Knowledge catalogue
arXiv:2407.12027v2 Announce Type: replace-cross Abstract: In the rapidly evolving Internet of Things (IoT) domain, we concentrate on enhancing energy efficiency in Deep Learning accelerators on FPGA-b
INT3 compression with fused Metal kernels enables large language models to compute attention operations directly on compressed (INT3/INT4) key-value cache representations using custom GPU kernels that
arXiv:2507.11889v2 Announce Type: replace Abstract: Adaptive mission control and dynamic parameter reconfiguration are essential for autonomous underwater vehicles (AUVs) operating in GPS-denied, comm
arXiv:2604.18637v1 Announce Type: cross Abstract: Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a worksho
arXiv:2604.18611v1 Announce Type: cross Abstract: Anomaly detection in nuclear industrial control systems (ICS) requires continuous, energy-efficient monitoring across multiple subsystems that are oft
arXiv:2512.09427v5 Announce Type: replace-cross Abstract: Existing memory management techniques severely hinder efficient Large Language Model serving on accelerators constrained by poor random-access
Qwen3.6-27B-TQ3_4S is a quantized 27 billion parameter language model that fits on 16GB of VRAM while supporting a 32k token context window, demonstrating strong performance across tested prompts. The
arXiv:2505.06335v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has the potential for simultaneous global learning amongst a large number of parallel agents, enabling emerging AI suc
arXiv:2410.03294v4 Announce Type: replace Abstract: This study addresses the deployment challenges of integer-only quantized Transformers on resource-constrained embedded FPGAs (Xilinx Spartan-7 XC7S1
arXiv:2604.19343v1 Announce Type: cross Abstract: Memristive devices present a promising foundation for next-generation information processing by combining memory and computation within a single physi
arXiv:2604.19275v1 Announce Type: cross Abstract: Modern UAV architectures increasingly aim to unify high-level autonomy and low-level flight control on a single General-Purpose Operating System (GPOS
The future is taking shape in Las Vegas this week, where the world’s leading startups are showcasing their groundbreaking AI work at Google Cloud Next. Whether they need the top AI models, infrastruct
arXiv:2604.18860v1 Announce Type: cross Abstract: GUI agents that control desktop computers via screenshot-and-click loops introduce a new class of vulnerability: the observation-to-action gap (mean 6
arXiv:2407.05102v2 Announce Type: replace-cross Abstract: Executing flow estimation using Deep Learning (DL)-based soft sensors on resource-limited IoT devices has demonstrated promise in terms of rel
arXiv:2604.18716v1 Announce Type: cross Abstract: Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine c
arXiv:2604.18607v1 Announce Type: cross Abstract: LLM-driven program evolution can discover high-quality programs, but its cost and run-to-run variance hinder reliable progress. We propose TurboEvolve
arXiv:2604.18655v1 Announce Type: cross Abstract: Deploying large language models (LLMs) on smartphones poses significant engineering challenges due to stringent constraints on memory, latency, and ru
arXiv:2604.19609v1 Announce Type: new Abstract: Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong domain p
This week at Google Cloud Next ‘26, we are sharing the evolution of Google Kubernetes Engine (GKE), delivering leading performance, efficiency, security, and scale for your most demanding and complex
While generative AI sparked a revolution, the true paradigm shift is the rapid evolution from standalone AI models to multi-agent autonomous systems. In this new era, the network transcends basic conn
arXiv:2604.16491v1 Announce Type: new Abstract: Pain is a multifaceted and widespread phenomenon with substantial clinical and societal burden, making reliable automated assessment a critical objectiv
arXiv:2604.17046v1 Announce Type: new Abstract: Collisions between cyclists and pedestrians at urban intersections remain a persistent source of injuries, yet few systems attempt real-time warnings to
arXiv:2604.16408v1 Announce Type: new Abstract: We present Speaking Memories, a distributed, stakeholder-in-the-loop robotic interaction platform for personalized cognitive exercise support. Rather th
arXiv:2604.16702v1 Announce Type: new Abstract: Road traffic accidents are a leading cause of fatalities worldwide. In the US, human error causes 94% of crashes, resulting in excess of 7,000 pedestria
b8870 is a release from llama.cpp, a project for LLM inference in C/C++ . This release represents a commit snapshot in the rapidly-developed open-source project that enables efficient inference of lar
b8874 is a release from llama.cpp, a C/C++ library for LLM inference. The llama.cpp project publishes multiple releases in a single day, and b8874 represents an intermediate build version containing u
B8875 is a release of llama.cpp, a C/C++ implementation for LLM inference . The llama.cpp project does not follow traditional release practices, with multiple releases published in a single day . This
> be Yann LeCun > spend years building JEPA at Meta > company focuses on LLaMA instead > his idea stays complicated and unused > robotics plans get dropped > decides to leave and start AMI Labs > buil
The Reddit post 'Benchmarking programs?' in r/ollama likely discusses tools and methods for measuring the performance of local language models running on Ollama. Available benchmarking tools for Ollam
arXiv:2604.16368v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by using a small draft model to propose k candidate tokens for a target model to verify. While effective
arXiv:2604.17789v1 Announce Type: cross Abstract: The MXFP4 microscaling format, which partitions tensors into blocks of 32 elements sharing an E8M0 scaling factor, has emerged as a promising substrat
arXiv:2505.21282v2 Announce Type: replace Abstract: Data-driven navigation algorithms are critically dependent on large-scale, high-quality real-world data collection for successful training and robus
arXiv:2604.16448v1 Announce Type: cross Abstract: As edge AI deployments scale to billions of devices running always-on, real-time compound AI pipelines, they represent a massive and largely unmanaged
The main stage at Google Cloud Next is where the vision is set. This year, we’re bridging the gap between those massive 'Cloud-scale' announcements and your local terminal. We are thrilled to announce
arXiv:2604.16496v1 Announce Type: cross Abstract: Continual learning, the ability to acquire new tasks sequentially without forgetting prior knowledge, is essential for deploying neural networks in dy
arXiv:2604.17745v1 Announce Type: new Abstract: Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental resu
arXiv:2604.17335v1 Announce Type: new Abstract: Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to vario
This DigitalOcean guide covers strategies and best practices for deploying and optimizing very large language models (600 billion+ parameters) on cloud infrastructure, focusing on inference performanc
arXiv:2511.21686v2 Announce Type: replace Abstract: Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sens
arXiv:2604.16722v1 Announce Type: new Abstract: Predicting full-field physics through the real-time virtual sensing of engineering systems can enhance limited physical sensors but often requires spars
arXiv:2604.17863v1 Announce Type: new Abstract: Spinning flexible objects, exemplified by traditional Chinese handkerchief performances, demands periodic steady-state motions under nonlinear dynamics
arXiv:2507.19205v2 Announce Type: replace Abstract: Real-time particle transverse momentum (p_T) estimation in high-energy physics demands algorithms that are both efficient and accurate under strict
arXiv:2604.16779v1 Announce Type: cross Abstract: Quantum feature maps offer expressive embeddings for classical learning tasks, and augmenting sparse identification of nonlinear dynamics (SINDy) with
arXiv:2604.18026v1 Announce Type: new Abstract: Many deployed systems expose black-box objectives whose minimizing configuration shifts with an externally observed context. When contexts revisit a sma
arXiv:2604.17530v1 Announce Type: cross Abstract: Posture is a critical factor for beginning instrumental learners. Most students receive instruction only once a week, and during the intervals between
arXiv:2604.16815v1 Announce Type: cross Abstract: Quantum error mitigation (QEM) provides a practical route for estimating reliable observables on noisy intermediate-scale quantum (NISQ) devices. Trad
arXiv:2604.16883v1 Announce Type: new Abstract: In long-context decoding for LLMs and LMMs, attention becomes increasingly memory-bound because each decoding step must load a large amount of KV-cache
arXiv:2604.16429v1 Announce Type: cross Abstract: We introduce Mosaic, a probabilistic weather forecasting model that addresses two principal sources of spectral degradation in ML-based weather predic
arXiv:2604.16913v1 Announce Type: cross Abstract: Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposa
arXiv:2604.16887v1 Announce Type: new Abstract: Robotic manipulators for aerospace applications require a delicate balance between lightweight construction and fault-tolerant operation to satisfy stri
arXiv:2602.09130v3 Announce Type: replace Abstract: Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning an
We're open-sourcing FlashKDA — our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. Achieves 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20,
Users training LTX-2.3 LoRAs on RTX 4090s typically use the official ltx-trainer tool, though the model officially targets H100 GPUs with lower VRAM setups requiring gradient checkpointing and reduced
A sophomore engineering student built a platform, landed corporate sponsors, and started a company. In three weeks. 🚀🤯 Engineering students have no way to build in public and get noticed by companies.
arXiv:2503.18970v3 Announce Type: replace Abstract: Structured State Space Models (SSMs) have emerged as a transformative paradigm in sequence modeling, addressing critical limitations of Recurrent Ne
b8853 is a release of llama.cpp , the open-source C/C++ library for running large language model inference. The project enables LLM inference with minimal setup and state-of-the-art performance on a w
Release b8855 addresses a crash in llama-tokenize when using the vocab_only flag with GLM-DSA models and fixes a crash in print_info for GLM-DSA when vocab_only is set. This is a bugfix release for th
B8857 is an intermediate build release from the llama.cpp project, which is a C/C++ implementation that enables LLM inference with minimal setup and state-of-the-art performance on a wide range of har
Release b8860 of llama.cpp addresses a tensor-parallel computation issue by fixing delayed AllReduce on Gemma-4 MoE models, including optimizations to skip forward past unused nodes and allow chains o
arXiv:2604.15821v1 Announce Type: cross Abstract: Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic mol