Knowledge catalogue
arXiv:2602.09850v2 Announce Type: replace Abstract: Industrial anomaly detection demands precise reasoning over fine-grained defect patterns. However, existing multimodal large language models (MLLMs)
arXiv:2508.10016v3 Announce Type: replace Abstract: Building interactive omni-modal assistants often relies on end-to-end multimodal alignment to fuse heterogeneous modalities, which incurs substantia
arXiv:2605.07068v1 Announce Type: cross Abstract: The LLM Wiki pattern, to compile and provide domain knowledge into a persistent artifact and serve it to LLMs via KV cache inference, promises context
arXiv:2605.05866v1 Announce Type: cross Abstract: Multiphase powder X-ray diffraction (PXRD) analysis remains a fundamental bottleneck in structure identification, as real-world synthesis often produc
arXiv:2605.04609v1 Announce Type: new Abstract: Composition is a cornerstone of visual aesthetics, influencing the appeal of an image. While its principles operate independently of specific content, i
arXiv:2505.13246v2 Announce Type: replace Abstract: Purpose: This paper introduces the concept of 'Agentic Publication,' a novel LLM-driven framework designed to complement traditional scientific publ
Anthropic just shipped sleep into agents. When you sleep, your hippocampus replays the day's neural sequences to the cortex during 150-220 Hz bursts called sharp-wave ripples. The replay runs about 20
arXiv:2605.03042v1 Announce Type: cross Abstract: This report describes ARIS (Auto-Research-in-sleep), an open-source research harness for autonomous research, including its architecture, assurance me
arXiv:2605.04857v1 Announce Type: new Abstract: This paper presents the development and validation of an eye-tracking dataset designed to investigate how second-language (L2) learners process idiomati
arXiv:2511.03475v4 Announce Type: replace Abstract: AI applications increasingly depend on long-context inference, where LLMs consume substantial context to support stronger reasoning. Common examples
arXiv:2605.04593v1 Announce Type: new Abstract: Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically leverages Class Activation Maps (CAMs) to achieve pixel-level predictio
arXiv:2605.04135v1 Announce Type: cross Abstract: Readers of applied-domain LLM capability evaluations want to know what AI systems can currently do. That literature answers a related, but consequenti
Pinecone has released full text search capabilities in public preview, expanding its vector database platform beyond semantic search to include traditional keyword-based search functionality. This fea
arXiv:2605.03383v1 Announce Type: new Abstract: Lithology classification aims to infer subsurface rock types from well-logging signals, supporting downstream applications like reservoir characterizati
arXiv:2605.04643v1 Announce Type: new Abstract: Approximating the ideological position of Members of Parliament (MPs) is a fundamental task in political science, helping researchers understand legisla
arXiv:2605.04506v1 Announce Type: new Abstract: We introduce Ilov3Splat, a novel framework for instance-level open-vocabulary 3D scene understanding built on 3D Gaussian Splatting (3D-GS). Most prior
arXiv:2605.05134v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently generate plausible but non-factual content, a phenomenon known as hallucination. While existing detection method
arXiv:2605.05185v1 Announce Type: new Abstract: Deep search has become a crucial capability for frontier multimodal agents, enabling models to solve complex questions through active search, evidence v
arXiv:2605.04003v1 Announce Type: cross Abstract: High-precision CNC machining of free-form aerospace components requires bounded compensations informed by inspection, simulation, and process knowledg
arXiv:2511.16567v3 Announce Type: replace Abstract: In this paper, we introduce POMA-3D, the first self-supervised 3D representation model learned from point maps. Point maps encode explicit 3D coordi
arXiv:2605.04887v1 Announce Type: new Abstract: The exponential expansion of digital commerce in Indonesia has significantly shifted consumer interactions toward video-centric social networks, particu
arXiv:2605.05103v1 Announce Type: new Abstract: We introduce the **Concept Field** of a text corpus: a local drift field with pointwise uncertainty, estimated in sentence-embedding space from the delt
arXiv:2605.04941v1 Announce Type: new Abstract: This paper describes our system submitted to SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models. We present an ef
Vultr has launched Archival Object Storage, a new storage service offering cost-effective long-term data retention and backup solutions. This service is designed for infrequently accessed data with lo
arXiv:2605.02366v1 Announce Type: new Abstract: Research funding discovery remains fundamentally fragmented: researchers navigate disparate agency portals (e.g., in the United States, NSF, NIH, DARPA,
arXiv:2605.01100v1 Announce Type: new Abstract: This work presents a knowledge-driven decision-support system that integrates structured defect knowledge with LLM-based reasoning to provide explainabl
arXiv:2605.02738v1 Announce Type: new Abstract: Solar photovoltaic (PV) deployment is expanding rapidly, yet detailed, up-to-date information on the spatial distribution and capacity of rooftop PV rem
arXiv:2605.00846v1 Announce Type: new Abstract: Clinical diagnosis requires answers that are accurate, verifiable, and explicitly grounded in official guidelines. While large language models excel at
arXiv:2510.12061v2 Announce Type: replace Abstract: Effective disaster response is essential for safeguarding lives and property. Existing statistical approaches often lack semantic context, generaliz
arXiv:2509.17677v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance on mathematical reasoning under well-defined conditions. However, real-world engineering
arXiv:2605.02199v1 Announce Type: new Abstract: Long-term LLM agents must compress streams of past interactions into persistent memory before future queries are known. Existing evaluations usually mea
arXiv:2605.01507v1 Announce Type: new Abstract: Prior studies report that partial driving automation can increase the cognitive demands on human drivers. This effect largely arises from human drivers'
arXiv:2605.01235v1 Announce Type: cross Abstract: Driven by the escalating global burden of mental health conditions, music-based interventions have attracted significant attention as a non-invasive,
arXiv:2605.03799v1 Announce Type: new Abstract: This preprint presents a systematic, research-oriented practicum that guides the reader through the entire modern NLP pipeline: from tokenisation and ve
arXiv:2605.01189v1 Announce Type: new Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transpar
arXiv:2605.03153v1 Announce Type: cross Abstract: Static benchmarks measure a model frozen at training time. Real systems face distribution shift: new categories, paraphrased queries, drift: and must
arXiv:2605.02728v1 Announce Type: new Abstract: This paper presents ORPilot, an open-source agentic AI system that translates real-world business problems into solver-ready optimization models. Unlike
arXiv:2605.03571v1 Announce Type: new Abstract: Patent examination is a complex, multi-stage process requiring both technical expertise and legal reasoning, increasingly challenged by rising applicati
arXiv:2605.02240v1 Announce Type: new Abstract: We introduce PhysicianBench, a benchmark for evaluating LLM agents on physician tasks grounded in real clinical setting within electronic health record
arXiv:2605.02940v1 Announce Type: new Abstract: The rapid spread of memes makes harmful content detection increasingly crucial, as effective identification can curb the circulation of misinformation.
arXiv:2605.00964v1 Announce Type: cross Abstract: Much research on LLMs has focused on increasing benchmark performance. However, the evaluation of such models in real-world collaborative human-AI wor
arXiv:2605.03780v1 Announce Type: cross Abstract: Transformers are effective at inferring the latent task from context via two inference modes: recognizing a task seen during training, and adapting to
Welcome to The Blueprint, a new feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hope to
arXiv:2605.03596v1 Announce Type: cross Abstract: Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a
arXiv:2605.01657v1 Announce Type: new Abstract: Vision-Language Models (VLMs) typically rely on static initial frames for video reasoning, restricting their ability to incorporate essential dynamic in
arXiv:2605.00943v1 Announce Type: new Abstract: Foundational models have advanced social robotics, enabling richer perception and communicative interaction with users. However, current systems still s
arXiv:2605.01368v1 Announce Type: new Abstract: Human-robot interaction (HRI) has long studied how agents and people coordinate to achieve shared goals. In this work, we formalize and benchmark the no
arXiv:2605.02567v1 Announce Type: new Abstract: The rapid advancement of generative Artificial Intelligence (AI) has introduced significant challenges for reliable AI-generated image detection. Existi
arXiv:2605.01555v1 Announce Type: new Abstract: We introduce an autonomous multiagent framework for mechanistic interpretability that automates both explaining and finding internal features in large l
arXiv:2604.16345v2 Announce Type: replace-cross Abstract: While advances in materials informatics have accelerated the development of Self-Driving Laboratories (SDLs), human-led experiments remain sta
arXiv:2507.23386v3 Announce Type: replace Abstract: Decoder-only large language models (LLMs) have been increasingly adopted to build embedding models for diverse tasks. To overcome the inherent limit
arXiv:2605.02159v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has delivered strong results in domains such as Atari and Go, but it still suffers from high sample cost and weak tran
arXiv:2605.02600v1 Announce Type: new Abstract: While Large Language Models (LLMs) and Vision-Language Models (VLMs) demonstrate remarkable capabilities in high-level reasoning and semantic understand
arXiv:2605.01448v1 Announce Type: cross Abstract: Cross-task generalization is a core challenge in open-world robotic manipulation, and the key lies in extracting transferable manipulation knowledge f
arXiv:2605.01113v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can g
arXiv:2601.21714v2 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) agents towards System~2 reasoning, characterized by deliberative, high-precision problem-solving, requir
arXiv:2605.01372v1 Announce Type: new Abstract: Large language models (LLMs) have been widely explored for embedding generation. While recent studies show that in-context learning (ICL) effectively en
arXiv:2605.02374v1 Announce Type: cross Abstract: Machine-generated text (MGT) detection is critical for regulating online information ecosystems, yet existing detectors often underperform in few-shot
arXiv:2605.02815v1 Announce Type: new Abstract: Text-to-SQL over large analytical databases requires navigating complex schemas, resolving ambiguous queries, and grounding decisions in actual data. Mo
arXiv:2605.02376v1 Announce Type: new Abstract: Automated medical report generation, MRG, holds substantial value for alleviating radiologist workload and enhancing diagnostic efficiency. However, mai