Knowledge catalogue
Choosing the right vector store for your Amazon Bedrock Knowledge Bases RAG application affects performance and cost. This post compares Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvect
arXiv:2609.19022v1 Announce Type: cross Abstract: Candidate-based decoding typically selects a completion for each prompt independently, but many applications require a collection of outputs that sati
arXiv:2609.17189v1 Announce Type: new Abstract: 3D hand mesh reconstruction is a challenging yet essential task for downstream applications, including human-robot interaction and AR/VR. Although conve
arXiv:2609.16842v1 Announce Type: new Abstract: Facial Landmark Detection(FLD) is a crucial task in various applications and has achieved significant advancements in recent years. However, current FLD
arXiv:2609.16350v1 Announce Type: new Abstract: Federated stochastic bilevel optimization has been actively studied in recent years due to its widespread applications in machine learning. However, mos
arXiv:2609.13677v1 Announce Type: cross Abstract: Modern real application problems involve matrix-valued parameters, yet conventional optimizers treat them as vectors, thereby motivating matrix-aware
arXiv:2507.23136v2 Announce Type: replace Abstract: Many prediction tasks can admit multiple models that can perform almost equally well. This phenomenon can undermine interpretability and safety when
arXiv:2609.17387v1 Announce Type: new Abstract: Real-time dense SLAM is a core capability for robotics applications that require robust localization and high- quality mapping in dynamic or fast-changi
arXiv:2601.10511v2 Announce Type: replace-cross Abstract: Model counting of Disjunctive Normal Form (DNF) formulas is a critical problem in applications such as probabilistic inference and network rel
arXiv:2609.17414v1 Announce Type: new Abstract: Text-conditioned latent diffusion models perform strongly in video generation and are promising backbones for robotic applications. However, existing ap
arXiv:2502.00550v2 Announce Type: replace Abstract: Surrogate models of parametric dynamical systems are essential for many-query and real-time predictions in engineering applications such as design o
arXiv:2609.15297v1 Announce Type: new Abstract: In order to make Reinforcement Learning algorithms applicable in real world scenarios, safety must be ensured even under adverse operating conditions. I
arXiv:2609.13869v1 Announce Type: cross Abstract: Automatic sentence function identification is important for many downstream natural language processing (NLP) applications such as dialogue systems, t
arXiv:2609.15561v1 Announce Type: new Abstract: Evaluating the factual correctness of large language models (LLMs) is vital for many applications. But are our evaluation tools themselves trustworthy?
arXiv:2508.02312v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs), now a foundation in advancing natural language processing, power applications such as text generation, machine t
arXiv:2512.15971v2 Announce Type: replace Abstract: Multispectral object detection is critical for safety-sensitive applications such as autonomous driving and surveillance, where robust perception un
arXiv:2609.15895v1 Announce Type: new Abstract: Physical AI relies on frequently-updated, latency-sensitive video stream to perceive, reason, and interact with the physical world, resulting in strict
arXiv:2609.15145v1 Announce Type: new Abstract: The reasoning ability of large language models (LLMs) is a critical factor for practical LLM-based applications. To investigate the current reasoning ca
arXiv:2510.13311v2 Announce Type: replace Abstract: Unsupervised tabular anomaly detection is a critical task with applications spanning offensive language detection, network security, and quality con
arXiv:2609.03301v1 Announce Type: cross Abstract: Wildfire seasons have become 84 days longer in the current days than in the 1970s, causing enormous threats to one's financial status and short- and l
arXiv:2609.14652v1 Announce Type: cross Abstract: Large Language Model (LLM) applications often transfer domain concepts into the model's context informally, through prompt prose, schema dumps, and ex
arXiv:2601.00900v2 Announce Type: replace-cross Abstract: As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target rec
arXiv:2609.15104v1 Announce Type: cross Abstract: We present the development and application of a three-dimensional Physics-Informed Neural Network (PINN) framework for the investigation of haemodynam
arXiv:2609.13734v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in real-world high-stakes applications, effective governance has become essential. Existing
arXiv:2609.15379v1 Announce Type: new Abstract: Machine learning can be crucial to help scale complex signal processing applications in scenarios such as healthcare. However, these machine learning mo
arXiv:2609.15915v1 Announce Type: new Abstract: Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience. Despite its promise, the application of meta-RL i
arXiv:2511.05890v2 Announce Type: replace Abstract: Synthetic Aperture Radar (SAR) images are inherently degraded by speckle noise that severely limits their reliability in high-precision applications
Application security company StackHawk Inc. today launched Wingman, a tool that fixes security vulnerabilities inside the artificial intelligence coding session that produced the flawed code. The soft
arXiv:2609.14124v1 Announce Type: cross Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy
arXiv:2609.14790v1 Announce Type: new Abstract: Detecting video highlights, the most informative or engaging moments in a video, is important for applications such as video summarization and content r
arXiv:2609.11997v1 Announce Type: new Abstract: Learning robust representations for time-series signals under noise and distribution shifts remains challenging, especially in clinical applications suc
arXiv:2609.12959v1 Announce Type: new Abstract: While offering significant promise for diverse applications, pattern-oriented swarms encounter multifaceted challenges in geometric control, self-organi
arXiv:2412.09378v4 Announce Type: replace-cross Abstract: Clinical trials bridge basic research and clinical application, serving as essential steps in drug development. This review examines clinical
arXiv:2309.06534v2 Announce Type: replace Abstract: Transfer learning is a critical technique that enables the application of knowledge gained from existing tasks or domains to improve performance on
Intelligence was yours. A founder’s intellectual property, business logic, differentiation, their intelligence, was once theirs alone. You built it, you owned it, and no tools/platform vendor stood be
Nvidia Corp. said today it’s tackling one of the major headaches for developers of quantum applications with the launch of a new orchestration layer within its open-source CUDA-Q platform. The announc
arXiv:2609.12971v1 Announce Type: new Abstract: Robots are increasingly used in diverse application areas, where autonomous navigation plays a central role. As these systems become more widespread, im
arXiv:2609.12342v1 Announce Type: new Abstract: The conditional generation of 3D motion has emerged as a key research topic due to its wide applicability across robotics, AR/VR, gaming, and content cr
Style transfer capabilities of different open-source methods 2026 Update This is the updated version of the study published at 2025.09.12. Link: https://www.reddit.com/r/StableDiffusion/comments/1nfoz
arXiv:2609.10248v1 Announce Type: cross Abstract: Traditional software delivery assumes a static paradigm: code is constructed prior to execution and deployed as a fixed artifact. We present Agentic J
arXiv:2408.02920v2 Announce Type: replace-cross Abstract: The rapid advancement of AI technology has led to widespread applications of agent systems across various domains. However, the need for detai
arXiv:2603.24125v3 Announce Type: replace Abstract: During training, Large Language Models (LLMs) learn social regularities that can lead to gender bias in downstream applications. Most mitigation eff
arXiv:2609.10647v1 Announce Type: new Abstract: We study the application of federated learning (FL) to indoor fire detection. Such fire-detection systems use edge cameras that record sensitive footage
ChatGPT Desktop (the Codex app) now supports integration with Ollama models, allowing users to run local LLMs within the application. To utilize this feature, simply download or upgrade to Ollama vers
arXiv:2609.09448v1 Announce Type: new Abstract: As agentic systems getting adopted rapidly in safety critical applications, it is vital to measure the confidence associated with the agentic actions. I
arXiv:2609.11078v1 Announce Type: new Abstract: Robot swarms are increasingly used in applications where accessible interaction with non-expert users is desirable. This paper investigates freehand ske
arXiv:2609.11209v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves knowledge-intensive large language model (LLM) applications by conditioning generation on retrieved docu
arXiv:2609.11524v1 Announce Type: cross Abstract: Many applications in engineering, including wireless broadcasting, require designs that provide performance certificates at different target outage le
arXiv:2609.09403v1 Announce Type: new Abstract: In the past decade, Bayesian optimization (BO) has emerged as a powerful and adaptable framework for automatic controller tuning and robot learning. Thi
arXiv:2606.16723v2 Announce Type: replace Abstract: Large language model (LLM) agents increasingly take actions (screening applicants, recommending credit, triaging patients), yet fairness for LLMs is
Your teams get an AI assistant that handles real work while your data stays in your environment and your conversations stay private Today, the Amazon Quick desktop application is generally available o
arXiv:2609.10371v1 Announce Type: new Abstract: High-resolution water mapping supports environmental monitoring and related applications, but accurate pixel-level labels are difficult and costly to pr
arXiv:2606.18425v2 Announce Type: replace-cross Abstract: Federated learning over medical images is a demanding workflow application. Each round fans out across parallel client jobs and converges on a
arXiv:2609.10434v1 Announce Type: new Abstract: Self-supervised speech foundation models are now used in a wide array of downstream applications, including traditional speech recognition and as the ba
arXiv:2609.09425v1 Announce Type: new Abstract: Educational data filters have become a practical way to improve language-model pre-training, but most filters treat educational value as a single scalar
arXiv:2609.10018v1 Announce Type: new Abstract: EdgeAI systems are increasingly employing computer vision applications to enable intelligent, on-device decision-making in real-time. However, these dep
arXiv:2609.08267v1 Announce Type: new Abstract: Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring fact
arXiv:2602.14536v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have seen remarkable advancements, achieving state-of-the-art results in diverse applications. Fine-tuning, an im
arXiv:2609.09953v1 Announce Type: new Abstract: Cross-lingual sentence encoders enable scalable transfer across hundreds of languages, powering applications such as translation mining and zero-shot le
arXiv:2609.09698v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications, yet they remain highly vulnerable to adversarial attacks that