src/mindflow/baselines.py

"""Baselines and ablations for Claim 2 / Claim 3.

- Generate, GenerateCoT: vanilla base models.
- Static pipeline: a fixed hand-crafted operator workflow (stand-in for the paper's
  fixed agentic pipelines AI-Scientist / AI-Researcher / VIRSCI, whose exact code we
  do not run; the point of the paper is *fixed* vs *learned/adaptive* flows).
- Single-operator: Generate -> <one operator> (Table 5 ablation).
- Shuffle operator: random flow, no controller (Appendix E baseline).
- MindFlow deploy: deterministic top-p rollout of the trained supernet controller.
"""
from __future__ import annotations
import numpy as np
from . import operators as ops
from .flow import execute_flow

STATIC_PIPELINE = ["Critical", "Constraint", "Convergent"]


def gen_idea(topic, related, model=None, seed=0):
    return ops.op_generate(topic, related, model=model, seed=seed)


def gen_cot_idea(topic, related, model=None, seed=0):
    return ops.op_generate_cot(topic, related, model=model, seed=seed)


def flow_idea(seq, topic, related, model=None, seed=0):
    idea, _ = execute_flow(seq, topic, related, model=model, seed=seed)
    return idea


def static_pipeline_idea(topic, related, model=None, seed=0):
    return flow_idea(STATIC_PIPELINE, topic, related, model=model, seed=seed)


def single_operator_idea(op_name, topic, related, model=None, seed=0):
    return flow_idea([op_name], topic, related, model=model, seed=seed)


def shuffle_flow(rng, min_ops=1, max_ops=3):
    k = rng.integers(min_ops, max_ops + 1)
    return list(rng.choice(ops.REFINE_OPS, size=k, replace=False))


def shuffle_idea(topic, related, rng, model=None, seed=0):
    return flow_idea(shuffle_flow(rng), topic, related, model=model, seed=seed)


def mindflow_deploy_idea(net, topic, related, model=None, seed=0, threshold=0.6):
    seq, _ = net.rollout_flow(topic, threshold=threshold)
    return flow_idea(seq, topic, related, model=model, seed=seed), seq