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import itertools
import random
from dataclasses import dataclass
from typing import Any
from .models import BenchCase
from .tokenizer import TokenizerProtocol
from .util import sha256_json
SYSTEM_PROMPT = (
"You are running a deterministic long-context evaluation. Read the complete "
"context, ignore stale or decoy records, and return only the requested bare JSON."
)
@dataclass(frozen=True)
class ContextSpec:
suite_id: str
matrix: str
family: str
target_tokens: int
position: float
seed: int
query_placement: str = "after"
@property
def case_id(self) -> str:
position = f"p{round(self.position * 100):02d}"
return (
f"{self.matrix}__{self.family}__t{self.target_tokens}__{position}"
f"__s{self.seed}__q{self.query_placement}"
)
def expand_context_config(config: dict[str, Any]) -> list[ContextSpec]:
suite_id = str(config["suite_id"])
context_window = int(config.get("context_window") or 0)
reserved_output = int(config.get("reserved_output_tokens") or 0)
maximum_prompt = context_window - reserved_output if context_window and reserved_output else None
specs: list[ContextSpec] = []
for matrix in config["matrices"]:
values = itertools.product(
matrix["families"],
matrix["lengths"],
matrix["positions"],
matrix["seeds"],
matrix.get("query_placements", ["after"]),
)
for family, length, position, seed, placement in values:
if not 0 < float(position) < 1:
raise ValueError(f"Position must be between zero and one: {position}")
if placement not in {"before", "after"}:
raise ValueError(f"Unsupported query placement: {placement}")
if maximum_prompt is not None and int(length) > maximum_prompt:
raise ValueError(
f"Prompt length {length} exceeds context-minus-output budget {maximum_prompt}"
)
specs.append(
ContextSpec(
suite_id=suite_id,
matrix=str(matrix["name"]),
family=str(family),
target_tokens=int(length),
position=float(position),
seed=int(seed),
query_placement=str(placement),
)
)
identifiers = [item.case_id for item in specs]
if len(identifiers) != len(set(identifiers)):
raise ValueError("Context configuration produces duplicate case identifiers")
return specs
def _secret(seed: int, label: str) -> str:
rng = random.Random(f"{seed}:{label}")
left = ["amber", "cedar", "indigo", "marble", "opal", "quartz", "saffron"]
right = ["falcon", "harbor", "lighthouse", "otter", "raven", "willow"]
return f"{rng.choice(left)}-{rng.randrange(100000, 999999)}-{rng.choice(right)}"
def _task_payload(spec: ContextSpec) -> tuple[list[tuple[float, str, str]], str, dict[str, Any]]:
target = _secret(spec.seed, spec.family)
decoy_a = _secret(spec.seed + 101, "decoy-a")
decoy_b = _secret(spec.seed + 202, "decoy-b")
marker = f"<<SX_PRIMARY_{spec.seed}_{spec.family.upper()}>>"
common: dict[str, Any] = {"record": target}
insertions: list[tuple[float, str, str]] = [
(0.08, "decoy-a", f"STALE RECORD record={decoy_a} status=revoked revision=2"),
(0.92, "decoy-b", f"DECOY RECORD record={decoy_b} status=simulation revision=99"),
]
if spec.family == "single_key":
primary = f"{marker} CANONICAL RECORD record={target} status=active"
query = "Return the active canonical record."
elif spec.family == "binding":
key = f"device-{(spec.seed % 83) + 10}"
primary = (
f"{marker} AUTHORITATIVE BINDING device={key} record={target} "
"status=active; similar device identifiers are unrelated."
)
common["device"] = key
query = f"Return the active record bound specifically to device {key}."
elif spec.family == "latest_record":
primary = f"{marker} CHANGELOG record={target} revision=12 state=current"
insertions.extend(
[
(0.19, "older", f"CHANGELOG record={decoy_a} revision=10 state=superseded"),
(0.77, "newer-decoy", f"CHANGELOG record={decoy_b} revision=13 state=test-only"),
]
)
common["revision"] = 12
query = "Return the highest production revision marked current, excluding test-only entries."
elif spec.family == "multi_hop":
relay = f"relay-{(spec.seed % 71) + 20}"
zone = ["north", "south", "east", "west"][spec.seed % 4]
primary = f"{marker} ROUTE MAP record={target} uses_relay={relay}"
insertions.append(
(1.0 - spec.position, "hop-two", f"RELAY DIRECTORY relay={relay} final_zone={zone}"),
)
common.update({"relay": relay, "zone": zone})
query = "Follow the route map through the relay directory and return record, relay, and final zone."
elif spec.family == "semantic":
callsign = f"unit-{(spec.seed % 89) + 10}"
primary = (
f"{marker} The only solar-powered courier approved for silent night delivery "
f"has registry value {target} and operational callsign {callsign}."
)
common["callsign"] = callsign
query = (
"Identify the registry value and callsign of the emission-free messenger "
"authorized to work after dark."
)
elif spec.family == "state_tracking":
counter = 10 + spec.seed % 7
add_a = 3 + spec.seed % 5
subtract = 1 + spec.seed % 3
final = counter + add_a - subtract
primary = f"{marker} STATE stream={target} value={counter} sequence=1"
insertions.extend(
[
(min(0.95, spec.position + 0.18), "update-a", f"UPDATE stream={target} add={add_a} sequence=2"),
(min(0.98, spec.position + 0.34), "update-b", f"UPDATE stream={target} subtract={subtract} sequence=3"),
]
)
common["final_value"] = final
query = "Apply the ordered updates and return the stream record and final value."
else:
raise ValueError(f"Unknown context family: {spec.family}")
primary_end_marker = f"<<SX_PRIMARY_END_{spec.seed}_{spec.family.upper()}>>"
insertions.append((spec.position, "primary", primary + " " + primary_end_marker))
return sorted(insertions, key=lambda item: (item[0], item[1])), query, common
def _filler_pool(tokenizer: TokenizerProtocol, seed: int) -> list[int]:
rng = random.Random(seed)
services = ["atlas", "beacon", "cinder", "delta", "ember", "fjord", "garnet", "helios"]
states = ["nominal", "queued", "retrying", "stable", "verified", "warming"]
regions = ["north-ridge", "west-field", "central-bay", "east-grove"]
lines = []
for index in range(5000):
service = rng.choice(services)
state = rng.choice(states)
region = rng.choice(regions)
trace = f"{rng.getrandbits(48):012x}"
lines.append(
f"event {index:05d}: service={service} region={region} state={state} "
f"latency_ms={rng.randrange(3, 997)} trace={trace}; routine telemetry only."
)
token_ids = tokenizer.encode("\n" + "\n".join(lines) + "\n")
if len(token_ids) < 1000:
raise ValueError("Tokenizer produced an unexpectedly small filler pool")
return token_ids
def _take(pool: list[int], count: int, offset: int) -> list[int]:
if count <= 0:
return []
start = offset % len(pool)
rotated = pool[start:] + pool[:start]
repeats, remainder = divmod(count, len(rotated))
return rotated * repeats + rotated[:remainder]
def _allocate_filler(total: int, insertion_positions: list[float]) -> list[int]:
gaps = []
previous = 0.0
for position in insertion_positions:
gaps.append(max(0.0, position - previous))
previous = position
gaps.append(max(0.0, 1.0 - previous))
raw = [total * gap for gap in gaps]
values = [int(value) for value in raw]
for index in sorted(range(len(raw)), key=lambda item: raw[item] - values[item], reverse=True):
if sum(values) >= total:
break
values[index] += 1
return values
def build_context_case(spec: ContextSpec, tokenizer: TokenizerProtocol) -> BenchCase:
insertions, query, expected = _task_payload(spec)
start_sentinel = f"SX_START_{spec.seed}_{spec.family}"
end_sentinel = f"SX_END_{spec.seed}_{spec.family}"
expected = {"start_sentinel": start_sentinel, **expected, "end_sentinel": end_sentinel}
keys = ", ".join(expected)
query_text = f"{query} Return exactly one JSON object with keys: {keys}."
prefix_query = query_text + "\n\n" if spec.query_placement == "before" else ""
suffix_query = "\n\n" + query_text if spec.query_placement == "after" else ""
fixed_user = (
prefix_query
+ f"BEGIN CONTEXT\nSTART SENTINEL: {start_sentinel}\n"
+ "\n".join(text for _, _, text in insertions)
+ f"\nEND SENTINEL: {end_sentinel}\nEND CONTEXT"
+ suffix_query
)
fixed_messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": fixed_user},
]
fixed_tokens = len(tokenizer.encode(tokenizer.render_chat(fixed_messages)))
available = spec.target_tokens - fixed_tokens
if available < 512:
raise ValueError(
f"Target {spec.target_tokens} is too small for {spec.family}; only {available} filler tokens remain"
)
pool = _filler_pool(tokenizer, spec.seed)
allocations = _allocate_filler(available, [item[0] for item in insertions])
segments = [_take(pool, size, spec.seed * 97 + index * 7919) for index, size in enumerate(allocations)]
primary_index = next(index for index, item in enumerate(insertions) if item[1] == "primary")
primary_marker = insertions[primary_index][2].split(" ", 1)[0]
primary_end_marker = f"<<SX_PRIMARY_END_{spec.seed}_{spec.family.upper()}>>"
def make_messages() -> list[dict[str, Any]]:
context_parts = [f"START SENTINEL: {start_sentinel}\n"]
for index, (_, _, insertion) in enumerate(insertions):
context_parts.append(tokenizer.decode(segments[index]))
context_parts.append("\n" + insertion + "\n")
context_parts.append(tokenizer.decode(segments[-1]))
context_parts.append(f"\nEND SENTINEL: {end_sentinel}")
user = prefix_query + "BEGIN CONTEXT\n" + "".join(context_parts) + "\nEND CONTEXT" + suffix_query
return [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": user},
]
diagnostics: list[tuple[int, int, int]] = []
for iteration in range(24):
messages = make_messages()
rendered = tokenizer.render_chat(messages)
actual_total = len(tokenizer.encode(rendered))
total_delta = spec.target_tokens - actual_total
primary_start = tokenizer.token_offset(rendered, primary_marker)
primary_end = tokenizer.token_offset(rendered, primary_end_marker)
actual_offset = round((primary_start + primary_end) / 2)
context_start = tokenizer.token_offset(rendered, "START SENTINEL:")
context_end = tokenizer.token_offset(rendered, "END SENTINEL:")
diagnostics.append((actual_total, actual_offset, total_delta))
if total_delta:
if total_delta > 0:
segments[-1].extend(_take(pool, total_delta, spec.seed + iteration * 3571))
else:
remaining = -total_delta
for segment in reversed(segments):
removed = min(remaining, len(segment))
if removed:
del segment[-removed:]
remaining -= removed
if remaining == 0:
break
if remaining:
raise ValueError("Unable to trim context to requested token count")
continue
desired_offset = context_start + round((context_end - context_start) * spec.position)
position_delta = desired_offset - actual_offset
tolerance = max(4, round(spec.target_tokens * 0.0025))
if abs(position_delta) <= tolerance:
break
before = segments[primary_index]
after = segments[primary_index + 1]
if position_delta > 0:
movement = min(position_delta, len(after))
before.extend(_take(pool, movement, spec.seed + iteration * 1237))
del after[:movement]
else:
movement = min(-position_delta, len(before))
del before[-movement:]
after[:0] = _take(pool, movement, spec.seed + iteration * 1237)
else:
raise RuntimeError(
f"Exact context construction did not converge for {spec.case_id}; "
f"last iterations={diagnostics[-6:]}"
)
messages = make_messages()
rendered = tokenizer.render_chat(messages)
actual_total = len(tokenizer.encode(rendered))
primary_start = tokenizer.token_offset(rendered, primary_marker)
primary_end = tokenizer.token_offset(rendered, primary_end_marker)
actual_offset = round((primary_start + primary_end) / 2)
context_start = tokenizer.token_offset(rendered, "START SENTINEL:")
context_end = tokenizer.token_offset(rendered, "END SENTINEL:")
if actual_total != spec.target_tokens:
raise AssertionError(f"Requested {spec.target_tokens} rendered tokens, produced {actual_total}")
actual_position = (actual_offset - context_start) / (context_end - context_start)
metadata = {
"matrix": spec.matrix,
"family": spec.family,
"seed": spec.seed,
"target_prompt_tokens": spec.target_tokens,
"actual_rendered_tokens": actual_total,
"requested_position": spec.position,
"actual_primary_token_offset": actual_offset,
"actual_primary_start_token_offset": primary_start,
"actual_primary_end_token_offset": primary_end,
"actual_primary_position": actual_position,
"context_start_token_offset": context_start,
"context_end_token_offset": context_end,
"query_placement": spec.query_placement,
"tokenizer_fingerprint": tokenizer.fingerprint,
"prompt_hash": sha256_json(messages),
"truncation_sentinels": [start_sentinel, end_sentinel],
}
return BenchCase(
case_id=spec.case_id,
suite_id=spec.suite_id,
lane="long-context",
messages=messages,
scorer="strict_json_exact",
expected=expected,
max_output_tokens=2048,
metadata=metadata,
)
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