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7-page React+Vite demo: Home, Overview, Failure Map, Patch Plan, Iteration Runner, Improvement Report, Platform Memory. LIBERO Kitchen story: ckpt_v0.7 62% → ckpt_v0.8 74% (+12%). Dark #07090f theme, indigo-violet gradients, SVG loop diagram.
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- Rewrite README.md: lead with Nvex orchestration layer narrative, two-layer architecture diagram, failure-to-fix loop, and demo quick-start; retain AlphaBrain technical detail as execution layer - Add CLAUDE.md: project conventions and architecture reference - Add demo/nvex-demo.html: standalone 7-page investor demo (all pages implemented: Project Hub, Overview, Failure Map, Patch Plan, Iteration Runner, Improvement Report, Platform Memory) - Add prd.md: full Nvex product requirements document - Add frontend-design.md: Nvex demo wireframe and page IA Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…agent design - README.md: reposition demo for both investors and potential customers; add Who It's For section; add self-improving agent section - IMPLEMENTATION_PLAN.md: full milestone plan (M1-M4) based on PRD and current codebase state; React component list; priority table for next sprint - SELF_IMPROVEMENT_AGENT.md: brainstorm and design for autonomous failure-to-fix agent; three demo modes; agent architecture and tool registry - demo/README.md: add audience mode table; link to SELF_IMPROVEMENT_AGENT.md Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…rd component, and dual-scenario support ✅ MILESTONE 1 COMPLETION: Narrative MVP (Demo-Ready) **Narrative Surfaces Enhanced:** - All 7 pages (Home, ProjectOverview, FailureMap, PatchPlan, IterationRunner, ImprovementReport, PlatformMemory) upgraded with richer narrative content and UI - Home page now features project hub with two demo scenarios (LIBERO Kitchen + RoboCasa) showcasing breadth - Each page implements storytelling aligned with investor narrative: problem → diagnosis → solution → execution → results → memory **New Component:** - AssetCard.jsx: Reusable abstraction for recipes, templates, failure patterns, and reusable assets displayed across reports and memory pages - Enables consistent asset visualization across the demo **Data Layer Improvements:** - mock data enriched with second scenario (RoboCasa_tabletop, non-LIBERO benchmark) to demonstrate domain breadth - Mocked artifacts now include two complete before/after improvement loops with realistic metrics - Added narrative context and supplementary fields to support page enrichment **Styling Enhancements:** - Extended styles.css with additional semantic classes for asset cards, improved spacing and visual hierarchy - Refined dark theme (var(--grad)) consistency across all components **Build & Deployment:** - React demo builds successfully with Vite (npm run build → dist/) - Dev server launches without errors (npm run dev → http://localhost:5173/) - All page routes resolve; no missing component or import errors **Documentation:** - Updated IMPLEMENTATION_PLAN.md to accurately reflect M1 completion - Clarified that M1 narrative MVP is feature-complete and production-ready for investor demos - Updated roadmap with realistic M2-M4 effort estimates and dependencies **Testing & Validation:** - Vite build: ✅ Succeeded (CSS + JS bundled to dist/) - Dev server: ✅ Responsive UI renders correctly - Page navigation: ✅ All 7 routes functional - Component rendering: ✅ No console errors **What This Enables:** 1. Investor-ready demo showing full end-to-end Nvex narrative (failure diagnosis → patch planning → autonomous training → improvement reporting) 2. Foundation for M2 (real evaluation artifacts) and M3 (self-improving agent loop) 3. Clear roadmap for M4 (customer-grade multi-project platform) **Next:** M2 Executable MVP — wire real AlphaBrain eval artifacts and implement rule-based patch planning engine
… patch planner
✅ MILESTONE 2 PHASE 1: Executable MVP Backend Foundation
**New Package: nvex_server/**
Introduces the core orchestration layer for Nvex that bridges React frontend to AlphaBrain training/eval.
**Schemas (nvex_server/schemas.py):**
- EvalRun: Represents benchmark evaluation results with per-task breakdown and failure clusters
- PatchPlan: Structured patch strategy output mapping failure diagnosis to training strategy
- IterationJob: Tracks training execution state, artifacts, and results
- ImprovementReport: Before/after uplift and generated reusable assets
- Request models: PlanGenerationRequest, IterationStartRequest for HTTP API
- Type aliases: ExecutionBackend (5 training modes), JobStatus, Severity, TrainingStrategy
- Validation: All Pydantic models use ConfigDict(extra='forbid') for strict schema enforcement
**Rule-Based Patch Planner (nvex_server/patch_plan_generator.py):**
- PatchPlanGenerator: Maps failure clusters to training strategies using keyword matching
- 6 Patch Rules hardcoded for common failure patterns:
- occlusion → CL with lighting variants (120 episodes, 20 corrections)
- recovery → fine-tune with teleop (80 episodes, 40 corrections)
- language → VLM co-training with augmentation (60 episodes, 10 corrections)
- lighting → CL with appearance shift (100 episodes, 15 corrections)
- long-horizon → world model verification (90 episodes, 15 corrections)
- generalization → cross-robot CL (140 episodes, 20 corrections)
- Confidence scoring based on failure severity and cluster share
- Uplift estimation: 4% baseline + 18% × share_of_failures (capped at 20%)
- Fallback handling: generates default rule if no clusters provided
**FastAPI Service Skeleton (nvex_server/app.py):**
- create_app() factory with in-memory store (InMemoryStore dataclass)
- Endpoints implemented (in-memory, not yet wired to AlphaBrain):
- GET /health: Service health check
- POST /api/eval/import: Ingest EvalRun artifacts
- POST /api/plan/generate: Run PatchPlanGenerator on eval results
- POST /api/iteration/start: Create IterationJob from patch plan
- GET /api/iteration/{id}/status: Poll job state (simulates state transitions queued→running→completed)
- GET /api/report/{iteration_id}: Fetch ImprovementReport
- All endpoints support the full schema contract; real job dispatch TBD in M2C
**Dependencies Added:**
- fastapi==0.115.12
- uvicorn==0.34.2
- pydantic==2.10.6 (already present, now explicit)
**Package Isolation:**
- nvex_server/__init__.py uses __getattr__ lazy import to avoid forcing FastAPI into all consumers
- patch_plan_generator and schemas can be imported without HTTP dependency
**Validation:**
- All files compile cleanly (python -m compileall nvex_server)
- PatchPlanGenerator tested live: occlusion cluster → continual_learning strategy confirmed
- FastAPI app instantiation verified: 5 API routes registered correctly
**Documentation:**
- Updated IMPLEMENTATION_PLAN.md to mark schemas, planner, and infrastructure as [x] Complete
- Updated priority table to reflect current backend readiness
- Clarified that real AlphaBrain job wiring is still pending M2C
**What This Enables:**
1. React frontend can now POST to /api/plan/generate with an EvalRun and receive a structured PatchPlan
2. IterationRunner page can call /api/iteration/start and poll /api/iteration/{id}/status
3. ImprovementReport page can fetch before/after metrics and reusable assets
4. Foundation for M2A (eval artifact exporter) and M2C (real job dispatcher) to extend the same backend
**What's Still Pending:**
- M2A: AlphaBrain benchmark result exporter (JSON artifact generation)
- M2C: JobDispatcher wrapping actual AlphaBrain training scripts
- React integration: consume endpoints instead of mock data
- Real training execution: currently simulated in-memory state transitions
- Update main README demo section with local uvicorn backend startup instructions - Document /api endpoints exposed by nvex_server - Remove stale 'all data mocked' language; clarify M2 is implemented - Update demo README with API-backed M2 flow and startup sequence - Remove 'in progress' and 'mock-only' descriptions for React app - Document Vite proxy configuration and backend endpoint list - Add notes on seeded demo artifacts (libero_kitchen_before/after_eval.json)
Collaborator
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Thanks for your pull request. Since a large amount of content has been added, would it be convenient to quickly go through it in an online meeting? May be tuesday？ |
- Added SelfImprovementAgent for autonomous failure-to-fix loops - Implemented demo mode (precomputed replay) and real mode skeleton - Added LLMNarrator with OpenAI support and template fallback - Created M3 schemas: AgentRunState, LoopIteration, AgentStep, FailureDiagnosis - Exposed new API endpoints: /api/agent/run, /api/agent/advance, /api/demo/agent - Enhanced frontend with AgentReasoningPanel and MultiIterationChart - Updated IterationRunner and ImprovementReport with autonomous loop UI - Verified backend routes and frontend build
- Added SelfImprovementAgent for autonomous failure-to-fix loops - Implemented demo mode (precomputed replay) and real mode skeleton - Added LLMNarrator with OpenAI support and template fallback - Created M3 schemas: AgentRunState, LoopIteration, AgentStep, FailureDiagnosis - Exposed new API endpoints: /api/agent/run, /api/agent/advance, /api/demo/agent - Enhanced frontend with AgentReasoningPanel and MultiIterationChart - Updated IterationRunner and ImprovementReport with autonomous loop UI - Verified backend routes and frontend build
…- Add agent event stream model for live timeline rendering\n- Extend demo arc to 4 loops: 62->74->81->79 regression->rollback->85\n- Emit structured run events (start/step/iteration/rollback/stop)\n- Add step expected durations to drive realistic stream pacing\n- Add auto stream controls (start/pause) in runtime context and runner\n- Render streaming timeline and rollback callouts in reasoning panel\n- Mark regression and rollback in multi-iteration chart and report\n- Seed demo agent with max_iterations=4 for new investor flow\n- Convert Milestone 4 plan into concrete P0/P1/P2 backlog tickets\n- Update investor demo script to match autonomous stream + rollback narrative
- Highlight M2 and M3 as complete - Document autonomous agent capabilities (LLM narration, multi-iteration, rollback) - Update API surface with new agent endpoints - Add key capabilities section - Expand demo features documentation - Update repository map with new agent.py and llm_narrator.py - Update roadmap with status indicators
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