Search: “reasoning”

arXiv:2512.19442v3 Announce Type: replace-cross Abstract: Diffusion-based generative models have greatly impacted the speech processing field in recent years, exhibiting high speech naturalness and sp

arXiv:2604.19299v1 Announce Type: cross Abstract: Despite the impressive capabilities of large language models, their substantial computational costs, latency, and privacy risks hinder their widesprea

arXiv:2604.19092v1 Announce Type: cross Abstract: Recent advances in large-scale video world models have enabled increasingly realistic future prediction, raising the prospect of leveraging imagined v

The future is taking shape in Las Vegas this week, where the world’s leading startups are showcasing their groundbreaking AI work at Google Cloud Next. Whether they need the top AI models, infrastruct

arXiv:2604.18951v1 Announce Type: cross Abstract: Adaptive multi-agent systems (MAS) are increasingly adopted to tackle complex problems.However, the narrow task coverage of their optimization raises

arXiv:2512.15907v2 Announce Type: replace Abstract: Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into te

arXiv:2510.08240v2 Announce Type: replace Abstract: Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing

Traditional lakehouses were engineered for the era of reporting, not the high-velocity, multimodal demands of AI agents. To bridge this gap, architecture must evolve into an AI-native foundation — one

The first wave of AI changed how we find information; the next wave is changing how we get work done. Today, we’re enhancing our most powerful AI tools and bringing them together under one roof. Gemin

arXiv:2604.19620v1 Announce Type: new Abstract: Free-association norms provide essential empirical data for investigating linguistic, semantic, and cultural phenomena in the cognitive sciences. Althou

arXiv:2604.18846v1 Announce Type: cross Abstract: Barren-plateau results have established exponential gradient suppression as a widely cited obstacle to the scalability of variational quantum algorith

arXiv:2603.22608v2 Announce Type: replace Abstract: Users often rely on Large Language Models (LLMs) for processing multiple documents or performing analysis over a number of instances. For example, a

VLM Performance：Qwen3.6-27B is natively multimodal, supporting both vision-language thinking and non-thinking modes in a single unified checkpoint — the same as Qwen3.6-35B-A3B. It handles images and

arXiv:2601.18891v3 Announce Type: replace Abstract: Caribou across the Arctic has declined in recent decades, motivating scalable and accurate monitoring approaches to guide evidence-based conservatio

By combining the analytical depth of Looker with Google’s Agentic Data Cloud, the potential to transform how we model, interact with, and act on our data appears limitless. This week at Google Cloud N

arXiv:2604.02368v4 Announce Type: replace Abstract: As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficienc

arXiv:2604.17679v1 Announce Type: new Abstract: Autonomous navigation requires planning to reach a goal safely and efficiently in complex and potentially dynamic environments. Graph search-based algor

arXiv:2604.17423v1 Announce Type: new Abstract: A unified framework for first-order optimization algorithms fornonconvex unconstrained optimization is proposed that uses adaptivelypreconditioned gradi

arXiv:2604.18549v1 Announce Type: new Abstract: In recent years, the Vision Transformer (ViT) has garnered significant attention within the computer vision community. However, the core component of Vi

arXiv:2603.23224v2 Announce Type: replace Abstract: Generative models have shown substantial impact across multiple domains, their potential for scene synthesis remains underexplored in robotics. This

arXiv:2604.16729v1 Announce Type: new Abstract: State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation remains: cur

arXiv:2604.16408v1 Announce Type: new Abstract: We present Speaking Memories, a distributed, stakeholder-in-the-loop robotic interaction platform for personalized cognitive exercise support. Rather th

arXiv:2604.18130v1 Announce Type: new Abstract: Digital marketplaces processing billions of dollars annually represent critical infrastructure in sociotechnical ecosystems, yet their performance optim

Vector Space Day 2026: Powered by Qdrant About We’re hosting our second-ever full-day in-person Vector Space Day (https://luma.com/vsd-sf) on June 11th at The Midway in San Francisco, and you’re invit

arXiv:2604.17487v1 Announce Type: new Abstract: Agentic systems often fail not by being entirely wrong, but by being too precise: a response may be generally useful while particular claims exceed what

APIs and limited releases for AI models are not a safety policy, they’re a business model (which is totally ok as long as you’re transparent about it). Especially on cyber-security, they give a false

arXiv:2510.07143v3 Announce Type: replace Abstract: Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectivene

arXiv:2604.18148v1 Announce Type: new Abstract: Automated fetal head segmentation in ultrasound images is critical for accurate biometric measurements in prenatal care. While existing deep learning ap

arXiv:2604.17894v1 Announce Type: new Abstract: Presentation slides are a primary medium for data-driven reporting, yet keeping complex, analytics-style decks up to date remains labor-intensive. Exist

arXiv:2006.12024v3 Announce Type: replace-cross Abstract: Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and cla

arXiv:2604.16349v1 Announce Type: cross Abstract: Retrieving real-time information is a fundamental capability for search-integrated agents in real-world applications. However, existing benchmarks are

arXiv:2604.18566v1 Announce Type: cross Abstract: We present a systematic evaluation of large language model families -- spanning both proprietary cloud APIs and locally-hosted open-source models -- o

arXiv:2604.17293v1 Announce Type: new Abstract: Reliable Large Language Models (LLMs) should abstain when confidence is insufficient. However, prior studies often treat refusal as a generic 'I don't k

arXiv:2604.17137v1 Announce Type: new Abstract: Navigating complex environments poses challenges for multi-agent systems, requiring efficient extraction of insights from limited information. In this p

arXiv:2604.16541v1 Announce Type: new Abstract: Recent advancements in Large Generative Models (LGMs) have revolutionized multi-modal generation. However, generating illustrated storybooks remains an

arXiv:2604.18578v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) has become the predominant algorithm for on-policy reinforcement learning due to its scalability and empirical robust

arXiv:2604.16370v1 Announce Type: new Abstract: Decoding natural language from non-invasive electroencephalography (EEG) remains fundamentally limited by low signal-to-noise ratio and restricted infor

arXiv:2604.16331v1 Announce Type: cross Abstract: Embodied task planning requires agents to execute long-horizon, goal-directed actions in complex 3D environments, where success depends on both immedi

arXiv:2508.14913v4 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated significant capabilities in solving mathematical problems expressed in natural language. However, mul

arXiv:2604.17316v1 Announce Type: new Abstract: Safe clinical deployment of Large Language Models (LLMs) requires not only high accuracy but also robust uncertainty calibration to ensure models defer

arXiv:2604.18134v1 Announce Type: new Abstract: Recent advancements in self-supervised learning have led to powerful surgical vision encoders capable of spatiotemporal understanding. However, extendin

arXiv:2602.10385v2 Announce Type: replace Abstract: Automatically discovering personalized sequential events from large-scale time-series data is crucial for enabling precision medicine in clinical re

arXiv:2512.11988v3 Announce Type: replace Abstract: Accurate capture of human-object interaction from ubiquitous sensors like RGB cameras is important for applications in human understanding, gaming,

arXiv:2604.16372v1 Announce Type: new Abstract: Multimodal sarcasm detection has recently garnered significant attention. However, existing benchmarks suffer from coarse-grained annotations and limite

arXiv:2604.18032v1 Announce Type: new Abstract: Traditional shadow removal networks often treat image restoration as an unconstrained mapping, lacking the physical interpretability required to balance

arXiv:2604.16411v1 Announce Type: new Abstract: We study asynchronous alignment, a first-class multimodal learning setting in which a dense primary stream must be fused with sporadic external context