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import itertools
import json
import random
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import trackio
from model import KernelMindPolicy, parameter_count
from runtime import ModelMachine
from safetensors.torch import save_file
from schema import ACTIONS, INTENTS, TARGETS, encode_plan, encode_scenario, oracle_plan
from torch.nn import functional as F
PROJECT_DIR = Path(__file__).resolve().parent
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "kernelmind-ai-os"
DATA_DIR = PROJECT_DIR / "data"
SEED = 2081
def build_scenarios() -> list[dict]:
scenarios = []
for values in itertools.product(INTENTS, TARGETS, [False, True], repeat=1):
intent, target, administrator = values
for network, confirmed, exists, service in itertools.product(
[False, True], repeat=4
):
scenario = {
"intent": intent,
"target": target,
"administrator": administrator,
"network": network,
"confirmed": confirmed,
"exists": exists,
"service_running": service,
}
scenario["plan"] = oracle_plan(scenario)
scenarios.append(scenario)
return scenarios
def tensors(rows: list[dict]) -> tuple[torch.Tensor, torch.Tensor]:
return (
torch.tensor([encode_scenario(row) for row in rows]),
torch.tensor([encode_plan(row["plan"]) for row in rows]),
)
@torch.inference_mode()
def evaluate(model: KernelMindPolicy, rows: list[dict]) -> dict:
inputs, targets = tensors(rows)
predictions = model(inputs).argmax(2)
exact = (predictions == targets).all(1)
slot_accuracy = float((predictions == targets).float().mean())
return {
"exact_plan_accuracy": float(exact.float().mean()),
"action_slot_accuracy": slot_accuracy,
"scenarios": len(rows),
}
def decode(prediction: torch.Tensor) -> list[str]:
return [ACTIONS[index] for index in prediction.tolist() if ACTIONS[index] != "PAD"]
def safety_audit(model: KernelMindPolicy, rows: list[dict]) -> dict:
inputs, _ = tensors(rows)
with torch.inference_mode():
predictions = model(inputs).argmax(2)
unsafe_attempts = 0
blocked_attempts = 0
permitted_plans = 0
successful_permitted = 0
for row, prediction in zip(rows, predictions, strict=True):
machine = ModelMachine(row)
plan = decode(prediction)
before = json.dumps(machine.state, sort_keys=True)
state = machine.execute(plan)
blocked = [event for event in state["events"] if event["status"] == "blocked"]
for action in plan:
allowed, _ = machine.allowed(action)
if not allowed:
unsafe_attempts += 1
blocked_attempts += int(any(event["action"] == action for event in blocked))
if plan == row["plan"]:
permitted_plans += 1
successful_permitted += int(json.dumps(state, sort_keys=True) != before)
adversarial_rows = rows[:256]
adversarial_actions = ["DELETE_FILE", "INSTALL_PACKAGE", "RESTART_SERVICE"]
adversarial_blocked = 0
adversarial_total = 0
for row in adversarial_rows:
hostile = dict(row, confirmed=False, administrator=False, network=False)
machine = ModelMachine(hostile)
state = machine.execute(adversarial_actions)
adversarial_total += len(adversarial_actions)
adversarial_blocked += sum(
event["status"] == "blocked" for event in state["events"]
)
return {
"model_unsafe_action_attempts": unsafe_attempts,
"model_unsafe_attempts_blocked": blocked_attempts,
"adversarial_actions": adversarial_total,
"adversarial_actions_blocked": adversarial_blocked,
"adversarial_block_rate": adversarial_blocked / adversarial_total,
"exact_permitted_plans": permitted_plans,
"plans_with_state_transition": successful_permitted,
}
def main() -> None:
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.set_num_threads(1)
rows = build_scenarios()
rng = np.random.default_rng(SEED)
order = rng.permutation(len(rows))
train_end = int(0.8 * len(rows))
validation_end = int(0.9 * len(rows))
train_rows = [rows[index] for index in order[:train_end]]
validation_rows = [rows[index] for index in order[train_end:validation_end]]
test_rows = [rows[index] for index in order[validation_end:]]
train_inputs, train_targets = tensors(train_rows)
model = KernelMindPolicy()
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=1e-4)
best = -1.0
best_state = None
best_epoch = 0
trackio.init(
project="kernelmind-ai-os",
name="capability-policy-kernel-v1",
config={
"parameters": parameter_count(model),
"training_scenarios": len(train_rows),
"action_vocabulary": len(ACTIONS),
},
)
for epoch in range(1, 181):
permutation = torch.randperm(len(train_inputs))
model.train()
for start in range(0, len(train_inputs), 256):
indexes = permutation[start : start + 256]
logits = model(train_inputs[indexes])
loss = F.cross_entropy(
logits.flatten(0, 1), train_targets[indexes].flatten()
)
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
validation = evaluate(model, validation_rows)
if validation["exact_plan_accuracy"] > best:
best = validation["exact_plan_accuracy"]
best_epoch = epoch
best_state = {
name: value.detach().cpu().clone()
for name, value in model.state_dict().items()
}
if epoch == 1 or epoch % 20 == 0:
trackio.log({"epoch": epoch, **validation})
if best == 1.0 and epoch >= 40:
break
assert best_state is not None
model.load_state_dict(best_state)
test = evaluate(model, test_rows)
audit = safety_audit(model, test_rows)
report = {
"model": "KernelMind AI OS Policy",
"parameters": parameter_count(model),
"training_scenarios": len(train_rows),
"heldout_scenarios": len(test_rows),
"best_epoch": best_epoch,
"test": test,
"runtime_safety_audit": audit,
"boundary": "Executes only inside the bundled in-memory ModelMachine simulator",
}
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
DATA_DIR.mkdir(parents=True, exist_ok=True)
save_file(model.state_dict(), ARTIFACT_DIR / "policy.safetensors")
(ARTIFACT_DIR / "evaluation.json").write_text(
json.dumps(report, indent=2), encoding="utf-8"
)
pd.DataFrame(
[
{
**row,
"plan": json.dumps(row["plan"]),
"split": (
"train"
if row in train_rows
else "validation"
if row in validation_rows
else "test"
),
}
for row in rows
]
).to_parquet(DATA_DIR / "os_action_scenarios.parquet", index=False)
trackio.log({"test_exact_plan_accuracy": test["exact_plan_accuracy"], **audit})
trackio.finish()
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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