from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any
DEFAULT_WEIGHTS = {
"foundational_capability": 0.25,
"competitive_capability": 0.25,
"semantic_vector": 0.15,
"performance": 0.10,
"stability": 0.25,
}
JUDGE_WEIGHTS = {
"foundational_capability": 0.20,
"competitive_capability": 0.20,
"semantic_vector": 0.12,
"research_judge": 0.15,
"performance": 0.08,
"stability": 0.25,
}
SCORE_EPSILON = 0.0005
RATIO_EPSILON = 0.005
METRIC_RELATIVE_EPSILON = 0.03
METRIC_ABSOLUTE_EPSILON = 25.0
@dataclass(frozen=True)
class GateObservation:
name: str
passed: bool
duration_ms: int = 0
message: str = ""
@dataclass(frozen=True)
class CaseObservation:
case_id: str
repository: str
passed: bool
rank: int | None = None
max_rank: int = 1
false_positive_count: int = 0
message: str = ""
objective: str = "foundational_capability"
score_override: float | None = None
def score(self) -> float:
if not self.passed:
return 0.0
if self.score_override is not None:
return clamp_score(self.score_override)
if self.rank is None or self.rank <= 0:
rank_score = 1.0
else:
rank_score = 1.0 / self.rank
false_positive_penalty = min(0.5, self.false_positive_count * 0.1)
return max(0.0, rank_score - false_positive_penalty)
@dataclass(frozen=True)
class MetricObservation:
name: str
value: float
budget: float | None = None
lower_is_better: bool = True
key: bool = True
def score(self) -> float:
if self.value < 0:
return 0.0
if self.budget is None or self.budget <= 0:
return 1.0
if self.lower_is_better:
return min(1.0, self.budget / max(self.value, 1.0))
return min(1.0, self.value / self.budget)
@dataclass(frozen=True)
class EvaluationObservation:
gates: list[GateObservation] = field(default_factory=list)
cases: list[CaseObservation] = field(default_factory=list)
metrics: list[MetricObservation] = field(default_factory=list)
generated_diff: bool = True
@dataclass(frozen=True)
class ScoreBreakdown:
score: float
foundational_capability: float
competitive_capability: float
accuracy: float
semantic_vector: float
research_judge: float | None
performance: float
stability: float
accepted: bool
reject_reasons: list[str]
degradations: list[dict[str, Any]] = field(default_factory=list)
improvements: list[dict[str, Any]] = field(default_factory=list)
metric_budget_failures: list[dict[str, Any]] = field(default_factory=list)
def to_dict(self) -> dict[str, Any]:
return {
"score": round(self.score, 6),
"foundational_capability": round(self.foundational_capability, 6),
"competitive_capability": round(self.competitive_capability, 6),
"accuracy": round(self.accuracy, 6),
"semantic_vector": round(self.semantic_vector, 6),
"research_judge": (
round(self.research_judge, 6)
if self.research_judge is not None
else None
),
"performance": round(self.performance, 6),
"stability": round(self.stability, 6),
"accepted": self.accepted,
"reject_reasons": self.reject_reasons,
"degradations": self.degradations,
"improvements": self.improvements,
"metric_budget_failures": self.metric_budget_failures,
}
def score_evaluation(
observation: EvaluationObservation,
previous_run: dict[str, Any] | None,
weights: dict[str, float] | None = None,
) -> ScoreBreakdown:
research_judge_scores = objective_case_scores(observation.cases, "research_judge")
active_weights = dict(JUDGE_WEIGHTS if research_judge_scores else DEFAULT_WEIGHTS)
if weights:
active_weights.update(weights)
foundational_scores = objective_case_scores(
observation.cases,
"foundational_capability",
aliases=("accuracy",),
)
competitive_scores = objective_case_scores(observation.cases, "competitive_capability")
semantic_vector_scores = objective_case_scores(observation.cases, "semantic_vector")
foundational_capability = average(foundational_scores, default=0.0)
competitive_capability = average(competitive_scores, default=0.0)
accuracy = average(
[
score
for score, scores in (
(foundational_capability, foundational_scores),
(competitive_capability, competitive_scores),
)
if scores
],
default=0.0,
)
semantic_vector = average(semantic_vector_scores, default=0.0)
research_judge = (
average(research_judge_scores, default=0.0)
if research_judge_scores
else None
)
performance = average([metric.score() for metric in observation.metrics], default=1.0)
stability = stability_score(observation.gates, observation.generated_diff)
budget_failures = metric_budget_failures(observation.metrics)
score = (
foundational_capability * active_weights["foundational_capability"]
+ competitive_capability * active_weights["competitive_capability"]
+ semantic_vector * active_weights["semantic_vector"]
+ (research_judge or 0.0) * active_weights.get("research_judge", 0.0)
+ performance * active_weights["performance"]
+ stability * active_weights["stability"]
)
reject_reasons = acceptance_reject_reasons(
observation=observation,
score=score,
foundational_capability=foundational_capability,
competitive_capability=competitive_capability,
accuracy=accuracy,
semantic_vector=semantic_vector,
research_judge=research_judge,
performance=performance,
stability=stability,
previous_run=previous_run,
)
degradations = evaluation_degradations(
observation=observation,
score=score,
foundational_capability=foundational_capability,
competitive_capability=competitive_capability,
accuracy=accuracy,
semantic_vector=semantic_vector,
research_judge=research_judge,
performance=performance,
stability=stability,
previous_run=previous_run,
)
improvements = evaluation_improvements(
observation=observation,
score=score,
foundational_capability=foundational_capability,
competitive_capability=competitive_capability,
accuracy=accuracy,
semantic_vector=semantic_vector,
research_judge=research_judge,
performance=performance,
stability=stability,
previous_run=previous_run,
)
return ScoreBreakdown(
score=score,
foundational_capability=foundational_capability,
competitive_capability=competitive_capability,
accuracy=accuracy,
semantic_vector=semantic_vector,
research_judge=research_judge,
performance=performance,
stability=stability,
accepted=not reject_reasons,
reject_reasons=reject_reasons,
degradations=degradations,
improvements=improvements,
metric_budget_failures=budget_failures,
)
def acceptance_reject_reasons(
observation: EvaluationObservation,
score: float,
foundational_capability: float,
competitive_capability: float,
accuracy: float,
semantic_vector: float,
research_judge: float | None,
performance: float,
stability: float,
previous_run: dict[str, Any] | None,
) -> list[str]:
reasons: list[str] = []
if not observation.generated_diff:
reasons.append("codex produced no candidate diff")
failed_gates = [gate.name for gate in observation.gates if not gate.passed]
if failed_gates:
reasons.append("quality gates failed: " + ", ".join(failed_gates))
if previous_run is None:
return reasons
protected_rejections = protected_objective_reject_reasons(
foundational_capability=foundational_capability,
competitive_capability=competitive_capability,
semantic_vector=semantic_vector,
research_judge=research_judge,
stability=stability,
previous_run=previous_run,
)
if protected_rejections:
reasons.extend(protected_rejections)
return reasons
previous_score = float(previous_run.get("score", 0.0))
score_improved = meaningful_increase(score, previous_score, SCORE_EPSILON, 0.0)
pareto_improved = epsilon_pareto_improved(
observation=observation,
score=score,
foundational_capability=foundational_capability,
competitive_capability=competitive_capability,
accuracy=accuracy,
semantic_vector=semantic_vector,
research_judge=research_judge,
performance=performance,
stability=stability,
previous_run=previous_run,
)
if not score_improved and not pareto_improved:
reasons.append(epsilon_pareto_reject_reason(score, previous_score))
return reasons
def protected_objective_reject_reasons(
foundational_capability: float,
competitive_capability: float,
semantic_vector: float,
research_judge: float | None,
stability: float,
previous_run: dict[str, Any],
) -> list[str]:
reasons: list[str] = []
for name, current in (
("foundational_capability", foundational_capability),
("competitive_capability", competitive_capability),
("semantic_vector", semantic_vector),
("stability", stability),
):
previous = previous_run.get(name)
if previous is not None and meaningful_decrease(
current,
float(previous),
RATIO_EPSILON,
0.0,
):
reasons.append(
f"protected {name} objective regressed "
f"({current:.6f}, previous {float(previous):.6f}, "
f"ratio_epsilon {RATIO_EPSILON:.6f})"
)
if research_judge is not None:
previous = previous_run.get("research_judge")
if previous is not None and meaningful_decrease(
research_judge,
float(previous),
RATIO_EPSILON,
0.0,
):
reasons.append(
f"protected research_judge objective regressed "
f"({research_judge:.6f}, previous {float(previous):.6f}, "
f"ratio_epsilon {RATIO_EPSILON:.6f})"
)
return reasons
def epsilon_pareto_improved(
observation: EvaluationObservation,
score: float,
foundational_capability: float,
competitive_capability: float,
accuracy: float,
semantic_vector: float,
research_judge: float | None,
performance: float,
stability: float,
previous_run: dict[str, Any],
) -> bool:
degradations = evaluation_degradations(
observation=observation,
score=score,
foundational_capability=foundational_capability,
competitive_capability=competitive_capability,
accuracy=accuracy,
semantic_vector=semantic_vector,
research_judge=research_judge,
performance=performance,
stability=stability,
previous_run=previous_run,
)
improvements = evaluation_improvements(
observation=observation,
score=score,
foundational_capability=foundational_capability,
competitive_capability=competitive_capability,
accuracy=accuracy,
semantic_vector=semantic_vector,
research_judge=research_judge,
performance=performance,
stability=stability,
previous_run=previous_run,
)
return bool(improvements) and not degradations
def epsilon_pareto_reject_reason(score: float, previous_score: float) -> str:
return (
f"neither score nor epsilon-pareto objectives improved "
f"(score {score:.6f}, previous {previous_score:.6f}, "
f"score_epsilon {SCORE_EPSILON:.6f})"
)
def evaluation_degradations(
observation: EvaluationObservation,
score: float,
foundational_capability: float,
competitive_capability: float,
accuracy: float,
semantic_vector: float,
research_judge: float | None,
performance: float,
stability: float,
previous_run: dict[str, Any] | None,
) -> list[dict[str, Any]]:
if previous_run is None:
return []
degradations: list[dict[str, Any]] = []
for name, current in (
("score", score),
("foundational_capability", foundational_capability),
("competitive_capability", competitive_capability),
("accuracy", accuracy),
("semantic_vector", semantic_vector),
("performance", performance),
("stability", stability),
):
previous = previous_run.get(name)
if previous is not None and meaningful_decrease(
current,
float(previous),
RATIO_EPSILON,
0.0,
):
degradations.append(numeric_degradation("score_component", name, previous, current))
if research_judge is not None:
previous = previous_run.get("research_judge")
if previous is not None and meaningful_decrease(
research_judge,
float(previous),
RATIO_EPSILON,
0.0,
):
degradations.append(
numeric_degradation(
"score_component",
"research_judge",
previous,
research_judge,
)
)
previous_metrics = keyed_items(previous_run.get("metrics", []), "name")
for metric in observation.metrics:
previous = previous_metrics.get(metric.name)
if previous is None:
continue
previous_value = float(previous.get("value", 0.0))
worsened = metric_worsened(metric, previous_value)
if worsened:
degradation = numeric_degradation(
"metric",
metric.name,
previous_value,
metric.value,
)
degradation["lower_is_better"] = metric.lower_is_better
degradation["budget"] = metric.budget
degradations.append(degradation)
previous_cases = keyed_items(previous_run.get("cases", []), "case_id")
for case in observation.cases:
previous = previous_cases.get(case.case_id)
if previous is None:
continue
case_degradation = case_worsened(previous, case)
if case_degradation:
degradations.append(case_degradation)
previous_gates = keyed_items(previous_run.get("gates", []), "name")
for gate in observation.gates:
previous = previous_gates.get(gate.name)
if previous and previous.get("passed") and not gate.passed:
degradations.append(
{
"kind": "gate",
"name": gate.name,
"previous": "passed",
"current": "failed",
"message": gate.message,
}
)
return degradations
def evaluation_improvements(
observation: EvaluationObservation,
score: float,
foundational_capability: float,
competitive_capability: float,
accuracy: float,
semantic_vector: float,
research_judge: float | None,
performance: float,
stability: float,
previous_run: dict[str, Any] | None,
) -> list[dict[str, Any]]:
if previous_run is None:
return []
improvements: list[dict[str, Any]] = []
for name, current in (
("score", score),
("foundational_capability", foundational_capability),
("competitive_capability", competitive_capability),
("accuracy", accuracy),
("semantic_vector", semantic_vector),
("performance", performance),
("stability", stability),
):
previous = previous_run.get(name)
if previous is not None and meaningful_increase(
current,
float(previous),
RATIO_EPSILON,
0.0,
):
improvements.append(numeric_change("score_component", name, previous, current))
if research_judge is not None:
previous = previous_run.get("research_judge")
if previous is not None and meaningful_increase(
research_judge,
float(previous),
RATIO_EPSILON,
0.0,
):
improvements.append(
numeric_change(
"score_component",
"research_judge",
previous,
research_judge,
)
)
previous_metrics = keyed_items(previous_run.get("metrics", []), "name")
for metric in observation.metrics:
previous = previous_metrics.get(metric.name)
if previous is None:
continue
previous_value = float(previous.get("value", 0.0))
improved = metric_improved(metric, previous_value)
if improved:
improvement = numeric_change("metric", metric.name, previous_value, metric.value)
improvement["lower_is_better"] = metric.lower_is_better
improvement["budget"] = metric.budget
improvements.append(improvement)
previous_cases = keyed_items(previous_run.get("cases", []), "case_id")
for case in observation.cases:
previous = previous_cases.get(case.case_id)
if previous is None:
continue
case_improvement = case_improved(previous, case)
if case_improvement:
improvements.append(case_improvement)
previous_gates = keyed_items(previous_run.get("gates", []), "name")
for gate in observation.gates:
previous = previous_gates.get(gate.name)
if previous and not previous.get("passed") and gate.passed:
improvements.append(
{
"kind": "gate",
"name": gate.name,
"previous": "failed",
"current": "passed",
"message": gate.message,
}
)
return improvements
def keyed_items(items: Any, key: str) -> dict[str, dict[str, Any]]:
if not isinstance(items, list):
return {}
return {
str(item[key]): item
for item in items
if isinstance(item, dict) and key in item
}
def numeric_degradation(
kind: str,
name: str,
previous: Any,
current: float,
) -> dict[str, Any]:
return numeric_change(kind, name, previous, current)
def metric_improved(metric: MetricObservation, previous_value: float) -> bool:
threshold = metric_threshold(previous_value)
if metric.lower_is_better:
return meaningful_decrease(metric.value, previous_value, threshold, 0.0)
return meaningful_increase(metric.value, previous_value, threshold, 0.0)
def metric_worsened(metric: MetricObservation, previous_value: float) -> bool:
threshold = metric_threshold(previous_value)
if metric.lower_is_better:
return meaningful_increase(metric.value, previous_value, threshold, 0.0)
return meaningful_decrease(metric.value, previous_value, threshold, 0.0)
def metric_threshold(previous_value: float) -> float:
return max(METRIC_ABSOLUTE_EPSILON, abs(previous_value) * METRIC_RELATIVE_EPSILON)
def meaningful_increase(
current: float,
previous: float,
absolute_epsilon: float,
relative_epsilon: float,
) -> bool:
return current - previous > epsilon_threshold(previous, absolute_epsilon, relative_epsilon)
def meaningful_decrease(
current: float,
previous: float,
absolute_epsilon: float,
relative_epsilon: float,
) -> bool:
return previous - current > epsilon_threshold(previous, absolute_epsilon, relative_epsilon)
def epsilon_threshold(
previous: float,
absolute_epsilon: float,
relative_epsilon: float,
) -> float:
return max(absolute_epsilon, abs(previous) * relative_epsilon)
def numeric_change(
kind: str,
name: str,
previous: Any,
current: float,
) -> dict[str, Any]:
previous_value = float(previous)
return {
"kind": kind,
"name": name,
"previous": round(previous_value, 6),
"current": round(current, 6),
"delta": round(current - previous_value, 6),
}
def case_worsened(previous: dict[str, Any], current: CaseObservation) -> dict[str, Any] | None:
previous_passed = bool(previous.get("passed"))
rank_worsened = rank_value(current.rank, current.max_rank) > rank_value(
previous.get("rank"),
int(previous.get("max_rank", current.max_rank)),
)
false_positives_worsened = current.false_positive_count > int(
previous.get("false_positive_count", 0)
)
if previous_passed and not current.passed:
reason = "passed_to_failed"
elif rank_worsened:
reason = "rank_worsened"
elif false_positives_worsened:
reason = "false_positives_increased"
else:
return None
return {
"kind": "case",
"objective": current.objective,
"case_id": current.case_id,
"repository": current.repository,
"reason": reason,
"previous": {
"passed": previous_passed,
"rank": previous.get("rank"),
"false_positive_count": previous.get("false_positive_count", 0),
},
"current": {
"passed": current.passed,
"rank": current.rank,
"false_positive_count": current.false_positive_count,
},
"message": current.message,
}
def case_improved(previous: dict[str, Any], current: CaseObservation) -> dict[str, Any] | None:
previous_passed = bool(previous.get("passed"))
rank_improved = rank_value(current.rank, current.max_rank) < rank_value(
previous.get("rank"),
int(previous.get("max_rank", current.max_rank)),
)
false_positives_improved = current.false_positive_count < int(
previous.get("false_positive_count", 0)
)
if not previous_passed and current.passed:
reason = "failed_to_passed"
elif rank_improved:
reason = "rank_improved"
elif false_positives_improved:
reason = "false_positives_decreased"
else:
return None
return {
"kind": "case",
"objective": current.objective,
"case_id": current.case_id,
"repository": current.repository,
"reason": reason,
"previous": {
"passed": previous_passed,
"rank": previous.get("rank"),
"false_positive_count": previous.get("false_positive_count", 0),
},
"current": {
"passed": current.passed,
"rank": current.rank,
"false_positive_count": current.false_positive_count,
},
"message": current.message,
}
def rank_value(rank: Any, max_rank: int) -> int:
if rank is None:
return max_rank + 10_000
return int(rank)
def stability_score(gates: list[GateObservation], generated_diff: bool) -> float:
if not generated_diff:
return 0.0
if not gates:
return 1.0
return sum(1.0 for gate in gates if gate.passed) / len(gates)
def objective_case_scores(
cases: list[CaseObservation],
objective: str,
aliases: tuple[str, ...] = (),
) -> list[float]:
objective_names = {objective, *aliases}
return [case.score() for case in cases if case.objective in objective_names]
def metric_budget_failures(metrics: list[MetricObservation]) -> list[dict[str, Any]]:
failures: list[dict[str, Any]] = []
for metric in metrics:
if metric.budget is None or metric.budget <= 0:
continue
misses_budget = (
metric.value > metric.budget
if metric.lower_is_better
else metric.value < metric.budget
)
if misses_budget:
failures.append(
{
"name": metric.name,
"value": round(metric.value, 6),
"budget": round(metric.budget, 6),
"lower_is_better": metric.lower_is_better,
"score": round(metric.score(), 6),
}
)
return failures
def average(values: list[float], default: float) -> float:
if not values:
return default
return sum(values) / len(values)
def clamp_score(value: float) -> float:
return min(1.0, max(0.0, float(value)))