All entries

arXiv:2610.16166v1 Announce Type: new Abstract: Interactive world models must capture how multiple participants jointly change a shared environment while observing different parts of it. Competitive g

arXiv:2610.16411v1 Announce Type: new Abstract: Deploying robots in human-shared environments requires deciding not only how to navigate, but also when dedicated social signals are worth emitting. Cla

arXiv:2610.18534v1 Announce Type: new Abstract: Flow Matching has recently become popular as a technique for generative modeling with applications in many domains. In the standard Flow Matching formul

European businesses are rapidly expanding their use of AI agents. More than half of the AI use among our EU enterprise and business customers now involves these tools, and the number of small business

Keeping a city running means making decisions that affect residents every day — from when to repair a road to how to use energy more efficiently. Across the EMEA region, cities and local software, inf

arXiv:2610.14324v1 Announce Type: new Abstract: Foundation models like V-JEPA are increasingly deployed across a heterogeneous edge-cloud continuum spanning smart glasses, phones, robots, and servers.

arXiv:2610.17326v1 Announce Type: new Abstract: Household robots must successfully navigate im-perfect circumstances, both in their internal models and the external world. We introduce a benchmark of

arXiv:2610.17898v1 Announce Type: new Abstract: Sparse autoencoders decompose language model representations into interpretable features, but feature interpretation in this literature remains predomin

arXiv:2509.17287v3 Announce Type: replace-cross Abstract: Visual teach-and-repeat (VT&R) navigation enables robots to autonomously traverse previously demonstrated paths using visual feedback. We pres

I've been working on Event2Vec, a small library for learning embeddings of discrete event sequences (purchases, logs, clinical codes, etc.) with a deliberately simple structure: a history is the runni

arXiv:2610.16172v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure tha

arXiv:2610.17724v1 Announce Type: cross Abstract: Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across

arXiv:2610.14609v1 Announce Type: new Abstract: Determining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality A

arXiv:2610.16879v1 Announce Type: new Abstract: The rapid spread of face forgeries on the Web challenges the credibility of visual content. In practical Web environments, manipulated media originate f

arXiv:2610.15244v1 Announce Type: new Abstract: Self-evolving mechanisms let vision-language model (VLM) agents continually adapt through evolving memory, but they also allow malicious behavior to eme

arXiv:2610.16679v1 Announce Type: new Abstract: Tool-augmented vision-language agents compose perception and measurement to reason about spatial relationships, but effective execution also requires tr

arXiv:2610.14948v1 Announce Type: new Abstract: Temporal point processes are used both to measure market mechanisms, for example how fast prices absorb news and how much of the reaction is the market

arXiv:2610.17980v1 Announce Type: new Abstract: Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted class, with ties typically broken at random.

arXiv:2610.18856v1 Announce Type: new Abstract: Persistent homology summarizes the topological evolution of a graph as persistence diagrams (PDs). Generating graphs whose topology matches a target des

arXiv:2610.15166v1 Announce Type: new Abstract: Coding agents are increasingly evolving beyond reproducing what a world looks like toward constructing executable worlds that capture how a world works.

arXiv:2511.20088v3 Announce Type: replace Abstract: In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanat

arXiv:2610.16447v1 Announce Type: cross Abstract: The estimation of heterogeneous treatment effects is a central objective of causal inference. However, state-of-the-art algorithms to solve this task

arXiv:2610.15241v1 Announce Type: cross Abstract: Hoffman constants quantify the distance to the solution set of a polyhedral system in terms of its residual; they control error bounds, geometric cond

arXiv:2610.16329v1 Announce Type: new Abstract: Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening. It can benef

arXiv:2610.16187v1 Announce Type: cross Abstract: Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output. This

arXiv:2610.14817v1 Announce Type: cross Abstract: State-of-the-art beat tracking is based on neural networks that model beats and downbeats as individual events, often followed by a separate Dynamic B

arXiv:2610.17734v1 Announce Type: new Abstract: Exploration bounds learning efficiency in reinforcement learning with verifiable rewards (RLVR). Current methods explore mostly through independent samp

arXiv:2610.15207v1 Announce Type: new Abstract: Large Language Models (LLMs) have been shown to be computationally expensive and vulnerable to jailbreak attacks, in which adversarial prompts bypass sa

arXiv:2604.02973v2 Announce Type: replace Abstract: Text-driven human motion generation aims to synthesize realistic motion sequences that follow textual descriptions. Despite recent advances, accurat

arXiv:2610.14138v1 Announce Type: cross Abstract: Retinal foundation models have demonstrated strong performance across a wide range of ophthalmic tasks, yet relatively little is known about how disea

arXiv:2610.15425v1 Announce Type: new Abstract: In embodied manipulation, Vision Language Action (VLA) models can execute atomic skills, but long-horizon manipulation requires recombining these skills

arXiv:2511.07923v3 Announce Type: replace Abstract: Accurate segmentation of marine organisms is vital for biodiversity monitoring and ecological assessment, yet existing datasets and models remain la

arXiv:2610.16173v1 Announce Type: new Abstract: Parallel wire-driven systems are a framework in which multiple wires are connected to an end effector, and the end effector is driven by the tensions of

arXiv:2610.16574v1 Announce Type: cross Abstract: Accurately assessing the convergence of Monte Carlo Markov Chains is crucial for ensuring their validity in Bayesian inference problems. However, conv

arXiv:2505.11884v3 Announce Type: replace Abstract: Face recognition performance based on deep learning heavily relies on large-scale training data, which is often difficult to acquire in practical ap

arXiv:2610.18082v1 Announce Type: new Abstract: Long-horizon agents often struggle to recover from failures whose causes lie several steps earlier, beyond what local reflection or backtracking can res

arXiv:2610.18593v1 Announce Type: new Abstract: Fairness constraints in predict-then-optimize pipelines are necessarily enforced on predicted outcomes hat{y}, since true outcomes y are unobserved at d

arXiv:2610.18260v1 Announce Type: new Abstract: Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora. Exi

arXiv:2610.17841v1 Announce Type: cross Abstract: Evaluating fashion retrieval systems remains challenging due to the limited availability of benchmarks that capture the fine-grained, open-ended queri

arXiv:2610.15921v1 Announce Type: new Abstract: With ever increasing personalized large language model (LLM) services, the next question to answer is how to improve the quality of services/users' expe

arXiv:2610.16289v1 Announce Type: new Abstract: Quantization has emerged as one of the primary techniques for reducing the memory footprint of large language models during inference. A prominent examp

arXiv:2610.18839v1 Announce Type: new Abstract: This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization. Recent studies have s

arXiv:2610.18451v1 Announce Type: cross Abstract: Large-scale expert parallelism (EP) has become the standard deployment configuration for Mixture-of-Experts (MoE) models in production. Meanwhile, the

arXiv:2610.14899v1 Announce Type: cross Abstract: The sudden growth in AI data centers is raising numerous environmental concerns. Low-frequency noise pollution is a concern for local residents, that

arXiv:2610.16694v1 Announce Type: new Abstract: Adversarial detection on evolving attributed graphs faces two challenges: fraud labels arrive late or not at all, and natural distributional drift erode

arXiv:2610.16845v1 Announce Type: cross Abstract: Standardizing a rare feature can help stochastic gradient descent learn its effect within a finite budget, but it also changes the noise cost of learn