Record 17032026 · captured 2026-08-25
The world looked up 98th Academy Awards. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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The 98th Academy Awards ceremony, presented by the Academy of Motion Picture Arts and Sciences (AMPAS), took place on March 15, 2026, at the Dolby Theatre in Hollywood, Los Angeles. During the gala, the AMPAS presented Academy Awards in 24 categories honoring
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
Michael Bakari Jordan is an American actor, producer, and director. His accolades include an Academy Award, three Actor Awards, and a Producers Guild Award, in addition to nominations for a British Academy Film Award, a Golden Globe Award and two Emmy Awards.
Paul Thomas Anderson, also known by his initials PTA, is an American filmmaker. Often described as one of the preeminent filmmakers of his generation, he is the recipient of numerous accolades, including three Academy Awards, three Golden Globe Awards and four
Jessie Buckley is an Irish actress and singer. Her accolades include an Academy Award, two BAFTAs, an Actor Award, a Golden Globe Award, a Critics' Choice Award and a Laurence Olivier Award.
Sinners is a 2025 American horror film produced, written, and directed by Ryan Coogler. Set in 1932 in the Mississippi Delta, it stars Michael B. Jordan in dual roles as criminal twin brothers who return to their hometown in the Jim Crow South, where they are
Amy Marie Madigan is an American actress. Known for her work on stage and screen, her accolades include an Academy Award, an Actor Award, a Golden Globe Award, and a Critics' Choice Award, in addition to a nomination for a Primetime Emmy Award.
Autumn Cheyenne Durald Arkapaw is an American cinematographer. For her work on the film Sinners (2025), she became the first woman of color to be nominated, and the first woman and first Black person to win, an Academy Award for Best Cinematography. On televis
Maya Rudolph is an American actress and comedian. In 2000, she became a cast member on the NBC sketch comedy show Saturday Night Live (SNL). During her tenure on the show, she appeared in supporting roles in the films 50 First Dates (2004), A Prairie Home Comp
Sean Justin Penn is an American actor and filmmaker. He is known for his intense leading man roles in film. His accolades include three Academy Awards, a Golden Globe Award, a British Academy Film Award, and nominations for an Emmy Award and a Grammy Award. He
The Academy Awards, more commonly known as the Oscars, are awards based on artistic and technical merit in film. They are presented annually by the Academy of Motion Picture Arts and Sciences (AMPAS) in the United States in recognition of excellence in cinemat
Hamnet is a 2025 historical drama film directed by Chloé Zhao, who co-wrote the screenplay with Maggie O'Farrell, based on the 2020 novel by O'Farrell. The film dramatises the family life of William Shakespeare and his wife Agnes Hathaway as they cope with the
Ryan Kyle Coogler is an American filmmaker. His accolades include an Academy Award, a British Academy Film Award, a Grammy Award, a Golden Globe Award, ten Black Reel Awards, and fourteen NAACP Image Awards.
Chase Infiniti Payne is an American actress, best known for her roles in the legal thriller anthology television series Presumed Innocent (2024), action-comedy-thriller film One Battle After Another (2025), and dystopian drama television series The Testaments
Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o
Weapons is a 2025 American supernatural mystery horror film directed, written, produced, and co-scored by Zach Cregger. It stars an ensemble cast including Josh Brolin, Julia Garner, Alden Ehrenreich, Austin Abrams, Cary Christopher, Toby Huss, Benedict Wong,
Benjamin Netanyahu, nicknamed "Bibi", is an Israeli politician and diplomat who has served as Prime Minister of Israel since 2022. Having previously held office from 1996 to 1999 and from 2009 to 2021, Netanyahu is Israel's longest-serving prime minister.
Barbara Joan "Barbra" Streisand is an American singer, songwriter, actress, author, and filmmaker. Over a career spanning more than six decades, she has achieved success across multiple fields of entertainment, earning her all four of the major performing art
Edward Allen Harris is an American actor and filmmaker. Harris received nominations for the Academy Award for Best Supporting Actor for his performances in Apollo 13 (1995), The Truman Show (1998), and The Hours (2002). He also directed and starred in Pollock
Timothée Hal Chalamet is an American and French actor. Known for his work in a diverse range of blockbusters and independent films, he is the recipient of numerous accolades including an Actor Award, a Golden Globe Award, and two Critics' Choice Awards, in add
The 2026 Revolution was a professional wrestling pay-per-view (PPV) event produced by All Elite Wrestling (AEW). It was the seventh annual Revolution and took place on March 15, 2026, at Crypto.com Arena in Los Angeles, California, marking the second consecuti
Charles Robert Redford Jr. was an American actor, director, and producer, celebrated for his magnetic presence as a leading man during the American New Wave. Across a career spanning more than six decades, Redford earned widespread recognition and numerous awa
Teyana Me Shay Jacqueline Taylor is an American singer, songwriter, actress, dancer, choreographer, and music video director. Her accolades include a Golden Globe Award, two Critics Choice Awards, and an NAACP Image Award, along with nominations for an Academy
Mr Nobody Against Putin is a 2025 documentary film directed by David Borenstein and Pavel Talankin. It follows Talankin in his job at a school in Karabash, a poor mining town near the Ural Mountains. While recording his students, Talankin also documents the Pu
Ludwig Emil Tomas Göransson is a Swedish musician, composer, and record producer. Based in the United States, he is often regarded as one of the most successful composers in Hollywood of the 21st century. For his work in music, film and television, he has rece
Marty Supreme is a 2025 American sports comedy-drama film directed by Josh Safdie, who co-wrote it with Ronald Bronstein. Set in the 1950s, it stars Timothée Chalamet as table tennis player Marty Mauser and follows his quest to become world champion. Gwyneth P
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
Leonardo Wilhelm DiCaprio is an American actor and film producer. Known for his work in biographical and period films, he is the recipient of numerous accolades, including an Academy Award, an Actor Award, a BAFTA Award, an Emmy Award, a Silver Bear and three
KPop Demon Hunters is a 2025 American animated musical urban fantasy film co-written and directed by Maggie Kang and Chris Appelhans. Produced by Sony Pictures Animation for Netflix, the film stars the voices of Arden Cho, Ahn Hyo-seop, May Hong, Ji-young Yoo,
Sentimental Value is a 2025 drama film directed by Joachim Trier, who co-wrote it with Eskil Vogt. It follows sisters Nora and Agnes in their reunion with their estranged father Gustav, who is preparing to make his next film with young American actress Rachel.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Forest structure in epigenetic landscapes
Morphogenesis is the biological process that causes the emergence and changes of patterns (tissues and organs) in living organisms. It is a robust, self-organising mechanism, governed by Genetic Regulatory Networks (GRN), that hasn't been thoroughly understood. In this work we propose Epigenetic Forests as a tool to study morphogenesis and to extract valuable information from GRN. Our method unfolds the richness
Unsupervised Point Cloud Pre-Training via Contrasting and Clustering
Annotating large-scale point clouds is highly time-consuming and often infeasible for many complex real-world tasks. Point cloud pre-training has therefore become a promising strategy for learning discriminative representations without labeled data. In this paper, we propose a general unsupervised pre-training framework, termed ConClu, which jointly integrates contrasting and clustering. The contrasting objective max
Conceptual Views of Neural Networks: A Framework for Neuro-Symbolic Analysis
We introduce \emph{conceptual views} as a formal framework grounded in Formal Concept Analysis for globally explaining neural networks. Experiments on twenty-four ImageNet models and Fruits-360 show that these views faithfully represent the original models, enable architecture comparison via Gromov--Wasserstein distance, and support abductive learning of human-comprehensible rules from neurons.
Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity
In this work, we study a natural nonparametric estimator of the transition probability matrices of a finite controlled Markov chain. We consider an offline setting with a fixed dataset, collected using a so-called logging policy. We develop sample complexity bounds for the estimator and establish conditions for minimaxity. Our statistical bounds depend on the logging policy through its mixing properties. We show that
Skeleton Regression: A Graph-Based Approach to Estimation with Manifold Structure
We introduce a new regression framework designed to deal with large-scale, complex data that lies around a low-dimensional manifold with noises. Our approach first constructs a graph representation, referred to as the skeleton, to capture the underlying geometric structure. We then define metrics on the skeleton graph and apply nonparametric regression techniques, along with feature transformations based on the graph
Measure transfer via stochastic slicing and matching
This paper studies iterative schemes for measure transfer and approximation problems, which are defined through a slicing-and-matching procedure. Similar to the sliced Wasserstein distance, these schemes benefit from the availability of closed-form solutions for the one-dimensional optimal transport problem and the associated computational advantages. While such schemes have already been successfully utilized in data
Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions
This paper considers stochastic first-order algorithms for minimax optimization under Polyak--Łojasiewicz (PL) conditions. We propose SPIDER-GDA for solving the finite-sum problem of the form $\min_x \max_y f(x,y)\triangleq \frac{1}{n} \sum_{i=1}^n f_i(x,y)$, where the objective function $f(x,y)$ is $μ_x$-PL in $x$ and $μ_y$-PL in $y$; and each $f_i(x,y)$ is $L$-smooth. We prove SPIDER-GDA could find an $ε$-optimal s
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
Dimensionality reduction-based dictionary learning methods in the literature have often used iterative random projections. The dimensionality of such a random projection matrix is a random number that might not lead to a separable subspace structure in the transformed space. The convergence of such methods highly depends on the initial seed values used. Also, gradient descent-based updates might result in local minim
We consider the problem of tensor completion with graphs serving as side information to represent interrelationships among variables. Existing approaches suffer from several limitations: (1) they are often task-specific and lack generality or systematic formulation; (2) they typically treat graphs as static structures, ignoring their inherent dynamism in tensor-based settings; (3) they lack theoretical guarantees on
Harvest Video Foundation Models via Efficient Post-Pretraining
Building video-language foundation models is costly and difficult due to the redundant nature of video data and the lack of high-quality video-language datasets. In this paper, we propose an efficient framework to harvest video foundation models from image ones. Our method is intuitively simple by randomly dropping input video patches and masking out input text during the post-pretraining procedure. The patch droppin
Fast Estimations of Hitting Time of Elitist Evolutionary Algorithms from Fitness Levels
The fitness level method is a widely used technique for estimating the mean hitting time of elitist evolutionary algorithms on level-based fitness functions. However, this paper identifies its main limitation: the linear lower bound derived from traditional fitness level partitioning is not tight when applied to many non-level-based fitness functions. A new subset level method is introduced to address this limitation
Modern large language models (LLMs) are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them. Unlike traditional learners, LLMs cannot use back-propagation to obtain feedback, and condition their output in situ in a phenomenon known as in-context learning (ICL). Many approaches to prompting and pre-training these models involve the automated generation of these prompts,
3D-LFM: Lifting Foundation Model
The lifting of 3D structure and camera from 2D landmarks is at the cornerstone of the entire discipline of computer vision. Traditional methods have been confined to specific rigid objects, such as those in Perspective-n-Point (PnP) problems, but deep learning has expanded our capability to reconstruct a wide range of object classes (e.g. C3DPO and PAUL) with resilience to noise, occlusions, and perspective distortio
Sparse Training for Federated Learning with Regularized Error Correction
Federated Learning (FL) has attracted much interest due to the significant advantages it brings to training deep neural network (DNN) models. However, since communications and computation resources are limited, training DNN models in FL systems face challenges such as elevated computational and communication costs in complex tasks. Sparse training schemes gain increasing attention in order to scale down the dimension
Computational efficiency and robustness are essential in process modeling, optimization, and control for real-world engineering applications. While neural network-based approaches have gained significant attention in recent years, conventional neural networks often fail to address these two critical aspects simultaneously or even independently. Inspired by natural physical systems and established literature, input co
All-weather Multi-Modality Image Fusion: Unified Framework and 100k Benchmark
Multi-modality image fusion (MMIF) combines complementary information from different image modalities to provide a comprehensive and objective interpretation of scenes. However, existing fusion methods cannot resist different weather interferences in real-world scenes, limiting their practical applicability. To bridge this gap, we propose an end-to-end, unified all-weather MMIF model. Rather than focusing solely on p
Combining Evidence Across Filtrations
In sequential anytime-valid inference, any admissible procedure must be based on e-processes: generalizations of test martingales that quantify the accumulated evidence against a composite null hypothesis at any stopping time. This paper proposes a method for combining e-processes constructed in different filtrations but for the same null. Although e-processes in the same filtration can be combined effortlessly (by a
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Computerized Adaptive Testing (CAT) offers an efficient and personalized method for assessing examinee proficiency by dynamically adjusting test questions based on individual performance. Compared to traditional, non-personalized testing methods, CAT requires fewer questions and provides more accurate assessments. As a result, CAT has been widely adopted across various fields, including education, healthcare, sports,
Federated Multi-Agent Mapping for Planetary Exploration
Multi-agent robotic exploration stands to play an important role in space exploration as the next generation of robotic systems ventures to far-flung environments. A key challenge in this new paradigm will be to effectively share and utilize the vast amount of data generated onboard while operating in bandwidth-constrained regimes typical of space missions. Federated learning (FL) is a promising tool for bridging thi
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty
This paper studies continuous-time risk-sensitive reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation with the exponential-form objective. The risk-sensitive objective arises either as the agent's risk attitude or as a distributionally robust approach against the model uncertainty. Owing to the martingale perspective in Jia and Zhou (J Mach Learn Res 24(161): 1--61
Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions
Neural Radiance Fields (NeRF) show impressive performance in photo-realistic free-view rendering of scenes. Recent improvements on the NeRF such as TensoRF and ZipNeRF employ explicit models for faster optimization and rendering, as compared to the NeRF that employs an implicit representation. However, both implicit and explicit radiance fields require dense sampling of images in the given scene. Their performance de
Policy Iteration for Two-Player General-Sum Stochastic Stackelberg Games
We address two-player general-sum stochastic Stackelberg games (SSGs), where the leader's policy is optimized considering the best-response follower whose policy is optimal for its reward under the leader. Existing policy gradient and value iteration approaches for SSGs do not guarantee monotone improvement in the leader's policy under the best-response follower. Consequently, their performance is not guarant
Random Scaling and Momentum for Non-smooth Non-convex Optimization
Training neural networks requires optimizing a loss function that may be highly irregular, and in particular neither convex nor smooth. Popular training algorithms are based on stochastic gradient descent with momentum (SGDM), for which classical analysis applies only if the loss is either convex or smooth. We show that a very small modification to SGDM closes this gap: simply scale the update at each time point by a
MetaGS: A Meta-Learned Gaussian-Phong Model for Out-of-Distribution 3D Scene Relighting
Out-of-distribution (OOD) 3D relighting requires novel view synthesis under unseen lighting conditions that differ significantly from the observed images. Existing relighting methods, which assume consistent light source distributions between training and testing, often degrade in OOD scenarios. We introduce MetaGS to tackle this challenge from two perspectives. First, we propose a meta-learning approach to train 3D
TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents
Long-separated research has been conducted on two highly correlated tracks: traffic and incidents. Traffic track witnesses complicating deep learning models, e.g., to push the prediction a few percent more accurate, and the incident track only studies the incidents alone, e.g., to infer the incident risk. We, for the first time, spatiotemporally aligned the two tracks in a large-scale region (16,972 traffic nodes) fr
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