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The project intentionally uses the Python standard library for its initial
schema layer. TOML inputs are converted into frozen dataclasses, validated, and
hashed using a canonical JSON representation.
"""
from __future__ import annotations
from dataclasses import asdict, dataclass
from hashlib import sha256
import json
from pathlib import Path
import re
import tomllib
from typing import Any, Iterable
class SpecError(ValueError):
"""Raised when a configuration cannot represent a valid treatment."""
def project_root() -> Path:
return Path(__file__).resolve().parents[2]
def _read_toml(path: Path) -> dict[str, Any]:
try:
with path.open("rb") as handle:
return tomllib.load(handle)
except (OSError, tomllib.TOMLDecodeError) as exc:
raise SpecError(f"Cannot read TOML configuration {path}: {exc}") from exc
def _canonical_hash(value: Any) -> str:
payload = json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True)
return sha256(payload.encode("utf-8")).hexdigest()
@dataclass(frozen=True, slots=True)
class HarnessSpec:
schema_version: int
harness_id: str
name: str
description: str
exact_search: bool
lexical: bool
syntax: str
dense: bool
graph_hops: int
query_policy: str
interface: str
packing: str
fusion: str
control: str
adaptive: bool
@classmethod
def from_mapping(cls, value: dict[str, Any], source: Path | None = None) -> "HarnessSpec":
try:
spec = cls(
schema_version=int(value["schema_version"]),
harness_id=str(value["harness_id"]),
name=str(value["name"]),
description=str(value.get("description", "")),
exact_search=bool(value["exact_search"]),
lexical=bool(value["lexical"]),
syntax=str(value["syntax"]),
dense=bool(value["dense"]),
graph_hops=int(value["graph_hops"]),
query_policy=str(value["query_policy"]),
interface=str(value["interface"]),
packing=str(value["packing"]),
fusion=str(value["fusion"]),
control=str(value.get("control", "none")),
adaptive=bool(value.get("adaptive", False)),
)
except (KeyError, TypeError, ValueError) as exc:
location = f" in {source}" if source else ""
raise SpecError(f"Malformed harness specification{location}: {exc}") from exc
spec.validate(source)
return spec
@classmethod
def load(cls, path: Path) -> "HarnessSpec":
return cls.from_mapping(_read_toml(path), path)
def validate(self, source: Path | None = None) -> None:
errors: list[str] = []
if self.schema_version != 1:
errors.append("schema_version must be 1")
if not re.fullmatch(r"H\d{3}", self.harness_id):
errors.append("harness_id must match H000-style identifiers")
if not re.fullmatch(r"[a-z][a-z0-9_]*", self.name):
errors.append("name must be a lowercase semantic slug")
if self.syntax not in {"raw", "tree_sitter"}:
errors.append("syntax must be raw or tree_sitter")
if self.graph_hops not in {0, 1, 2}:
errors.append("graph_hops must be 0, 1, or 2")
if self.graph_hops and self.syntax != "tree_sitter":
errors.append("graph expansion requires the tree_sitter structural index")
if self.query_policy not in {"one_shot", "iterative"}:
errors.append("query_policy must be one_shot or iterative")
if self.interface not in {"unified", "specialized"}:
errors.append("interface must be unified or specialized")
if self.packing not in {"ranked_snippets", "skeletons", "whole_files", "role_summaries"}:
errors.append("unsupported context packing strategy")
if self.packing == "skeletons" and self.syntax != "tree_sitter":
errors.append("skeleton packing requires tree_sitter syntax")
if self.fusion not in {"none", "rrf"}:
errors.append("fusion must be none or rrf")
active_advanced_sources = int(self.lexical) + int(self.syntax == "tree_sitter") + int(self.dense)
expected_fusion = "rrf" if active_advanced_sources >= 2 else "none"
if self.control == "none" and self.fusion != expected_fusion:
errors.append(
f"fusion must be {expected_fusion} for {active_advanced_sources} advanced retrieval sources"
)
allowed_controls = {"none", "no_search", "random_context", "oracle_file", "oracle_function"}
if self.control not in allowed_controls:
errors.append(f"control must be one of {sorted(allowed_controls)}")
if self.control != "none":
if self.exact_search or self.lexical or self.dense or self.syntax != "raw" or self.graph_hops:
errors.append("control harnesses cannot enable repository retrieval capabilities")
if self.fusion != "none" or self.adaptive:
errors.append("control harnesses cannot enable fusion or adaptive routing")
elif not self.exact_search:
errors.append("non-control harnesses must retain the exact/regex baseline")
if self.adaptive:
if not (self.lexical and self.dense and self.syntax == "tree_sitter"):
errors.append("adaptive routing requires lexical, syntax, and dense retrieval")
if self.graph_hops < 1 or self.query_policy != "iterative":
errors.append("adaptive routing requires graph expansion and iterative queries")
if errors:
location = f" ({source})" if source else ""
raise SpecError(f"Invalid harness {self.harness_id}{location}: " + "; ".join(errors))
@property
def config_hash(self) -> str:
return _canonical_hash(asdict(self))
@property
def treatment_hash(self) -> str:
"""Hash only causal treatment fields, excluding labels and prose."""
value = asdict(self)
for field in ("harness_id", "name", "description"):
value.pop(field)
return _canonical_hash(value)
@property
def uses_embedding(self) -> bool:
return self.dense
@dataclass(frozen=True, slots=True)
class EditInterfaceSpec:
"""Immutable action-interface treatment for protocol-normalized studies."""
schema_version: int
interface_id: str
name: str
description: str
edit_tool: str
prompt_contract: str
@classmethod
def load(cls, path: Path) -> "EditInterfaceSpec":
value = _read_toml(path)
try:
spec = cls(
schema_version=int(value["schema_version"]),
interface_id=str(value["interface_id"]),
name=str(value["name"]),
description=str(value.get("description", "")),
edit_tool=str(value["edit_tool"]),
prompt_contract=str(value["prompt_contract"]),
)
except (KeyError, TypeError, ValueError) as exc:
raise SpecError(f"Malformed edit-interface specification {path}: {exc}") from exc
spec.validate(path)
return spec
def validate(self, source: Path | None = None) -> None:
errors: list[str] = []
if self.schema_version != 1:
errors.append("schema_version must be 1")
if not re.fullmatch(r"P\d{3}", self.interface_id):
errors.append("interface_id must match P000-style identifiers")
if not re.fullmatch(r"[a-z][a-z0-9_]*", self.name):
errors.append("name must be a lowercase semantic slug")
if self.edit_tool not in {"apply_patch", "replace_text", "write_file"}:
errors.append("unsupported edit tool")
if not self.prompt_contract.strip():
errors.append("prompt_contract must be non-empty")
if errors:
location = f" ({source})" if source else ""
raise SpecError(
f"Invalid edit interface {self.interface_id}{location}: "
+ "; ".join(errors)
)
@property
def config_hash(self) -> str:
return _canonical_hash(asdict(self))
@property
def treatment_hash(self) -> str:
value = asdict(self)
for field in ("interface_id", "name", "description"):
value.pop(field)
return _canonical_hash(value)
@dataclass(frozen=True, slots=True)
class ModelSpec:
schema_version: int
model_id: str
canonical_name: str
expected_identity: str
expected_inference_key: str
expected_variant: str
expected_format: str
expected_quantization: str
provider: str
base_url: str
api_token_env: str
discovery_endpoint: str
native_discovery_endpoint: str
inference_endpoint: str
temperature: float
top_p: float
max_tokens: int
seed: int
context_length: int
reasoning_mode: str
@classmethod
def load(cls, path: Path) -> "ModelSpec":
value = _read_toml(path)
try:
spec = cls(
schema_version=int(value["schema_version"]),
model_id=str(value["model_id"]),
canonical_name=str(value["canonical_name"]),
expected_identity=str(value["expected_identity"]),
expected_inference_key=str(value["expected_inference_key"]),
expected_variant=str(value["expected_variant"]),
expected_format=str(value["expected_format"]),
expected_quantization=str(value["expected_quantization"]),
provider=str(value["provider"]),
base_url=str(value["base_url"]).rstrip("/"),
api_token_env=str(value.get("api_token_env", "LM_STUDIO_API_TOKEN")),
discovery_endpoint=str(value.get("discovery_endpoint", "/v1/models")),
native_discovery_endpoint=str(value.get("native_discovery_endpoint", "/api/v1/models")),
inference_endpoint=str(value.get("inference_endpoint", "/v1/chat/completions")),
temperature=float(value["temperature"]),
top_p=float(value["top_p"]),
max_tokens=int(value["max_tokens"]),
seed=int(value["seed"]),
context_length=int(value["context_length"]),
reasoning_mode=str(value["reasoning_mode"]),
)
except (KeyError, TypeError, ValueError) as exc:
raise SpecError(f"Malformed model specification {path}: {exc}") from exc
spec.validate(path)
return spec
def validate(self, source: Path | None = None) -> None:
errors: list[str] = []
if self.schema_version != 1:
errors.append("schema_version must be 1")
if not re.fullmatch(r"M\d{3}", self.model_id):
errors.append("model_id must match M000-style identifiers")
if not self.canonical_name.strip() or not self.expected_identity.strip():
errors.append("canonical_name and expected_identity must be pinned")
if not self.expected_inference_key or not self.expected_variant:
errors.append("the LM Studio model key and selected variant must be pinned")
if not self.expected_format or not self.expected_quantization:
errors.append("model format and quantization must be pinned")
if self.provider != "lm_studio_local":
errors.append("provider must be lm_studio_local")
if self.base_url not in {"http://127.0.0.1:1234", "http://localhost:1234"}:
errors.append("LM Studio must be configured on local port 1234")
if not (0.0 <= self.temperature <= 2.0 and 0.0 < self.top_p <= 1.0):
errors.append("invalid sampling parameters")
if self.max_tokens <= 0:
errors.append("max_tokens must be positive")
if self.context_length <= 0:
errors.append("context_length must be positive")
if self.reasoning_mode not in {"none", "off", "on", "low", "medium", "high"}:
errors.append("unsupported reasoning_mode")
if errors:
location = f" ({source})" if source else ""
raise SpecError("Invalid model specification" + location + ": " + "; ".join(errors))
@property
def config_hash(self) -> str:
return _canonical_hash(asdict(self))
@dataclass(frozen=True, slots=True)
class EmbeddingSpec:
schema_version: int
embedding_id: str
status: str
provider: str
base_url: str
api_token_env: str
discovery_endpoint: str
inference_endpoint: str
model_key: str
expected_display_name: str
expected_format: str
expected_quantization: str
expected_size_bytes: int
max_context_length: int
loaded_context_length: int
vector_dimension: int
normalized: bool
query_instruction: str
document_prefix_template: str
chunk_lines: int
chunk_overlap_lines: int
chunk_char_limit: int
batch_size: int
notes: str
@classmethod
def load(cls, path: Path) -> "EmbeddingSpec":
value = _read_toml(path)
try:
spec = cls(
schema_version=int(value["schema_version"]),
embedding_id=str(value["embedding_id"]),
status=str(value["status"]),
provider=str(value["provider"]),
base_url=str(value["base_url"]).rstrip("/"),
api_token_env=str(value.get("api_token_env", "LM_STUDIO_API_TOKEN")),
discovery_endpoint=str(value.get("discovery_endpoint", "/api/v1/models")),
inference_endpoint=str(value.get("inference_endpoint", "/v1/embeddings")),
model_key=str(value["model_key"]),
expected_display_name=str(value["expected_display_name"]),
expected_format=str(value["expected_format"]),
expected_quantization=str(value["expected_quantization"]),
expected_size_bytes=int(value["expected_size_bytes"]),
max_context_length=int(value["max_context_length"]),
loaded_context_length=int(value["loaded_context_length"]),
vector_dimension=int(value["vector_dimension"]),
normalized=bool(value["normalized"]),
query_instruction=str(value["query_instruction"]),
document_prefix_template=str(value["document_prefix_template"]),
chunk_lines=int(value["chunk_lines"]),
chunk_overlap_lines=int(value["chunk_overlap_lines"]),
chunk_char_limit=int(value["chunk_char_limit"]),
batch_size=int(value["batch_size"]),
notes=str(value.get("notes", "")),
)
except (KeyError, TypeError, ValueError) as exc:
raise SpecError(f"Malformed embedding specification {path}: {exc}") from exc
if spec.schema_version != 1 or not re.fullmatch(r"EMB\d{3}", spec.embedding_id):
raise SpecError(f"Invalid embedding specification {path}")
errors: list[str] = []
if spec.status != "ready":
errors.append("the pinned embedding profile must have status ready")
if spec.provider != "lm_studio_local":
errors.append("embedding provider must be lm_studio_local")
if spec.base_url not in {"http://127.0.0.1:1234", "http://localhost:1234"}:
errors.append("embedding service must use local LM Studio on port 1234")
if not all(
(
spec.model_key,
spec.expected_display_name,
spec.expected_format,
spec.expected_quantization,
)
):
errors.append("embedding identity, format, and quantization must be pinned")
if min(
spec.expected_size_bytes,
spec.max_context_length,
spec.loaded_context_length,
spec.vector_dimension,
) <= 0:
errors.append("embedding size, context length, and vector dimension must be positive")
if spec.loaded_context_length > spec.max_context_length:
errors.append("loaded embedding context cannot exceed the model maximum")
if not spec.normalized:
errors.append("the initial cosine-similarity protocol requires normalized embeddings")
if not spec.query_instruction or "{path}" not in spec.document_prefix_template:
errors.append("embedding query instruction and path-aware document prefix must be frozen")
if min(spec.chunk_lines, spec.chunk_char_limit, spec.batch_size) <= 0:
errors.append("embedding chunk size and batch size must be positive")
if not 0 <= spec.chunk_overlap_lines < spec.chunk_lines:
errors.append("embedding chunk overlap must be non-negative and smaller than the chunk")
if errors:
raise SpecError(f"Invalid embedding specification {path}: " + "; ".join(errors))
return spec
@property
def config_hash(self) -> str:
return _canonical_hash(asdict(self))
@dataclass(frozen=True, slots=True)
class TaskSpec:
schema_version: int
task_id: str
repository_url: str
base_commit: str
gold_commit: str
language: str
statement: str
gold_patch: str
test_patch: str
gold_files: tuple[str, ...]
gold_symbols: tuple[str, ...]
fail_to_pass_tests: tuple[str, ...]
pass_to_pass_tests: tuple[str, ...]
difficulty: str
provenance: str
validation_status: str
@classmethod
def load(cls, path: Path) -> "TaskSpec":
value = _read_toml(path)
try:
spec = cls(
schema_version=int(value["schema_version"]),
task_id=str(value["task_id"]),
repository_url=str(value["repository_url"]),
base_commit=str(value["base_commit"]),
gold_commit=str(value["gold_commit"]),
language=str(value["language"]),
statement=str(value["statement"]),
gold_patch=str(value["gold_patch"]),
test_patch=str(value.get("test_patch", "")),
gold_files=tuple(str(item) for item in value["gold_files"]),
gold_symbols=tuple(str(item) for item in value["gold_symbols"]),
fail_to_pass_tests=tuple(str(item) for item in value["fail_to_pass_tests"]),
pass_to_pass_tests=tuple(str(item) for item in value["pass_to_pass_tests"]),
difficulty=str(value["difficulty"]),
provenance=str(value["provenance"]),
validation_status=str(value["validation_status"]),
)
except (KeyError, TypeError, ValueError) as exc:
raise SpecError(f"Malformed task specification {path}: {exc}") from exc
spec.validate(path)
return spec
def validate(self, source: Path | None = None) -> None:
errors: list[str] = []
if self.schema_version != 1:
errors.append("schema_version must be 1")
if not re.fullmatch(r"TASK_[A-Z0-9_]+", self.task_id):
errors.append("task_id must match TASK_UPPERCASE_STYLE")
if not self.repository_url.startswith("https://gitlab.com/"):
errors.append("initial benchmark tasks must use a version-pinned GitLab repository")
if not re.fullmatch(r"[0-9a-fA-F]{40}", self.base_commit):
errors.append("base_commit must be a full 40-character Git commit SHA")
if not re.fullmatch(r"[0-9a-fA-F]{40}", self.gold_commit):
errors.append("gold_commit must be a full 40-character Git commit SHA")
if not self.language or not self.statement.strip():
errors.append("language and statement must be non-empty")
if not self.gold_files or not self.gold_symbols:
errors.append("gold_files and gold_symbols must be non-empty")
allowed_statuses = {"template_only", "retrieval_ready", "end_to_end_ready"}
if self.validation_status not in allowed_statuses:
errors.append(f"validation_status must be one of {sorted(allowed_statuses)}")
if self.validation_status == "end_to_end_ready":
if not self.gold_patch:
errors.append("end-to-end-ready tasks require a gold patch")
if not self.test_patch:
errors.append("end-to-end-ready tasks require a hidden test patch")
if not self.fail_to_pass_tests or not self.pass_to_pass_tests:
errors.append("end-to-end-ready tasks require both test sets")
if not self.provenance.strip() or not self.difficulty.strip():
errors.append("difficulty and provenance must be documented")
for repository_path in (
*self.gold_files,
*((self.gold_patch,) if self.gold_patch else ()),
*((self.test_patch,) if self.test_patch else ()),
):
path = Path(repository_path)
if path.is_absolute() or ".." in path.parts:
errors.append(f"repository path must be safe and relative: {repository_path}")
if errors:
location = f" ({source})" if source else ""
raise SpecError(f"Invalid task {self.task_id}{location}: " + "; ".join(errors))
@property
def config_hash(self) -> str:
return _canonical_hash(asdict(self))
@dataclass(frozen=True, slots=True)
class ExperimentSpec:
schema_version: int
experiment_id: str
name: str
mode: str
description: str
harness_ids: tuple[str, ...]
edit_interface_ids: tuple[str, ...]
agent_system_ids: tuple[str, ...]
repository_ids: tuple[str, ...]
backend_ids: tuple[str, ...]
model_ids: tuple[str, ...]
embedding_id: str
task_split: str
context_budgets: tuple[int, ...]
seeds: tuple[int, ...]
repetitions: int
max_tool_calls: int
max_test_runs: int
timeout_seconds: int
@classmethod
def load(cls, path: Path) -> "ExperimentSpec":
value = _read_toml(path)
try:
spec = cls(
schema_version=int(value["schema_version"]),
experiment_id=str(value["experiment_id"]),
name=str(value["name"]),
mode=str(value["mode"]),
description=str(value.get("description", "")),
harness_ids=tuple(str(item) for item in value["harness_ids"]),
edit_interface_ids=tuple(
str(item) for item in value.get("edit_interface_ids", [])
),
agent_system_ids=tuple(str(item) for item in value.get("agent_system_ids", [])),
repository_ids=tuple(str(item) for item in value.get("repository_ids", [])),
backend_ids=tuple(str(item) for item in value.get("backend_ids", [])),
model_ids=tuple(str(item) for item in value["model_ids"]),
embedding_id=str(value["embedding_id"]),
task_split=str(value["task_split"]),
context_budgets=tuple(int(item) for item in value["context_budgets"]),
seeds=tuple(int(item) for item in value["seeds"]),
repetitions=int(value["repetitions"]),
max_tool_calls=int(value["max_tool_calls"]),
max_test_runs=int(value["max_test_runs"]),
timeout_seconds=int(value["timeout_seconds"]),
)
except (KeyError, TypeError, ValueError) as exc:
raise SpecError(f"Malformed experiment specification {path}: {exc}") from exc
spec.validate(path)
return spec
def validate(self, source: Path | None = None) -> None:
errors: list[str] = []
if self.schema_version != 1:
errors.append("schema_version must be 1")
if not re.fullmatch(r"E\d{2}", self.experiment_id):
errors.append("experiment_id must match E00-style identifiers")
if not self.harness_ids or len(set(self.harness_ids)) != len(self.harness_ids):
errors.append("harness_ids must be non-empty and unique")
if len(set(self.edit_interface_ids)) != len(self.edit_interface_ids):
errors.append("edit_interface_ids must be unique")
if len(set(self.agent_system_ids)) != len(self.agent_system_ids):
errors.append("agent_system_ids must be unique")
if len(set(self.repository_ids)) != len(self.repository_ids):
errors.append("repository_ids must be unique")
is_study2 = self.mode in {"study2_live_agent", "study2_reliability"}
is_protocol = self.mode == "protocol_interface"
if is_study2 and (not self.agent_system_ids or not self.repository_ids):
errors.append("Study 2 experiments must enumerate agent systems and repositories")
if is_protocol and (not self.edit_interface_ids or not self.repository_ids):
errors.append("protocol experiments must enumerate edit interfaces and repositories")
if not is_protocol and self.edit_interface_ids:
errors.append("only protocol experiments may enumerate edit interfaces")
if not (is_study2 or is_protocol) and (self.agent_system_ids or self.repository_ids):
errors.append(
"only repository-scale Study 2/protocol experiments may enumerate "
"agent systems or repositories"
)
if is_protocol and self.agent_system_ids:
errors.append("protocol experiments cannot enumerate agent systems")
if len(set(self.backend_ids)) != len(self.backend_ids):
errors.append("backend_ids must be unique")
if self.mode == "index_backend" and not self.backend_ids:
errors.append("index_backend experiments must enumerate backend_ids")
if self.mode != "index_backend" and self.backend_ids:
errors.append("only index_backend experiments may enumerate backend_ids")
if not self.model_ids or len(set(self.model_ids)) != len(self.model_ids):
errors.append("model_ids must be non-empty and unique")
if not self.context_budgets or any(value <= 0 for value in self.context_budgets):
errors.append("context budgets must be positive")
if not self.seeds or self.repetitions <= 0:
errors.append("seeds and repetitions must be positive/non-empty")
if min(self.max_tool_calls, self.max_test_runs, self.timeout_seconds) <= 0:
errors.append("execution limits must be positive")
if errors:
location = f" ({source})" if source else ""
raise SpecError("Invalid experiment specification" + location + ": " + "; ".join(errors))
def cells_per_task(self) -> int:
return (
(len(self.harness_ids) + len(self.agent_system_ids))
* max(len(self.edit_interface_ids), 1)
* max(len(self.backend_ids), 1)
* len(self.model_ids)
* len(self.context_budgets)
* len(self.seeds)
* self.repetitions
)
@dataclass(frozen=True, slots=True)
class BackendSpec:
schema_version: int
backend_id: str
name: str
kind: str
distance: str
description: str
library: str
version: str
index_type: str
query_repetitions: int
neighbors: int | None = None
ef_construction: int | None = None
ef_search: int | None = None
@classmethod
def load(cls, path: Path) -> "BackendSpec":
value = _read_toml(path)
try:
spec = cls(
schema_version=int(value["schema_version"]),
backend_id=str(value["backend_id"]),
name=str(value["name"]),
kind=str(value["kind"]),
distance=str(value["distance"]),
description=str(value.get("description", "")),
library=str(value["library"]),
version=str(value["version"]),
index_type=str(value["index_type"]),
query_repetitions=int(value["query_repetitions"]),
neighbors=int(value["neighbors"]) if "neighbors" in value else None,
ef_construction=int(value["ef_construction"]) if "ef_construction" in value else None,
ef_search=int(value["ef_search"]) if "ef_search" in value else None,
)
except (KeyError, TypeError, ValueError) as exc:
raise SpecError(f"Malformed backend specification {path}: {exc}") from exc
errors: list[str] = []
if spec.schema_version != 1 or not re.fullmatch(r"B\d{3}", spec.backend_id):
errors.append("invalid backend schema or identifier")
if not all((spec.library, spec.version, spec.index_type)) or spec.query_repetitions < 2:
errors.append("backend version, index type, and at least two repetitions are required")
if spec.backend_id == "B002" and None in (
spec.neighbors, spec.ef_construction, spec.ef_search
):
errors.append("B002 must freeze HNSW parameters")
if errors:
raise SpecError(f"Invalid backend specification {path}: " + "; ".join(errors))
return spec
@property
def config_hash(self) -> str:
return _canonical_hash(asdict(self))
@dataclass(frozen=True, slots=True)
class AgentSystemSpec:
schema_version: int
system_id: str
name: str
family: str
description: str
implementation: str
model_calls: int
max_tool_calls: int
max_test_runs: int
interactive: bool
same_model_required: bool
reference: str
@classmethod
def load(cls, path: Path) -> "AgentSystemSpec":
value = _read_toml(path)
try:
spec = cls(
schema_version=int(value["schema_version"]),
system_id=str(value["system_id"]),
name=str(value["name"]),
family=str(value["family"]),
description=str(value["description"]),
implementation=str(value["implementation"]),
model_calls=int(value["model_calls"]),
max_tool_calls=int(value["max_tool_calls"]),
max_test_runs=int(value["max_test_runs"]),
interactive=bool(value["interactive"]),
same_model_required=bool(value["same_model_required"]),
reference=str(value["reference"]),
)
except (KeyError, TypeError, ValueError) as exc:
raise SpecError(f"Malformed agent-system specification {path}: {exc}") from exc
errors: list[str] = []
if spec.schema_version != 1 or not re.fullmatch(r"A\d{3}", spec.system_id):
errors.append("invalid agent-system schema or identifier")
if not re.fullmatch(r"[a-z][a-z0-9_]*", spec.name):
errors.append("agent-system name must be a lowercase semantic slug")
if spec.implementation != "local_reimplementation":
errors.append("system baselines must be explicitly labeled local_reimplementation")
if spec.model_calls <= 0 or min(spec.max_tool_calls, spec.max_test_runs) < 0:
errors.append("agent-system budgets must be nonnegative and model_calls positive")
if not spec.same_model_required or not spec.reference.startswith("https://"):
errors.append("same-model control and a public reference are required")
if errors:
raise SpecError(f"Invalid agent-system specification {path}: " + "; ".join(errors))
return spec
@property
def config_hash(self) -> str:
return _canonical_hash(asdict(self))
@dataclass(frozen=True, slots=True)
class RepositorySpec:
schema_version: int
repository_id: str
name: str
repository_url: str
local_path: str
language: str
source_suffixes: tuple[str, ...]
test_pattern: str
test_runner: str
pinned_head: str
@classmethod
def load(cls, path: Path) -> "RepositorySpec":
value = _read_toml(path)
try:
spec = cls(
schema_version=int(value["schema_version"]),
repository_id=str(value["repository_id"]),
name=str(value["name"]),
repository_url=str(value["repository_url"]),
local_path=str(value["local_path"]),
language=str(value["language"]),
source_suffixes=tuple(str(item) for item in value["source_suffixes"]),
test_pattern=str(value["test_pattern"]),
test_runner=str(value["test_runner"]),
pinned_head=str(value["pinned_head"]),
)
except (KeyError, TypeError, ValueError) as exc:
raise SpecError(f"Malformed repository specification {path}: {exc}") from exc
errors: list[str] = []
if spec.schema_version != 1 or not re.fullmatch(r"R\d{3}", spec.repository_id):
errors.append("invalid repository schema or identifier")
if not spec.repository_url.startswith("https://gitlab.com/"):
errors.append("Study 2 repositories must be public GitLab URLs")
local = Path(spec.local_path)
if local.is_absolute() or ".." in local.parts:
errors.append("local_path must be a safe project-relative path")
if spec.language not in {"go", "python"}:
errors.append("unsupported Study 2 language")
if not spec.source_suffixes or any(not item.startswith(".") for item in spec.source_suffixes):
errors.append("source_suffixes must be non-empty file suffixes")
if not re.fullmatch(r"[0-9a-f]{40}", spec.pinned_head):
errors.append("pinned_head must be a lowercase full Git SHA")
if errors:
raise SpecError(f"Invalid repository specification {path}: " + "; ".join(errors))
return spec
@property
def config_hash(self) -> str:
return _canonical_hash(asdict(self))
def _load_specs(directory: Path, loader: Any, identifier_field: str) -> dict[str, Any]:
result: dict[str, Any] = {}
for path in sorted(directory.glob("*.toml")):
spec = loader(path)
identifier = getattr(spec, identifier_field)
if identifier in result:
raise SpecError(f"Duplicate specification identifier {identifier} in {directory}")
result[identifier] = spec
return result
def load_harnesses(root: Path | None = None) -> dict[str, HarnessSpec]:
base = root or project_root()
return _load_specs(base / "configs" / "harnesses", HarnessSpec.load, "harness_id")
def load_edit_interfaces(root: Path | None = None) -> dict[str, EditInterfaceSpec]:
base = root or project_root()
return _load_specs(
base / "configs" / "edit_interfaces",
EditInterfaceSpec.load,
"interface_id",
)
def load_models(root: Path | None = None) -> dict[str, ModelSpec]:
base = root or project_root()
return _load_specs(base / "configs" / "models", ModelSpec.load, "model_id")
def load_embeddings(root: Path | None = None) -> dict[str, EmbeddingSpec]:
base = root or project_root()
return _load_specs(base / "configs" / "embeddings", EmbeddingSpec.load, "embedding_id")
def load_experiments(root: Path | None = None) -> dict[str, ExperimentSpec]:
base = root or project_root()
return _load_specs(base / "configs" / "experiments", ExperimentSpec.load, "experiment_id")
def load_backends(root: Path | None = None) -> dict[str, BackendSpec]:
base = root or project_root()
return _load_specs(base / "configs" / "backends", BackendSpec.load, "backend_id")
def load_agent_systems(root: Path | None = None) -> dict[str, AgentSystemSpec]:
base = root or project_root()
return _load_specs(
base / "configs" / "agent_systems", AgentSystemSpec.load, "system_id"
)
def load_repositories(root: Path | None = None) -> dict[str, RepositorySpec]:
base = root or project_root()
return _load_specs(
base / "configs" / "repositories", RepositorySpec.load, "repository_id"
)
def load_tasks(root: Path | None = None) -> dict[str, TaskSpec]:
base = root or project_root()
return _load_specs(base / "tasks" / "manifests", TaskSpec.load, "task_id")
def load_task_split(path: Path) -> tuple[str, ...]:
try:
identifiers = tuple(
line.strip()
for line in path.read_text(encoding="utf-8").splitlines()
if line.strip() and not line.lstrip().startswith("#")
)
except OSError as exc:
raise SpecError(f"Cannot read task split {path}: {exc}") from exc
if len(set(identifiers)) != len(identifiers):
raise SpecError(f"Task split contains duplicate identifiers: {path}")
return identifiers
def _validate_plain_toml(paths: Iterable[Path], errors: list[str]) -> None:
for path in paths:
try:
_read_toml(path)
except SpecError as exc:
errors.append(str(exc))
def validate_configuration_tree(root: Path | None = None) -> tuple[list[str], list[str]]:
base = root or project_root()
errors: list[str] = []
warnings: list[str] = []
try:
harnesses = load_harnesses(base)
edit_interfaces = load_edit_interfaces(base)
models = load_models(base)
embeddings = load_embeddings(base)
experiments = load_experiments(base)
backends = load_backends(base)
agent_systems = load_agent_systems(base)
repositories = load_repositories(base)
tasks = load_tasks(base)
except SpecError as exc:
return [str(exc)], []
expected_harness_ids = {f"H{number:03d}" for number in range(21)}
if set(harnesses) != expected_harness_ids:
errors.append(
"Initial catalog must contain exactly H000-H020; found "
+ ", ".join(sorted(harnesses))
)
if set(models) != {"M001", "M002", "M003", "M004"}:
errors.append("The study must define exactly the pinned runtime profiles M001-M004")
if set(edit_interfaces) != {"P001", "P002", "P003"}:
errors.append("Study 3 must define exactly edit interfaces P001-P003")
if set(embeddings) != {"EMB001", "EMB002"}:
errors.append(
"The study must define exactly EMB001 (Study 1) and EMB002 (Study 2)"
)
if set(backends) != {"B001", "B002", "B003"}:
errors.append("The backend study must define exactly B001-B003")
if set(agent_systems) != {"A001", "A002"}:
errors.append("Study 2 must define exactly controlled agent systems A001-A002")
if set(repositories) != {"R001", "R002", "R003"}:
errors.append("Study 2 must define exactly repositories R001-R003")
for experiment in experiments.values():
missing_harnesses = set(experiment.harness_ids) - set(harnesses)
missing_edit_interfaces = set(experiment.edit_interface_ids) - set(edit_interfaces)
missing_models = set(experiment.model_ids) - set(models)
missing_systems = set(experiment.agent_system_ids) - set(agent_systems)
missing_repositories = set(experiment.repository_ids) - set(repositories)
if missing_harnesses:
errors.append(f"{experiment.experiment_id} references missing harnesses {sorted(missing_harnesses)}")
if missing_edit_interfaces:
errors.append(
f"{experiment.experiment_id} references missing edit interfaces "
f"{sorted(missing_edit_interfaces)}"
)
if missing_models:
errors.append(f"{experiment.experiment_id} references missing models {sorted(missing_models)}")
if missing_systems:
errors.append(
f"{experiment.experiment_id} references missing agent systems {sorted(missing_systems)}"
)
if missing_repositories:
errors.append(
f"{experiment.experiment_id} references missing repositories {sorted(missing_repositories)}"
)
missing_backends = set(experiment.backend_ids) - set(backends)
if missing_backends:
errors.append(
f"{experiment.experiment_id} references missing backends {sorted(missing_backends)}"
)
if experiment.embedding_id not in embeddings:
errors.append(
f"{experiment.experiment_id} references missing embedding profile {experiment.embedding_id}"
)
continue
uses_dense = any(harnesses[item].uses_embedding for item in experiment.harness_ids)
if uses_dense and embeddings[experiment.embedding_id].status != "ready":
warnings.append(
f"{experiment.experiment_id} includes dense retrieval but {experiment.embedding_id} "
"is not configured; execution must remain blocked"
)
split_path = base / "tasks" / "splits" / f"{experiment.task_split}.txt"
try:
split = load_task_split(split_path)
except SpecError as exc:
errors.append(str(exc))
continue
missing_tasks = set(split) - set(tasks)
if missing_tasks:
errors.append(
f"{experiment.experiment_id} split references missing tasks {sorted(missing_tasks)}"
)
if "TASK_EXAMPLE" in split:
errors.append(f"{experiment.experiment_id} cannot include the template TASK_EXAMPLE")
if not split:
warnings.append(
f"{experiment.experiment_id} task split {experiment.task_split} is empty; "
"execution must remain blocked"
)
if experiment.mode in {
"study2_live_agent",
"study2_reliability",
"protocol_interface",
}:
allowed_urls = {
repositories[item].repository_url for item in experiment.repository_ids
}
unexpected = {
tasks[item].repository_url
for item in split
if item in tasks and tasks[item].repository_url not in allowed_urls
}
if unexpected:
errors.append(
f"{experiment.experiment_id} split contains repositories outside its registry: "
f"{sorted(unexpected)}"
)
hashes: dict[str, str] = {}
for harness in harnesses.values():
if harness.treatment_hash in hashes:
errors.append(
f"{harness.harness_id} duplicates the causal treatment of "
f"{hashes[harness.treatment_hash]}"
)
hashes[harness.treatment_hash] = harness.harness_id
interface_hashes: dict[str, str] = {}
for interface in edit_interfaces.values():
if interface.treatment_hash in interface_hashes:
errors.append(
f"{interface.interface_id} duplicates the causal treatment of "
f"{interface_hashes[interface.treatment_hash]}"
)
interface_hashes[interface.treatment_hash] = interface.interface_id
_validate_plain_toml((base / "configs" / "scenarios").glob("*.toml"), errors)
_validate_plain_toml((base / "configs" / "backends").glob("*.toml"), errors)
return errors, warnings
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