Knowledge catalogue
arXiv:2508.07683v2 Announce Type: replace-cross Abstract: Video Temporal Grounding (VTG) aims to localize specific video segments corresponding to natural language queries. While recent Large Vision-L
arXiv:2606.29034v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly summarize clinical evidence, where a claim's weight depends on how strongly it is supported. Yet these model
Today, we give robots a /skills library that self-evolves and compounds indefinitely! Introducing ASPIRE: a robot solving its 100th task is no longer as clueless as solving its first. Coding agents ob
arXiv:2602.23294v2 Announce Type: replace Abstract: In real scenarios, videos can span several minutes or even hours. However, existing research on spatio-temporal video grounding (STVG), given a text
arXiv:2606.29502v1 Announce Type: new Abstract: Skill memories can improve agentic reinforcement learning by reusing past experience as textual guidance, but retrieved skills are not oracular: they ma
arXiv:2510.16325v3 Announce Type: replace Abstract: Ultra-high-resolution text-to-image generation is increasingly vital for applications requiring fine-grained textures and global structural fidelity
arXiv:2606.29099v1 Announce Type: cross Abstract: Artificial neural networks (ANN) provide accurate continuous-valued representation, whereas spiking neural networks (SNN) offer event-driven temporal
arXiv:2606.30288v1 Announce Type: new Abstract: Large Vision Language Models (LVLMs) have achieved remarkable success on vision-language tasks, yet fine-grained perception over high-resolution images
arXiv:2606.30045v1 Announce Type: new Abstract: Interactive video generation systems for camera-controlled world exploration roll out growing sequences of latent video frames, entangling state transit
arXiv:2606.30265v1 Announce Type: cross Abstract: Speculative decoding accelerates language model inference by using a fast drafter to propose candidate tokens that are then verified by a larger targe
arXiv:2606.30248v1 Announce Type: new Abstract: Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.g., via reward models or DPO) to align generated content with hu
🚨 A year ago, OpenMed didn't exist. Today: 340M model downloads. 1,500+ open medical models, all Apache 2.0. 650+ run on your iPhone, fully on-device. Started by one person. Now built by a community.
arXiv:2603.02794v2 Announce Type: replace-cross Abstract: We present TVF (Time-Varying Filtering), an interpretable, low-latency speech enhancement model for real-time, on-device assistive hearing. A
Build b9839 of llama.cpp adds offline mode support to the llama download command for checking cached models without network access, and fixes a use-after-free bug in the URL-task callback. llama.cpp i
b9840 is a release of llama.cpp, a tool for LLM inference in C/C++. The release enables running AI models locally on Windows, Linux, and macOS. This specific build tag likely contains bug fixes, perfo
arXiv:2603.10863v2 Announce Type: replace Abstract: Despite the remarkable capabilities of Multimodal Large Language Models (MLLMs), they still suffer from visual fading in long-context scenarios. Spe
arXiv:2504.01882v2 Announce Type: replace Abstract: The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network s
arXiv:2606.27634v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly being considered for deployment on edge devices such as laptops, enabling private, low-latency, and locall
arXiv:2603.12001v2 Announce Type: replace-cross Abstract: Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and clo
arXiv:2606.27677v1 Announce Type: cross Abstract: World-action models have shown promising robot-manipulation performance by jointly predicting future visual states and actions. However, existing meth
arXiv:2606.27663v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models leverage large-scale vision-language pretraining for flexible robot manipulation, yet at test time they remain britt
arXiv:2606.27731v1 Announce Type: cross Abstract: Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason
arXiv:2511.20687v3 Announce Type: replace-cross Abstract: This paper presents a novel hybrid approach for coupling subdomain-local non-intrusive Operator Inference (OpInf) reduced order models (ROMs)
arXiv:2501.16947v2 Announce Type: replace Abstract: The advances in Vision-Language models (VLMs) offer exciting opportunities for robotic applications involving image geo-localization - the problem o
arXiv:2606.27900v1 Announce Type: new Abstract: Human motion describes the three-dimensional full-body movement of a person. Anticipating such motion holds significant relevance across a wide range of
arXiv:2606.27771v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training improves the reward alignment of flow-based generators, but often degrades perceptual quality in ways that a
arXiv:2606.27593v1 Announce Type: new Abstract: We introduce a categorical framework called ODYSSEY for constructing verifiable, local truth-preserving foundation models as compositions of foundries:
arXiv:2606.28094v1 Announce Type: cross Abstract: Real-world object removal is challenging due to two key difficulties: the target object's non-local effects, such as shadows and reflections, which ar
arXiv:2606.27575v1 Announce Type: new Abstract: Predicting how a scene will evolve after a desired 3D transformation from images is a central goal in vision, graphics, and robotics. Yet unlike ideal s
arXiv:2606.27624v1 Announce Type: cross Abstract: Using robots to estimate the location of the radiation source is an effective way to improve efficiency and safety. Existing methods focus on planning
arXiv:2601.14264v2 Announce Type: replace-cross Abstract: Large language models (LLMs) act as digital twins for human respondents, yet their psychometric comparability remains uncertain. We propose a
arXiv:2606.27556v1 Announce Type: new Abstract: Radar camera fusion is widely used in Automatic Emergency Braking AEB systems because radar provides reliable range and velocity measurements while came
arXiv:2606.27720v1 Announce Type: new Abstract: Reconstructing humans in dynamic scenes from moving monocular cameras remains challenging due to scale ambiguity, human-scene misalignment, and occlusio
arXiv:2606.27491v1 Announce Type: new Abstract: Automated instance segmentation of forest LiDAR point clouds is increasingly critical as forest monitoring moves toward scalable, detailed, 3D measureme
arXiv:2606.27655v1 Announce Type: new Abstract: Accurately localizing and segmenting small targets in low signal-to-noise ratio (SNR) infrared sequences remains a challenging task. Since targets are o
arXiv:2606.27514v1 Announce Type: new Abstract: Geolocation encoders, which map geographic coordinates to learned representations, are emerging as an effective means of capturing visual and non-visual
Tomorrow, we're releasing an ML model for on-device PII redaction, which is far smaller than existing models, but is surprisingly effective. It is a first iteration. Will be live at 12:30! A big relea
arXiv:2606.27505v1 Announce Type: new Abstract: AI generated images are proliferating across the Internet. While some are used for entertainment, others are weaponized for fraud and social engineering
arXiv:2603.11229v2 Announce Type: replace-cross Abstract: Machine learning forecast systems are moving beyond point predictions to full predictive distributions for future outcomes y conditional on co
arXiv:2606.27780v1 Announce Type: new Abstract: World models are often used for planning by rolling learned dynamics forward. Many planning environments, however, are not vectors or images; they are g
v0.30.12 is a release candidate (rc0) that fixes a gemma4:12b floating point exception crash on x86, CUDA, Linux, and Windows systems. It includes improvements to ollama launch for Hermes Desktop, all
arXiv:2606.27926v1 Announce Type: new Abstract: Geometry Problem Solving have increasingly adopt the neuro-symbolic paradigm, combining neural intuition with symbolic rigor. However, current framework
Swyx announced two workshops on Local AI scheduled for the following day. The announcement was made via X (formerly Twitter) to invite the community to attend these early-morning sessions.
Build b9829 is an intermediate release of llama.cpp, the open-source C/C++ project that enables users to run large language models on consumer hardware without expensive GPUs or cloud infrastructure.
llama.cpp b9830 is a release of the open-source LLM inference project enabling local language model execution with minimal setup . The release likely includes backend improvements and optimizations fo
Release b9831 of llama.cpp includes backend detection improvements and synchronization enhancements, particularly for async CUDA copies and Vulkan backend operations. Llama.cpp is designed to enable L
B9832 is a build release of llama.cpp from the ggml-org project , which is a free and open-source tool that allows you to run AI models locally on Windows, Linux and macOS . The release likely contain
Release b9833 is a build of llama.cpp, an open-source project for LLM inference in C/C++. This release represents an incremental update within the llama.cpp development cycle, following the standard r
Ends Tuesday. Top winners each get $ + DGX Spark + Stripe credits. The Hermes Agent Accelerated Business Hackathon presented by @NVIDIAAI × @stripe × @NousResearch starts now, for builders making agen
MASSIVE NEWS has partnered with NVIDIA to advance local AI deployment as a default option, addressing the shift toward on-device and edge AI processing rather than cloud-dependent models. This collabo
b9822 is a release of llama.cpp, a project for LLM inference in C/C++ . As a build version in the ongoing development sequence of the llama.cpp project, this release likely includes bug fixes, perform
The search results show information about other llama.cpp releases and the project generally, but do not contain specific details about release b9824. Based on the available information about llama.cp
b9825 is a release of llama.cpp, the C/C++ implementation for large language model inference . This build represents an intermediate version in the ongoing development of llama.cpp, part of the ggml-o
B9826 is a release of llama.cpp, an LLM inference project written in C/C++ . llama.cpp enables users to run large language models on consumer hardware without expensive GPUs or cloud infrastructure .
Release b9827 of llama.cpp, released on June 27, 2026, added a cudaMemcpy2DAsync fast path to ggml_cuda_cpy for improved CUDA tensor copying performance. When tensors are not fully contiguous but each
B9828 is a llama.cpp release featuring OpenCL flash attention improvements, including reworked FA kernels for f16 and f32, prefill prepass kernels, and FA kernels for q4_0 and q8_0 quantization format
Ornith is a model available through the Ollama library, a platform for running large language models locally. The specific capabilities and parameters of this model can be found on its dedicated model
arXiv:2606.27304v1 Announce Type: new Abstract: Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engine
arXiv:2606.26585v1 Announce Type: new Abstract: With the gradual introduction of AI into telescope scheduling, AI-based decision-making has shown advantages in handling complex multi-constraint proble
arXiv:2606.26881v1 Announce Type: cross Abstract: We introduce an accelerated Langevin-based sampling method that is based on two complementary devices: SamAdams adaptive timestepping, which automatic