BitCache: Spectral Isoperimetric KV-Cache Compression (Cheeger Contraction)

Author: Kelvin Mateus | Bit++ Technologies
Architecture: BitCacheGPT2LMHeadModel (Spectral KV-Cache Decimation Engine)

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1. Overview

BitCache is an ultra-fast, $O(N)$ spectral KV-cache compression middleware for Large Language Models. It decimates redundant attention states in Key and Value projection matrices during auto-regressive generation, cutting KV-cache memory footprint by up to 65% while preserving strict factual recall and language fluency.

Unlike sliding-window methods (such as StreamingLLM) that suffer from catastrophic context loss in the middle (Lost in the Middle), BitCache leverages:

• Discrete Cheeger Boundary Flux: Cross-alignment isoperimetric cuts between the observation boundary (the prompt question) and the sequence bulk ($h(G) \ge \lambda_2/2$).
• Catalan Thresholding ($\tau = 0.4785305$): Non-softmax $O(N)$ spectral pruning running in sub-millisecond latency (~0.77 ms).
• Positional Integrity & Anti-Degeneration: Preserves logical sequence IDs to eliminate auto-regressive repetition loops.

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2. Benchmark Results: 5-Depth Needle Retrieval

In controlled benchmarks across 5 prompt depths (10%, 30%, 50%, 70%, and 90% of total sequence length):

Finding: BitCache achieves 100% parity with the full uncompressed baseline, maintaining identical answer recall while freeing 65% of physical memory.

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3. Quick Start (Hugging Face Transformers)

You can load and run this model directly using standard Hugging Face pipelines with trust_remote_code=True:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "KelvinAxhcar/bitcache-distilgpt2"

# 1. Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)

# 2. Run generation - KV-cache is automatically compressed in VRAM
prompt = "In enterprise cloud telemetry architectures, the database master password is PHOENIX-771. Question: What is the database password?\nAnswer: The database password is"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=15, do_sample=False)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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4. Citation & Enterprise Licensing

For commercial deployments, high-throughput multi-GPU serving (vLLM / TGI integration), or enterprise PoC trials, contact:

• Lead Developer: Kelvin Mateus
• Organization: Bit++ Technologies
• Inquiries: kaxhcar@gmail.com