Knowledge catalogue
arXiv:2605.03163v1 Announce Type: new Abstract: Scientific time series often encode predictive geometric structure, including connectivity, cycles, shell-like geometry, directional changes, and nonlin
arXiv:2605.03377v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) achieve strong performance on node classification tasks but remain difficult to interpret, particularly with respect to whi
arXiv:2605.02682v1 Announce Type: new Abstract: Authorizing Large Language Model (LLM)-driven agents to dynamically invoke tools and access protected resources introduces significant security risks, a
arXiv:2602.16138v2 Announce Type: replace Abstract: We introduce IRIS (Intent Resolution via Inference-time Saccades), a novel training-free approach that uses eye-tracking data in real-time to resolv
arXiv:2601.06445v2 Announce Type: replace Abstract: Computational narrative analysis aims to capture rhythm, tension, and emotional dynamics in literary texts. Existing large language models can gener
arXiv:2605.03343v1 Announce Type: new Abstract: Medical image super-resolution (MedSR) is essential for improving diagnostic precision across diverse imaging modalities such as MRI, CT, X-ray, Ultraso
arXiv:2605.03103v1 Announce Type: new Abstract: Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical h
arXiv:2605.03485v1 Announce Type: new Abstract: Multidimensional human understanding is essential for real-world applications such as film analysis and virtual digital humans, yet current LVLM benchma
arXiv:2605.01797v1 Announce Type: new Abstract: Integration of Answer Set Programming (ASP) with neural networks has emerged as a promising tool in Neuro-symbolic AI. While existing approaches extend
arXiv:2605.02092v1 Announce Type: new Abstract: The automation of scientific research workflows has emerged as a transformative frontier in artificial intelligence, yet existing autonomous research ag
arXiv:2505.08203v2 Announce Type: replace-cross Abstract: While recent advancements in AI music generation have predominantly focused on direct audio synthesis, these systems suffer from inherent rigi
arXiv:2605.02255v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in interactive and retrieval-augmented settings, raising significant privacy concerns. While at
arXiv:2605.02974v1 Announce Type: cross Abstract: Structured launch signals on Product Hunt contain statistically significant predictive information for Series A funding outcomes. We construct PHBench
arXiv:2605.01147v1 Announce Type: new Abstract: As large language models are increasingly deployed as interacting agents in high-stakes decisions, the AI safety community assumes that safety propertie
arXiv:2602.01842v3 Announce Type: replace Abstract: Inference-time compute has re-emerged as a practical way to improve LLM reasoning. Most test-time scaling (TTS) algorithms rely on autoregressive de
arXiv:2603.05965v2 Announce Type: replace-cross Abstract: We present PROBE (PRobabilistic Occupancy BEV Encoding), a learning-free LiDAR place recognition descriptor that models each BEV cell's occupa
arXiv:2605.03542v1 Announce Type: cross Abstract: The dual norm characterisation of weak solutions of second-order linear elliptic partial differential equations is mathematically natural but computat
arXiv:2605.02964v1 Announce Type: new Abstract: Reinforcement learning (RL) trained language model agents with tool access are increasingly deployed in coding assistants, research tools, and autonomou
arXiv:2605.03821v1 Announce Type: new Abstract: Existing robot video world models are typically trained with low-level objectives such as reconstruction and perceptual similarity, which are poorly ali
arXiv:2605.03244v1 Announce Type: new Abstract: Movie scripts pose a fundamental challenge for automatic summarization due to their non-linear, cross-cut narrative structure, which makes surface-level
arXiv:2605.04039v1 Announce Type: new Abstract: Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation
arXiv:2605.03301v1 Announce Type: new Abstract: De-identification of clinical text remains essential for secondary use of electronic health records (EHRs), yet public benchmarks such as i2b2 2006/2014
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:2605.02269v1 Announce Type: new Abstract: Specification gaming is a critical failure mode of LLM agents. Despite this, there has been little systematic research into when it arises and what driv
arXiv:2605.03789v1 Announce Type: cross Abstract: We propose Conformal Seasonal Pools (CSP), a training-free probabilistic time-series forecaster that mixes same-season empirical draws with signed res
arXiv:2605.01999v1 Announce Type: new Abstract: Classifying brain tumors using magnetic resonance imaging (MRI) is crucial for early diagnosis and treatment; however, tumor heterogeneity and a dearth
arXiv:2605.02463v1 Announce Type: cross Abstract: Multi-agent LLM systems are increasingly used to solve complex tasks through decomposition, debate, specialization, and ensemble reasoning. However, t
arXiv:2402.05284v2 Announce Type: replace Abstract: In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-worl
arXiv:2605.02667v1 Announce Type: cross Abstract: Dense and accurate depth estimation is essential for robotic manipulation, grasping, and navigation, yet currently available depth sensors are prone t
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:2605.01217v1 Announce Type: new Abstract: Face Recognition systems are widely deployed in real-world applications, but they also raise privacy concerns due to unauthorized collection and misuse
arXiv:2505.16850v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data privacy across decentralize
arXiv:2601.06034v1 Announce Type: cross Abstract: Software testing is critical in the software development lifecycle, yet translating requirements into executable test scripts remains manual and error
arXiv:2503.07557v2 Announce Type: replace Abstract: We present a novel method, AutoSpatial, an efficient approach with structured spatial grounding to enhance VLMs' spatial reasoning. By combining min
arXiv:2605.02520v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) offers a well-established path to grounding large language model (LLM) outputs in external knowledge, yet the quest
arXiv:2601.12024v2 Announce Type: replace-cross Abstract: Customer reviews contain valuable signals about service quality, but converting large-scale review corpora into actionable business recommenda
arXiv:2605.01242v1 Announce Type: new Abstract: Reinforcement learning (RL) is a fundamental framework for sequential decision-making, in which an agent learns an optimal policy through interactions w
arXiv:2504.02293v3 Announce Type: replace Abstract: Gloss is a written approximation that bridges Sign Language (SL) and its corresponding spoken language. Despite a deaf and hard-of-hearing populatio
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:2605.02234v1 Announce Type: cross Abstract: We present a method for diagnosing interpretation in neural networks by identifying an input subspace where a proposed interpretation is highly faithf
arXiv:2605.02357v1 Announce Type: new Abstract: In 3D point cloud understanding, the core challenge lies in accurately capturing discriminative features within complex neighborhoods, which directly af
arXiv:2601.04376v3 Announce Type: replace Abstract: Reliable stress recognition is critical in applications such as medical monitoring and safety-critical systems, including real-world driving. While
arXiv:2605.01815v1 Announce Type: new Abstract: Generative Adversarial Networks (GANs) offer a pragmatic route to mitigate data scarcity in vision tasks. We study generative augmentation across two lo
arXiv:2605.00894v1 Announce Type: new Abstract: Vision foundation models (VFMs), such as DINOv3, provide rich semantic representations that are promising for computational pathology. However, many cur
arXiv:2503.15984v3 Announce Type: replace Abstract: Modern image restoration and super-resolution methods utilize deep learning due to its superior performance compared to traditional algorithms. Howe
arXiv:2603.12612v2 Announce Type: replace Abstract: Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a fundamental challenge, as the ''curse of dimensio
arXiv:2605.01567v1 Announce Type: cross Abstract: Large language model (LLM) coding agents increasingly operate over repositories, terminals, tests, and execution traces across long software-engineeri
arXiv:2605.01264v1 Announce Type: cross Abstract: Traditional approaches to test case generation often involve manual effort and incur significant computational overhead. Additionally, these approache
arXiv:2508.15202v2 Announce Type: replace Abstract: Process Reward Models (PRMs) supervise intermediate reasoning steps in large language models (LLMs), but existing PRMs are mainly trained on general
arXiv:2605.02130v1 Announce Type: new Abstract: Human-level agentic intelligence extends beyond low-level geometric perception, evolving from recognizing where things are to understanding what they ar
arXiv:2604.05134v2 Announce Type: replace Abstract: We study how reasoning evolves in a language model -- from supervised fine-tuning (SFT) to reinforcement learning (RL) -- by analyzing how a set of
arXiv:2505.10887v3 Announce Type: replace Abstract: This paper introduces extsc{InfantAgent-Next}, a generalist agent capable of interacting with computers in a multimodal manner, encompassing text, i
arXiv:2605.02392v1 Announce Type: new Abstract: Novelty assessment is a critical yet complex task in the examination process for patent acceptance, requiring examiners to determine whether an inventio
arXiv:2605.01461v1 Announce Type: new Abstract: Swarm foraging algorithms, such as the central-place foraging algorithm (CPFA), typically rely on offline parameter optimization using genetic algorithm
arXiv:2605.02133v1 Announce Type: new Abstract: AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale
arXiv:2605.00969v1 Announce Type: cross Abstract: We present MedMosaic, a medical audio question-answering dataset designed to benchmark language and audio reasoning models under realistic clinical co
arXiv:2605.01961v1 Announce Type: new Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents. However
arXiv:2605.02471v1 Announce Type: new Abstract: Mapping standing dead trees is crucial for acquiring information on the effects of climate change on forests and forest biodiversity. However, leveragin
arXiv:2603.10584v2 Announce Type: replace Abstract: We introduce Marigold-SSD, a single-step, late-fusion depth completion framework that leverages strong diffusion priors while eliminating the costly
arXiv:2510.07731v3 Announce Type: replace-cross Abstract: Organic reaction mechanisms are the stepwise elementary reactions by which reactants form intermediates and products, and are fundamental to u