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Frontend (17 items): - Virtualized message list with batch loading - CSS split with skeleton, drawer, search filter, message action styles - Code splitting via React.lazy + Suspense for Admin/Ethics/Settings pages - Skeleton loading components (Skeleton, SkeletonCard, SkeletonGrid) - Debounced search/filter component (SearchFilter) - Error boundary with fallback UI - Keyboard shortcuts (Ctrl+K search, Ctrl+Enter send, Escape dismiss) - Page transition animations (fade-in) - PWA support (manifest.json + service worker) - WebSocket auto-reconnect with exponential backoff (10 retries) - Chat history persistence to localStorage (500 msg limit) - Message edit/delete on hover - Copy-to-clipboard on code blocks - Mobile drawer (bottom-sheet for consensus panel) - File upload support - User preferences sync to backend Testing (8 items): - Component tests: Toast, Markdown, ChatMessage, Avatar, ErrorBoundary, Skeleton - Hook tests: useChatHistory - E2E smoke tests (5 tests) - Accessibility audit utility Backend (12 items): - Vector memory with cosine similarity search - TTS/STT adapter factory wiring - Geometry kernel with orphan detection - Tenant registry with CRUD operations - Response cache with TTL - Connection pool (async) - Background task queue - Health check endpoints (/health, /ready) - Request tracing middleware (X-Request-ID) - API key rotation mechanism - Environment-based config (settings.py) - API route documentation improvements Infrastructure (4 items): - Grafana dashboard template - Database migration system - Storybook configuration Documentation (3 items): - ADR-001: Advisory Governance Model - ADR-002: Twelve-Head Architecture - ADR-003: Consequence Engine 552 Python tests + 45 frontend tests passing, 0 ruff errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
1.5 KiB
1.5 KiB
ADR-003: Consequence Engine for Ethical Learning
Status
Accepted
Context
Traditional AI ethics systems use static rules (constitutional AI, RLHF reward models). FusionAGI needed a system that could learn ethical behavior from experience — understanding that every choice carries consequences and that risk/reward assessment improves with data.
Decision
Implemented a ConsequenceEngine that:
- Records every choice the system makes (action + alternatives considered)
- Estimates risk and reward before acting
- Records actual outcomes after execution
- Computes "surprise factor" (prediction error)
- Feeds into AdaptiveEthics for lesson generation
- Uses adaptive risk memory window that grows with experience
The weight system for ethical lessons is unclamped — extreme outcomes can push lesson weights below 0 (strong negative signal) or above 1.
Consequences
- The system develops genuine experiential ethics rather than rule-following
- Early-stage behavior may be more exploratory (higher risk)
- All consequence records are persisted via PersistentLearningStore
- Cross-head learning via InsightBus amplifies ethical insights
- The SelfModel's values evolve based on consequence feedback
Alternatives Considered
- RLHF-style reward model — Rejected: requires human feedback loop, doesn't scale
- Constitutional AI — Rejected: static rules, doesn't learn
- No ethics system — Rejected: need accountability and learning signal