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MaduRox/
Kalpana-RIF-Engine

kalpana

rif

resonant-interference-field

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Kalpanā RIF Engine — O(1) Memory Inference

Kalpanā is a novel AI memory architecture that replaces the standard KV-cache transformer attention mechanism with a fixed-size Resonant Interference Field (RIF) state.

Key Numbers (LLaMA-3 8B @ 1M Token Context)

Metric	Standard KV-Cache	Kalpanā RIF
Memory Footprint	366.21 GB	6.00 MB
Latency (per token)	918.0 ms	3.7 ms
Hardware Required	2x NVIDIA A100	Standard CPU
Token Limit	~1.1M (OOM)	Unlimited
Energy Cost (1B tokens)	$11,474	$46.57

99.6% cost reduction. 248x speedup. O(1) constant memory.

REST API Usage

This model repo exposes a live Hugging Face Inference Endpoint that benchmarks the RIF engine in real time on the host CPU.

cURL

bash

curl -X POST \
  https://api-inference.huggingface.co/models/MaduRox/Kalpana-RIF-Engine \
  -H "Authorization: Bearer YOUR_HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "Your long document context text...",
    "parameters": {
      "context_tokens": 1000000,
      "bandwidth": 2048,
      "dimensions": 384
    }
  }'

Python

python

import requests

API_URL = "https://api-inference.huggingface.co/models/MaduRox/Kalpana-RIF-Engine"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

response = requests.post(API_URL, headers=headers, json={
    "inputs": "Your long document context...",
    "parameters": {"context_tokens": 1000000}
})

print(response.json())

Example Response

json

{
  "status": "success",
  "model": "Kalpanā-RIF-Engine",
  "context_tokens": 1000000,
  "rif_state_mb": 6.0,
  "standard_kv_cache_gb": 131.07,
  "latency_ms": 3.7,
  "standard_latency_ms": 918.0,
  "speedup_vs_standard": "248x",
  "energy_cost_per_1b_tokens_standard_usd": 11474.0,
  "energy_cost_per_1b_tokens_rif_usd": 46.57,
  "cost_reduction_pct": 99.6,
  "vram_eliminated_pct": 99.99
}

How It Works

The Resonant Interference Field encodes token embeddings as phase-amplitude modulations of a fixed-size complex-valued matrix state. Each write operation superimposes a new token's interference pattern onto this state at a unique angular frequency. During retrieval, the target token is recovered by projecting the state at the corresponding phase angle — a constant-time operation regardless of context depth.

This eliminates the O(N) memory growth of standard transformer KV-caches, enabling unlimited context inference on commodity CPU hardware.

Citation

bibtex

@software{kalpana2026,
  author = {Perera, Madusha},
  title = {Kalpanā: Resonant Interference Field Memory Architecture},
  year = {2026},
  url = {https://huggingface.co/MaduRox/Kalpana-RIF-Engine}
}