feat(sona): Save/load learned state to disk for persistence across restarts

[dmoellenbeck]

Feature Request

SONA's learned weights (MicroLoRA, BaseLoRA, EWC++ Fisher matrix, ReasoningBank patterns) are in-memory only. On process restart, all learning is lost and must be re-acquired by replaying trajectories.

Motivation

For long-running applications (coaching systems, production chatbots, persistent agents), SONA needs to survive process restarts without losing learned adaptations. Currently, the only workaround is to re-feed all trajectories on every startup, which:

• Requires >= 100 trajectories before forceLearn() activates
• Takes ~30s for 200 trajectories via MCP stdio
• Loses any patterns that were learned during the previous session

Proposed API

Rust:

// Save learned state (LoRA weights, EWC Fisher, patterns)
engine.save_state("path/to/sona-state.bin")?;

// Load from previous state
let engine = SonaEngine::load_state("path/to/sona-state.bin")?;

Node.js (NAPI):

engine.saveState("path/to/sona-state.json");
const engine = SonaEngine.loadState("path/to/sona-state.json");

What should be persisted

Component	Estimated Size (256-dim)
MicroLoRA weights (rank-2)	~2 KB
BaseLoRA weights (rank-8)	~16 KB
EWC++ Fisher information matrix	~256 KB
ReasoningBank cluster centroids	~40 KB
Pattern metadata	~5 KB
Total	~320 KB

Current workaround

We store trajectories in a JSON file (intelligence.json) and replay them on startup. The @ruvector/cli hooks session-start command initializes the intelligence layer, but SONA's neural weights start fresh each time.

Additional context

The IntelligenceEngine in ruvector npm already persists Q-tables, memories, and trajectories to intelligence.json. SONA state could be added to this same file, or stored separately as a binary blob for efficiency.

Related: hooks_force_learn requires 100 trajectories minimum even with force=true (#273 covers the stats counter bug). State persistence would eliminate the need for trajectory replay entirely.