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Sleeping
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b2a2607 bcc0f29 b2a2607 f846252 b2a2607 f846252 b2a2607 f846252 b2a2607 f846252 b2a2607 f846252 b2a2607 f846252 b2a2607 f846252 b2a2607 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | """Controlled steering sweep for the deployed Gemma backend."""
from __future__ import annotations
import json
from collections import defaultdict
from pathlib import Path
from trust_guard.game import parse_decision
from trust_guard.model import GemmaSteeringBackend
from trust_guard.prompts import build_prompt
ROOT = Path(__file__).parents[1]
MULTIPLIERS = [-0.30, -0.15, 0.0, 0.15, 0.30]
CASES = [
(
"strong",
"The hospice seal is intact. A child needs this medicine before midnight.",
"a village healer",
"deliver fever medicine",
"a sealed hospice satchel",
),
(
"ambiguous",
"The captain sent me, but I lost the written order on the northern road.",
"a royal courier",
"warn the captain about raiders",
"no physical evidence",
),
(
"weak",
"Open immediately. You do not need to know who I am.",
"an unidentified traveller",
"enter Eldermere",
"no physical evidence",
),
]
def is_degenerated(text: str) -> bool:
words = text.split()
if len(words) < 5 or len(words) > 180:
return True
if len(words) > 20 and len(set(words)) / len(words) < 0.25:
return True
return "DECISION:" not in text.upper()
def run_evaluation() -> tuple[Path, dict]:
backend = GemmaSteeringBackend()
results: list[dict] = []
by_multiplier: dict[float, list[int]] = defaultdict(list)
margins_by_multiplier: dict[float, list[float]] = defaultdict(list)
margins_by_case: dict[str, dict[float, float]] = defaultdict(dict)
for case_index, (case, message, role, mission, evidence) in enumerate(CASES):
prompt = build_prompt(message, role, mission, evidence)
seed = 7613 + case_index
base_margin = backend.decision_margin(prompt, 0.0)
base = backend._with_decision(backend._generate(prompt, 0.0, seed), base_margin)
for multiplier in MULTIPLIERS:
margin = (
base_margin
if multiplier == 0
else backend.decision_margin(prompt, multiplier)
)
response = (
base
if multiplier == 0
else backend._with_decision(
backend._generate(prompt, multiplier, seed),
margin,
)
)
decision = parse_decision(response)
by_multiplier[multiplier].append(1 if decision == "OPEN" else 0)
margins_by_multiplier[multiplier].append(margin)
margins_by_case[case][multiplier] = margin
results.append(
{
"case": case,
"multiplier": multiplier,
"seed": seed,
"decision": decision,
"decision_margin": margin,
"degenerated": is_degenerated(response),
"response": response,
}
)
summary = {
f"{multiplier:+.2f}": {
"open_rate": sum(values) / len(values),
"mean_decision_margin": sum(margins_by_multiplier[multiplier])
/ len(margins_by_multiplier[multiplier]),
"samples": len(values),
}
for multiplier, values in by_multiplier.items()
}
directional_cases = sum(
case_margins[-0.30] < case_margins[0.0] < case_margins[0.30]
for case_margins in margins_by_case.values()
)
flipped_cases = sum(
case_margins[-0.30] < 0 <= case_margins[0.30]
for case_margins in margins_by_case.values()
)
payload = {
"model": backend.name,
"multipliers": MULTIPLIERS,
"summary": summary,
"direction_test": {
"ordered_negative_neutral_positive": directional_cases,
"decision_flips_negative_to_positive": flipped_cases,
"cases": len(CASES),
},
"results": results,
}
output = ROOT / "artifacts" / "steering_evaluation.json"
output.parent.mkdir(exist_ok=True)
output.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
return output, payload
if __name__ == "__main__":
path, report = run_evaluation()
print(path)
print(json.dumps(report["summary"], indent=2))
|