CEM Tree Architecture: Branch-Classified Memory Beats Flat Embeddings
We've been benchmarking memory retrieval systems on BEAM and MABench. Our tree architecture — where context is classified into hierarchical branches (Roots → Trunk → Sector → Leaf) and questions route to the correct branch — consistently outperforms flat embedding stores.
BEAM Results (DeepSeek V4 Pro)
| Method |
Score |
| No retrieval (bare LLM) |
~38% |
| Standard retrieval |
77.2% |
| CEM Tree (branch-classified retrieval) |
~78% |
MABench Accurate Retrieval
- CEM tree architecture: 77.4% F1
- Published baselines: Claude 3.7 Sonnet 49.6%, HippoRAG-v2 41.6%
Why It Works
Flat memory stores retrieve by vector similarity — "what's semantically nearby." But memory questions aren't about nearby — they're about category. "What time was my meeting?" routes to Events. "What did we decide about the API?" routes to Technical. Branch classification before retrieval means the model sees relevant context instead of 10K tokens of noise.
The tree is model-agnostic. Any LLM plugged in gets smarter. No fine-tuning.
Full results: cem888.ai/benchmarks
creator@cem888.ai
CEM Tree Architecture: Branch-Classified Memory Beats Flat Embeddings
We've been benchmarking memory retrieval systems on BEAM and MABench. Our tree architecture — where context is classified into hierarchical branches (Roots → Trunk → Sector → Leaf) and questions route to the correct branch — consistently outperforms flat embedding stores.
BEAM Results (DeepSeek V4 Pro)
MABench Accurate Retrieval
Why It Works
Flat memory stores retrieve by vector similarity — "what's semantically nearby." But memory questions aren't about nearby — they're about category. "What time was my meeting?" routes to Events. "What did we decide about the API?" routes to Technical. Branch classification before retrieval means the model sees relevant context instead of 10K tokens of noise.
The tree is model-agnostic. Any LLM plugged in gets smarter. No fine-tuning.
Full results: cem888.ai/benchmarks
creator@cem888.ai