Knowledge catalogue
arXiv:2606.02638v1 Announce Type: cross Abstract: Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. However, most systems fail to mo
arXiv:2603.18599v2 Announce Type: replace Abstract: Speculative Jacobi Decoding (SJD) offers a draft-model-free approach to accelerate autoregressive text-to-image synthesis. However, the high-entropy
Computex 2024 is expected to feature significant announcements around local AI deployment, with major industry players including NVIDIA and Microsoft actively promoting and discussing on-device AI wor
arXiv:2606.03332v1 Announce Type: new Abstract: Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation.
arXiv:2606.02956v1 Announce Type: new Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We prese
This post documents a creative project combining multiple AI image and video generation tools—SDXL, DMD-2, SEEDVR2, and LTX-2.3—with video editing software Shotcut to create a multi-day production. Th
arXiv:2606.02894v1 Announce Type: new Abstract: Small edge devices such as IoT surveillance nodes and search-and-rescue (SAR) platforms are increasingly expected to run computer vision locally. On ult
arXiv:2606.02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive
TripoSplat is an open-source tool developed by TripoAI that converts single 2D images into 3D Gaussian representations with variable density and high quality. The method enables efficient 3D reconstru
Ollama v0.30.2 is a patch release from the 0.30 series, which features improved compatibility and performance using llama.cpp, augmented MLX engine support on Apple Silicon, and broader model support
Ollama v0.30.3 adds support for the Gemma 4-12B model . This is a minor patch release that builds on the v0.30 series, which provides improved compatibility and performance improvements. The release w
Ollama v0.30.4 is a patch release within the v0.30 series, which offers improved compatibility and performance using llama.cpp, augments the MLX engine on Apple Silicon with wider hardware support, an
arXiv:2606.03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text. Yet, it remains unc
arXiv:2603.13384v2 Announce Type: replace-cross Abstract: Software vulnerabilities often depend on cross-file data flow, build options, framework conventions, and runtime guards, so isolated function
arXiv:2606.03220v1 Announce Type: cross Abstract: Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions
This Reddit discussion from r/ollama explores user experiences with uncensored Ollama language models, featuring anecdotal accounts of unusual or extreme use cases and recommendations for popular unce
arXiv:2606.03238v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) makes large-scale post-training possible by replacing an underspecified human objective with learned
You can use Hermes Desktop with Ollama using local or cloud models. Get started 👇👇👇 The next evolution of Hermes Agent is here! Introducing Hermes Desktop: everything you love about Hermes, now native
arXiv:2507.19702v1 Announce Type: cross Abstract: Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains. However, existing app
arXiv:2606.00016v1 Announce Type: cross Abstract: Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that r
arXiv:2606.02551v1 Announce Type: cross Abstract: Affordance understanding bridges visual perception and physical action, serving as an explainable interface for robot manipulation in open and unstruc
arXiv:2606.00092v1 Announce Type: cross Abstract: Weakly-supervised classification of whole-slide images with attention-based multiple instance learning (ABMIL) on top of foundation features now reach
arXiv:2606.00386v1 Announce Type: new Abstract: Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreg
arXiv:2606.01635v1 Announce Type: cross Abstract: Token selection is pivotal for effective LLM post-training. However, existing methods mostly rely on local heuristics and rarely formulate token selec
arXiv:2602.16224v2 Announce Type: replace Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal da
arXiv:2606.00483v1 Announce Type: cross Abstract: Genotype-based cis-expression prediction depends on accurately modeling local regulatory architecture. We present block-sparse Bayesian sparse linear
arXiv:2606.02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference
arXiv:2606.00393v1 Announce Type: cross Abstract: Geometric distortion in prostate diffusion-weighted imaging (DWI) can impair lesion localization and reduce the reliability of MRI-based clinical asse
B9466 is a build release in the llama.cpp project that includes fixes and improvements to speculative decoding functionality, specifically addressing n_outputs_max issues and extracting helper functio
The search results show recent llama.cpp releases but do not contain specific information about release b9467. Based on the context of llama.cpp releases and the GitHub repository structure, b9467 is
B9468 is an intermediate build release of llama.cpp, the open-source C/C++ library that enables large language model inference on consumer hardware. This release continues the project's rapid developm
b9469 is an intermediate build release of llama.cpp, a C/C++ implementation that enables large language model inference on consumer hardware with minimal dependencies. Build releases like b9469 repres
The search results don't contain specific information about release b9470. Based on available context, b9470 is a build release from llama.cpp, a C/C++ implementation that enables LLM inference with m
B9471 is a build release of llama.cpp, an open-source C/C++ implementation for LLM inference that enables running large language models on consumer hardware with optimized performance. This intermedia
B9473 is a release of llama.cpp, a C/C++ framework for large language model inference . The release represents a specific build commit from the ggml-org/llama.cpp repository. This entry likely documen
B9474 is a release version of llama.cpp, a tool for LLM inference in C/C++ . The release likely contains bug fixes, feature updates, and performance improvements to the llama.cpp project, which enable
b9478 is a release of llama.cpp, a project for LLM inference in C/C++ . This release represents one of the project's frequent build updates, as the llama.cpp project releases new versions regularly wi
B9480 is a release of llama.cpp, an LLM inference project in C/C++ . The release likely contains updates, bug fixes, or feature improvements to the llama.cpp codebase for running large language models
Build b9483 is an intermediate release of llama.cpp, the C/C++ inference engine for running large language models locally. This build represents a specific commit snapshot from the ggml-org llama.cpp
arXiv:2606.00737v1 Announce Type: new Abstract: This paper investigates the optimization mechanisms of non-convex Model Predictive Control (MPC) using the Maximum Entropy Differential Dynamic Programm
arXiv:2606.01007v1 Announce Type: cross Abstract: Sparsely activated Mixture-of-Experts (MoE) models scale capacity via conditional computation, but distributed inference suffers from cross-GPU expert
Build on-device personal AI agents on Windows PCs with new tools from NVIDIA and Microsoft, including secure sandboxing, faster local inference, multi-GPU support, and RTX acceleration for Windows AI
arXiv:2606.00251v1 Announce Type: new Abstract: The ability to recognize one's own limitations and decide whether to solve a problem or delegate is fundamental for reliable intelligent systems. Yet we
arXiv:2606.00811v1 Announce Type: cross Abstract: Data centers now account for 4.4% of United States electricity demand, yet the grid-level effectiveness of the renewable energy certificates (RECs) an
arXiv:2606.01946v1 Announce Type: new Abstract: Embedded magnetic tracking holds highly attractive prospects for remote navigation of endoluminal medical devices. However, existing six-degree-of-freed
arXiv:2606.00837v1 Announce Type: cross Abstract: Diffusion models provide strong priors for generating structured data, but many tasks require outputs beyond the scale on which these models are typic
arXiv:2606.01513v1 Announce Type: cross Abstract: High-stakes enterprise document generation, including financial dispute narratives, compliance notices, and audit summaries, demands schema correctnes
arXiv:2606.01149v1 Announce Type: new Abstract: Video Moment Retrieval (MR) and Highlight Detection (HD) are crucial tasks in video analysis that aim to localize specific moments and estimate clip-wis
arXiv:2606.01544v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) in practice incurs substantial memory and computational costs. Post-training pruning (PTP) is an effective approa
arXiv:2606.00955v1 Announce Type: new Abstract: Despite the growing availability of cryo-electron microscopy (cryo-EM) density maps, effectively leveraging them for protein representation remains chal
arXiv:2602.19848v2 Announce Type: replace Abstract: Skin lesion classification datasets often suffer from severe class imbalance, with malignant cases significantly underrepresented, leading to biased
arXiv:2606.01212v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are widely deployed and increasingly influential, but their reliance on external corpora exposes new secu
arXiv:2606.00690v1 Announce Type: new Abstract: Sequential conformal prediction (CP) provides valid uncertainty quantification under the assumption of residual exchangeability. However, this assumptio
arXiv:2606.01294v1 Announce Type: new Abstract: Linear attention reduces the quadratic cost of softmax attention by maintaining a recurrent fast-weight state, but it consistently lags on in-context re
arXiv:2606.01024v1 Announce Type: cross Abstract: Discrete Masked diffusion language models generate text by iterative parallel decoding, but few-step decoding suffers from a tradeoff between length a
arXiv:2606.00576v1 Announce Type: new Abstract: Reliable mobile manipulation in dynamic indoor environments requires a scene representation that remains geometrically consistent, semantically queryabl
arXiv:2606.02418v1 Announce Type: cross Abstract: Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any c
Excited for Ollama to be working with @nvidia for the NVIDIA RTX Spark launch! NVIDIA RTX Spark: a 1-petaflop superchip, the full CUDA and RTX ecosystem, and Windows-native agents. A new beginning for
arXiv:2411.05196v3 Announce Type: replace Abstract: This study presents DhondtXAI as a SHAP-independent, D'Hondt-based attribution framework for tabular XAI. Instead of model-native feature importance
arXiv:2606.01521v1 Announce Type: new Abstract: A central problem in machine learning is that models can achieve near-perfect training performance while generalizing substantially less well to unseen