Knowledge catalogue
arXiv:2608.09250v1 Announce Type: new Abstract: Federated learning (FL) must serve devices with varying computational capabilities. A fixed model cannot suit all devices, while training one model per
arXiv:2608.09208v1 Announce Type: cross Abstract: Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL). Ho
arXiv:2608.08906v1 Announce Type: new Abstract: Outlier detection in decentralized data environments is a challenging task for many machine learning implementations, particularly in settings where dat
arXiv:2608.09687v1 Announce Type: new Abstract: Federated learning (FL) in Low Earth Orbit (LEO) satellite constellations is affected by non-IID data and irregular ground-station visibility, both driv
arXiv:2608.09221v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces signif
arXiv:2608.09369v1 Announce Type: cross Abstract: Visual object tracking requires effective temporal integration, yet most Transformer trackers still rely on predominantly feed-forward feature extract
arXiv:2608.08244v1 Announce Type: new Abstract: General wearable foundation models are pretrained across broad sensor streams and populations, but are not designed around women's-health tasks. We intr
arXiv:2608.09880v1 Announce Type: new Abstract: Financial prediction typically relies on task-specific regression, ranking, or policy heads, separating the language model from the numerical object ult
arXiv:2608.08852v1 Announce Type: new Abstract: AI agents can now solve problems, answer like subject experts, and generate long-form multimodal content. However, whether they can adapt a lesson to fi
arXiv:2608.08114v1 Announce Type: cross Abstract: In this work, viscous fluid flow governed by the Stokes equations in highly perforated domains is studied using physics-informed neural networks (PINN
arXiv:2608.07725v1 Announce Type: new Abstract: Average-reward reinforcement-learning regret is known up to logarithmic factors, but the numerical content of published guarantees is difficult to compa
arXiv:2608.08664v1 Announce Type: new Abstract: Visual counterfactual explanations aim to change classifier decisions through realistic and localized edits while preserving decision-irrelevant content
Learn how First Orion, a branded communications company, shifted from brittle script-based UI testing to AI-driven QA automation with Amazon Nova Act. By describing tests in plain English instead of m
arXiv:2608.08736v1 Announce Type: new Abstract: Fitness Action Quality Assessment (AQA) is important for intelligent sports training, yet the capabilities of Multimodal Large Language Models (MLLMs) i
arXiv:2607.18171v2 Announce Type: replace Abstract: Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose effici
arXiv:2608.07937v1 Announce Type: new Abstract: We present FlexSplat, a feed-forward framework for novel view synthesis (NVS) from uncalibrated, object-centric multi-view image collections. A recent l
arXiv:2510.23389v2 Announce Type: replace-cross Abstract: The behaviour of neural network components must be proven correct before deployment in safety-critical systems. Unfortunately, existing neural
arXiv:2608.09460v1 Announce Type: cross Abstract: Cardiac digital twin research is moving from subject-specific anatomical replicas toward virtual cohorts that represent clinically relevant population
arXiv:2608.07474v1 Announce Type: new Abstract: Prior work showed that human-in-the-loop oversight becomes structurally untenable in high-loss domains when AI output velocity V exceeds human cognitive
arXiv:2511.00079v2 Announce Type: replace Abstract: flowengineR is an R package designed to provide a modular and extensible framework for building reproducible algorithmic workflows for general-purpo
arXiv:2608.07620v1 Announce Type: new Abstract: Recent advances in flow matching models have substantially improved the quality of text-to-image generation, but have also raised increasing safety conc
arXiv:2608.08227v1 Announce Type: new Abstract: Focus particles such as 'even' and 'only' are central to formal semantic theories that posit structured representations over sets of alternatives. 'Even
arXiv:2505.16941v4 Announce Type: replace-cross Abstract: Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of label
arXiv:2608.08605v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on Large Language Models (LLMs) are proliferating rapidly, but their heterogeneous execution traces provide no common ba
arXiv:2608.07920v1 Announce Type: new Abstract: Multimodal LLM judge panels can cross-reference peers, but a quoted peer judgment may itself be untrusted. We expose source-blind anchoring as a text-le
arXiv:2602.19471v2 Announce Type: replace Abstract: Source-free domain adaptation (SFDA) aims to adapt a model trained in the source domain to perform well in the target domain, with only unlabeled ta
arXiv:2608.08445v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to gr
arXiv:2608.09427v1 Announce Type: new Abstract: Pretrained self-supervised representations have emerged as a core component of current deepfake detection methods, yet it remains unclear which of their
arXiv:2608.08963v1 Announce Type: cross Abstract: Generalized Category Discovery aims to recognize known categories while identifying novel ones within unlabeled data. Existing methods, typically base
arXiv:2608.08436v1 Announce Type: new Abstract: Precipitation nowcasting predicts the spatiotemporal evolution of future radar echoes from historical radar echo sequences, thereby estimating the occur
arXiv:2608.08307v1 Announce Type: cross Abstract: Medical Visual Question Answering (VQA) requires aligning subtle visual evidence, including lesion texture, boundary sharpness, and diffuse density ch
arXiv:2608.08207v1 Announce Type: cross Abstract: Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while rema
Runtime intelligence startup FriskAI Inc. launched today with 3.6 million in pre-seed funding to give enterprises a record of what their artificial intelligence agents actually do once they go into pr
arXiv:2506.00633v3 Announce Type: replace-cross Abstract: Generating semantically controllable 3D CT volumes from radiology reports requires more than a rich text encoder, it requires vision-language
arXiv:2608.09450v1 Announce Type: new Abstract: Betting-based sequential tests and Blackwell approachability are linked by a rate-explicit reduction through support-function residuals. For a compact c
arXiv:2608.07770v1 Announce Type: cross Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial con
arXiv:2601.17593v3 Announce Type: replace Abstract: Recent progress in large language models has renewed interest in how multi-step reasoning is represented internally. While prior work often treats r
arXiv:2608.09842v1 Announce Type: new Abstract: Recent document parsers achieve table TEDS scores above 93 on OmniDocBench v1.6, yet community feedback and our audit reveal persistent failures on comp
arXiv:2608.07523v1 Announce Type: cross Abstract: Difficulty differences across parallel-class programming examinations affect the fairness of course assessment. This study repositions large language
arXiv:2308.04553v4 Announce Type: replace Abstract: Visual recognition models are prone to learning spurious correlations induced by a biased training set where certain conditions B (eg, Indoors) are
arXiv:2608.09158v1 Announce Type: cross Abstract: Large audio-language models (LALMs) have demonstrated strong capabilities in understanding diverse audio inputs. This diversity includes low-frequency
arXiv:2603.27996v2 Announce Type: replace Abstract: Diffusion models have emerged as a powerful framework for generative tasks in deep learning. They decompose generative modeling into two computation
arXiv:2608.08896v1 Announce Type: new Abstract: Imaging device downtime is a major barrier to healthcare delivery in low- and middle-income countries (LMICs), often driven by limited access to special
arXiv:2509.14474v3 Announce Type: replace Abstract: The debate around Artificial General Intelligence (AGI) remains open due to two fundamentally different goals: replicating human-level performance v
arXiv:2608.08924v1 Announce Type: new Abstract: Popularity of AI-based face recognition system directly demands protection of sensitive biometric data used for training. Visual secret sharing is an in
arXiv:2608.09396v1 Announce Type: new Abstract: Invariant learning seeks representations that remain predictive across environments, yet the behavior of its objectives along the regularization path is
arXiv:2608.08941v1 Announce Type: cross Abstract: Operational Design Domain (ODD) specifications describe where an automated driving system (ADS) is permitted to operate, but they do not prescribe wha
arXiv:2608.08395v1 Announce Type: cross Abstract: Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents. We compare
arXiv:2608.09480v1 Announce Type: new Abstract: A model alone does not determine how a programming agent acts. What the model sees, how actions enter the environment, how feedback returns, and how one
arXiv:2604.01476v2 Announce Type: replace-cross Abstract: Reinforcement learning for LLMs is vulnerable to reward hacking, where models exploit shortcuts to maximize reward without solving the intende
arXiv:2608.08904v1 Announce Type: cross Abstract: How much of a vision-language model's (VLM) spatial understanding remains after the action post-training process of building a vision-language-action
arXiv:2608.09168v1 Announce Type: new Abstract: Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge. Although recent work has substantially
arXiv:2608.09564v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments from egoc
From @sequoia's own your intelligence event. We're starting to see a new category of company emerge, the Full-Stack AI company, where innovation happens at both the product and intelligence layer. Tha
arXiv:2608.07627v1 Announce Type: new Abstract: Hospitals are racing to embed AI, while coping with the surge in adaptation of the technology in other industries, into the triage management, documenta
arXiv:2608.08510v1 Announce Type: new Abstract: Humans have the remarkable ability to engage in spontaneous informal conversations and selectively attend to individual speakers while filtering out com
arXiv:2608.09595v1 Announce Type: new Abstract: Compressing large language models to two bits or fewer is increasingly feasible through block-wise post-training quantization; cross-block variants reco
arXiv:2608.07827v1 Announce Type: cross Abstract: Confidence estimation for large language models (LLMs) aims to estimate the probability that a generated answer is correct, while calibration aligns t
arXiv:2608.05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajector
arXiv:2608.07953v1 Announce Type: new Abstract: Distribution shift poses a significant challenge to the robustness of machine learning models, but the current solutions only aim to detect out-of-distr