Record 13102026 · captured 2026-10-14
The world looked up RJ Mitte. 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.
Roy Frank "RJ" Mitte III was an American actor and disability rights activist. Born in Jackson, Mississippi, Mitte was diagnosed with cerebral palsy when he was three years old. He moved to Los Angeles in 2006 and worked with a personal talent manager to find
Selma Blair is an American actress. She is known for her roles in Cruel Intentions, Legally Blonde, The Sweetest Thing, and the Hellboy franchise.
Mathematics is a field of knowledge concerned with abstract concepts such as numbers, geometric shapes, sets, functions, and probabilities. It uses logical reasoning and proof to study and establish their properties, often expressed as theorems, formulas, and
This is a list of equations, indicating each Wikipedia page under appropriate bands of their field.
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
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
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.
Nidal Malik Hasan is an American mass murderer, former United States Army major, and former physician, known for perpetrating the 2009 Fort Hood shooting, in which he killed 13 people and injured 32 others.
Jennifer Meyer is an American jewelry designer.
Ilhan Abdullahi Omar is an American politician serving as the U.S. representative for Minnesota's 5th congressional district since 2019. The district includes all of Minneapolis and some of its first-ring suburbs. From 2017 to 2019, Omar served in the Minnesot
Jacob Walter Misiorowski, nicknamed "the Miz", is an American professional baseball pitcher for the Milwaukee Brewers of Major League Baseball (MLB). He made his MLB debut in 2025 and his first MLB All-Star appearance the same year. In 2026, he set the record
Shane Michael Gillis is an American stand-up comedian, actor, and podcaster. He started performing stand-up in 2012, and in 2016, along with fellow stand-up comedian Matt McCusker, began co-hosting Matt and Shane's Secret Podcast, the most subscribed-to podcas
Digger is a 2026 American satirical black comedy film directed, co-written, and co-produced by Alejandro González Iñárritu. Tom Cruise, who also produced the film, stars as Digger Rockwell, an aging oil magnate whose drilling sets off an ecological disaster. T
Kimberly Ann Guilfoyle is an American media personality and politician who served as the United States ambassador to Greece from 2025 until her resignation in 2026. She previously served as an advisor and led the fundraising division of the Donald Trump 2020 p
Christa Gail Pike is an American convicted murderer on death row in Tennessee. In 1995, when she was 18, Pike and her boyfriend lured their 19-year-old classmate Colleen Slemmer into a wooded area in Knoxville where they tortured and killed her. Slemmer was be
East of Eden (2026 miniseries)
East of Eden is a 2026 American historical drama television limited series created by Zoe Kazan, starring Florence Pugh. It is adapted from the 1952 novel by John Steinbeck and premiered on Netflix on October 1, 2026. It received generally positive reviews fro
Animals is a 2026 American crime thriller film directed by Ben Affleck and written by Affleck, Connor O. McIntyre and Billy Ray. It stars Affleck, Kerry Washington, Gillian Anderson, Adriana Paz with Luis Gerardo Méndez, and Steven Yeun. The film received nega
Tobias Vincent Maguire, is an American actor and film producer. He began his career in supporting roles, before gaining international recognition and critical praise for his role as Spider-Man in Sam Raimi's 2002 film Spider-Man. Maguire reprised the role in t
East of Eden is a 1952 family saga novel by American author and Nobel Prize winner John Steinbeck. Many regard the work as Steinbeck's most ambitious novel, and Steinbeck himself considered it his magnum opus. Steinbeck said of East of Eden, "It has everything
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.
Christopher Columbus was an Italian explorer and navigator from the Republic of Genoa who completed four Spanish transatlantic voyages in the name of the Catholic Monarchs, opening the way for the widespread European exploration and colonization of the America
Drishyam: The Conclusion is a 2026 Indian Hindi-language crime thriller film co-written, produced, and directed by Abhishek Pathak. Produced by Panorama Studios and Star Studio18, it is a sequel to Drishyam 2 (2022). Unlike its predecessors, the film features
Columbus Day is a national holiday in many countries of the Americas and elsewhere, and a federal holiday in the United States, intended to celebrate the anniversary of the arrival of Christopher Columbus in the Americas. He went ashore at Guanahaní, an island
Tyler Isaiah Huntley is an American professional football quarterback for the Baltimore Ravens of the National Football League (NFL). Nicknamed "Snoop", he played college football for the Utah Utes, leading them to consecutive Pac-12 South Division titles betw
Other Mommy is a 2026 American supernatural psychological horror film directed by Rob Savage from a screenplay by Nathan Elston, based on the 2024 novel Incidents Around the House by Josh Malerman. The film stars Jessica Chastain as a mother who must do everyt
Neha Bora is an Indian activist and PhD student. They are the national president of the All India Students' Association (AISA) and an active participant in India's Gen-Z movements.
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
Susan Lynn Lieberman Dell is an American businesswoman, philanthropist, and the wife of Michael Dell. They married in 1989, four years after Michael Dell formed his eponymous computer company. She is the co-founder and current chair of the Michael & Susan Dell
Josie Totah, formerly known as J. J. Totah, is an American actress. She is known for her recurring role on the Disney Channel series Jessie and a supporting role on the 2013 ABC comedy series Back in the Game. She received praise from critics for her role in t
The Houthis, officially known as Ansar Allah or Ansarullah, are a Zaydi revivalist and Shia Islamist political and paramilitary organization that emerged from Yemen in the 1990s. It is predominantly made up of Zaydis, whose namesake leadership is drawn largely
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
ChunkAct: Action Chunking for Agentic Robotics
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under the guidance of a memory and thereby generalizes to new tasks without retraining. This generality comes at a steep price in efficiency. Every primitive requires a round of LLM reasoning over the full interaction history, so the cost of an episode grow
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study
How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filtering, and conditioning across reward definitions and label granularities using 5,000 naturally suboptimal human demonstrations and 24 target tasks. Within each VLA, we match demonstrations and training budgets. Additional comparisons examine pretrain
Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards
Long-horizon agents have drawn growing attention for their ability to work autonomously -- from hours to days -- on economically valuable tasks, a capability essential to automated discovery and recursive self-improvement (RSI). Yet open-source models still struggle on long-horizon tasks, largely for lack of both training data and a working recipe, and scaling both is hard: long, complex tasks are difficult to curate
DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning
Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem. Existing ways to shorten the CoT require additional training for each model, add a fixed steering vector to the model's hidden states, or use decoding heuristics such as stopping early or banning words like "wait", which can reduce accuracy. We propose DoubtLess, a training-fre
AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards
Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable, and verifiable. Yet code models cannot see how their choices of layout and color come together on the canvas, often limiting spatial planning and aesthetic quality. Image generators excel at visual design but can misrender requested content, making their outputs unrel
How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through text, while latent reasoning offers an efficient internal medium but lacks native tool access and reasoning transparency. We bridge these two directions with Hybrid Reasoning Agents (HybridAgents), which dynamically navigate between latent reasoning an
Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root learning-rate scaling. Reported to approximately preserve progress per sample with one optimizer step per rollout, this rule is less understood in practical multi-step training. We develop a two-clock framework that separates the samples needed to make progress from those
MFG: Multi-turn Feedback Guided Molecular Optimization
Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining a similar overall structure. This process often requires iterative modifications guided by computational or experimental feedback. Recent large language model (LLM)-based methods have begun to explore this iterative approach; however, these
LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models
Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, calibrations drift, controllers run at different rates, and end-effector pads wear down. We present LIBERO-RIG, a benchmark that perturbs only the robot's hardware and setup while holding every LIBERO task fixed. The benchmark spans eleven perturbation axes, including c
Who Improves the Robot? Toward Physical Recursive Self-Improvement
Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive Self-Improvement (Physical RSI), in which physical experience guides lasting updates to both a robot's capabilities and its improvement procedure, making subsequent improvement more effective across tasks. Physical RSI faces challenges from noisy observations, continuous
Fast Rates for Offline Q-Learning with KL Regularization
This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization. Recent studies have shown that KL regularization can yield fast rates in offline decision-making problems. However, it remains unclear whether Q-learning, which adopts incremental stochastic approximation updates, can achieve such fast rates in the offline setting. We first propose KL-reg
RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks
It is difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, deformations, stiffness and friction. We present RoboSimStudio (RSS), an interactive toolkit that makes choices of solver, contact model, and physical parameters explicit and editable, allowing both humans and coding agents to iteratively refine simu
Read the Room Before Your Model Generates
Large language models (LLMs) generally rely on sampling temperature to regulate the amount of randomness in their outputs. Mainstream methods for selecting temperature typically involve fixed, heuristic global settings and context or token-level calibration, rather than a geometrically interpretable representation of the communicative scenario. In this work, we introduce a simple and interpretable temperature selecti
Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource languages, the obvious options are few-shot prompting and fine-tuning on a small parallel set. We ask: what if moderate amounts of low-resource monolingual data are also available? We argue that, rather than continued pretraining, a more effective use of this data is the pre-
Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from proposal and admission mechanisms. It distinguishes changes to interpreted state from revisions to tools and code, repositor
Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning
In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator of effective coordination is behavioral diversity, i.e., the agents' potential to enact distinct yet complementary control policies given the same set of observations. Although behavioral diversity can enable role specialization and richer joint behaviors, its appropria
SEAR: Self-Evolving Agentic Robotics
Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for the next task. We introduce SEAR (Self-Evolving Agentic Robotics), a framework and benchmark built around two questions. Is the agent really learning, or just trying more often? And does what it learns still help on new tasks, in a new simulator, and on a real robot? The
Bytes to Threats: Byte-Sequence Transformer For Malware Detection
Existing malware detection approaches heavily rely on handcrafted features derived from static or dynamic analysis of the executable files. These methods require specialized domain expertise, dedicated analysis tools, and time-consuming analysis, which limits their scalability and robustness in the face of the increasing complexity and diversity of modern malware. Consequently, there is a growing interest in approach
Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation
LLM ensemble gains are often measured against a single baseline, but the value attributed to additional candidates can change with the comparison used. We study an LLM aggregator under four matched conditions: Direct (no candidates), Single-slot (one candidate in a multi-slot interface), Duplicate (one candidate repeated), and Multi-candidate (separately generated candidates). Under either non-diverse control, overal
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents
Long-horizon agents accumulate progress in persistent task state, yet repeatedly process interaction histories describing how that progress was achieved. This raises a natural question: can long-horizon context instead be organized around reusable progress rather than the history that produced it? In this paper, we introduce CarryOn, an object-oriented context runtime that organizes continuation around reusable task
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems
Large language model multi-agent systems extend individual agents' capabilities through collaboration across specialized roles, tools, and workflows. Yet collaboration makes failures harder to diagnose: one agent may misinterpret a correct result while others carry the mistake forward. Failure attribution therefore seeks to identify the responsible agent and decisive error step. Recent methods trace dependencies, but
Jeva: System One Models from First Principles
System one models enable direct, low-latency decision-making. Yet recent practice of predicting an answer from serialized text does not, by itself, respect the structure of a decision problem. We argue that current designs underemphasize four important structural requirements. (i) null-aware: Models should distinguish answer absence from uncertainty among candidates; (ii) permutation-invariant: Reordering alternative
Beyond the Verdict: Agentic Code Judging with Verifiable Feedback
Test-time scaling for large language models (LLMs) can involve generating multiple candidate solutions for selection or revising an earlier attempt. To guide selection and revision, we need a verifier that can estimate candidate correctness and provide evidence of failure. In competitive programming, however, official judging systems provide authoritative verdicts under finite submission budgets, without revealing fa
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards
Reinforcement learning (RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes. We analyze what this unit-test-only reward teaches a model during debugging and find that, while it raises pass rates, it also teaches the model to edit more and to hallucinate bugs in code that is already correct. We introduce PreciseCoder, a training algorithm t
SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts
Large language models increasingly generate Solidity smart contracts that, once deployed, can never be patched, making a single latent fault permanent and exploitable. Yet current evaluations mostly ask only whether generated code compiles. We introduce SolBehave, a benchmark for evaluating the correctness of model-generated contracts at scale, moving beyond ad hoc unit tests. Across 250 real, reproducible Ethereum c
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) | 5.6% (56) | 74.55% (82) |
| llms.txt (apex domain) | 10.5% (105) | 4.7% (47) | 60% (66) |
| llms.txt (docs subdomain) | 4.2% (42) | 1% (10) | 46.36% (51) |
| .well-known/mcp.json | 0.6% (6) | 0.3% (3) | 9.09% (10) |
| .well-known/agents.txt | 0.3% (3) | 0.2% (2) | 0% (0) |
| ai.txt | 0.2% (2) | 0.1% (1) | 0% (0) |
| ai-plugin.json (deprecated) | 0.6% (6) | 0.3% (3) | 0.91% (1) |
Notable events recorded on this day and month across all years.