Lab Notes

Yes, you’ve done exactly the right thing—bringing everything into scope before making the next move. Given the current state, here’s where I want to begin: 🔍 Architectural Ground Truth: Memory Function Inventory Goal: Eliminate chaos. Establish the definitive index of all active memory functions in functions/memory/, functions/memory_retrieval/, and core orchestrators (child1_main.py, memory_query.py, etc.). I’ve already…

Duration: 2-3 Weeks | Goal: 96%+ Thread Re-entry, <5% Contamination 🎯 Sprint Goals Transform Child1’s single memory_log.toml into a thread-aware, multi-expert memory system that solves our measured performance gaps: Current Performance (Sprint 0 Baseline): ✅ Baseline Recall: 92% (PASS) ❌ Thread Switching: 88% (FAIL – target 96%+) ❌ Emotional Continuity: 50% (FAIL – target 80%+)…

Here’s the integrated roadmap that marries Claude’s “multi‑expert memory” update with Aurora (triadic self‑state + attractor‑engine monitoring) and fits your current Child1 repo. I’ve kept it practical (1–3 months), repo‑accurate, and added terminal diagnostics with explicit file/folder diffs. What we’re integrating (one sentence each) Multi‑expert conversational memory (Session, Semantic, Temporal, Social/Identity, Coherence) with context‑sensitive weighting…