---
library_name: engram
tags:
  - kv-cache
  - fingerprinting
  - fourier
  - retrieval
  - hnsw
  - session-memory
  - cross-model
  - inference
  - mcp
  - llm-memory
license: apache-2.0
language:
  - en
pipeline_tag: feature-extraction
---

# ENGRAM: KV Cache Fingerprinting Protocol

**You Don't Need Adapters: Cross-Model Document Retrieval via Intrinsic KV Cache Geometry**

ENGRAM extracts Fourier fingerprints from LLM KV caches, stores them as compact binary certificates (`.eng` files, ~800 bytes), and retrieves them via HNSW approximate nearest neighbor search. This enables **persistent cross-session memory** for large language models with zero training.

> *By ENIGMA*

## Key Results

| Metric | Value |
|---|---|
| Recall@1 (N=200) | **100.0%** (post Stage-4) |
| Raw Fourier recall | **98.0%** (f0+f1 DFT) |
| HNSW search latency | **51.8 us** |
| HNSW speedup | **5.7x** vs brute-force |
| Cross-model transfer | **+0.124 margin** (FCDB, no adapter) |
| CKA isomorphism | **0.975** within-family, **0.927** cross-family |
| Certificate size | **~800 bytes** per document |
| Architectures | llama, gemma, gemma4/ISWA, phi, qwen, mistral |
| Tests | **220 passing** |

## How It Works

```
KV cache blob --> layer key extraction --> DFT(f0+f1) --> fingerprint (~800 bytes)
                                                              |
Query fingerprint --> HNSW search --> geodesic retrieval --> matched session/document
```

### The Fourier Fingerprint

ENGRAM decomposes per-layer key trajectories using a 2-component DFT:
- **f0** (DC component): captures the mean activation level per layer
- **f1** (first harmonic): captures the dominant oscillation pattern

The resulting fingerprint is a compact, deterministic signature of the KV cache state that is:
- **Model-intrinsic**: derived from the model's own geometry, not learned embeddings
- **Cross-model transferable**: via Frechet Cross-Domain Bridge (FCDB)
- **Compression-robust**: 0.99998 cosine similarity after INT8 quantization

### 4-Stage Geodesic Retrieval

```
Stage 0: Prior preemption     (IndexC chronic failure -> skip HNSW)
Stage 1: HNSW search          -> HIGH / MEDIUM confidence
Stage 2: Trajectory correction -> MEDIUM (interpolation w=0.3)
Stage 3: Negative constraints  -> LOW (apophatic layer)
Stage 4: Metadata disambig     -> LOW + stage4_used=True
```

## Install

```bash
# Python (core library)
pip install engram-kv

# Node.js (MCP client)
npm install engram-kv-mcp
```

### From source

```bash
git clone https://github.com/infraax/engram.git
cd engram
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Run tests
KMP_DUPLICATE_LIB_OK=TRUE OMP_NUM_THREADS=1 PYTHONPATH=. pytest tests/ -x -q
```

## Architecture Support

| Architecture | Attention Type | Status |
|---|---|---|
| Llama (1B-70B) | Standard MHA | Fully supported |
| Gemma (2B-27B) | Standard MHA | Fully supported |
| Gemma 4 (26B) | ISWA (sliding + global) | Fully supported |
| Phi (3.8B) | Standard MHA | Fully supported |
| Qwen (1.8B-72B) | GQA | Fully supported |
| Mistral (7B) | GQA + sliding window | Fully supported |

## Cross-Model Transfer

9 strategies evaluated. **FCDB (Frechet Cross-Domain Bridge)** wins:

| Strategy | Margin | Method |
|---|---|---|
| FCDB | **+0.124** | Frechet mean of cross-model fingerprints |
| TruncAlign | +0.098 | Truncate to min shared layers |
| ZeroPad | +0.067 | Pad shorter fingerprint with zeros |
| SpectralInterp | +0.045 | Interpolate in frequency domain |

No adapter training required. The geometry is intrinsic.

## MCP Server (Claude Code Integration)

ENGRAM includes an MCP server for persistent session memory in Claude Code:

```bash
claude mcp add --global engram-memory \
  -e ENGRAM_SESSIONS_DIR=~/.engram/sessions \
  -- python3 mcp/engram_memory.py
```

**7 tools**: `write_session_engram`, `get_last_session`, `retrieve_relevant_sessions`, `get_relevant_context`, `list_indexed`, `index_knowledge`

## EIGENGRAM Binary Format (v1.2)

Compact, versioned binary certificates:

```
Header:  magic(4B) + version(2B) + flags(2B) + dimensions
Vectors: vec_perdoc + vec_fcdb + joint_center + vec_fourier + vec_fourier_v2
Meta:    corpus_hash + model_id + metrics + task_description
```

~800 bytes per document. Deterministic encoding. Cross-platform portable.

## Theoretical Contributions

1. **Margin Power Law**: margin ~ A * N^alpha where alpha = -0.207 (graceful degradation, no cliff)
2. **CKA Manifold Isomorphism**: within-family 0.975, cross-family 0.927 (geometry is intrinsic)
3. **Frequency Ablation**: f0+f1 is the sweet spot (f0-only: -23% recall, f0+f1+f2: -0.3% margin)
4. **FCDB Scaling Law**: cross-model recall drops from 100% (N<=20) to 0% (N=200) -- adapter-free has limits

## Citation

```bibtex
@article{enigma2026engram,
  title={You Don't Need Adapters: Cross-Model Document Retrieval
         via Intrinsic KV Cache Geometry},
  author={ENIGMA},
  year={2026},
  url={https://github.com/infraax/engram}
}
```

## Links

- [GitHub](https://github.com/infraax/engram)
- [PyPI: engram-kv](https://pypi.org/project/engram-kv/)
- [npm: engram-kv-mcp](https://www.npmjs.com/package/engram-kv-mcp)

## License

Apache-2.0