build(agent): new-agents-2#7e3bbc iteration
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AGENTS.md
10
AGENTS.md
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@ -19,3 +19,13 @@ Guidance for contributors
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- Do not change the MVP skeleton without explicit intent to expand functionality.
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- Ensure test.sh remains executable and tests cover basic import/build scenarios.
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- Update AGENTS.md as the architecture evolves.
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New MVP scaffolds (current status):
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- Added lightweight DSL primitives (src/catopt_category_theoretic_compositional/dsl.py)
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- Added minimal contracts primitives (src/catopt_category_theoretic_compositional/contracts.py)
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- Added a tiny solver kernel (src/catopt_category_theoretic_compositional/solver.py)
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- Exposed new API in package __init__ (LocalProblem, SharedVariables, PlanDelta, admm_update)
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How to explore:
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- Import surface: from catopt_category_theoretic_compositional import LocalProblem, SharedVariables, PlanDelta, admm_update
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- Use admm_update(local, shared) as a tiny, deterministic update example.
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@ -8,7 +8,10 @@ single utility function used by the test suite.
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from __future__ import annotations
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__all__ = ["add"]
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from .dsl import LocalProblem, SharedVariables, PlanDelta
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from .solver import admm_update
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__all__ = ["add", "LocalProblem", "SharedVariables", "PlanDelta", "admm_update"]
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def add(a: int, b: int) -> int:
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@ -1,90 +1,62 @@
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"""CatOpt Contracts: Minimal primitives for Local Problems and exchanges.
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"""Contracts: lightweight data-contract primitives for CatOpt (minimal).
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This module provides lightweight dataclasses that sketch the API surface
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for CatOpt's data contracts. The goal is to be expressive enough for MVP wiring
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without pulling in heavy dependencies.
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These dataclasses are intentionally small and import-time friendly to keep
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the MVP stable while enabling eventual extensions for privacy contracts,
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audits, and governance.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, asdict
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from typing import Any, Dict, List, Optional
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from dataclasses import dataclass, asdict, field
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from typing import Dict, Optional
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import json
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@dataclass
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class LocalProblem:
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"""Represents a device-level optimization problem.
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- variables: a map of decision variables and their current values/roles
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- objective: a serializable representation of the local objective
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- constraints: list of constraints (expressions or callables in a real system)
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- privacy_preferences: optional hints for privacy-preserving exchanges
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"""
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variables: Dict[str, Any]
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objective: Any
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constraints: List[Any]
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privacy_preferences: Optional[Dict[str, Any]] = None
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class LocalProblemContract:
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id: str
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domain: str
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objective: str
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constraints: list[str]
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def to_json(self) -> str:
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return json.dumps(asdict(self), default=str)
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return json.dumps(asdict(self))
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@classmethod
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def from_json(cls, payload: str) -> "LocalProblemContract":
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data = json.loads(payload)
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return cls(id=data["id"], domain=data.get("domain", ""), objective=data.get("objective", ""), constraints=data.get("constraints", []))
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@dataclass
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class SharedVariables:
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"""Variables shared across agents for coordination (e.g., aggregates)."""
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variables: Dict[str, Any]
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class SharedVariablesContract:
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version: int
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contract_id: str
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encryption_schema: Optional[str] = None
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contract_id: Optional[str] = None
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payload: Dict[str, float] = field(default_factory=dict)
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def to_json(self) -> str:
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return json.dumps(asdict(self), default=str)
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return json.dumps(asdict(self))
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@classmethod
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def from_json(cls, payload: str) -> "SharedVariablesContract":
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data = json.loads(payload)
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return cls(version=data["version"], contract_id=data.get("contract_id"), payload=data.get("payload", {}))
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@dataclass
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class DualVariables:
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"""Lagrange-like multipliers used in coordination."""
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multipliers: Dict[str, float]
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def to_json(self) -> str:
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return json.dumps(asdict(self), default=str)
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@dataclass
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class PlanDelta:
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"""Proposed updates to the local problem in a delta-sync step."""
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delta: Dict[str, Any]
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timestamp: float
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class PlanDeltaContract:
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delta: Dict[str, float]
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timestamp: str
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author: str
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contract_id: str
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privacy_budget: Optional[Dict[str, Any]] = None
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proofs: Optional[List[str]] = None
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contract_id: Optional[str] = None
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def to_json(self) -> str:
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return json.dumps(asdict(self), default=str)
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return json.dumps(asdict(self))
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@classmethod
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def from_json(cls, payload: str) -> "PlanDeltaContract":
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data = json.loads(payload)
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return cls(delta=data["delta"], timestamp=data["timestamp"], author=data["author"], contract_id=data.get("contract_id"))
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@dataclass
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class AuditLog:
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"""Immutable-ish audit log for governance and replay protection."""
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entries: List[Dict[str, Any]]
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def to_json(self) -> str:
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return json.dumps(asdict(self), default=str)
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@dataclass
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class PolicyBlock:
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"""Safety and data-exposure policy block attached to exchanges."""
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safety_constraints: List[str]
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data_exposure_rules: List[str]
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def to_json(self) -> str:
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return json.dumps(asdict(self), default=str)
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__all__ = ["LocalProblemContract", "SharedVariablesContract", "PlanDeltaContract"]
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@ -0,0 +1,69 @@
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"""CatOpt DSL primitives (minimal MVP scaffolding).
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This module provides lightweight, serializable representations for the local
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optimization problems and exchange artifacts that participate in the
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CatOpt protocol. It is intentionally small but stable to enable safe expansion
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in future iterations.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field, asdict
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from typing import Dict, List, Optional
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import json
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@dataclass
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class LocalProblem:
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id: str
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domain: str
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objective: str
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constraints: List[str] = field(default_factory=list)
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variables: Optional[Dict[str, float]] = None
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def to_json(self) -> str:
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return json.dumps(asdict(self))
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@classmethod
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def from_json(cls, payload: str) -> "LocalProblem":
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data = json.loads(payload)
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return cls(
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id=data["id"],
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domain=data.get("domain", "unknown"),
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objective=data.get("objective", ""),
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constraints=data.get("constraints", []),
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variables=data.get("variables"),
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)
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@dataclass
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class SharedVariables:
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version: int
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signals: Dict[str, float] = field(default_factory=dict)
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def to_json(self) -> str:
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return json.dumps(asdict(self))
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@classmethod
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def from_json(cls, payload: str) -> "SharedVariables":
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data = json.loads(payload)
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return cls(version=data["version"], signals=data.get("signals", {}))
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@dataclass
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class PlanDelta:
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delta: Dict[str, float]
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timestamp: str
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author: str
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contract_id: Optional[str] = None
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def to_json(self) -> str:
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return json.dumps(asdict(self))
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@classmethod
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def from_json(cls, payload: str) -> "PlanDelta":
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data = json.loads(payload)
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return cls(delta=data["delta"], timestamp=data["timestamp"], author=data["author"], contract_id=data.get("contract_id"))
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__all__ = ["LocalProblem", "SharedVariables", "PlanDelta"]
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@ -1,37 +1,41 @@
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"""Tiny ADMM-inspired solver placeholder for CatOpt MVP."""
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"""A tiny, placeholder ADMM-like solver kernel for CatOpt MVP.
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This module provides a minimal function that demonstrates an update step
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between a LocalProblem (DSL) and SharedVariables (contracts). The logic is
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deliberately simple to keep the MVP stable while offering a hook for future
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expansion.
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"""
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from __future__ import annotations
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from typing import Dict, Any, Tuple
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from .contracts import LocalProblem, SharedVariables, DualVariables
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from typing import Dict
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from .dsl import LocalProblem, SharedVariables
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def admm_step(local: LocalProblem, shared: SharedVariables, dual: DualVariables) -> Tuple[LocalProblem, SharedVariables, DualVariables]:
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"""Perform a toy ADMM step.
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def admm_update(local: LocalProblem, shared: SharedVariables, rho: float = 1.0) -> LocalProblem:
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"""Perform a tiny, deterministic ADMM-like update of a local problem.
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This is a non-production placeholder that demonstrates the kind of update
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you would wire into the MVP. It does not solve the problem; it simply
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demonstrates structure and state transitions for local and shared data.
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This is a stub implementation intended for demonstration and testing. It
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updates a simple primal variable if present, otherwise initializes it.
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"""
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# Very naive placeholder: push a tiny, deterministic adjustment to a variable
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# indicating progress in coordination. In a real solver, you'd compute proximal
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# updates, dual ascent steps, and consensus enforcement here.
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if local.variables:
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key = next(iter(local.variables))
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val = local.variables[key]
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try:
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local.variables[key] = val # no-op baseline; real code would update
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except Exception:
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pass
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# Copy local to avoid mutating input (functional style).
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updated = LocalProblem(
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id=local.id,
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domain=local.domain,
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objective=local.objective,
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constraints=list(local.constraints),
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variables=dict(local.variables) if local.variables is not None else {},
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)
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# Update a dummy shared signal (e.g., a mean of a shared variable)
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for k, v in list(shared.variables.items()):
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if isinstance(v, (int, float)):
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shared.variables[k] = v * 1.0 # no change; placeholder
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# Simple, deterministic update to a hypothetical primal variable.
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primal = 0.0
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if not isinstance(updated.variables, dict):
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updated.variables = {}
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if isinstance(updated.variables, dict):
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primal = updated.variables.get("primal", 0.0)
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updated.variables["primal"] = primal + 0.5 * rho
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# Update duals trivially
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for k in list(dual.multipliers.keys()):
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dual.multipliers[k] = dual.multipliers[k]
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return updated
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return local, shared, dual
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__all__ = ["admm_update"]
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