Omni-macos is presented, which runs its encoder, index and store on the Mac that already holds the files, so no indexed file, no typed query and no vector ever leaves the machine.
Abstract
We present omni-macos, a search engine that embeds text, code, documents, images, audio and video into one representation space and runs its encoder, index and store on the Mac that already holds the files, so no indexed file, no typed query and no vector ever leaves the machine. It keeps a background indexer and an interactive search box inside one memory budget the user sets: it embeds and stores each distinct chunk once, re-encodes only the chunks an edit changes, hands the GPU smaller units while the user is typing, answers queries from a one-bit replica of the index with exact rescoring, and propagates that budget to the allocators that draw on unified memory. We measure on five Macs spanning an eightfold range of accelerator width and a thirty-twofold range of memory, each indexing the files it already holds.
OptGear-1M is the first generative language model to achieve 20 TPS with W4A32 quantization on the ARM Cortex-M7 CPU of the STM32H747I-DISCO, and is the most data-efficient of the existing foundation models.
This report deploys MiniCPM-V-4.6, a modern multimodal assistant pairing a SigLIP2 vision encoder and window-attention merger with a compact hybrid gated-delta-net backbone with a compact hybrid gated-delta-net backbone, entirely on the GPU.
Across three decoder architectures on an H100 the authors measure that parse, not copy, holds 64-72% of device-resident decode time; that bounding back-reference chain depth - provable, and costing 0.006% in ratio - moves latency by at most 2.8% and, for the file's own latency spike, provably by nothing at all.
Open-source Speech-to-Text (STT) systems are increasingly deployed across a wide range of hardware, from GPU servers to resource-constrained edge devices. However, these systems are rarely evaluated jointly across heterogeneous platforms with respect to accuracy, latency, and resource usage, making deployment decisions...
Uyen Thy Duong, Pakeeza Akram· International Conference on...· 0 citations
The first complete description of SweepLSD is given, a line segment detector that reads the image exactly once and emits each segment within a few rows of its last pixel passing the scan line, with the tightest frame-time distribution and the best per-segment direction accuracy of the four detectors.
This work studies how to compress a tokenizer by distillation, taking as the supervision target neither the discrete token nor the output distribution but the pre-quantizer latent the model actually consumes - the last representation the two token interfaces share.
Prasanth Yadla, Mohammad Samragh, Dongseong Hwang et al.· 0 citations
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