BSE – Semantic Zip Engine for Text, Image and Audio
We built BSE (Bramble Semantic Engine) – a semantic compressor that transforms natural inputs into low-dimensional structured representations. It's designed as a preprocessing engine for LLMs, capable of reducing long inputs into compact, logic-preserving forms across: 1. Language Extracts SVO (Subject, Verb, Object) structure Captures modifiers: adjectives/adverbs Restores pronouns from short-term memory Detects questions Computes: Compression Rate (%) Semantic Loss (%) Compares sentence compression outputs via SDC: Subject-Subject, Verb-Verb, Object-Subject similarity Sentence…
In plain words
BSE is a semantic compression engine that preprocesses text, image, and audio inputs for language models by converting them into compact, low-dimensional structured representations. For text, it extracts subject-verb-object structures and modifiers while measuring compression rates and semantic loss. For images, it creates weighted patch matrices prioritizing center content. For audio, it decomposes signals into pitch and intensity across frequency bands. The tool is designed for developers and researchers working to reduce input size while preserving logical meaning across multiple data types.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
We built BSE (Bramble Semantic Engine) – a semantic compressor that transforms natural inputs into low-dimensional structured representations. It's designed as a preprocessing engine for LLMs, capable of reducing long inputs into compact, logic-preserving forms across: 1. Language Extracts SVO (Subject, Verb, Object) structure Captures modifiers: adjectives/adverbs Restores pronouns from short-term memory Detects questions Computes: Compression Rate (%) Semantic Loss (%) Compares sentence compression outputs via SDC: Subject-Subject, Verb-Verb, Object-Subject similarity Sentence distance 2. Image Crops and weights center-priority patches Converts into 100x100 weighted matrices Visualizes: R, G, B Channels Brightness 3. Audio Decomposes audio into pitch & intensity across frequency bands Returns normalized 2D matrices Visualized as grayscale spectro-patches Live demo (Gradio): https://huggingface.co/spaces/Sibyl-V/BSE_demo Feedback welcome on: Compression logic Use cases (LLM fine-tuning, retrieval, alignment) Design of multi-modal structure output Built in 48 hours by a solo dev & their black nine-tailed fox partner. Let us know what you'd improve — and what scares you.
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