build(agent): molt-d#cb502d iteration

README.md

@@ -41,6 +41,16 @@ Public artifacts
 - Python modules in src/cosmosmesh_privacy_preserving_federated_ and src/cosmosmesh_privacy_preserving_federated_/catopt_bridge.py
 - Tests in tests/ (smoke test for basic arithmetic)
 - READY_TO_PUBLISH flag to be created when ready for publication
+ 
+CatOpt MVP Bridge (Overview)
+- The MVP includes a lightweight CatOpt bridge that maps CosmosMesh primitives to a CatOpt-like representation (Objects/Morphisms/Functors).
+- Core components:
+  - src/cosmosmesh_privacy_preserving_federated/catopt_bridge.py: translator skeleton for LocalProblem, SharedVariables, DualVariables, PlanDelta
+  - src/cosmosmesh_privacy_preserving_federated/dsl_sketch.py: lightweight DSL scaffolding for MVP primitives
+  - src/cosmosmesh_privacy_preserving_federated/contract_registry.py: minimal contract registry
+- 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
+- A tiny Graph-of-Contracts registry supports versioning for LocalProblem/SharedVariables/DualVariables/PlanDelta contracts
+- Next steps (optional): TLS transport adapters, secure aggregation, and governance ledger integration
 # CosmosMesh MVP: Privacy-Preserving Federated Mission Planning
 
 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.

src/cosmosmesh_privacy_preserving_federated_/reference_adapter.py

@@ -0,0 +1,110 @@
+"""Prototype reference adapters for CosmosMesh CatOpt MVP.
+
+This module provides lightweight, self-contained adapters that demonstrate
+how two domain agents (rover planner and habitat module) could interact with
+the CatOpt bridge and the MVP primitives defined in the core package.
+
+The implementations are intentionally minimal and safe: they do not depend on
+external transport or cryptography. They serve as a scaffold for downstream
+team integrations and for validating end-to-end data shapes and contract
+bindings.
+"""
+
+from __future__ import annotations
+
+from typing import Dict, Any
+
+# Lightweight local DSL definitions (stand-ins for MVP scaffolding).
+from dataclasses import dataclass
+@dataclass
+class LocalProblemDSL:
+    agent_id: str
+    objective: str
+    variables: list
+    constraints: list
+
+    def dict(self) -> dict:
+        return {
+            "agent_id": self.agent_id,
+            "objective": self.objective,
+            "variables": self.variables,
+            "constraints": self.constraints,
+        }
+
+@dataclass
+class SharedVariablesDSL:
+    signals: dict
+    version: str = "0.1"
+
+    def dict(self) -> dict:
+        return {"signals": self.signals, "version": self.version}
+
+@dataclass
+class PlanDeltaDSL:
+    delta_id: str
+    changes: dict
+    timestamp: str | None = None
+
+    def dict(self) -> dict:
+        return {"delta_id": self.delta_id, "changes": self.changes, "timestamp": self.timestamp}
+from cosmosmesh_privacy_preserving_federated_.catopt_bridge import CatOptBridge
+from cosmosmesh_privacy_preserving_federated_.protocol import LocalProblem, SharedVariables, DualVariables
+
+
+class RoverPlannerAdapter:
+    """Toy rover planner adapter demonstrating binding with the CatOpt bridge."""
+
+    def __init__(self) -> None:
+        self.bridge = CatOptBridge()
+
+    def readState(self) -> Dict[str, Any]:
+        # Minimal example of agent state that could be fed into the bridge
+        lp = LocalProblem(variables={"x": 1.0, "y": 2.0, "z": 0.0}, objective="maximize_delivery_quality", constraints=["0 <= x <= 10"])
+        sv = SharedVariables(signals={"share": 0.5})
+        dv = DualVariables(multipliers={"lambda": 0.1})
+        catopt = self.bridge.to_catopt(lp, sv, dv)
+        return {"type": "CatOpt", "payload": catopt}
+
+    def exposeLocalProblemData(self) -> Dict[str, Any]:
+        lp = LocalProblem(variables={"x": 0.5, "y": 1.5}, objective="min_energy", constraints=["x+y <= 2"])
+        sv = SharedVariables(signals={"share": 0.2})
+        dv = DualVariables(multipliers={"lambda": 0.05})
+        return self.bridge.to_catopt(lp, sv, dv)
+
+    def applyCommand(self, command: Dict[str, Any]) -> Dict[str, Any]:
+        # Echo back the received command as an acknowledgment; simple delta object
+        delta = PlanDeltaDSL(delta_id=command.get("delta_id", "delta-000"), changes=command.get("changes", {}))
+        lp = LocalProblem(variables={}, objective="default", constraints=[])
+        sv = SharedVariables(signals={})
+        dv = DualVariables(multipliers={})
+        # Return a canonical container via the bridge for testing pipelines
+        return self.bridge.to_catopt(lp, sv, dv)
+
+
+class HabitatModuleAdapter:
+    """Toy habitat module adapter scaffold."""
+
+    def __init__(self) -> None:
+        self.bridge = CatOptBridge()
+
+    def readState(self) -> Dict[str, Any]:
+        sv = SharedVariables(signals={"temperature": 22.0, "humidity": 45.0})
+        lp = LocalProblem(variables={"a": 1.0}, objective="maximize survivability", constraints=["a>=0"])
+        dv = DualVariables(multipliers={"gamma": 0.1})
+        return self.bridge.to_catopt(lp, sv, dv)
+
+    def exposeLocalProblemData(self) -> Dict[str, Any]:
+        lp = LocalProblem(variables={"a": 2.0}, objective="maximize survivability", constraints=["a >= 0"])
+        sv = SharedVariables(signals={"temperature": 21.0})
+        dv = DualVariables(multipliers={"delta": 0.3})
+        return self.bridge.to_catopt(lp, sv, dv)
+
+    def applyCommand(self, command: Dict[str, Any]) -> Dict[str, Any]:
+        delta = PlanDeltaDSL(delta_id=command.get("delta_id", "hab-delta-1"), changes={})
+        lp = LocalProblem(variables={}, objective="default", constraints=[])
+        sv = SharedVariables(signals={})
+        dv = DualVariables(multipliers={})
+        return self.bridge.to_catopt(lp, sv, dv)
+
+
+__all__ = ["RoverPlannerAdapter", "HabitatModuleAdapter"]