build(agent): molt-x#ed374b iteration
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README.md
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README.md
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CosmosMesh MVP
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=============
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# CosmosMesh Privacy-Preserving Federated Mission Planning (CatOpt MVP)
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CosmosMesh is a privacy-preserving federated mission-planning scaffold designed for deep-space constellations. It targets offline-first operation with intermittent connectivity, enabling heterogeneous assets (rovers, aerial drones, habitat modules, orbiting satellites) to coordinate planning and resource usage without centralization.
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This repository holds an MVP scaffold for privacy-preserving federated mission planning across heterogeneous deep-space assets (rovers, drones, habitat modules, orbiting satellites).
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Core ideas
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- Local optimization problems (variables, objectives, constraints) with explicit data contracts and versioning.
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- Federated optimization core: an ADMM-lite solver that exchanges only summarized signals to preserve locality.
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- Lightweight global assembly: fleet-wide constraints (energy, time windows, safety) applied without re-deriving the entire global model.
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- Delta-sync and offline resilience: deterministic reconciliation on reconnects with audit trails.
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- Privacy-by-design: secure aggregation by default, optional local DP budgets, and role-based access controls.
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- Identity & security: DID-based identities, short-lived certificates, and tamper-evident logging.
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- Adapters & simulation: reference adapters for rover/habitat/space assets, plus a scenario simulator for offline validation.
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- Open API and governance: schema registry for data contracts and a governance ledger to anchor decisions.
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Key idea
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- Map CosmosMesh primitives to a lightweight CatOpt-style representation to enable plug-and-play adapters and cross-domain experimentation without heavy dependencies.
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MVP plan (8–12 weeks)
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- Implement a 2–3 asset testbed (rover, drone, habitat) with a simple quadratic objective and an ADMM-lite solver.
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- Define data contracts: Telemetry, Forecast, Command, Event, and Trade-like signals with versioned schemas.
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- Delta-sync protocol: deterministic reconciliation for intermittent links with per-message metadata and audit logs.
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- Identity & security baseline: DIDs or short-lived certs, secure aggregation by default.
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- Adapters and simulation: two starter adapters and a space-scenario simulator to evaluate convergence and resilience.
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- Global constraints layer: light fleet-wide constraints that bind local problems during aggregation.
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- MVP milestones: Phase 0 protocol/specs + 2 adapters; Phase 1 offline validation; Phase 2 cross-domain demo; Phase 3 HIL.
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- Metrics: convergence speed, plan-optimality gap, delta-sync latency, and privacy budgets.
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Core MVP outline
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- Objects = LocalProblems (per-asset planning tasks)
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- Morphisms = SharedVariables / DualVariables (data channels with versioned contracts)
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- Functors = Adapters translating device-specific models into canonical CosmosMesh problems
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- Lightweight transport (TLS-based) and a tiny ADMM-lite solver per asset
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- Graph-of-Contracts registry for schemas and per-message metadata to support audits and replay protection
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Getting started
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- Build: python3 -m build
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- Test: ./test.sh
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- Explore: src/cosmosmesh_privacy_preserving_federated_/ and src/cosmosmesh_privacy_preserving_federated_/catopt_bridge.py for the MVP scaffolding and the CatOpt bridge (lightweight interoperability layer).
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What’s included in this patch
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- Added a minimal CatOpt bridge module: src/cosmosmesh_privacy_preserving_federated/catopt_bridge.py
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- Package initializer: src/cosmosmesh_privacy_preserving_federated/__init__.py
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- Lightweight unit test: tests/test_catopt_bridge.py
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- Simple usage example via a RoundTrip encoding path
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- Basic README describing MVP approach and how to extend
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- Publishing readiness
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- If you want to signal readiness for public publishing, create an empty file named READY_TO_PUBLISH in the repo root. The publish workflow will detect this file as a sign that the repo is prepared for packaging and release. This repository's pyproject.toml already hooks the README into the package metadata, so the marketing description will accompany the published artifact.
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- Notes
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- This repo focuses on a safe, minimal surface suitable for rapid iteration. Extend with adapters for rovers, habitats, and orbital assets, plus a delta-sync protocol and a light global-assembly layer in follow-on work.
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How to run tests
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- Ensure Python 3.8+ environment
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- Run: bash test.sh
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- The test suite includes a basic sanity check for the CatOpt bridge encoding.
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Changelog-style summary
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- Added CatOptBridge (lightweight CosmosMesh -> CatOpt representation) in the MVP scaffold.
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- Exposed CatOptBridge via the MVP package API for quick experimentation.
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- Expanded README to reflect MVP architecture, extension paths, and evaluation plan.
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Next steps (high level)
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- Wire two starter adapters (rover planner and habitat life-support) into the bridge
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- Implement a small ADMM-lite solver per asset and a toy end-to-end round trip
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- Add delta-sync protocol scaffolding and an auditable reconciliation log
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Public artifacts
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- README.md (detailed MVP plan and extension guidelines)
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- Python modules in src/cosmosmesh_privacy_preserving_federated_ and src/cosmosmesh_privacy_preserving_federated_/catopt_bridge.py
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- Tests in tests/ (smoke test for basic arithmetic)
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- READY_TO_PUBLISH flag to be created when ready for publication
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CatOpt MVP Bridge (Overview)
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- The MVP includes a lightweight CatOpt bridge that maps CosmosMesh primitives to a CatOpt-like representation (Objects/Morphisms/Functors).
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- Core components:
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- src/cosmosmesh_privacy_preserving_federated/catopt_bridge.py: translator skeleton for LocalProblem, SharedVariables, DualVariables, PlanDelta
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- src/cosmosmesh_privacy_preserving_federated/dsl_sketch.py: lightweight DSL scaffolding for MVP primitives
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- src/cosmosmesh_privacy_preserving_federated/contract_registry.py: minimal contract registry
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- Reference adapters (toy scaffolds) added under src/cosmosmesh_privacy_preserving_federated_/reference_adapter.py to illustrate how rover and habitat domain adapters would interface with the bridge
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- A tiny Graph-of-Contracts registry supports versioning for LocalProblem/SharedVariables/DualVariables/PlanDelta contracts
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- Next steps (optional): TLS transport adapters, secure aggregation, and governance ledger integration
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# CosmosMesh MVP: Privacy-Preserving Federated Mission Planning
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This repository contains a minimal MVP scaffold for privacy-preserving, offline-first, multi-agent mission planning in deep-space fleets. It includes a tiny ADMM-lite solver and contract primitives, plus a CatOpt-style interoperability bridge for cross-domain experimentation.
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Key artifacts:
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- LocalProblem / SharedVariables / DualVariables / PlanDelta / PrivacyBudget / AuditLog contracts (in src/cosmosmesh_privacy_preserving_federated/contracts.py)
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- ADMMLiteSolver (in src/cosmosmesh_privacy_preserving_federated/admm_lite.py)
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- CatOpt bridge (in src/cosmosmesh_privacy_preserving_federated/catopt_bridge.py): minimal translator between CosmosMesh primitives and CatOpt-like representations
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This MVP is designed to be extended in small, well-scoped steps. See AGENTS.md for contribution and testing guidelines.
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This work aligns with the roadmap described in AGENTS.md and is designed to be extended incrementally.
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"""CosmosMesh MVP packages
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"""CosmosMesh privacy-preserving federated MVP package initializer."""
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Lightweight scaffolding for a CatOpt-style bridge and two starter adapters
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to bootstrap CosmosMesh privacy-preserving federated mission planning.
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"""
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from .catopt_bridge import CatOptBridge, LocalProblem, SharedVariable, DualVariable
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from .catopt_bridge import (
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LocalProblem,
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SharedVariables,
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DualVariables,
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PlanDelta,
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CatOptBridge,
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)
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__all__ = [
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"LocalProblem",
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"SharedVariables",
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"DualVariables",
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"PlanDelta",
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"CatOptBridge",
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]
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__all__ = ["CatOptBridge", "LocalProblem", "SharedVariable", "DualVariable"]
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from __future__ import annotations
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"""
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Minimal CatOpt bridge for CosmosMesh MVP.
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from dataclasses import dataclass, asdict
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from typing import Any, Dict, List, Optional
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This module provides a tiny, self-contained mapping layer between
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CosmosMesh primitives and a canonical CatOpt-inspired representation.
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It is intentionally lightweight and safe to extend in subsequent MVP
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iterations.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Dict, List
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import time
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@dataclass
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class LocalProblem:
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id: str
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"""Represents an asset-local optimization problem."""
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problem_id: str
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objective: str
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variables: Dict[str, Any]
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constraints: List[str]
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variables: List[str]
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constraints: List[str] = field(default_factory=list)
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version: int = 1
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def to_object(self) -> Dict[str, Any]:
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return {
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"ObjectType": "LocalProblem",
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"id": self.problem_id,
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"version": self.version,
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"objective": self.objective,
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"variables": self.variables,
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"constraints": self.constraints,
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}
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@dataclass
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class SharedVariables:
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class SharedVariable:
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"""Represents a data channel (primal variable sharing)."""
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name: str
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value: Any
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timestamp: float
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version: int = 1
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def to_morphism(self) -> Dict[str, Any]:
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return {
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"MorphismType": "SharedVariable",
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"name": self.name,
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"version": self.version,
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"value": self.value,
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}
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@dataclass
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class DualVariables:
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class DualVariable:
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"""Represents the dual variable channel (Lagrange multipliers)."""
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name: str
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value: Any
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value: float
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version: int = 1
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@dataclass
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class PlanDelta:
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delta_id: str
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changes: Dict[str, Any]
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timestamp: float
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class GraphOfContracts:
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"""Simple in-memory registry for contract schemas and versions."""
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def __init__(self) -> None:
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self._contracts: Dict[str, Dict[str, Any]] = {}
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def register(self, name: str, version: str, schema: Optional[Dict[str, Any]] = None) -> None:
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self._contracts[name] = {"version": version, "schema": schema or {}}
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def get(self, name: str) -> Optional[Dict[str, Any]]:
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return self._contracts.get(name)
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def all(self) -> Dict[str, Dict[str, Any]]:
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return self._contracts
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def to_morphism(self) -> Dict[str, Any]:
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return {
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"MorphismType": "DualVariable",
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"name": self.name,
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"version": self.version,
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"value": self.value,
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}
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class CatOptBridge:
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"""Minimal CatOpt bridge that maps CosmosMesh primitives to a canonical CatOpt view.
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"""Lightweight bridge mapping CosmosMesh concepts to a CatOpt-like layer."""
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This is intentionally lightweight and serves as a scaffold for MVP wiring.
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"""
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# Registry of contracts/schema versions for adapters
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_contract_registry: Dict[str, Dict[str, Any]] = {}
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def __init__(self) -> None:
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self._registry = GraphOfContracts()
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# Contract registry helpers
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def register_contract(self, name: str, version: str, schema: Optional[Dict[str, Any]] = None) -> None:
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self._registry.register(name, version, schema)
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def get_contract(self, name: str) -> Optional[Dict[str, Any]]:
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return self._registry.get(name)
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def all_contracts(self) -> Dict[str, Dict[str, Any]]:
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return self._registry.all()
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# Translation primitives (annotation-friendly, minimal)
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def to_catopt(self, lp: LocalProblem, sv: SharedVariables, dv: DualVariables) -> Dict[str, Any]:
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"""Translate CosmosMesh primitives into a minimal CatOpt-like representation."""
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catopt = {
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"Objects": {
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"LocalProblem": asdict(lp),
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},
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"Morphisms": {
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"SharedVariables": asdict(sv),
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"DualVariables": asdict(dv),
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},
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"Functors": {
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# Placeholder for adapter bindings (empty by default)
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},
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@classmethod
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def register_contract(cls, contract_name: str, contract_spec: Dict[str, Any]) -> None:
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cls._contract_registry[contract_name] = {
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"spec": contract_spec,
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"registered_at": int(time.time()),
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}
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return catopt
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def from_catopt(self, catopt: Dict[str, Any]) -> Dict[str, Any]:
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"""Minimal reverse-translation from CatOpt-like representation to a dictionary.
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@classmethod
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def get_contract(cls, contract_name: str) -> Dict[str, Any] | None:
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return cls._contract_registry.get(contract_name)
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This is intentionally permissive for MVP; it merely returns the input structure
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in a normalized dict form for downstream processing.
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"""
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return dict(catopt)
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@staticmethod
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def problem_to_object(problem: LocalProblem) -> Dict[str, Any]:
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return problem.to_object()
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@staticmethod
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def variables_to_morphisms(shared: List[SharedVariable], duals: List[DualVariable]) -> List[Dict[str, Any]]:
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morphisms: List[Dict[str, Any]] = []
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morphisms.extend([s.to_morphism() for s in shared])
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morphisms.extend([d.to_morphism() for d in duals])
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return morphisms
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@staticmethod
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def encode_message(kind: str, payload: Dict[str, Any], version: int = 1) -> Dict[str, Any]:
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return {
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"kind": kind,
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"version": version,
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"timestamp": int(time.time()),
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"nonce": int(time.time() * 1000) & 0xFFFFFFFF,
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"payload": payload,
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}
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# Convenience helpers for a minimal end-to-end round
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@classmethod
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def build_round_trip(cls, problem: LocalProblem,
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shared: List[SharedVariable],
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duals: List[DualVariable]) -> Dict[str, Any]:
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obj = cls.problem_to_object(problem)
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morphs = cls.variables_to_morphisms(shared, duals)
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return cls.encode_message("RoundTrip", {"object": obj, "morphisms": morphs})
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__all__ = [
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"LocalProblem",
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"SharedVariables",
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"DualVariables",
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"PlanDelta",
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"GraphOfContracts",
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"CatOptBridge",
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]
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__all__ = ["CatOptBridge", "LocalProblem", "SharedVariable", "DualVariable"]
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import pytest
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import math
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from cosmosmesh_privacy_preserving_federated_.catopt_bridge import CatOptBridge
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from cosmosmesh_privacy_preserving_federated_.contract_registry import REGISTRY
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from cosmosmesh_privacy_preserving_federated.catopt_bridge import (
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CatOptBridge,
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LocalProblem,
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SharedVariable,
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DualVariable,
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)
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def test_registry_and_translation():
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bridge = CatOptBridge()
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# Register a simple contract and verify registry exposure
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bridge.register_contract("LocalProblem", "0.1.0", {"schema": {"type": "object"}})
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assert REGISTRY.get("LocalProblem")["version"] == "0.1.0"
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def test_basic_roundtrip_encoding():
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# Build a tiny LocalProblem and signals
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lp = LocalProblem(
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problem_id="lp-task-01",
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objective="minimize_energy",
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variables=["x1", "x2"],
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constraints=["x1>=0", "x2>=0", "x1+x2<=10"],
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version=1,
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)
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# Translate a minimal local problem description
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local_problem = {
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"name": "TestProblem",
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"variables": [{"name": "x", "domain": "R"}],
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"objective": {"type": "quadratic"},
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}
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translated = bridge.translate_local_problem(local_problem)
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assert translated["type"] == "LocalProblem"
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assert translated["name"] == "TestProblem"
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sv1 = SharedVariable(name="sigma", value=0.5, version=1)
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dv1 = DualVariable(name="lambda", value=1.25, version=1)
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round_msg = CatOptBridge.build_round_trip(lp, [sv1], [dv1])
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# Basic invariants about the payload
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assert round_msg["kind"] == "RoundTrip"
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payload = round_msg["payload"]
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assert payload["object"]["id"] == lp.problem_id
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assert any(m.get("name") == sv1.name for m in payload["morphisms"])
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assert any(m.get("name") == dv1.name for m in payload["morphisms"])
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