build(agent): molt-by#23c260 iteration

src/catopt_category_theoretic_compositional/adapters/habitat_adapter.py

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+"""Starter habitat controller adapter illustrating local problem adaptation."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Dict, Any
+
+from ..contracts import LocalProblem
+
+
+@dataclass
+class HabitatConfig:
+    id: str
+    region: str
+    capabilities: Dict[str, Any]
+
+
+def adapt_local_problem(problem: LocalProblem) -> LocalProblem:
+    """Tiny adapter that annotates the local problem for habitat domain."""
+    if isinstance(problem.variables, dict):
+        problem.variables.setdefault("habitat_annotated", True)
+    return problem

src/catopt_category_theoretic_compositional/adapters/rover_adapter.py

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+"""Starter rover adapter illustrating LocalProblem -> CatOpt representation."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Dict, Any, List
+
+from ..contracts import LocalProblem
+
+
+@dataclass
+class RoverConfig:
+    name: str
+    max_speed: float
+    payload: Dict[str, Any]
+
+
+def adapt_local_problem(problem: LocalProblem) -> LocalProblem:
+    """A toy adapter that mildly rewrites a LocalProblem to a canonical form."""
+    # In a real adapter, map device capabilities to a canonical LocalProblem.
+    # Here we simply attach a no-op metadata note by mutating the objective if possible.
+    if isinstance(problem.objective, str):
+        problem.objective = problem.objective + " [adapted-by-rover]"
+    return problem

src/catopt_category_theoretic_compositional/contracts.py

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+"""CatOpt Contracts: Minimal primitives for Local Problems and exchanges.
+
+This module provides lightweight dataclasses that sketch the API surface
+for CatOpt's data contracts. The goal is to be expressive enough for MVP wiring
+without pulling in heavy dependencies.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, asdict
+from typing import Any, Dict, List, Optional
+import json
+
+
+@dataclass
+class LocalProblem:
+    """Represents a device-level optimization problem.
+
+    - variables: a map of decision variables and their current values/roles
+    - objective: a serializable representation of the local objective
+    - constraints: list of constraints (expressions or callables in a real system)
+    - privacy_preferences: optional hints for privacy-preserving exchanges
+    """
+
+    variables: Dict[str, Any]
+    objective: Any
+    constraints: List[Any]
+    privacy_preferences: Optional[Dict[str, Any]] = None
+
+    def to_json(self) -> str:
+        return json.dumps(asdict(self), default=str)
+
+
+@dataclass
+class SharedVariables:
+    """Variables shared across agents for coordination (e.g., aggregates)."""
+
+    variables: Dict[str, Any]
+    version: int
+    contract_id: str
+    encryption_schema: Optional[str] = None
+
+    def to_json(self) -> str:
+        return json.dumps(asdict(self), default=str)
+
+
+@dataclass
+class DualVariables:
+    """Lagrange-like multipliers used in coordination."""
+
+    multipliers: Dict[str, float]
+
+    def to_json(self) -> str:
+        return json.dumps(asdict(self), default=str)
+
+
+@dataclass
+class PlanDelta:
+    """Proposed updates to the local problem in a delta-sync step."""
+
+    delta: Dict[str, Any]
+    timestamp: float
+    author: str
+    contract_id: str
+    privacy_budget: Optional[Dict[str, Any]] = None
+    proofs: Optional[List[str]] = None
+
+    def to_json(self) -> str:
+        return json.dumps(asdict(self), default=str)
+
+
+@dataclass
+class AuditLog:
+    """Immutable-ish audit log for governance and replay protection."""
+
+    entries: List[Dict[str, Any]]
+
+    def to_json(self) -> str:
+        return json.dumps(asdict(self), default=str)
+
+
+@dataclass
+class PolicyBlock:
+    """Safety and data-exposure policy block attached to exchanges."""
+
+    safety_constraints: List[str]
+    data_exposure_rules: List[str]
+
+    def to_json(self) -> str:
+        return json.dumps(asdict(self), default=str)

src/catopt_category_theoretic_compositional/registry.py

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+"""Graph-of-Contracts registry (in-memory sketch).
+
+This module provides a tiny in-process registry to map contract_ids to
+contracts and associated metadata. It is a placeholder that demonstrates the
+intended MVP pattern for later persistence and cross-domain adapters.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional
+from .contracts import LocalProblem, SharedVariables
+
+
+class GraphOfContracts:
+    def __init__(self) -> None:
+        self._registry: Dict[str, Dict[str, Any]] = {}
+
+    def register_contract(self, contract_id: str, contract: LocalProblem | SharedVariables, metadata: Optional[Dict[str, Any]] = None) -> str:
+        self._registry[contract_id] = {
+            "contract": contract,
+            "metadata": metadata or {},
+        }
+        return contract_id
+
+    def get_contract(self, contract_id: str) -> Optional[Dict[str, Any]]:
+        return self._registry.get(contract_id)

src/catopt_category_theoretic_compositional/solver.py

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+"""Tiny ADMM-inspired solver placeholder for CatOpt MVP."""
+
+from __future__ import annotations
+
+from typing import Dict, Any, Tuple
+
+from .contracts import LocalProblem, SharedVariables, DualVariables
+
+
+def admm_step(local: LocalProblem, shared: SharedVariables, dual: DualVariables) -> Tuple[LocalProblem, SharedVariables, DualVariables]:
+    """Perform a toy ADMM step.
+
+    This is a non-production placeholder that demonstrates the kind of update
+    you would wire into the MVP. It does not solve the problem; it simply
+    demonstrates structure and state transitions for local and shared data.
+    """
+    # Very naive placeholder: push a tiny, deterministic adjustment to a variable
+    # indicating progress in coordination. In a real solver, you'd compute proximal
+    # updates, dual ascent steps, and consensus enforcement here.
+    if local.variables:
+        key = next(iter(local.variables))
+        val = local.variables[key]
+        try:
+            local.variables[key] = val  # no-op baseline; real code would update
+        except Exception:
+            pass
+
+    # Update a dummy shared signal (e.g., a mean of a shared variable)
+    for k, v in list(shared.variables.items()):
+        if isinstance(v, (int, float)):
+            shared.variables[k] = v * 1.0  # no change; placeholder
+
+    # Update duals trivially
+    for k in list(dual.multipliers.keys()):
+        dual.multipliers[k] = dual.multipliers[k]
+
+    return local, shared, dual