Knowledge catalogue
arXiv:2610.09577v1 Announce Type: new Abstract: We study finite-horizon online resource allocation with i.i.d. requests and an endogenous Markov state on a finite state space: each action affects the
arXiv:2610.10385v1 Announce Type: new Abstract: Embedding tables are among the largest components of modern language models. Most compression methods fix a coding geometry such as coordinate blocks, l
arXiv:2610.10018v1 Announce Type: new Abstract: Effective long-range propagation remains a central challenge in graph neural networks, as increasing a model's propagation depth does not guarantee that
arXiv:2610.10298v1 Announce Type: new Abstract: Machine-learned interatomic potentials (MLIPs) excel at in-distribution tasks, accelerating drug and material development, yet they struggle to generali
arXiv:2610.10314v1 Announce Type: new Abstract: Multiphase flow in porous microstructures is central to CO_2 storage, fuel-cell operation, and flip-chip packaging. Predicting these flows remains chall
arXiv:2610.09709v1 Announce Type: new Abstract: Can we determine whether a foundation model will generalize out-of-distribution (OOD) before any target data is available? Existing diagnostics require
I spent the past year independently building Slow Vale, an LLM-driven life simulation. The Chinese server now has 800+ AI residents sharing one continuously running city. This is an engineering write-
arXiv:2610.09409v1 Announce Type: new Abstract: The widespread deployment of generative AI has made it increasingly difficult to distinguish synthetic content from real data. Consequently, synthetic d
arXiv:2604.03315v2 Announce Type: replace Abstract: Storyboarding is a core skill in visual storytelling for film, animation, and games. However, automating this process requires a system to achieve t
arXiv:2610.07707v1 Announce Type: new Abstract: Conversational AI assistants with long-term memory extract facts from user messages into a store consulted in later conversations. A stated plan can ent
arXiv:2610.07459v1 Announce Type: new Abstract: Organizations make claims about their AI agents: a person approves every external email, every action is logged, an evaluation shows the agent is safe t
arXiv:2610.07668v1 Announce Type: cross Abstract: Modern cache replacement designs saturate because they operate within fixed representational structures, hand-crafted and heuristic based feature-engi
arXiv:2610.07340v1 Announce Type: cross Abstract: Protein-molecule virtual screening is increasingly cast as a problem of representation learning in a shared embedding space. Existing methods rely on
arXiv:2610.07277v1 Announce Type: new Abstract: Scenario-based MPC is an attractive strategy for chance-constrained motion planning that approximates uncertainty via a finite set of sampled scenarios.
arXiv:2610.08102v1 Announce Type: new Abstract: Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management
arXiv:2610.08320v1 Announce Type: new Abstract: Cluttered indoor environments, where large and heavy objects are scattered across diverse surfaces, require humanoid robots to sequentially navigate, gr
arXiv:2610.08501v1 Announce Type: cross Abstract: Depression has no diagnostic blood test. Language models promise tireless, consistent assessment, but can accurate raters disagree about individuals?
arXiv:2610.07758v1 Announce Type: cross Abstract: Training-free token reduction accelerates vision transformers by removing redundant tokens across layers, recovering most of the original accuracy at
arXiv:2610.07884v1 Announce Type: cross Abstract: Adaptive multiresolution methods reduce representation cost by concentrating fine-scale degrees of freedom where needed, but their tree updates are us
arXiv:2610.07979v1 Announce Type: new Abstract: As agents continuously improve by generating and revising Skills, the process that discovers and refines those Skills becomes a learnable object in its
arXiv:2610.08176v1 Announce Type: new Abstract: Decentralized learning is highly sensitive to communication topology under non-IID data. Adaptive peer-selection methods can exploit local model informa
arXiv:2610.07817v1 Announce Type: cross Abstract: Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually
arXiv:2610.07846v1 Announce Type: cross Abstract: The scenario approach is a well-established statistical framework for data-driven decision-making. In particular, in data-driven optimization, the sce
arXiv:2610.07655v1 Announce Type: cross Abstract: Discrete diffusion models have emerged as a practically successful framework for generative modeling on discrete product spaces, yet their statistical
NVIDIA DSX Air creates a node‑based digital twin that models an AI factory’s infrastructure and software interfaces, allowing agentic workflows to interrogate the virtual environment, run configuratio
arXiv:2512.13708v2 Announce Type: replace Abstract: Inferring interaction structure from steady-state observations is a central inverse problem when transient trajectories are unavailable. Here we for
arXiv:2610.07100v1 Announce Type: new Abstract: Persistent agent memory is only as reliable as its retention decision: an assertion weakly supported by its source can be stored and later reused as est
arXiv:2610.07018v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance in multimodal reasoning, yet they remain prone to generating plausible but incorrect answ
arXiv:2610.04939v1 Announce Type: new Abstract: We ask whether physically meaningful correspondences discovered inside a trained scientific model remain meaningful outside the conditions under which t
arXiv:2610.04671v1 Announce Type: cross Abstract: A federated objective is a weighted sum of client risks, and the weights are almost always fixed in advance. We treat them instead as the only instrum
arXiv:2610.04405v1 Announce Type: new Abstract: Despite the growing popularity of physics-informed neural networks (PINNs), their applicability in the long-time integration of partial differential equ
arXiv:2610.06138v1 Announce Type: new Abstract: Graph Convolutional Networks (GCNs) have emerged as a powerful framework for learning from graph-structured data, yet their deployment on resource-const
arXiv:2610.05379v1 Announce Type: new Abstract: Model performance monitoring is a standard practice in machine learning deployments. Detection performance is tracked continuously, and model decay is e
arXiv:2610.05337v1 Announce Type: new Abstract: We identify a mismatch between the physical role of transport fields in many PDEs and their usual role in neural operators: PDEs use them to select read
arXiv:2610.05723v1 Announce Type: new Abstract: Given incomplete measurements of a single physical field in a coupled system with unknown parameters, can we infer its full physical state and identify
arXiv:2610.04297v1 Announce Type: cross Abstract: Latent safety filters certify safety on a learned low-dimensional representation of the state, enabling constraints that resist analytic description.
arXiv:2610.05552v1 Announce Type: new Abstract: Three-dimensional gait analysis guides rehabilitation after spinal cord injury but depends on marker-based motion capture and force plates, which few cl
arXiv:2610.06365v1 Announce Type: new Abstract: Sinkholes are a widespread geohazard in karst terrain. In Florida, soluble carbonate bedrock, shallow groundwater, and intense rainfall combine to make
arXiv:2610.03829v1 Announce Type: cross Abstract: Real-world outpatient oncology notes contain specialised terminology, tumour staging expressions, treatment names, toxicity descriptions, and institut
arXiv:2610.04249v1 Announce Type: new Abstract: Recent successes of matrix- and curvature-based optimizers have renewed interest in how update geometry shapes learning. These methods normalize or prec
arXiv:2603.09693v2 Announce Type: replace Abstract: Predicting interfacial degradation and microstructural evolution through phase-field modelling is computationally intensive, particularly for parame
arXiv:2610.05476v1 Announce Type: new Abstract: Scaling decentralized learning changes not only the number of clients N, but also the dynamics of information propagation and consensus. We argue that t
arXiv:2610.06404v1 Announce Type: new Abstract: We consider the stability of multi-step reasoning processes, which have extensive applications in language models, including chain-of-thought and algori
arXiv:2610.05129v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) provide a natural way to make video anomaly detection more explainable. However, their final decisions do not a
arXiv:2606.19894v2 Announce Type: replace Abstract: Score-based diffusion models have achieved remarkable empirical success, motivating extensive theoretical work to establish their foundations. Howev
arXiv:2610.04117v1 Announce Type: new Abstract: Policy mirror descent (PMD) has a mature finite-time theory in discounted Markov decision processes (MDPs), but less is known in the average-reward sett
arXiv:2603.07865v2 Announce Type: replace-cross Abstract: Text-to-audio (T2A) diffusion models generate high-quality audio but require tens of neural function evaluations (NFEs), resulting in substant
arXiv:2610.04144v1 Announce Type: new Abstract: Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged robots due to reduced shear strength and gravity-induced ani
arXiv:2610.05885v1 Announce Type: new Abstract: Many works approach continual learning through the lens of infinite context windows. As an agent puts more observation into context (concretely the KV c
arXiv:2610.04533v1 Announce Type: new Abstract: Weight decay is widely used as a regularization technique in neural network training, yet its role in neuron condensation (parameter direction alignment
arXiv:2610.02763v1 Announce Type: new Abstract: Tropical forest monitoring is essential for global climate stability and biodiversity preservation. To address the urgent need for rapid, reliable detec
arXiv:2610.03413v1 Announce Type: new Abstract: Sequential learning systems often make decisions from accumulated experience while receiving high-dimensional inputs whose distribution may change over
arXiv:2610.03425v1 Announce Type: new Abstract: Suspicion is an important, yet elusive concept in anti-money laundering and counter-terrorist financing (AML/CFT), which allows for intervention below t
arXiv:2603.13856v3 Announce Type: replace-cross Abstract: Building AI systems that can plan, act, and create in the physical world requires more than pattern recognition. Such systems must reason abou
arXiv:2610.02631v1 Announce Type: new Abstract: Post-deployment feedback from users can be a cost-effective, scalable, and representative means to monitor and improve generative AI systems and feature
arXiv:2610.03273v1 Announce Type: new Abstract: Reinforcement learning (RL) for learning path recommendation (LPR) faces two coupled obstacles. First, the policy must commit to a sequence of L concept
arXiv:2610.03591v1 Announce Type: new Abstract: Understanding and assessing natural hazards is essential for disaster preparedness and risk reduction. Recent advances in large language models have spu
arXiv:2610.02954v1 Announce Type: new Abstract: Biologically-informed neural networks (BINNs) have emerged as a flexible subclass of physics-informed neural networks (PINNs) for learning terms in part
Today, we’re announcing Google Cloud Modernize, an end-to-end transformation portfolio to help enterprises collapse multi-year roadmaps with the power of AI. Google Cloud Modernize brings together Goo
arXiv:2610.02665v1 Announce Type: cross Abstract: Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajecto