Knowledge catalogue
arXiv:2504.09844v4 Announce Type: replace-cross Abstract: Modern frameworks for training large foundation models (LFMs) employ dataloaders in a data-parallel manner, with each loader processing a disj
arXiv:2604.22865v1 Announce Type: new Abstract: We introduce MeshLAM, a feed-forward framework for one-shot animatable mesh head reconstruction that generates high-fidelity, animatable 3D head avatars
arXiv:2604.23289v1 Announce Type: cross Abstract: Due to the unprecedented success of deep learning, it has become an integral component in several multimedia computing applications in todays world. U
arXiv:2412.07160v3 Announce Type: replace Abstract: To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts
arXiv:2604.23620v1 Announce Type: new Abstract: We present Move-Then-Operate, a Vision language action framework that explicitly decouples robotic manipulation into two distinct behavioral phases: coa
arXiv:2604.23960v1 Announce Type: new Abstract: In this paper, we extend the recent Vector-Accelerated Motion Planning (VAMP) framework to multi-robot motion planning (MRMP). We develop two vector-acc
arXiv:2603.12365v2 Announce Type: replace-cross Abstract: History-dependent constitutive models serve as macroscopic closures for the aggregated effects of micromechanics. Their parameters are typical
arXiv:2505.16888v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed via third-party system prompts downloaded from public marketplaces. We identify a criti
arXiv:2604.24415v1 Announce Type: cross Abstract: Quantitative analysis of the kinematic chain in sports motion is essential for performance evaluation and injury prevention. Conventional methods such
arXiv:2604.24020v1 Announce Type: cross Abstract: Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering, yet
arXiv:2604.24356v1 Announce Type: cross Abstract: What do recurrent neural networks, polynomial ODEs, and discrete polynomial maps each bring to computation, and what do they lack? All three operate o
arXiv:2604.23922v1 Announce Type: cross Abstract: In this paper, we introduce the Quasi-Quadratic Gradient (QQG), a novel search direction designed to accelerate the BFGS method within the quasi-Newto
arXiv:2604.24230v1 Announce Type: new Abstract: Skull-base meningiomas are often characterized by favorable long-term prognosis, yet their anatomical complexity and proximity to critical neurovascular
arXiv:2508.01635v2 Announce Type: replace-cross Abstract: Microservice architectures enable scalable cloud-native applications; however, the distributed nature of these systems complicates the mainten
arXiv:2603.20711v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) depl
arXiv:2604.23577v1 Announce Type: new Abstract: Serving diverse NLP workloads with large language models is costly: at one enterprise partner, inference costs exceeded $200K/month despite over 70% of
arXiv:2604.24368v1 Announce Type: new Abstract: Generating high-fidelity synthetic tabular data remains a critical challenge for enhancing data availability in privacy-sensitive and low-resource domai
arXiv:2604.24514v1 Announce Type: new Abstract: Accurate trajectory prediction is fundamentally challenging due to high scene heterogeneity - the severe variance in motion velocity, spatial density, a
arXiv:2601.03555v2 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.
arXiv:2512.12844v2 Announce Type: replace-cross Abstract: Reliable uncertainty quantification is essential for deploying machine learning systems in high-stakes domains. Conformal prediction provides
arXiv:2604.22807v1 Announce Type: cross Abstract: Optimal transport with quadratic cost provides a geometric framework for steering an ensemble, modeled by a probability law, with minimal effort. Yet
arXiv:2604.24729v1 Announce Type: new Abstract: Specification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specificat
arXiv:2604.24544v1 Announce Type: new Abstract: The increasing reliance on Large Language Models (LLMs) across diverse sectors highlights the need for robust domain-specific and language-specific eval
We present StereoFoley, a video-to-audio generation framework that produces semantically aligned, temporally synchronized, and spatially accurate stereo sound at 48 kHz. While recent generative video-
arXiv:2512.10959v2 Announce Type: replace Abstract: We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning,
arXiv:2604.23605v1 Announce Type: new Abstract: The application of large language models (LLMs) in clinical decision support faces significant challenges of 'tunnel vision' and diagnostic hallucinatio
arXiv:2604.24562v1 Announce Type: new Abstract: Driving in compliance with traffic laws and regulations is a basic requirement for human drivers, yet autonomous vehicles (AVs) can violate these requir
arXiv:2604.23066v1 Announce Type: new Abstract: Urban flooding affects lives and infrastructure worldwide. Mapping inundation in complex urban environments from satellite imagery remains challenging d
Z-Image-Omni-Base is a versatile foundation model capable of both generation and editing tasks , designed for use within the ComfyUI framework. It is released to unlock the full potential for communit
arXiv:2604.22283v1 Announce Type: new Abstract: This study investigates the kinematic role of palm degrees of freedom (DoF) in enhancing thumb opposability in a five-finger robotic hand. A hand model
arXiv:2602.11931v2 Announce Type: replace-cross Abstract: Evolutionary agentic systems intensify the trade-off between computational efficiency and reasoning capability by repeatedly invoking large la
arXiv:2604.22378v1 Announce Type: new Abstract: Robot-to-human handovers often rely on static, open-loop strategies (or, at best, approaches that adapt only the position), which generally do not consi
arXiv:2604.22569v1 Announce Type: cross Abstract: Machine learning-based malware detectors are increasingly vulnerable to adversarial examples. Traditional defenses, such as one-shot adversarial train
arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a centra
arXiv:2604.22224v1 Announce Type: cross Abstract: AI is increasingly used to accelerate engineering design by improving decision-making and shortening iteration cycles. Application to marine propeller
arXiv:2604.22043v1 Announce Type: cross Abstract: Background: The classroom discourse analysis has been transformed by the growing use of audio-video multimodal data, which demands analytical methods
arXiv:2604.22678v1 Announce Type: new Abstract: A common approach to question answering with retrieval-augmented generation (RAG) is to concatenate documents into a single context and pass it to a lan
arXiv:2604.22433v1 Announce Type: new Abstract: Heat exposure connects the built environment and public health, directly shaping the livability and sustainability of urban areas. Understanding the spa
arXiv:2511.13193v2 Announce Type: replace Abstract: Multi-agent systems (MAS) built on large language models (LLMs) often suffer from inefficient 'free-for-all' communication, leading to exponential t
arXiv:2604.22494v1 Announce Type: cross Abstract: We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with Stiefel-con
arXiv:2604.22446v1 Announce Type: new Abstract: Individual agent capabilities have advanced rapidly through modular skills and tool integrations, yet multi-agent systems remain constrained by fixed te
arXiv:2604.22700v1 Announce Type: new Abstract: Understanding and predicting the progression of neurodegenerative diseases remains a major challenge in medical AI, with significant implications for ea
arXiv:2601.10386v2 Announce Type: replace-cross Abstract: Accurate survival prediction in Non-Small Cell Lung Cancer (NSCLC) requires integrating clinical, radiological, and histopathological data. Mu
arXiv:2604.22526v1 Announce Type: new Abstract: Wireless localization of permanent magnets enables occlusion-free guidance for medical interventions, yet its practical accuracy is fundamentally limite
arXiv:2411.04680v5 Announce Type: replace Abstract: Differential privacy (DP) is a formal privacy framework that enables training machine learning (ML) models while protecting individuals' data. As po
arXiv:2509.14127v2 Announce Type: replace Abstract: We consider the problem of delivering multiple packages from a single depot to distinct goal locations using a homogeneous fleet of robots with limi
arXiv:2604.22154v1 Announce Type: cross Abstract: Emerging AI systems in behavioral health and psychiatry use multi-step or multi-agent LLM pipelines for tasks like assessing self-harm risk and screen
This post compares TensorRT-LLM and llama.cpp (GGUF) as inference frameworks for running coding agents like Cline and RooCode on RTX 5090 GPUs, examining the tradeoff between inference speed and VRAM
arXiv:2604.22269v1 Announce Type: cross Abstract: This paper presents a semantic-enhanced receiver framework for transmitting natural language sentences over noisy wireless channels using multiple sho
arXiv:2603.02709v2 Announce Type: replace-cross Abstract: We propose a novel framework for sensory-aware sequential recommendation that enriches item representations with linguistically extracted sens
arXiv:2509.24004v2 Announce Type: replace Abstract: Generating high-fidelity 3D head avatars from a single image is challenging, as current methods lack fine-grained, intuitive control over expression
arXiv:2604.22065v1 Announce Type: new Abstract: We present Selective Non-Gaussian Refinement (SNGR), a SLAM framework that augments iSAM2 with targeted nested sampling on windows where Gaussian approx
arXiv:2604.22452v1 Announce Type: new Abstract: Collective intelligence refers to the ability of a group to achieve outcomes beyond what any individual member can accomplish alone. As large language m
The April 2026 LangChain newsletter from Harrison Chase covers the latest updates, developments, and announcements from the LangChain team regarding their language model framework and tools. The newsl
arXiv:2506.05038v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in various reasoning-intensive tasks. However, these models exhibit unexpecte
arXiv:2511.22277v2 Announce Type: replace Abstract: Large language models (LLMs) have shown remarkable ability to generate code, yet their outputs often violate syntactic or semantic constraints when
b8931 is a release version of llama.cpp published on April 25, 2026 . llama.cpp is an LLM inference framework in C/C++ that enables running large language models efficiently on various hardware. The r
arXiv:2604.20932v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems are increasingly deployed in sensitive domains such as healthcare and law, where they rely on private, do
arXiv:2502.04416v3 Announce Type: replace-cross Abstract: Scaling large language models (LLMs) improves performance but significantly increases inference costs, with feed-forward networks (FFNs) consu
B8924 is a release build number from the llama.cpp project, a C/C++ framework for efficient large language model inference on consumer hardware. The llama.cpp project publishes multiple releases in a