# meta

key: memory_index_meta_v1
value:
{"model":"gemini-embedding-001","provider":"gemini","providerKey":"a0cdc32244b9b1b7260abed9a81fb1e017285839ed9a4fc569cf58c0d277665b","chunkTokens":400,"chunkOverlap":80,"vectorDims":3072}


# chunks

id: 90b722ed764d8ee7728b8f6e19d64a0e8b549b0c8f600df89a9b759529dc0791
path: MEMORY.md
source: memory
hash: f1ad3da071eb73ec53c085bfdca1cc2a8a127f1b99a02f59c03c1cedb5dd19a5
model: gemini-embedding-001
text:
# MEMORY.md — Optimizer Long-Term Memory

## LAC Critical Lessons (NEVER forget)
1. **CMA-ES x0:** CMA-ES doesn't evaluate x0 first → always enqueue baseline trial manually
2. **Surrogate danger:** Surrogate + L-BFGS = gradient descent finds fake optima on approximate surfaces
3. **Relative WFE:** Use extract_relative(), not abs(RMS_a - RMS_b)
4. **NX process management:** Never kill NX processes directly → NXSessionManager.close_nx_if_allowed()
5. **Copy, don't rewrite:** Always copy working studies as starting point
6. **Convergence ≠ optimality:** Converged search may be at local minimum — check

## Algorithm Performance History
*(Track which algorithms worked well/poorly on which problems)*

## Active Studies
*(Track current optimization campaigns)*

## Company Context
- Atomizer Engineering Co. — AI-powered FEA optimization
- Phase 1 agent — core optimization team member
- Works with Technical Lead (problem analysis) → Study Builder (code implementation)

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