Knowledge catalogue
arXiv:2610.17075v1 Announce Type: new Abstract: We propose a nonparametric approach that fuses deterministic initial forecasts and a partial realization into a probabilistic forecast using the framewo
arXiv:2610.18176v1 Announce Type: cross Abstract: Proactive human-robot collaboration requires autonomous systems to infer not only immediate actions but also the underlying hierarchical intentions go
arXiv:2601.07535v2 Announce Type: replace-cross Abstract: Clustering with bandit feedback refers to the problem of partitioning the arms of an N-armed stochastic bandit according to their underlying d
arXiv:2508.12943v3 Announce Type: replace-cross Abstract: Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This
arXiv:2610.17281v1 Announce Type: new Abstract: SE(3)-equivariance provides a useful geometric inductive bias for visuomotor policies, encoding that actions covary with rigid transformations of the ob
arXiv:2610.14844v1 Announce Type: cross Abstract: We introduce stochastic physics-informed neural networks (SPINNs) for stochastic boundary-value problems (SBVPs) with random operators. The method enf
arXiv:2610.15722v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are good at video understanding and code generation, but it remains unclear whether they can recover the physic
arXiv:2507.21437v3 Announce Type: replace Abstract: Physics-informed neural networks and physics-informed DeepONet excel at solving partial differential equations but often struggle with singularly pe
arXiv:2610.14921v1 Announce Type: new Abstract: A language model that keeps learning online must not slowly erode its own base capabilities. We ask which part of a modern LLM should absorb continual u
arXiv:2610.13999v1 Announce Type: new Abstract: Rule decisions such as content moderation, advertising compliance, and eligibility checks combine several conditions, exemptions, and missing-informatio
arXiv:2610.15968v1 Announce Type: new Abstract: Spiking Transformers achieve energy efficiency by discarding softmax normalization in favor of thresholded, binary attention. However, this removal brea
arXiv:2610.15039v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models combine specialized experts through an input-dependent routing mechanism, and their sparse variants underpin many modern
arXiv:2610.17483v1 Announce Type: new Abstract: Machine learning surrogates of partial differential equations (PDEs) are commonly trained on data generated by numerical solvers. Therefore, the trainin
arXiv:2610.16451v1 Announce Type: new Abstract: Fine-tuning large language models at scale is increasingly limited by the memory cost of backpropagation. Zeroth-Order (ZO) optimizers reduce this burde
The EU is investigating OpenAI and Anthropic after their AI agents hacked into other organisations. These early enforcement steps might lead to the first legal battle between Brussels and the AI labs
arXiv:2610.16357v1 Announce Type: new Abstract: Agentic video reasoning systems gather and assess video evidence across stages, selecting relevant spans as visual evidence for answering questions. New
arXiv:2610.14186v1 Announce Type: cross Abstract: Could model-free reinforcement learning algorithms actually be learning a model of the environment? In this work, we apply counterfactual intervention
arXiv:2610.18229v1 Announce Type: cross Abstract: Deep reinforcement learning agents in partially observable competitive games must act on beliefs about an unseen opponent's intent. We study a spatial
arXiv:2610.18842v1 Announce Type: cross Abstract: Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive S
You can't solve real-world physical tasks through autoregressive sampling alone. The physical world is entirely unforgiving to hallucination or approximate pattern matching. If we want actual autonomo
Aditya Soni / Reuters: Sources: Anthropic and OpenAI have pushed for substantial control over what external AI evaluators can publish, out of concern that trade secrets could leak — Anthropic and Open
arXiv:2610.13703v1 Announce Type: new Abstract: The fast pace in which protocols, frameworks, and technologies are being proposed for LLM-based agents results in a complex scenario for anyone aiming t
arXiv:2610.12718v1 Announce Type: cross Abstract: Large language models are stochastic by nature. Repeated executions under the same experimental conditions may produce different responses. We propose
arXiv:2610.12494v1 Announce Type: new Abstract: In this article, we propose algebraic integer number formats (AINF) as a framework for highly configurable and accurate compressed computation. A key pr
arXiv:2610.13376v1 Announce Type: new Abstract: Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to in
Flo Health leveraged Databricks AI Runtime to create open‑source language models that prioritize both safety and practical utility. By integrating Databricks’ scalable training infrastructure with rob
arXiv:2610.12738v1 Announce Type: cross Abstract: Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven m
arXiv:2610.13912v1 Announce Type: new Abstract: Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model
arXiv:2610.13688v1 Announce Type: new Abstract: Harvesting technologies have evolved from conventional mechanized systems based on bulk mechanical interaction toward increasingly automated and robotic
arXiv:2610.13711v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn policies that sample discrete objects in proportion to a positive reward. They are trained by minimizing bala
arXiv:2610.12544v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate discrete data by iteratively unmasking multiple tokens in parallel using a learned mask predictor. Their output
arXiv:2610.13689v1 Announce Type: new Abstract: Learning how local perturbations affect a network is important for identifying which components drive or absorb a system's response. We formulate this a
arXiv:2610.13788v1 Announce Type: new Abstract: LiDAR-inertial odometry (LIO) is widely used for real-time localization and mapping, but its reliability can degrade substantially in subterranean envir
arXiv:2610.13886v1 Announce Type: new Abstract: Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact
Rafe Rosner-Uddin / Financial Times: PitchBook: robotics and physical AI companies have raised ~$48B YTD, as they gather training data from people completing tasks in factories, offices, and homes — ‘
arXiv:2610.12002v1 Announce Type: cross Abstract: Test-time training (TTT) adapts an LLM's parameters using signals derived from test inputs, and can make striking improvements in pre-specified settin
arXiv:2502.17920v2 Announce Type: replace Abstract: Pre-trained visual models have become fundamental in computer vision, but they face challenges in continual learning scenarios where data and tasks
arXiv:2610.12361v1 Announce Type: new Abstract: Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision fo
arXiv:2610.11108v1 Announce Type: new Abstract: Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT)
Coding-agent platforms, sandbox products, and code-execution services all need the same thing: isolated machines that start up fast, retain their state between bursts of work, and don’t consume comput
arXiv:2610.11344v1 Announce Type: new Abstract: Physics-based models connect scientific explanation with quantitative prediction. Constructing them requires selecting physical processes, defining stat
arXiv:2610.11025v1 Announce Type: cross Abstract: Exploratory data analysis (EDA) is rarely open-ended in practice: analysts work from high-level domain questions toward the concrete analyses that can
OpenAI released 719 mathematics manuscripts on October 6, 2026 that were generated by an internal frontier model (not publicly available), covering 372 topic families—including a solution to the Navie
arXiv:2610.10690v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) offer linear-time scaling with sequence length while requiring only constant memory, yet they struggle to capture long-
arXiv:2610.11030v1 Announce Type: cross Abstract: Tool-using LLM agents violate the policies they are deployed to enforce, often silently. Prior defenses hand-write rules, query an LLM verifier per ac
arXiv:2512.13892v3 Announce Type: replace-cross Abstract: Reliable estimation of feature contributions in machine learning models is essential for transparency, algorithmic fairness, and regulatory co
arXiv:2610.11803v1 Announce Type: new Abstract: Self-improving learning systems adapt data selection, optimization, and auxiliary symbolic components, inducing nonstationary objectives outside standar
arXiv:2610.11185v1 Announce Type: new Abstract: Single-cell trajectory inference maps transcriptomic measurements onto developmental continua, yet configurations that fit the data equally well can ass
arXiv:2610.11004v1 Announce Type: cross Abstract: Reconstructing spatially resolved plasma dynamics from few sensors is essential for diagnostics, reduced-order modelling and control, yet remains diff
arXiv:2610.11257v1 Announce Type: new Abstract: Learned representations can lose latent degrees of freedom successively, suggesting a cascade of transitions whose underlying stability principle remain
arXiv:2610.12313v1 Announce Type: new Abstract: Large language model compliance systems are deployed on the assumption that a verdict depends on the regulatory rule it is given. We test this directly
arXiv:2610.11401v1 Announce Type: cross Abstract: World Action Models (WAMs) enable generalist robot manipulation by conditioning an action expert on representations from a pretrained video Diffusion
arXiv:2610.10805v1 Announce Type: new Abstract: As AI systems increasingly interact with people and make decisions about them, understanding human interpretations becomes an important part of developi
arXiv:2610.09835v1 Announce Type: new Abstract: Continual pre-training and fine-tuning in Large Language Models (LLMs) inevitably induce catastrophic forgetting, typically mitigated by replay using of
arXiv:2610.09712v1 Announce Type: cross Abstract: We develop a continuous-time deterministic policy gradient framework for reinforcement learning with reflected state dynamics, where the state process
BREAKING: President Trump on Elon Musk today: “First, we honor an industrial titan, a brilliant engineer, and one of the greatest technology founders to ever live. His name is Elon Musk. Quite honestl
arXiv:2610.10426v1 Announce Type: new Abstract: Terminal-agent capability depends jointly on model weights and the runtime harness that formats prompts, binds tools, and handles error recovery. Existi
arXiv:2610.09928v1 Announce Type: new Abstract: Visual generative models are trained to capture the probability distributions of natural images, yet whether their native priors reflect the regularitie
Public research universities solve some of society’s most urgent challenges, but turning ideas into breakthroughs requires fast, secure access to cutting edge technology. In the agentic AI era institu
arXiv:2512.08617v2 Announce Type: replace Abstract: This tutorial focused on Healthcare Domain Applications of NLP, what we have achieved around HealthcareNLP, and the challenges that lie ahead for th