Record 19102026 · captured 2026-10-20
The world looked up Jailer 2. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
Complete record JSON
What the most people looked up, ranked by Wikipedia pageviews for that day.
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 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 film crew to docum
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
The 2026 WrestleDream, also promoted as WrestleDream: Orlando, was a professional wrestling pay-per-view (PPV) event produced by All Elite Wrestling (AEW). It was the fourth annual WrestleDream and took place on Saturday, October 17, 2026, at the Addition Fina
Kate Sharon Zacharia (née Black) is an American political commentator who served as the 37th White House press secretary in October 2026, during the second presidency of Donald Trump. She held the title for five days, making her the shortest-serving White Hous
Michael "Bear" Bachmeier is an American college football quarterback for the BYU Cougars.
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
Kaviya Maran is an Indian businesswoman. She is the executive director of Sun TV Network and chief executive of the Indian Premier League franchise Sunrisers Hyderabad, owned by the Sun Group founded by her father Kalanithi Maran.
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
Abdulrahman Mohamed El-Sayed is an American epidemiologist and politician who is the Democratic nominee in the 2026 United States Senate election in Michigan.
Love of Your Life is a 2026 American romantic drama film directed by Rachel Morrison, from a script by Julia Cox. It stars Margaret Qualley, Aaron Pierre, Patrick Schwarzenegger, Gabriel Basso, and Catherine Keener. It is produced by Ryan Gosling and Jessie He
Nathan Joseph Fielder is a Canadian filmmaker, actor, and comedian. He is known for his awkward, deadpan persona and his use of satire and surreal humor.
Devanesan Chokkalingam, popularly known as Deva, was an Indian composer and singer who predominantly worked in Tamil cinema. He composed songs and provided background music for Tamil, Kannada, Telugu, and Malayalam films in a career closing in on four decades.
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
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
Since 18 September 2026, a series of demonstrations and blockades by lycée students has been taking place in France and later, to a lesser degree, in Belgium, Albania, Greece, and Italy, having originated in Créteil, a commune in the suburbs of Paris. The prot
Anirudh Ravichander, also credited mononymously as Anirudh, is an Indian composer and playback singer who works primarily in Tamil cinema. He has also composed for Telugu and Hindi films. He has won two Filmfare Awards South, ten SIIMA Awards, six Edison Award
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
Tenzing Norgay, born Namgyal Wangdi, and also referred to as Sherpa Tenzing, was a Nepalese-Indian Sherpa mountaineer. On 29 May 1953, he and Edmund Hillary were the first people confirmed to have reached the summit of Mount Everest, as part of the 1953 Britis
The Hôtel de Ville is a municipal building in Limoges, Haute-Vienne, France, standing on the Place Jacques Chirac. Completed in 1883, it was designated a monument historique by the French government in 1975. It was badly damaged by arson during the 17 October
Below is a 2026 Canadian thriller miniseries created by Jesse McKeown and starring Josh Hartnett, Mackenzie Davis, and Charlie Heaton. It premiered on Netflix on October 8, 2026. The series received generally positive reviews from critics.
Whalefall is a 2026 American survival thriller film directed and produced by Brian Duffield, based on the 2023 novel by Daniel Kraus, who co-wrote the script with Duffield. The film follows a scuba diver who, while searching for the remains of his estranged fa
Lizzie Andrew Borden was an American woman who was tried and acquitted of the murders of her father and stepmother in Fall River, Massachusetts. No other individuals were charged with the murders.
Kalanithi Maran is an Indian billionaire media proprietor. He is the founder and chairman of Sun Group, one of India's largest media conglomerates. He owns multiple television channels, newspapers, weeklies, FM radio stations, DTH services, a movie production
Kelaokalani Fifita "Kalani" 'Sitake is a Tongan–American football coach and former player who is the head football coach at Brigham Young University.
The Diplomat (American TV series)
The Diplomat is an American political thriller television series with elements of a romantic comedy. It was created by Debora Cahn. First airing in 2023, it stars Keri Russell as a veteran US diplomat who is unexpectedly appointed as the ambassador to the Unit
On the afternoon of October 17, 2026, a fire ignited in a training area at CFB Wainwright, a Canadian military base in Alberta, and spread rapidly beyond it. Four service members were killed, and 39 others were wounded, 19 of them critically. A National Defenc
Curtis Jason Carr is an American college football quarterback for the Notre Dame Fighting Irish.
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'.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory
Multimodal large language models (MLLMs) have made rapid progress on visual reasoning, yet many tasks require evidence that is difficult to obtain through internal reasoning alone: our controlled study on 22 benchmarks shows that simply increasing the inference budget of the base model yields only modest gains. Such tasks demand grounded perception, external knowledge, and numerical measurements obtained through hete
Reinforcement Learning with Visually Attributable On-Policy Self-Distillation
Multimodal reinforcement learning with verifiable rewards (RLVR) provides reliable task-level supervision, yet offers limited guidance for localizing errors in visual reasoning. On-policy self-distillation (OPSD) complements this supervision with dense token-level corrections from a privileged teacher equipped with an evidence-centered visual view. However, the resulting corrections may conflate visual evidence with
CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting
Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments. However, directly extending single-robot approaches to multi-robot settings often fails to ensure safety and efficiency, especially under occlusions and out-of-distribution (OOD) scenarios, where individual robots may overlook potential risks or make inaccurate predictions. Leveraging
Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy
In this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of physical Recursive self-improvement. We introduce PhysRSI-eval to quantify physical RSI through a five-axis metric: First-Attempt Performance, Efficiency, Success, Budget and Improvement from failure. Different VLMs are evaluated on 18 manipulation tasks under a fixed interaction budget. The analysis of experimental r
WeAct: Dynamic Weight Switching as Agent Actions
Long-horizon agents must learn heterogeneous capabilities from interleaved trajectories. Updating shared parameters to learn new capabilities can degrade previously acquired ones, resulting in catastrophic forgetting. As a trajectory unfolds, different stages may require different capabilities, yet conventional joint training and static adapter merging maintain a fixed parameter configuration throughout execution. We
A seemingly reasonable push in Sokoban can make the puzzle unsolvable. To avoid such mistakes, an agent has to anticipate the consequences of its actions before execution, especially when feedback from the real environment is sparse or actions are irreversible. We introduce Agentic Imagination (Agima), a training framework that trains an agent to imagine before acting, i.e. to be imaginative. Agima trains a single om
AnyGoal: On World Model Generalization Beyond Expert Rollouts
The goal of a world model is to capture the dynamics of an environment. This has the great appeal that it allows agents to plan and simulate a course of actions with the help of a world model. For these uses to be reliable, however, the learned dynamics must behave well beyond the specific state-action combinations observed in expert rollouts. Despite this, the relationship between data coverage, dynamics generalizat
LatComm: Reusable Context Compression for Latent Communication between Large Language Models
Systems that combine several large language models (LLMs) often use one context for many questions. When the context is passed between models as text, the receiving model processes all of its tokens again for every question. Avoiding this cost requires a context representation that is compact, reusable across questions, and readable by a different LLM. Existing compression and latent communication methods each provid
Same Simulation, Different Evidence: Where Retrieved Tool Output Costs LLM Agents Accuracy
LLM agents that compute the right answer can still answer wrongly. Agent harnesses increasingly keep long tool outputs out of the context window, saving them to files that the agent searches or reads on demand. We ask where this costs accuracy with a real scientific tool: PHREEQC, a widely used geochemistry simulator from the USGS, whose reports reach 846.8k characters and whose determinism lets every printed value b
Kronecker Delta Rule for Long Context Linear Attention
We propose KronLSTM, a linear attention architecture based on the Kronecker Delta Rule, a novel update rule for associative matrix memories. KronLSTM extends the xLSTM family with a memory-revision mechanism that corrects targeted associations while modifying the memory only along a single direction in matrix space. This minimally invasive memory revision reduces interference with previously stored information compar
ProteinGeoBench: Frontier Structural-Biology Agents Are Fluent at Lookup but Lost in Geometry
Frontier agents retrieve structural metadata well and compute geometry from coordinates less reliably. We ask how much of a benchmark score reflects that gap and how much reflects the evaluation configuration around the model. We study ProteinGeoBench, a suite of 131 protein-structure tasks, using 4,249 rollouts from four frontier models and 4,486 further trials across 26 models. Three of the four frontier models fal
Agent World Model in the Wild: Computer-Use Agent Environment Synthesis
Agent learning is shifting from learning from human data toward learning through interaction with the world. However, scaling such learning directly in real-world computer systems is challenging and can introduce substantial security risks. We therefore propose Agent World Model in the Wild (WildAWM), a scalable pipeline for synthesizing controllable environments and tasks for training general computer-use agents (CU
RefCraft: Benchmarking Visual-Reference-Conditioned Agentic Generation through Web Development
Beyond basic tool use and coding skills, effectively incorporating visual references into agentic generation is equally essential for applying AI agents to real-world productivity tasks. We study this crucial capability through webpage development, a practical and readily evaluable setting in which existing benchmarks remain largely idealized: they typically focus on reconstructing visual targets or generating webpag
AfriBED: a Speech Recognition Bias Evaluation Dataset for Low Resource African Languages
Despite advancements in automatic speech recognition systems (ASR), current systems exhibit speaker-attribute bias, i.e., their speech recognition performance is biased against certain speakers based on their demographic attributes such as gender, accents, and speaker type. However, accurately assessing speaker attribute bias in a given ASR system requires the availability of a bias evaluation dataset with comparable
Next Best Action: Causal Constrained Offline RL for Business Action Targeting
Next Best Action (NBA) systems must select among customer interventions (e.g., discounts, promotions, and lifecycle actions), maximize long-term economic value, satisfy complex operational constraints, and remain causally grounded. Existing approaches lack a rigorous framework for meeting these requirements jointly. We address this gap by formulating NBA as a Constrained Markov Decision Process (CMDP). We then integr
Mise-en-Video: Fine-Grained 3D Control for Multi-Shot Video Generation
Recent advances in text-to-video generation have enabled high-fidelity single-shot synthesis, yet practical storytelling creation requires multi-shot videos with both strong inter-shot consistency and fine-grained controllability. Existing multi-shot video generation approaches achieve strong consistency but still fall short in controllability. In this paper, we propose Mise-en-Video (MeV), a highly controllable mult
SOMA: Continuous Monitoring and Intervention for System Two Reasoning with System One Models
Reasoning models often run into problems without noticing them, even when symptoms are visible in their own text. In humans, metacognition separates monitoring from control: fast, intuitive signals keep watch while slow, deliberate reasoning unfolds and help decide whether to continue, change course, or stop. System One (S1) decision models, such as Jev, are a natural candidate for this role: they return a calibrated
LG-Bench: A Graph-Structured Evaluation Benchmark for Life Sciences
Traditional evaluation benchmarks reduce inherently interconnected scientific knowledge in life sciences into flat lists of questions, disregarding the underlying topological structure of the knowledge. We introduce, the first graph-structured benchmark for life sciences, featuring over 10,000 high-quality multiple-choice questions across medicine, biology, and chemistry. Our approach constructs a weighted evaluation
Game of Phones: Harnessing Game Theory and LLMs in 'Bot Wars' to Counteract Phone Scams
Phone scams impose substantial and growing costs on individuals and financial systems worldwide, yet existing countermeasures impose no resource cost on perpetrators. Strategic scam-baiting, in which automated agents engage scammers to deplete their operational capacity, offers a proactive alternative but requires dynamic strategy adaptation that static systems cannot provide. We introduce a game-theoretic framework
TokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning
Expert human readers process large inputs (e.g., code repositories and long documents) by skimming to closely read only what matters. On the other hand, language models process all input tokens through each model layer, resulting in significant processing costs for long-context inputs. We introduce TokenGate, a small, drop-in component that extends existing trained models to enable end-to-end learnable token pruning
recAMPLIFY: Large-Scale Pretraining of Recursive Protein Language Models
Protein language models (pLMs) are powerful tools for protein annotation and mutation-effect prediction, but progress has largely depended on scaling model size and data, making them expensive and prone to memorization. Test-time scaling offers a complementary path, but methods developed for autoregressive LLMs do not transfer directly: masked encoders lack a generation loop, and amino acids do not verbalize in a rea
Towards Robust Model Organisms for Eliciting Latent Knowledge
A key problem in AI safety is eliciting latent knowledge (ELK), extracting information from a model that is misaligned and more knowledgeable than its overseer. For example, a misaligned model used to scan code for vulnerabilities may want to withhold its findings, i.e., sandbag, in order to exploit them later. To study ELK empirically, we need model organisms that mirror this setting. Existing model organisms fall s
Hierarchical Token Merging with Recursive Autoencoders for Unsupervised Grammar Induction
Hierarchical abstraction is a fundamental principle across many data modalities, enabling complex sequences to be represented compositionally at multiple levels. Learning such hierarchies directly from data, without explicit structural supervision, remains a central challenge in representation learning. We investigate whether meaningful hierarchical structure can emerge from compression alone. While autoencoders lear
Rubric hacking limits both performance and training robustness in rubric-based reinforcement learning. By decomposing rubrics into sub-dimensions, we reveal the Intra-Rubric Reward Divergence: as the total rubric score increases, certain sub-dimension scores systematically decrease, suggesting the model sacrifices specific quality aspects to optimize the aggregated objective. From the sub-dimension perspective, we ob
StebBench: Diagnosing Real-World Procedural Understanding in AI Agents
Evaluating an agent's planning ability remains challenging for open-domain tasks, where verifying procedures through execution is often impractical. Existing evaluations typically assess only the final plan and do not distinguish procedural errors such as missing, incorrect, or misordered actions. We introduce STEPBENCH, a benchmark of 6,885 verified procedures crawled from wikiHow across 81 open-domain categories. F
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 | 12.8% (128) | 5.1% (51) | 74.55% (82) |
| llms.txt (apex domain) | 10.7% (107) | 4.2% (42) | 60.91% (67) |
| llms.txt (docs subdomain) | 4.1% (41) | 0.9% (9) | 46.36% (51) |
| .well-known/mcp.json | 0.6% (6) | 0.3% (3) | 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.