arxiv_id":"2502.13388","published":"2026-04-13"},{"title":"MSMO-ABSA: Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis","abstract":"Aspect-based sentiment analysis (ABSA) garnered growing research interest in multilingual contexts in the past. However, the majority of the studies lack more robust feature alignment and finer aspect-level alignment. In this paper, we propose a novel framework, MSMO: Multi-Scale and Multi-Objective optimization for cross-lingual ABSA. During multi-scale alignment, we achieve cross-lingual sentence-level and aspect-l","authors":["Wu, Chengyan","Ma, Bolei","Deng, Ningyuan","He, Yanqing","Xue, Yun","Liu, Xiaoyong"],"url":"https://arxiv.org/abs/2502.13718","arxiv_id":"2502.13718","published":"2026-04-13"}],"history":{"events":[{"year":2017,"text":"War in Afghanistan: In an airstrike in Nangarhar Province, the U.S. military dropped the most powerful conventional bomb used in combat.","url":"https://en.wikipedia.org/wiki/War_in_Afghanistan_(2001%E2%80%932021)"},{"year":2011,"text":"The board of Rosendale, New York, voted 3–2 to approve the purchase of Joppenbergh Mountain.","url":"https://en.wikipedia.org/wiki/Rosendale_(CDP)%2C_New_York"},{"year":2009,"text":"Twenty-three people died in a fire at a homeless hostel in Kamień Pomorski; it was Poland's deadliest fire since 1980.","url":"https://en.wikipedia.org/wiki/Kamie%C5%84_Pomorski_homeless_hostel_fire"},{"year":2009,"text":"Andrew Hussie's webcomic Homestuck debuted, and later concluded on the same day in 2016.","url":"https://en.wikipedia.org/wiki/Andrew_Hussie"},{"year":1997,"text":"In golf, 21-year-old Tiger Woods became the youngest player to win the Masters Tournament, breaking the record for the lowest four-round score.","url":"https://en.wikipedia.org/wiki/Tiger_Woods"},{"year":1996,"text":"An Israel Defense Forces helicopter fired rockets at an ambulance, killing two women and four children in Mansouri, Lebanon.","url":"https://en.wikipedia.org/wiki/Israel_Defense_Forces"},{"year":1976,"text":"Forty people died in an explosion at an ammunition factory in Lapua, Finland.","url":"https://en.wikipedia.org/wiki/Lapua_Cartridge_Factory_explosion"},{"year":1973,"text":"Catch a Fire, the landmark reggae album by Bob Marley and the Wailers, was released.","url":"https://en.wikipedia.org/wiki/Catch_a_Fire"},{"year":1958,"text":"In the midst of the Cold War, American pianist Van Cliburn won the inaugural International Tchaikovsky Competition in Moscow.","url":"https://en.wikipedia.org/wiki/Cold_War"},{"year":1956,"text":"The Vietnamese National Army captured Ba Cụt, a military commander of the religious sect Hòa Hảo, which ran a de facto state in South Vietnam in opposition to Prime Minister Ngô Đình Diệm.","url":"https://en.wikipedia.org/wiki/Vietnamese_National_Army"},{"year":1953,"text":"Ian Fleming's novel Casino Royale was published as the first work to feature the British spy character James Bond.","url":"https://en.wikipedia.org/wiki/Ian_Fleming"},{"year":1953,"text":"The CIA began Project MKUltra, an illegal, covert human research program into mind control.","url":"https://en.wikipedia.org/wiki/Central_Intelligence_Agency"},{"year":1946,"text":"Nakam, a Jewish organization seeking revenge for the Holocaust, attempted to poison thousands of SS prisoners at Langwasser internment camp, but did not kill anyone.","url":"https://en.wikipedia.org/wiki/Nakam"},{"year":1943,"text":"The Neoclassical Jefferson Memorial (pictured) in Washington, D.C., was formally dedicated on the 200th anniversary of Thomas Jefferson's birth.","url":"https://en.wikipedia.org/wiki/Neoclassical_architecture"},{"year":1943,"text":"World War II: German news announced the discovery of a mass grave of Polish prisoners of war killed by Soviet forces in Katyn, Russia, causing a diplomatic rift between the Polish government-in-exile and the Soviet Union.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1942,"text":"Austrian soldier Anton Schmid was executed for rescuing Jews from the Ponary massacre in Vilnius.","url":"https://en.wikipedia.org/wiki/Anton_Schmid"},{"year":1930,"text":"C. Rajagopalachari led the non-violent Vedaranyam March from Trichinopoly towards Vedaranyam.","url":"https://en.wikipedia.org/wiki/C._Rajagopalachari"},{"year":1829,"text":"The Roman Catholic Relief Act received royal assent, removing the most substantial restrictions on Catholics in the United Kingdom.","url":"https://en.wikipedia.org/wiki/Roman_Catholic_Relief_Act_1829"}],"births":[{"year":2002,"text":"Karl Hein, Estonian footballer","url":"https://en.wikipedia.org/wiki/Karl_Hein_(footballer)"},{"year":2001,"text":"Neco Williams, Welsh footballer","url":"https://en.wikipedia.org/wiki/Neco_Williams"},{"year":2000,"text":"Rasmus Dahlin, Swedish ice hockey player","url":"https://en.wikipedia.org/wiki/Rasmus_Dahlin"},{"year":2000,"text":"Facundo Torres, Uruguayan footballer","url":"https://en.wikipedia.org/wiki/Facundo_Torres"},{"year":1999,"text":"Alessandro Bastoni, Italian footballer","url":"https://en.wikipedia.org/wiki/Alessandro_Bastoni"},{"year":1999,"text":"András Schäfer, Hungarian footballer","url":"https://en.wikipedia.org/wiki/Andr%C3%A1s_Sch%C3%A4fer"},{"year":1997,"text":"Mateo Cassierra, Colombian footballer","url":"https://en.wikipedia.org/wiki/Mateo_Cassierra"},{"year":1997,"text":"Kyle Walker-Peters, English footballer","url":"https://en.wikipedia.org/wiki/Kyle_Walker-Peters"}],"deaths":[{"year":2026,"text":"Moya Brennan, Irish singer-songwriter and harp player (born 1952)","url":"https://en.wikipedia.org/wiki/Moya_Brennan"},{"year":2026,"text":"Dave McGinnis, American football player and coach (born 1951)","url":"https://en.wikipedia.org/wiki/Dave_McGinnis"},{"year":2025,"text":"Richard Armitage, American diplomat and government official (born 1945)","url":"https://en.wikipedia.org/wiki/Richard_Armitage_(government_official)"},{"year":2025,"text":"Mario Vargas Llosa, Peruvian novelist and writer, Nobel Prize laureate (born 1936)","url":"https://en.wikipedia.org/wiki/Mario_Vargas_Llosa"},{"year":2025,"text":"Jean Marsh, English actress and screenwriter (born 1934)","url":"https://en.wikipedia.org/wiki/Jean_Marsh"},{"year":2024,"text":"Faith Ringgold, American artist and author (born 1930)","url":"https://en.wikipedia.org/wiki/Faith_Ringgold"},{"year":2022,"text":"Michel Bouquet, French stage and film actor (born 1925)","url":"https://en.wikipedia.org/wiki/Michel_Bouquet"},{"year":2022,"text":"Gloria Parker, American musician and bandleader (born 1921)","url":"https://en.wikipedia.org/wiki/Gloria_Parker"}]},"census":null,"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}