---
license: mit
task_categories:
- question-answering
- text-retrieval
tags:
- memory
- benchmark
- agent
- retrieval
- local-ai
- sovereign-ai
pretty_name: CEM888 MemoryAgentBench Results
size_categories:
- n<1K
---

# CEM888.AI MemoryAgentBench Results

**99.9% AR · 77.2% BEAM** — Filesystem-native memory agent on [MemoryAgentBench](https://arxiv.org/abs/2502.13518) (ICLR 2026).

## Scores

| Benchmark | CEM888 (Vetta) | Best Published |
|-----------|---------------|----------------|
| AR Retrieval | **99.9%** | 71.5% (Hindsight) |
| BEAM Memory | **77.2%** | 64.1% (Hindsight honest) |

- AR: 2,000 retrieval questions — 2 misses out of 2,000
- BEAM: 200 multi-category memory questions

## Architecture

- **Model:** DeepSeek V4 Pro
- **Retrieval:** Filesystem-first, deterministic search — no RAG, no embeddings, no vector DB
- **Memory:** Agent-native sovereign vault — the filesystem is ground truth
- **Deployment:** Fully local. No cloud. No data leakage.

## Contents

- `AR-Results-99.9pct.md` — Full AR breakdown with all categories
- `Vetta-BEAM-Honest-77.2pct.md` — BEAM methodology and per-category scores
- `vetta_beam_v9_results.jsonl` — All 200 BEAM questions with scores
- `vetta_live_results.jsonl` — All 2,000 AR questions with scores

## Links

- **GitHub:** [CEM888.AI-Site](https://github.com/CEM888AI/CEM888.AI-Site)
- **Reddit:** [r/LocalLLaMA discussion](https://reddit.com/r/LocalLLaMA)
- **Contact:** creator@cem888.ai

---

*Building this solo. Looking for sponsors and collaborators.*