| 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. |
It extracts SVO structure + modifiers from natural language, compresses visual and auditory data into weighted semantic matrices, and returns structured input usable by LLMs or downstream models.
All components are live and testable. Ask me anything about the math, compression logic, or integration plans.