{"date":"2026-01-30","schema_version":1,"captured_at":"2026-08-25T23:25:59.389Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Ajit Pawar","views":649912,"rank":3,"extract":"Ajit Anantrao Pawar was an Indian politician who served as Maharashtra's longest-serving deputy chief minister for more than eight years, between 2010 and his death in 2026, for six terms. He held the office under various governments, including the cabinets of","url":"https://en.wikipedia.org/wiki/Ajit_Pawar","thumb":"https://upload.wikimedia.org/wikipedia/commons/f/fc/Shri_Ajit_Anantrao_Pawar.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail_unscaled"},{"title":"Nicki Minaj","views":227248,"rank":5,"extract":"Onika Tanya Maraj-Petty, known professionally as Nicki Minaj, is a Trinidadian rapper, singer, and songwriter. 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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 ","authors":["Colas, Cédric","Fournier, Pierre","Sigaud, Olivier","Chetouani, Mohamed","Oudeyer, Pierre-Yves"],"url":"https://arxiv.org/abs/1810.06284","arxiv_id":"1810.06284","published":"2026-01-30"},{"title":"Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey","abstract":"Building autonomous machines that can explore open-ended environments, discover possible interactions and build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that this can only be achieved by $autotelic$ $agents$: intrinsically motivated learning agents that can learn to represent, generate, select and solve their own problems. In recent years, the convergence","authors":["Colas, Cédric","Karch, Tristan","Sigaud, Olivier","Oudeyer, Pierre-Yves"],"url":"https://arxiv.org/abs/2012.09830","arxiv_id":"2012.09830","published":"2026-01-30"},{"title":"Active Inference Tree Search in Large POMDPs","abstract":"The ability to plan ahead efficiently is key for both living organisms and artificial systems. Model-based planning and prospection are widely studied in cognitive neuroscience and artificial intelligence (AI), but from different perspectives--and with different desiderata in mind (biological realism versus scalability) that are difficult to reconcile. Here, we introduce a novel method to plan in POMDPs--Active Infer","authors":["Maisto, Domenico","Gregoretti, Francesco","Friston, Karl","Pezzulo, Giovanni"],"url":"https://arxiv.org/abs/2103.13860","arxiv_id":"2103.13860","published":"2026-01-30"},{"title":"BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models","abstract":"We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset of them) are being modified. We show that with small-to-medium training data, applying BitFit on pre-trained BERT models is competitive with (and sometimes better than) fine-tuning the entire model. For larger data, the method is competitive with other sparse fine-tuning methods. Besides their practical utility, these ","authors":["Ben-Zaken, Elad","Ravfogel, Shauli","Goldberg, Yoav"],"url":"https://arxiv.org/abs/2106.10199","arxiv_id":"2106.10199","published":"2026-01-30"},{"title":"HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information","abstract":"Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified using their position in this hierarchy. In addition, section titles usually indicate the common topic of their respective sentences. We propose a novel approach to formulate, extract, encode and inject hierarchical structure information ex","authors":["Ruan, Qian","Ostendorff, Malte","Rehm, Georg"],"url":"https://arxiv.org/abs/2203.09629","arxiv_id":"2203.09629","published":"2026-01-30"},{"title":"Divergence Results and Convergence of a Variance Reduced Version of ADAM","abstract":"Stochastic optimization algorithms using exponential moving averages of the past gradients, such as ADAM, RMSProp and AdaGrad, have been having great successes in many applications, especially in training deep neural networks. ADAM in particular stands out as efficient and robust. Despite of its outstanding performance, ADAM has been proved to be divergent for some specific problems. We revisit the divergent question","authors":["Wang, Ruiqi","Klabjan, Diego"],"url":"https://arxiv.org/abs/2210.05607","arxiv_id":"2210.05607","published":"2026-01-30"},{"title":"Automated Search for Conjectures on Mathematical Constants using Analysis of Integer Sequences","abstract":"Formulas involving fundamental mathematical constants had a great impact on various fields of science and mathematics, for example aiding in proofs of irrationality of constants. However, the discovery of such formulas has historically remained scarce, often perceived as an act of mathematical genius by great mathematicians such as Ramanujan, Euler, and Gauss. Recent efforts to automate the discovery of formulas for ","authors":["Razon, Ofir","Harris, Yoav","Gottlieb, Shahar","Carmon, Dan","David, Ofir","Kaminer, Ido"],"url":"https://arxiv.org/abs/2212.09470","arxiv_id":"2212.09470","published":"2026-01-30"},{"title":"Gaussian kernels on non-simply-connected closed Riemannian manifolds are never positive definite","abstract":"We show that the Gaussian kernel $\\exp\\left\\{-λd_g^2(\\bullet, \\bullet)\\right\\}$ on any non-simply-connected closed Riemannian manifold $(\\mathcal{M},g)$, where $d_g$ is the geodesic distance, is not positive definite for any $λ> 0$, combining analyses in the recent preprint~[9] by Da Costa--Mostajeran--Ortega and classical comparison theorems in Riemannian geometry.","authors":["Li, Siran"],"url":"https://arxiv.org/abs/2303.06558","arxiv_id":"2303.06558","published":"2026-01-30"},{"title":"Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models","abstract":"In this perspective paper, we first comprehensively review existing evaluations of Large Language Models (LLMs) using both standardized tests and ability-oriented benchmarks. We pinpoint several problems with current evaluation methods that tend to overstate the capabilities of LLMs. We then articulate what artificial general intelligence should encompass beyond the capabilities of LLMs. We propose four characteristi","authors":["Ma, Yuxi","Zhang, Chi","Zhu, Song-Chun"],"url":"https://arxiv.org/abs/2307.03762","arxiv_id":"2307.03762","published":"2026-01-30"},{"title":"Machine learning for option pricing: an empirical investigation of network architectures","abstract":"We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output data (option prices or implied volatilities). The majority of articles in this literature considers a (plain) feed forward neural network architecture in order to connect the neurons used for learning the function mapping inputs to outputs. I","authors":["Della Corte, Serena","Van Mieghem, Laurens","Papapantoleon, Antonis","Papazoglou-Hennig, Jonas"],"url":"https://arxiv.org/abs/2307.07657","arxiv_id":"2307.07657","published":"2026-01-30"},{"title":"Low-redundancy Distillation for Continual Learning","abstract":"Continual learning (CL) aims to learn new tasks without erasing previous knowledge. However, current CL methods primarily emphasize improving accuracy while often neglecting training efficiency, which consequently restricts their practical application. Drawing inspiration from the brain's contextual gating mechanism, which selectively filters neural information and continuously updates past memories, we propose L","authors":["Liu, RuiQi","Diao, Boyu","Huang, Libo","An, Zijia","Liu, Hangda","An, Zhulin"],"url":"https://arxiv.org/abs/2309.16117","arxiv_id":"2309.16117","published":"2026-01-30"},{"title":"ACES: Generating Diverse Programming Puzzles with with Autotelic Generative Models","abstract":"The ability to invent novel and interesting problems is a remarkable feature of human intelligence that drives innovation, art, and science. We propose a method that aims to automate this process by harnessing the power of state-of-the-art generative models to produce a diversity of challenging yet solvable problems, here in the context of Python programming puzzles. Inspired by the intrinsically motivated literature","authors":["Pourcel, Julien","Colas, Cédric","Molinaro, Gaia","Oudeyer, Pierre-Yves","Teodorescu, Laetitia"],"url":"https://arxiv.org/abs/2310.10692","arxiv_id":"2310.10692","published":"2026-01-30"},{"title":"Fair Graph Machine Learning under Adversarial Missingness Processes","abstract":"Graph Neural Networks (GNNs) have achieved state-of-the-art results in many relevant tasks where decisions might disproportionately impact specific communities. However, existing work on fair GNNs often assumes that either sensitive attributes are fully observed or they are missing completely at random. We show that an adversarial missingness process can inadvertently disguise a fair model through the imputation, lea","authors":["Lina, Debolina Halder","Silva, Arlei"],"url":"https://arxiv.org/abs/2311.01591","arxiv_id":"2311.01591","published":"2026-01-30"},{"title":"Mesh Neural Cellular Automata","abstract":"Texture modeling and synthesis are essential for enhancing the realism of virtual environments. Methods that directly synthesize textures in 3D offer distinct advantages to the UV-mapping-based methods as they can create seamless textures and align more closely with the ways textures form in nature. We propose Mesh Neural Cellular Automata (MeshNCA), a method that directly synthesizes dynamic textures on 3D meshes wi","authors":["Pajouheshgar, Ehsan","Xu, Yitao","Mordvintsev, Alexander","Niklasson, Eyvind","Zhang, Tong","Süsstrunk, Sabine"],"url":"https://arxiv.org/abs/2311.02820","arxiv_id":"2311.02820","published":"2026-01-30"},{"title":"Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring","abstract":"Self-supervised methods have recently proved to be nearly as effective as supervised ones in various imaging inverse problems, paving the way for learning-based approaches in scientific and medical imaging applications where ground truth data is hard or expensive to obtain. These methods critically rely on invariance to translations and/or rotations of the image distribution to learn from incomplete measurement data ","authors":["Scanvic, Jérémy","Davies, Mike","Abry, Patrice","Tachella, Julián"],"url":"https://arxiv.org/abs/2312.11232","arxiv_id":"2312.11232","published":"2026-01-30"},{"title":"Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video","abstract":"Generating dynamic 3D object from a single-view video is challenging due to the lack of 4D labeled data. An intuitive approach is to extend previous image-to-3D pipelines by transferring off-the-shelf image generation models such as score distillation sampling.However, this approach would be slow and expensive to scale due to the need for back-propagating the information-limited supervision signals through a large pr","authors":["Pan, Zijie","Yang, Zeyu","Zhu, Xiatian","Zhang, Li"],"url":"https://arxiv.org/abs/2401.08742","arxiv_id":"2401.08742","published":"2026-01-30"},{"title":"Scaling Laws for Downstream Task Performance of Large Language Models","abstract":"Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (upstream) loss. However, in transfer learning settings, in which LLMs are pretrained on an unsupervised dataset and then finetuned on a downstream task, we often also care about the downstream performance. In this work, we study the scaling be","authors":["Isik, Berivan","Ponomareva, Natalia","Hazimeh, Hussein","Paparas, Dimitris","Vassilvitskii, Sergei","Koyejo, Sanmi"],"url":"https://arxiv.org/abs/2402.04177","arxiv_id":"2402.04177","published":"2026-01-30"},{"title":"Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling","abstract":"Point cloud processing methods leverage local and global point features %at the feature level to cater to downstream tasks, yet they often overlook the task-level context inherent in point clouds during the encoding stage. We argue that integrating task-level information into the encoding stage significantly enhances performance. To that end, we propose SMTransformer which incorporates task-level information into a v","authors":["He, Yong","Yu, Hongshan","Mu, Chaoxu","Feng, Mingtao","Chen, Tongjia","Li, Zechuan"],"url":"https://arxiv.org/abs/2403.14124","arxiv_id":"2403.14124","published":"2026-01-30"},{"title":"Towards Identifiable Latent Additive Noise Models","abstract":"Causal representation learning (CRL) offers the promise of uncovering the underlying causal model by which observed data was generated, but the practical applicability of existing methods remains limited by the strong assumptions required for identifiability and by challenges in applying them to real-world settings. Most current approaches are applicable only to relatively restrictive model classes, such as linear or","authors":["Liu, Yuhang","Zhang, Zhen","Gong, Dong","Gao, Erdun","Huang, Biwei","Gong, Mingming"],"url":"https://arxiv.org/abs/2403.15711","arxiv_id":"2403.15711","published":"2026-01-30"},{"title":"Enhancing Multiple Object Tracking Accuracy via Quantum Annealing","abstract":"Multiple object tracking (MOT), a key task in image recognition, presents a persistent challenge in balancing processing speed and tracking accuracy. This study introduces a novel approach that leverages quantum annealing (QA) to expedite computation speed, while enhancing tracking accuracy through the ensembling of object tracking processes. A method to improve the matching integration process is also proposed. By u","authors":["Ihara, Yasuyuki"],"url":"https://arxiv.org/abs/2403.18908","arxiv_id":"2403.18908","published":"2026-01-30"},{"title":"Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound","abstract":"Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic framework designed for deploying image-based AI models in such settings. Using this framework, we deployed a trained model for fetal ultrasound standard plane detection, and evaluated it in real-time sessions with both novice and expert users. ","authors":["Wong, Chun Kit","Ngo, Mary","Lin, Manxi","Bashir, Zahra","Heen, Amihai","Svendsen, Morten Bo Søndergaard"],"url":"https://arxiv.org/abs/2404.00032","arxiv_id":"2404.00032","published":"2026-01-30"},{"title":"NoiseNCA: Noisy Seed Improves Spatio-Temporal Continuity of Neural Cellular Automata","abstract":"Neural Cellular Automata (NCA) is a class of Cellular Automata where the update rule is parameterized by a neural network that can be trained using gradient descent. In this paper, we focus on NCA models used for texture synthesis, where the update rule is inspired by partial differential equations (PDEs) describing reaction-diffusion systems. To train the NCA model, the spatio-temporal domain is discretized, and Eul","authors":["Pajouheshgar, Ehsan","Xu, Yitao","Süsstrunk, Sabine"],"url":"https://arxiv.org/abs/2404.06279","arxiv_id":"2404.06279","published":"2026-01-30"},{"title":"Emergent Dynamics in Neural Cellular Automata","abstract":"Neural Cellular Automata (NCA) models are trainable variations of traditional Cellular Automata (CA). Emergent motion in the patterns created by NCA has been successfully applied to synthesize dynamic textures. However, the conditions required for an NCA to display dynamic patterns remain unexplored. Here, we investigate the relationship between the NCA architecture and the emergent dynamics of the trained models. Sp","authors":["Xu, Yitao","Pajouheshgar, Ehsan","Süsstrunk, Sabine"],"url":"https://arxiv.org/abs/2404.06406","arxiv_id":"2404.06406","published":"2026-01-30"},{"title":"Endmember Extraction from Hyperspectral Images Using Self-Dictionary Approach with Linear Programming","abstract":"Hyperspectral imaging technology has a wide range of applications, including forest management, mineral resource exploration, and Earth surface monitoring. A key step in utilizing this technology is endmember extraction, which aims to identify the spectral signatures of materials in observed scenes. Theoretical studies suggest that self-dictionary methods using linear programming (LP), known as Hottopixx methods, are","authors":["Mizutani, Tomohiko"],"url":"https://arxiv.org/abs/2404.13098","arxiv_id":"2404.13098","published":"2026-01-30"},{"title":"Federated Learning for Heterogeneous Electronic Health Record Systems with Cost Effective Participant Selection","abstract":"The increasing volume of electronic health records (EHRs) presents the opportunity to improve the accuracy and robustness of models in clinical prediction tasks. Unlike traditional centralized approaches, federated learning enables training on data from multiple institutions while preserving patient privacy and complying with regulatory constraints. In practice, healthcare institutions (i.e., hosts) often need to bui","authors":["Kim, Jiyoun","Kim, Junu","Hur, Kyunghoon","Choi, Edward"],"url":"https://arxiv.org/abs/2404.13318","arxiv_id":"2404.13318","published":"2026-01-30"}],"history":{"events":[{"year":2020,"text":"The World Health Organization declared the COVID-19 pandemic to be a public health emergency of international concern.","url":"https://en.wikipedia.org/wiki/World_Health_Organization"},{"year":2013,"text":"The Korea Aerospace Research Institute launched Naro-1, South Korea's first carrier rocket and their first launch vehicle to achieve Earth orbit.","url":"https://en.wikipedia.org/wiki/Korea_Aerospace_Research_Institute"},{"year":2005,"text":"Forty-six years to the day after the sinking of the Danish ocean liner MS Hans Hedtoft, Queen Margrethe II unveiled a memorial in Copenhagen to the 95 passengers and crew who perished.","url":"https://en.wikipedia.org/wiki/MS_Hans_Hedtoft"},{"year":2000,"text":"Kenya Airways Flight 431 crashed into the Atlantic Ocean near Ivory Coast shortly after takeoff, killing 169 on board.","url":"https://en.wikipedia.org/wiki/Kenya_Airways_Flight_431"},{"year":1979,"text":"Varig Flight 967 disappeared shortly after takeoff from Tokyo's Narita International Airport en route to Rio de Janeiro.","url":"https://en.wikipedia.org/wiki/Varig_Flight_967"},{"year":1972,"text":"The Troubles: On Bloody Sunday, members of the British Parachute Regiment shot 26 civil-rights protesters in Derry, Northern Ireland, killing at least 13 people.","url":"https://en.wikipedia.org/wiki/The_Troubles"},{"year":1968,"text":"Vietnam War: Forces of the Viet Cong and the Vietnamese People's Army launched the Tet Offensive to strike military and civilian command and control centers throughout South Vietnam.","url":"https://en.wikipedia.org/wiki/Vietnam_War"},{"year":1964,"text":"In a bloodless coup, Nguyễn Khánh overthrew Dương Văn Minh's military junta in South Vietnam, less than three months after Minh's own coup.","url":"https://en.wikipedia.org/wiki/Nguy%E1%BB%85n_Kh%C3%A1nh"},{"year":1959,"text":"Danish ocean liner MS Hans Hedtoft sank, resulting in the deaths of all 95 passengers and crew.","url":"https://en.wikipedia.org/wiki/MS_Hans_Hedtoft"},{"year":1948,"text":"Mahatma Gandhi (pictured), the political and spiritual leader of India and the Indian independence movement, was fatally shot by Nathuram Godse at Birla House in Delhi.","url":"https://en.wikipedia.org/wiki/Mahatma_Gandhi"},{"year":1945,"text":"World War II: Allied forces liberated more than 500 prisoners of war (pictured) from a Japanese POW camp near Cabanatuan in the Philippines.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1939,"text":"In a speech to the Reichstag, Adolf Hitler threatened the \"annihilation of the Jewish race in Europe\".","url":"https://en.wikipedia.org/wiki/30_January_1939_Reichstag_speech"},{"year":1862,"text":"The United States Navy launched USS Monitor (pictured), the first American ironclad warship.","url":"https://en.wikipedia.org/wiki/United_States_Navy"},{"year":1835,"text":"Richard Lawrence became the first person to attempt to assassinate a sitting US president when he failed to kill Andrew Jackson at the US Capitol (assassination attempt pictured) and was subdued by the crowd.","url":"https://en.wikipedia.org/wiki/Richard_Lawrence_(failed_assassin)"},{"year":1661,"text":"Two years after his death, Oliver Cromwell's remains were exhumed for a posthumous execution and his head was placed on a spike above Westminster Hall in London, where it remained until 1685.","url":"https://en.wikipedia.org/wiki/Oliver_Cromwell"},{"year":1649,"text":"King Charles I, who was defeated in both the First and the Second English Civil Wars, was beheaded for high treason in front of the Banqueting House in London.","url":"https://en.wikipedia.org/wiki/Charles_I_of_England"},{"year":1607,"text":"Low-lying areas flooded around the coasts of the Bristol Channel in southwest England and south Wales, resulting in around 2,000 deaths.","url":"https://en.wikipedia.org/wiki/1607_Bristol_Channel_floods"},{"year":1287,"text":"Wareru created the Hanthawaddy Kingdom in today's Lower Burma and declared himself king following the collapse of the Pagan Empire.","url":"https://en.wikipedia.org/wiki/Wareru"}],"births":[{"year":2005,"text":"Prince Hashem, second son of King Abdullah II of Jordan","url":"https://en.wikipedia.org/wiki/Prince_Hashem_bin_Abdullah"},{"year":2003,"text":"Amen Thompson, American basketball player","url":"https://en.wikipedia.org/wiki/Amen_Thompson"},{"year":2003,"text":"Ausar Thompson, American basketball player","url":"https://en.wikipedia.org/wiki/Ausar_Thompson"},{"year":2002,"text":"Tyla, South African singer and songwriter.","url":"https://en.wikipedia.org/wiki/Tyla"},{"year":2002,"text":"Bijan Robinson, American football player","url":"https://en.wikipedia.org/wiki/Bijan_Robinson"},{"year":2001,"text":"Curtis Jones, English footballer","url":"https://en.wikipedia.org/wiki/Curtis_Jones_(footballer%2C_born_2001)"},{"year":2000,"text":"Markella Kavenagh, Australian actress","url":"https://en.wikipedia.org/wiki/Markella_Kavenagh"},{"year":2000,"text":"Bryan Woo, American baseball player","url":"https://en.wikipedia.org/wiki/Bryan_Woo"}],"deaths":[{"year":2026,"text":"Catherine O'Hara, Canadian-American actress, comedian and screenwriter (born 1954)","url":"https://en.wikipedia.org/wiki/Catherine_O'Hara"},{"year":2025,"text":"Dick Button, American figure skater and actor (born 1929)","url":"https://en.wikipedia.org/wiki/Dick_Button"},{"year":2025,"text":"Julius Chan, Papua New Guinean politician, 2nd Prime Minister of Papua New Guinea (born 1939)","url":"https://en.wikipedia.org/wiki/Julius_Chan"},{"year":2025,"text":"Marianne Faithfull, English singer-songwriter and actress (born 1946)","url":"https://en.wikipedia.org/wiki/Marianne_Faithfull"},{"year":2025,"text":"Edcel Lagman, Filipino politician (born 1942)","url":"https://en.wikipedia.org/wiki/Edcel_Lagman"},{"year":2024,"text":"Chita Rivera, American actress, singer, and dancer (born 1933)","url":"https://en.wikipedia.org/wiki/Chita_Rivera"},{"year":2023,"text":"Bobby Beathard, American Pro Football Hall of Fame executive (born 1937)","url":"https://en.wikipedia.org/wiki/Bobby_Beathard"},{"year":2023,"text":"Bobby Hull, Canadian ice hockey player (born 1939)","url":"https://en.wikipedia.org/wiki/Bobby_Hull"}]},"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."}