Record 21102026 · captured 2026-10-22
The world looked up Kim Jong Un. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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What the most people looked up, ranked by Wikipedia pageviews for that day.
Kim Jong Un is a North Korean politician and dictator who has been serving as the supreme leader of North Korea since 2011, following the death of his father, Kim Jong Il. He is the third ruler from the Kim family to lead the country, which was founded by his
Jailer 2 is a 2026 Indian Tamil-language action comedy film written and directed by Nelson Dilipkumar. It was produced by Kalanithi Maran through Sun Pictures. A sequel to Jailer (2023), the film stars Rajinikanth, Ramya Krishnan, Yogi Babu, Mirnaa and Rithvik
Elizabeth Anne Holmes is an American businesswoman convicted of fraud in connection with her health technology company Theranos. Holmes founded Theranos in 2003, and its valuation soared in the early 2010s after it claimed to have revolutionized blood testing
You Can See Everything is a 2026 American experimental meta-documentary film directed by Nathan Fielder and Lance Oppenheim. It follows Elizabeth Holmes, the founder of the defunct health technology company Theranos, and her partner Billy Evans, who invited a
Helen Mary Haines is an Australian politician and health researcher who has served as the independent member of Parliament (MP) for the Victorian division of Indi since 2019. Prior to entering politics, she worked in nursing and midwifery services, and as a he
Luke McCaffrey is an American professional football wide receiver and kick returner for the Washington Commanders of the National Football League (NFL). He played college football for the Nebraska Cornhuskers and Rice Owls, originally at quarterback before swi
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
Hunter Schafer is an American actress, model, and artist. Born in New Jersey and raised in North Carolina, she came to public attention after joining a 2016 lawsuit against North Carolina's Public Facilities Privacy & Security Act, a later-repealed "bathroom b
Street Fighter is a 2026 martial arts comedy film directed by Kitao Sakurai and written by Sakurai and T. J. Fixman, with a story by Dalan Musson and Gary Dauberman. It is the third live-action feature-length film based on the Street Fighter video game series
Barbara Palvin Sprouse is a Hungarian model and actress. She first appeared in the Sports Illustrated Swimsuit Issue in 2016. In 2019, she became a Victoria's Secret Angel, although she first walked in 2012. She is also an ambassador for Longines and Armani Be
Richard Riehle was an American character actor, best known for playing Tom Smykowski in Office Space (1999) and Walt Finnerty on Grounded for Life (2001–2005). He appeared in over 400 films, television shows and other projects.
Nathan Joseph Fielder is a Canadian and American filmmaker, actor, and comedian. He is known for his awkward, deadpan persona and his use of satire and surreal humor.
Christian Jackson McCaffrey is an American professional football running back for the San Francisco 49ers of the National Football League (NFL). He played college football for the Stanford Cardinal and was selected by the Carolina Panthers eighth overall in th
Dylan Thomas Sprouse is an American actor. He is known for his role as Zack Martin on the Disney Channel series The Suite Life of Zack & Cody and its spin-off, The Suite Life on Deck, where he starred alongside his twin brother Cole Sprouse. He is one of the o
Crystal Lake is an American slasher television series developed by Brad Caleb Kane that is based on the Friday the 13th film franchise. It is a prequel to the 1980 film and stars Linda Cardellini in the leading role of Pamela Voorhees, with Callum Vinson as a
2026 Alberta independence referendum
The independence question of the 2026 Alberta referendum is one of 10 questions of the 2026 Alberta referendum election, which took place on October 19, 2026. The question was a choice of two options: whether to remain in Canada or for the province of Alberta
Brock Richard Purdy is an American professional football quarterback for the San Francisco 49ers of the National Football League (NFL). He played college football for the Iowa State Cyclones and was selected by the 49ers with the final pick in the 2022 NFL dra
Abdulrahman Mohamed El-Sayed is an American epidemiologist, public health administrator, academic and politician who is the Democratic nominee in the 2026 United States Senate election in Michigan.
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone 'lemon'.
The London Monster was the name given to an attacker of women in London between 1788 and 1790. Renwick Williams, a Welsh maker of artificial flowers, was arrested for the attacks in June 1790, given two trials and sent to Newgate Prison for six years. The Mons
The Tampa Bay Rays are an American professional baseball team based in the Tampa Bay area. The Rays compete in Major League Baseball (MLB) as a member club of the American League (AL) East Division. They are one of two major league clubs based in Florida, alon
Neatsville is an unincorporated community in Adair County, located in the U.S. state of Kentucky. It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as
Carrie is an American supernatural horror television miniseries developed by Mike Flanagan, and produced by Amazon MGM Studios for Amazon Prime Video. The first series adaptation of the eponymous franchise, it is based on the 1974 novel by Stephen King, and is
Demonstrations and protests against poor school and economic conditions by lycée high school students have taken place in France since September 2026. In early October, the protests developed into a nationwide mobilisation involving students, teachers, parents
Call of Duty: Modern Warfare 4
Call of Duty: Modern Warfare 4 is a 2026 first-person shooter game developed by Infinity Ward and published by Activision. It is the twenty-third installment in the Call of Duty series and the fourth and final entry in the rebooted Modern Warfare sub-series, f
Ansel Easton Adams was an American landscape photographer and environmentalist known for his black-and-white images of the American West. He helped found Group f/64, an association of photographers advocating "pure" photography which favored sharp focus and th
Kylie Kristen Jenner is an American media personality, socialite, and businesswoman. She starred in the E! reality television series Keeping Up with the Kardashians from 2007 to 2021 and the Hulu reality television series The Kardashians starting in 2022. She
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
Osiris was the god of fertility, agriculture, the afterlife, the dead, resurrection, life, and vegetation in ancient Egyptian religion. He was classically depicted with a pharaoh's beard, partially mummy-wrapped at the legs, wearing a distinctive atef crown an
Clayface is a 2026 American body horror-psychological thriller film based on the eponymous character from DC Comics. Directed by James Watkins from a screenplay by Mike Flanagan and Hossein Amini, it is the third film in the DC Universe (DCU). Tom Rhys Harries
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction. We challenge this de facto design with two diagnostic findings. (1) Non-uniform computational need: items within the same sequence vary drastically in how much computation they require. Most converge after shallow processing while
PhysiCalWorld: In-Context Physics Calibration for World Models
Existing world models typically perceive a scene through current visual frame or a narrow history window. However, these observations may not reveal the physics properties that determine transition dynamics, such as an object's mass distribution, joint damping, or surface friction. Without calibrating these physics factors for world models, predictions tend to regress toward an empirical prior and induce severe drift
Environment Duels: A Microcosm of LLM Co-Evolution
Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predecessors and competitors create. How well does each model play its role in this ecosystem, as a creator and as a solver? We introduce Environment Duels, a dynamic agentic benchmark that evaluates both capabilities on interactive environments. Each model writes environments
The End of Consensus Research?
Consensus is a fundamental problem in distributed computing and at the core of state machine replication (SMR). Despite decades of active human research, discovering practical low-latency solutions to the problem is still open, as it requires combining protocol knowledge, correctness reasoning, implementation, and experimental evaluation. We report on how AI helped us discover A-greement, a new consensus protocol for
Quant-Arena: Benchmarking LLM Agents for Quantitative Trading Research
Quantitative research is an iterative loop: a researcher proposes a market hypothesis, implements it as an executable signal, tests it on historical data, and decides what to keep. Existing benchmarks score formulas, code, or individual trading decisions, leaving open whether large language models (LLMs) can complete this loop and produce strategies that work beyond the research period. We introduce Quant-Arena, a be
Multimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use
Multimodal agents (MMAs) sustain interaction through reasoning, memory, tools, and feedback, most visibly as computer-use agents, while robotic systems (RSs) couple sensing and actuation under physical dynamics. We define multimodal embodied agents (MMEAs) as goal-directed systems that couple multimodal task reasoning with physical action and revise decisions from the resulting feedback. This raises a central questio
Asymmetric On-Policy Distillation Learns In-Context Exploration
Sequential decision-making under partial observability is difficult because agents must explore to infer hidden, task-relevant information before acting effectively. Learning such information-gathering behaviors tabula rasa via online reinforcement learning is sample-inefficient, whereas offline imitation averages expert actions across hidden states and fails to learn exploration. In this work, we propose ASTEROID, a
Despite recent advances, current AI systems remain highly susceptible to domain shifts, particularly in biomedical imaging, where images are usually acquired under substantially different experimental conditions. Although recent domain adaptation methods have made substantial progress, significant performance gaps under domain shift persist. To measure progress in this regard, we introduce BatchBench, a leaderboard t
Differentially Private Sparse Reward Estimation with Preference Feedback
Reinforcement Learning with Human Feedback (RLHF) has become a central paradigm for aligning Large Language Models with human values. However, the reliance on sensitive preference data necessitates rigorous differential privacy (DP) guarantees to prevent user information leakage. Existing private alignment methods typically assume dense reward parameterizations, resulting in estimation error bounds that scale polynom
AtomicRL: Atomic Reinforcement Learning for Long-Horizon Robotic Manipulation
Vision-language-action (VLA) models have shown promise in robotic manipulation, but imitation learning (IL) alone often struggles to master complex long-horizon skills. Reinforcement learning (RL) offers an effective avenue for further improving VLA policies. However, existing VLA-RL methods typically suffer from delayed and biased credit assignment due to trajectory-level sparse rewards, while imbalanced subtask mas
Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization
Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined. We model this coordination problem as discrete block optimization with an exactly evaluable objective and a known dependency graph. Independent sets certify compatible edits whose verified gains, measured against the same current solution, add exactly. A coordinator constructs a safe compatible candidate and
Charts as Epistemic Actions for Experience-Driven Agentic Data Analysis
Data agents built on ReAct-style loops now execute complex analytical workflows over structured data, yet their intermediate reasoning still runs almost entirely through text. Human analysts instead move between numbers, tables, and charts, reading visual evidence to expose trends and anomalies that text summaries drop. We ask whether charts can serve as internal epistemic actions inside a data agent's reasoning loop
From Reasoning to Action: Benchmarking Frontier AI Models on LIBERO-10 Robot Manipulation Tasks
Recent advances in frontier AI models have demonstrated remarkable capabilities in visual understanding, reasoning, planning, and tool use, raising the possibility of using general-purpose AI models for robot manipulation without task-specific training. However, their manipulation capabilities have not been systematically evaluated on standardized robotics benchmarks. In this report, we present a unified closed-loop
SWE-DIVE: Multi-Turn Coding Agent Evaluation with Dynamic User Simulation
Coding agents are typically evaluated using one-shot, well-formulated prompts. Benchmarks such as SWE-bench use existing GitHub issues as input for a coding agent and evaluate whether the agent successfully solves the task. However, in real-world use cases, prompts are often not as well-formulated or structured as existing GitHub issues. Additionally, a coding agent session is not limited to a single prompt; it typic
Decision models map stated rules and observed states to discrete answers. Aggregate accuracy leaves unresolved which rule and information changes guide those answers. We connect controlled input changes to transfer, joint reliability, and directed information use through executable arithmetic rules. Semantic rule separation and crossed rule and state tests make transfer and joint success separately measurable. We com
SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control for Humanoid Loco-Manipulation
Humanoid loco-manipulation requires an effective interface between high-level task policies and whole-body control. Existing interfaces typically rely on a single action representation for both task intent and whole-body coordination. This may burden the high-level policy or leave one aspect underspecified. We present SP-HLM, a framework built on a sparse-pose-driven interface. The interface combines five task-critic
HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning
Harness optimization, which improves an agent's prompts, tools, and workflows, is an increasingly effective way to enhance agents built on frozen or closed-source LLMs. Leading approaches such as the Darwin--Godel Machine and the Huxley--Godel Machine (HGM) rely on recursive self-improvement and were developed mainly for coding, where the competence needed to improve the agent coincides with the competence being impr
Robust Soft Skill Adaptation via Behavioral Localization in Tool-Using Agents
Text skills can extend the capabilities of tool-using agents, but long skill descriptions increase computational costs. Soft prompts provide a compact alternative, yet learning from entire successful trajectories may also introduce unnecessary behavioral changes. We find that the useful effect of a text skill is highly concentrated in a small subset of trajectory positions. Based on this observation, we propose Behav
What the Author Wrote, What the Agent Reads: Silent Cue Loss in Enterprise Document Ingestion
Enterprise agents act on documents. Before an agent can follow a policy, answer a compliance question or file a ticket, some ingestion layer must turn a Word file or PDF into tokens - and that layer is where an agent deployed in the wild silently loses information. We show that one layer is lost by every mainstream converter - the meaning an author encodes in how text looks - and that the loss makes agents confidentl
Empirical exchange-correlation functionals are fitted on fixed orbitals but used self-consistently, so better energies often come with worse densities. We show that the Brillouin residual of a trained functional on the Kohn-Sham orbitals of an accurate density controls, to first order, both the self-consistent density shift and the gap between fitted and self-consistent energies. Penalizing it requires no self-consis
GuideEvolve: Self-Evolving Agent Framework with Expert Guidance and 3Ts Mechanisms
This study introduces GuideEvolve, a self-evolving agent framework incorporating expert guidance and three autonomous regulatory mechanisms, collectively termed the 3Ts: Think Fast and Slow, Throwback, and Termination by choice. The framework addresses critical limitations in extant self-evolving agent paradigms through dual innovations: (1) a task-specific expert-evaluation-driven evolutionary pipeline, and (2) the
Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories
Understanding large, complex, and rapidly evolving code bases remains a core challenge for software engineering at scale, hindering onboarding, maintenance, refactoring, and impact analysis. Providing this information to a Large-Language Model (LLM) is another hurdle, as the LLM must understand not only the required code method but also its associated methods in the grand scheme of the repository. We propose Code Com
Agent Behavior Maps: Locating Agents by What They Would Do, Without Running Them
LLM agents with similar success rates can follow different action sequences. An Agent Behavior Map represents LLM-harness combinations using centered action log-likelihoods on shared reference trajectories, without requiring new environment rollouts from the target agent. We derive error bounds relating squared coordinate distances to trajectory KL under explicit assumptions, and across eight cross-scored models the
Building Auditable AI Text Detectors with Sparse Autoencoders
AI-generated text detectors are increasingly deployed in consequential settings---peer review, education, admissions, journalism, and content moderation---where their outputs directly affect individuals. Yet the strongest detectors are fine-tuned neural classifiers whose decisions are opaque, while transparent alternatives (stylometric and linguistic-feature classifiers) underperform them by wide margins. We present
Alarm root-cause analysis is studied on data that cannot be shared. Operator logs are confidential, so what the field publishes is a causal graph between alarm types rather than per-incident causes, a fixed scenario set, gated, or never released. Two recent benchmarks carry per-incident root causes at scale; neither states a download location or a licence. We give a generator rather than a dataset. It runs on real ba
Origins publishing files written for machines rather than people. Measured against a frozen cohort, so a change in the number means a change in adoption.
| Signal | Web head Tranco top 1,000 n=1,000 | Web tail sampled to rank 100k n=1,000 | AI-native model & dev platforms n=110 |
|---|---|---|---|
| Any agent-facing signal | 13.5% (135) | 4.9% (49) | 74.55% (82) |
| llms.txt (apex domain) | 11.3% (113) | 4.2% (42) | 60.91% (67) |
| llms.txt (docs subdomain) | 4.3% (43) | 1% (10) | 46.36% (51) |
| .well-known/mcp.json | 0.6% (6) | 0.2% (2) | 9.09% (10) |
| .well-known/agents.txt | 0.3% (3) | 0.1% (1) | 0% (0) |
| ai.txt | 0.3% (3) | 0.1% (1) | 0% (0) |
| ai-plugin.json (deprecated) | 0.6% (6) | 0.2% (2) | 0.91% (1) |
Notable events recorded on this day and month across all years.