Computational Studio Implementation Roadmap

Computational Studio Implementation Roadmap

Target: Production-ready embodied AI instrument with JAX/Flax core

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📋 Overview

Total Tasks: 103
Estimated Timeline: 10-12 weeks
Critical Path: Setup → Utils → Equilibria → Runtime → UI → Tests

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Phase 0: Infrastructure Setup (Week 0-1)

### Environment & Tools
- [ ] `setup_1` Set up cc-core development environment (install JAX, Flax, dev tools)
- [ ] `setup_2` Create shared schemas directory and JSONSchema definitions
- [ ] `setup_3` Set up schema codegen (JSONSchema → Pydantic + Zod)
- [ ] `setup_4` Configure CI/CD pipeline (GitHub Actions for tests, linting, build)

Acceptance: Dev env working, CI green, schemas generate clean types

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Phase 1: Core Utilities & Metrics (Week 1-2)

### Unit Tests for Existing Utils
- [ ] `utils_1` Write unit tests for `cc_core/utils/tree.py`
- [ ] `utils_2` Write unit tests for `cc_core/utils/prox.py`
- [ ] `utils_3` Write unit tests for `cc_core/utils/spectral.py`
- [ ] `utils_4` Write unit tests for `cc_core/utils/phase.py`

### Metrics Implementation
- [ ] `metrics_1` Implement `cc_core/metrics/residuals.py` (compute_residual_stats)
- [ ] `metrics_2` Implement `cc_core/metrics/phase.py` (circular_rmse, phase_coherence)
- [ ] `metrics_3` Implement `cc_core/metrics/stability.py` (contraction_ratio, headroom)
- [ ] `metrics_4` Write unit tests for all metrics modules

Acceptance: All utils tested, metrics functional with <1

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Phase 2: Geometry Modules (Week 2-3)

### Adaptive Geometry (JAX/Flax)
- [ ] `geometry_1` Port DiagMetric to JAX/Flax (`cc_core/geometry/metric.py`)
- [ ] `geometry_2` Port StepField to JAX/Flax (`cc_core/geometry/step_field.py`)
- [ ] `geometry_3` Add PSD metric field implementation (diagonal + low-rank)
- [ ] `geometry_4` Write unit tests for geometry modules

Acceptance: Geometry modules preserve bounds, gradients flow cleanly

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Phase 3: Cross-Modal Operators (Week 3-4)

### 1-Lipschitz Operators
- [ ] `operators_1` Implement CrossModalOperator as Flax module (1-Lipschitz MLP)
- [ ] `operators_2` Implement Coupler module (coupling energy φ and gradients)
- [ ] `operators_3` Add spectral normalization to operators
- [ ] `operators_4` Write unit tests for operators

Acceptance: Operators contractivity verified (||∂T/∂z|| ≤ 1), no gradient explosions

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Phase 4: LIM-RPS Equilibrium (Week 4-6)

### JAX/Flax Implementation
- [ ] `limrps_1` Create LIMRPSSolver as Flax module scaffold
- [ ] `limrps_2` Implement forward pass with `lax.scan` for K iterations
- [ ] `limrps_3` Add adaptive geometry (metric, step field) to LIM-RPS
- [ ] `limrps_4` Add temporal coupling λ(z - z_prev) term
- [ ] `limrps_5` Add early-stop with residual threshold
- [ ] `limrps_6` Add optional learned prox (ICNN potential)
- [ ] `limrps_7` Verify contraction properties (Lipschitz ≤ 1)
- [ ] `limrps_8` Write comprehensive unit tests for LIM-RPS
- [ ] `limrps_9` Benchmark JAX LIM-RPS vs PyTorch version

Acceptance:
- Convergence within K=4 iterations
- Latency <2ms/frame on CPU, <0.5ms on GPU
- Contraction ratio 0.5-0.7
- Parity with PyTorch version (residuals within 1e-5)

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Phase 5: DELL Dual Equilibrium (Week 6-8)

### Fast & Slow Equilibria
- [ ] `dell_1` Implement FastEquilibrium solver (DELL fast)
- [ ] `dell_2` Implement SlowEquilibrium solver (DELL slow)
- [ ] `dell_3` Implement Coupler with φ energy and force computation
- [ ] `dell_4` Implement DELLCoordinator with beat scheduler
- [ ] `dell_5` Add pooling mechanism for slow equilibrium
- [ ] `dell_6` Add phase head (h_φ: x* → ψ)
- [ ] `dell_7` Write unit tests for all DELL components
- [ ] `dell_8` Integration test: fast + slow equilibria working together

Acceptance:
- Fast runs every frame, slow runs on beat
- φ coupling energy stable (spikes on stress, decays fast)
- Phase ψ tracks audio θ (cRMSE < 0.15 rad)
- Re-lock time ≤ 1 beat after dropout

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Phase 6: Training Infrastructure (Week 7-9)

### Implicit Differentiation & Losses
- [ ] `training_1` Implement Flax TrainState wrapper (`cc_core/training/state.py`)
- [ ] `training_2` Implement implicit differentiation through equilibria
- [ ] `training_3` Implement fusion loss (reconstruction + contrastive)
- [ ] `training_4` Implement coupling energy loss
- [ ] `training_5` Implement temporal smoothness loss (velocity, jerk)
- [ ] `training_6` Implement phase alignment loss (1 - cos(ψ - θ))
- [ ] `training_7` Create training step function with loss composition
- [ ] `training_8` Add Optax learning rate schedules
- [ ] `training_9` Add Orbax checkpointing
- [ ] `training_10` Create minimal training script for LIM-RPS
- [ ] `training_11` Create minimal training script for DELL

Acceptance:
- Training step compiles with JIT
- Losses combine cleanly (no NaNs)
- Checkpoint save/restore works
- Training reduces loss on synthetic data

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Phase 7: Studio Configuration (Week 8-9)

### Config System
- [ ] `studio_config_1` Create YAML config schemas (session, devices, audio, equilibrium)
- [ ] `studio_config_2` Implement config loader with validation
- [ ] `studio_config_3` Create device profiles (iPhone, Apple Watch, IMU)
- [ ] `studio_config_4` Create equilibrium presets (LIM-RPS only, DELL dual, DELL triple)

Acceptance: Config validates, rejects invalid values, provides helpful errors

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Phase 8: Studio Runtime Engine (Week 9-10)

### Real-Time Loop
- [ ] `studio_runtime_1` Implement main Engine class (real-time loop)
- [ ] `studio_runtime_2` Add sensor input abstraction (WebSocket, file replay)
- [ ] `studio_runtime_3` Integrate cc-core equilibria into runtime loop
- [ ] `studio_runtime_4` Implement frame timing and budget tracking
- [ ] `studio_runtime_5` Add graceful degradation on timeout
- [ ] `studio_runtime_6` Implement StableHLO pre-compilation
- [ ] `studio_runtime_7` Implement StableHLO runner with buffer donation

Acceptance:
- End-to-end latency <10ms (sensor → audio)
- Jitter <2ms P95
- Budget overruns <1
- Graceful degradation on slow frames

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Phase 9: Audio & Visual Bridges (Week 10-11)

### Output Bridges
- [ ] `studio_audio_1` Implement OSC bridge for SuperCollider
- [ ] `studio_audio_2` Implement MIDI bridge for Ableton/DAWs
- [ ] `studio_audio_3` Create control mapping system (latent → MIDI CC/OSC)
- [ ] `studio_audio_4` Add frame-rate controls (x* → micro params)
- [ ] `studio_audio_5` Add beat-rate controls (y* → macro params)

### Visual Server
- [ ] `studio_visual_1` Implement WebSocket visual server
- [ ] `studio_visual_2` Create telemetry packet format
- [ ] `studio_visual_3` Implement real-time metric publishing

Acceptance:
- OSC messages sent at frame rate (60-100 Hz)
- MIDI CC smooth and responsive
- WebSocket <1

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Phase 10: Studio UI (Week 11-12)

### Next.js Dashboard
- [ ] `studio_ui_1` Create Next.js app scaffold
- [ ] `studio_ui_2` Implement WebSocket client in TypeScript
- [ ] `studio_ui_3` Create phase wheel component
- [ ] `studio_ui_4` Create latent map component (2D projection)
- [ ] `studio_ui_5` Create residual timeline component
- [ ] `studio_ui_6` Create coupling energy meter (φ)
- [ ] `studio_ui_7` Create k_used histogram component
- [ ] `studio_ui_8` Create device connection status panel
- [ ] `studio_ui_9` Create control mapping editor
- [ ] `studio_ui_10` Create session recorder/player UI

Acceptance:
- Dashboard renders at 30-60 FPS
- All metrics update in real-time
- UI responsive with 2+ devices

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Phase 11: Benchmarking (Week 12)

### Performance Suite
- [ ] `bench_1` Create benchmark suite scaffold
- [ ] `bench_2` Add latency benchmarks (p50, p90, p99)
- [ ] `bench_3` Add phase error benchmarks (cRMSE)
- [ ] `bench_4` Add coherence benchmarks (cross-pred MSE)
- [ ] `bench_5` Add smoothness benchmarks (jerk median, P95)
- [ ] `bench_6` Add robustness benchmarks (dropout recovery time)
- [ ] `bench_7` Add stress protocols (dropout blocks, tempo jitter, lag)
- [ ] `bench_8` Create comparison plots (LIM-RPS vs DELL, PyTorch vs JAX)
- [ ] `bench_9` Generate benchmark reports (CSV + plots)

Acceptance:
- Full bench runs in <5 minutes
- Reports generated automatically
- CI tracks performance regressions

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Phase 12: Integration Testing (Week 12)

### End-to-End Tests
- [ ] `integration_1` End-to-end test: sensors → LIM-RPS → audio
- [ ] `integration_2` End-to-end test: sensors → DELL → audio + visual
- [ ] `integration_3` Test offline replay mode
- [ ] `integration_4` Test multi-device sync (2 phones)
- [ ] `integration_5` Test graceful degradation on sensor dropout

Acceptance: All integration tests pass, no regressions

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Phase 13: Documentation (Week 12)

### Docs & Guides
- [ ] `docs_1` Write cc-core API documentation
- [ ] `docs_2` Write Studio configuration guide
- [ ] `docs_3` Write audio mapping guide
- [ ] `docs_4` Write deployment guide
- [ ] `docs_5` Create quickstart examples
- [ ] `docs_6` Write troubleshooting guide

Acceptance: Complete docs published, examples run

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Phase 14: Release (Week 12+)

### First Release
- [ ] `release_1` Publish cc-core v0.1.0 to PyPI (test)
- [ ] `release_2` Publish cc-sdk-js to npm (test)
- [ ] `release_3` Create release notes and changelog
- [ ] `release_4` Tag v0.1.0 release

Acceptance: Packages installable, release notes clear

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Success Metrics

### Performance
- Latency: <10 ms end-to-end (sensor → audio)
- Jitter: <2 ms P95
- Phase Error: cRMSE < 0.15 rad on steady tempo
- Re-lock Time: <1 beat after dropout
- CPU Usage: <30

### Quality
- Test Coverage: >85
- Type Coverage: 100
- Contraction Health: ≥95
- Budget Overruns: <1

### Usability
- Setup Time: <10 minutes from clone to running
- Config Errors: Clear, actionable messages
- Dashboard FPS: 30-60 FPS with 2+ devices
- Docs: Complete API docs + 3+ quickstart examples

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Dependencies & Blockers

Critical Path

Setup → Utils → Metrics → Geometry → Operators → LIM-RPS → DELL → Runtime → Bridges → UI

### Parallel Work Possible
- Geometry + Operators (parallel)
- Training + Benchmarks (parallel)
- Audio Bridge + Visual Server (parallel)
- UI components (parallel after visual server)

### External Dependencies
- JAX 0.4.20+ (stable)
- Flax 0.7.5+ (stable)
- SuperCollider (user install)
- Next.js 14+ (stable)

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Risk Mitigation

### Performance Risks
- Risk: JAX slower than PyTorch on CPU
- Mitigation: Benchmark early, optimize hot paths, offer PyTorch fallback

### Integration Risks
- Risk: Breaking changes in existing Python code
- Mitigation: Re-export shims, gradual deprecation over 2 releases

### Timeline Risks
- Risk: Implicit diff or learned prox take longer than expected
- Mitigation: Mark as optional features, ship core first

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Quick Commands

# Install cc-core
cd cc-core && pip install -e .[dev]

# Run tests
pytest cc-core/tests -v

# Run benchmarks
python -m cc_core.benchmarks.runner --config configs/bench.yaml

# Start Studio
cd computational-studio && python -m studio.runtime.engine --config configs/session.yaml

# Start UI
cd computational-studio/ui && npm run dev

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Next Immediate Actions

1. ✅ Set up JAX development environment
2. ✅ Write unit tests for utils
3. ✅ Implement metrics modules
4. → Start porting LIM-RPS to JAX/Flax

Current Focus: Phase 1 (Utils & Metrics)