self-optimization ยท diff
git:20260507.56f011d to git:20260507.8dfdcb4
6 added, 14 removed. Audit A to C.
---
name: self-optimization
description: SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
allowed-tools: Read, Write, Edit, Bash, Grep, Glob, WebFetch, WebSearch, Agent, AskUserQuestion
- graph:
- domains: [domain:software-engineering]
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- ---
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- # Self-Optimization
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- ## Overview
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- Implements the SONA (Self-Optimizing Neural Architecture) adaptation cycle with sub-millisecond weight updates, EWC++ to prevent catastrophic forgetting, and a ReasoningBank for trajectory-based learning.
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- ## When to Use
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- - After task completion to extract and persist learnings
+ graph:
+ domains: [domain:software-engineering]
+ skillAreas: [skill-area:agentic-loops, skill-area:orchestration-loop]
+ workflows: [workflow:feature-development]
+ topics: [topic:developer-experience]
+ roles: [role:tech-lead, role:backend-engineer]
- Improving routing and agent selection over time
- Adapting to new project patterns without forgetting old ones
- Building cross-session intelligence
## SONA Cycle
1. **Extract Patterns** - Mine execution data for recurring patterns
2. **RETRIEVE** - Search ReasoningBank for matching trajectories
3. **JUDGE** - Evaluate trajectory applicability in current context
4. **DISTILL** - Compress and store new entries
5. **Adapt** - Update weights with EWC++ regularization
## Anti-Forgetting (EWC++)
- Elastic Weight Consolidation prevents overwriting previously learned patterns
- Fisher information matrix tracks parameter importance
- Configurable regularization penalty for new adaptations
## RL Algorithms
Q-Learning, SARSA, PPO, DQN, A2C, TD3, SAC, DDPG, Rainbow
## Agents Used
- `agents/optimizer/` - Performance tuning
- `agents/adaptive-queen/` - Real-time adaptation
## Tool Use
Invoke via babysitter process: `methodologies/ruflo/ruflo-intelligence`