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.