Knowledge catalogue
arXiv:2606.30084v1 Announce Type: new Abstract: MLLM-based GUI grounding methods commonly formulate target localization as autoregressive coordinate generation, enabling models to leverage the strong
arXiv:2606.30077v1 Announce Type: cross Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged a
arXiv:2606.28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic mal
arXiv:2606.29855v1 Announce Type: new Abstract: In precipitation forecasting, not only accuracy but also temporal resolution is critical. However, increasing temporal resolution is constrained by obse
arXiv:2606.30139v1 Announce Type: new Abstract: Relevance is not permission. Attention lets a model read key-value items related to the current query, but it does not guarantee that the value contribu
arXiv:2606.29513v1 Announce Type: new Abstract: A 3D scene is understood through its objects, not the primitives that compose them. Yet feed-forward reconstruction methods output dense, unstructured s
arXiv:2606.29847v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) excel at multimodal tasks but remain prone to object hallucinations. Prior training-free remedies often uniformly s
arXiv:2606.29858v1 Announce Type: new Abstract: Language model loss follows remarkably regular scaling laws over model and data size, yet it remains unclear why the aggregate loss should exhibit a pow
arXiv:2606.29586v1 Announce Type: cross Abstract: Vision-language foundation models have shown strong potential in medical image analysis. Although foundation models for ultrasound imaging have recent
SpaceXAI has become a major force for good in Memphis and surrounding communities. • SpaceXAI’s expansion is projected to push local tax revenue above 100 million • Employs almost 3,000 people locally
arXiv:2606.28432v1 Announce Type: cross Abstract: We study the spectral perturbation of the empirical Fisher Information Matrix (FIM) of a parametric statistical model under two structured perturbatio
arXiv:2506.20771v2 Announce Type: replace Abstract: We propose a latent score-based generative AI framework for learning stochastic, non-local closure models and constitutive laws in nonlinear dynamic
arXiv:2412.19897v3 Announce Type: replace-cross Abstract: We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve th
arXiv:2606.29567v1 Announce Type: cross Abstract: LLM-based assistants transmit user queries verbatim to third-party API endpoints that lie outside the user's audit or control. When those queries cont
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