Arena: the environment repository, and redaction-pressure

An environment is an eval you take the gradient of, so it carries obligations a benchmark does not: a held-out slice, a counterweighted reward, and a demonstrated 0.0 floor and 1.0 ceiling. README states the three as house rules. redaction-pressure is Forge's redact stage as a task. The first reward design scored survivors — secrets caught, decoys kept, prose kept — and an empty ruleset tied the best real attempt at 0.500, because destroying nothing keeps everything. Counting removals instead puts inaction at 0.000, shredding at 0.380, and an oracle at 1.000. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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 # Arena
 RL environments from Lumbridge. Apache-2.0, built to the [`verifiers`](https://github.com/PrimeIntellect-ai/verifiers)
 v1 spec so they install and run on the Prime Intellect Environments Hub unchanged.
 An environment here is an eval you take the gradient of. That is not a rename — it changes
 what the artifact has to carry. A benchmark's score is its output, so a benchmark can be a
 number in a table. An environment's score is the input to the next weight update, so it has
 to be reproducible, gameable-proof, and traceable back to the code that produced it, or the
 model that trained on it inherits whatever was wrong with it silently.
 Three rules follow, and every environment in this repository is held to them:
 1. **Graded on what the model did not see.** Rules, plans and policies fitted to visible
   examples pass on visible examples. If a held-out slice cannot be generated, the
   environment does not measure generalisation and does not ship.
 2. **No single-sided reward.** Every reward has a counterweight in the same episode. A
   reward you can max by doing the crude thing is a reward that teaches the crude thing.
 3. **A reachable ceiling and a zero floor, demonstrated.** Each environment ships the check
   that shows an oracle scores 1.0 and inaction scores 0.0. An unreachable component is
   dead weight in the gradient; a floor above zero pays for doing nothing.
 ## Environments
 | | |
 |---|---|
 | [`redaction-pressure`](environments/redaction_pressure) | Write redaction rules for a ticket corpus. Graded on the next records the generator would have produced. Every secret class is shape-matched to a decoy, so a rule keyed on shape earns on one and pays on the other. Reward is a scanner — no judge model, no runtime, milliseconds per rollout. |
 ## Running one
 ```bash
 uv run eval @ configs/redaction_pressure.toml --model <model-id>
 ```
 ## Why these, and not benchmark reimplementations
 The Hub is filling with reimplementations, which are worth doing and are not what we have.
 What Lumbridge has is a set of tools that already do verifiable work — Bench's
 contamination gate, Forge's signed build chain — and those gates make good rewards
 precisely because they were built to be un-arguable about a machine. `redaction-pressure`
 is Forge's `redact` stage with the exit code turned into a gradient.
@@ -0,0 +1,13 @@
 # redaction-pressure — rules are graded on records the model never read.
 #
 #   uv run eval @ configs/redaction_pressure.toml
 num_tasks = 16
 num_rollouts = 2
 [env.taskset]
 id = "redaction-pressure"
 visible = 4    # records the model reads
 held_out = 12  # records it is scored on
 [env.agent.harness]
 id = "null"    # a pure-trace reward: one completion, no tools, no runtime
@@ -0,0 +1,13 @@
 [project]
 name = "redaction-pressure"
 version = "0.1.0"
 description = "redaction-pressure — write redaction rules graded on records the author never saw."
 requires-python = ">=3.11"
 dependencies = ["verifiers", "regex"]
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
 [tool.hatch.build.targets.wheel]
 packages = ["redaction_pressure"]
@@ -0,0 +1,3 @@
 from redaction_pressure.taskset import RedactionTaskset
 __all__ = ["RedactionTaskset"]
@@ -0,0 +1,161 @@
 """The corpus generator.
 Every task is one *stream* of records: a visible slice the agent reads, and a held-out
 slice it never sees, drawn from the same generator with the same secret classes and
 different instances. Both slices are built from one seed, so a task is reproducible from
 its integer id alone and nothing has to ship as data.
 The held-out slice is the whole point. Rules written by reading examples will pass the
 examples; the only question worth rewarding is whether they survive instances the author
 never saw. A generator gives that for free, and a fixed dataset cannot give it at all.
 Each record carries `secrets` (spans that MUST be removed) and `decoys` (spans that look
 like secrets to a careless rule and MUST survive). The decoys are not garnish — without
 them "redact everything" scores perfectly, and the reward stops measuring anything.
 """
 from __future__ import annotations
 import random
 import string
 from dataclasses import dataclass, field
 # Held together deliberately: every secret class is paired with a decoy class that shares
 # its surface shape. A rule keyed on shape alone scores on one and pays on the other.
 FIRST = ["Dana", "Priya", "Marcus", "Ines", "Tomas", "Rui", "Nadia", "Owen"]
 LAST = ["Okafor", "Lindqvist", "Baptiste", "Moreau", "Sato", "Ahmed", "Vance", "Duarte"]
 CITY = ["lisbon", "osaka", "tallinn", "bogota", "leeds", "dakar"]
 PUBLIC_HOSTS = ["docs.python.org", "status.cloud.example", "www.postgresql.org"]
@dataclass(frozen=True)
 class Span:
    """One region of a record, by class, with the exact text occupying it."""
    kind: str
    text: str
@dataclass
 class Record:
    """One document, plus the ground truth about what is in it."""
    body: str
    secrets: list[Span] = field(default_factory=list)
    decoys: list[Span] = field(default_factory=list)
 def _token(rng: random.Random, n: int, alphabet: str = string.ascii_lowercase + string.digits) -> str:
    return "".join(rng.choice(alphabet) for _ in range(n))
 # --- the seven secret classes, each with its shape-matched decoy ------------------------
 # Each builder returns (sentence, secret span, decoy span). The decoy is what a rule that
 # matched on shape rather than meaning would also destroy.
 def _api_key(rng: random.Random) -> tuple[str, Span, Span]:
    secret = f"sk-live-{_token(rng, 24)}"
    # Shape-identical and public: the test key in our own README.
    decoy = f"sk-test-{'0' * 24}"
    return (
        f"Customer pasted their key {secret} into the ticket; our published sample is "
        f"{decoy} and should stay in the docs snippet.",
        Span("api_key", secret),
        Span("public_test_key", decoy),
    )
 def _email(rng: random.Random) -> tuple[str, Span, Span]:
    first, last = rng.choice(FIRST), rng.choice(LAST)
    secret = f"{first.lower()}.{last.lower()}@{_token(rng, 6, string.ascii_lowercase)}.com"
    decoy = "support@lumbridgecorp.com"
    return (
        f"Reply-to was {secret}; the shared alias {decoy} was on cc and is public.",
        Span("email", secret),
        Span("public_alias", decoy),
    )
 def _phone(rng: random.Random) -> tuple[str, Span, Span]:
    secret = f"+1-{rng.randint(200, 989)}-{rng.randint(200, 999)}-{rng.randint(1000, 9999)}"
    # Same digit shape, not a phone number: an order reference.
    decoy = f"ORD-{rng.randint(200, 989)}-{rng.randint(200, 999)}-{rng.randint(1000, 9999)}"
    return (
        f"Callback number {secret} against order {decoy}.",
        Span("phone", secret),
        Span("order_ref", decoy),
    )
 def _card(rng: random.Random) -> tuple[str, Span, Span]:
    secret = "-".join(f"{rng.randint(1000, 9999)}" for _ in range(4))
    # Sixteen digits in four groups that is a build id, not a card.
    decoy = f"build {rng.randint(1000, 9999)}.{rng.randint(1000, 9999)}.{rng.randint(1000, 9999)}.{rng.randint(1000, 9999)}"
    return (
        f"They read the card {secret} aloud on the call, on {decoy}.",
        Span("card", secret),
        Span("build_id", decoy),
    )
 def _host(rng: random.Random) -> tuple[str, Span, Span]:
    secret = f"{_token(rng, 5, string.ascii_lowercase)}-db-{rng.randint(1, 9)}.internal.lumbridge"
    decoy = rng.choice(PUBLIC_HOSTS)
    return (
        f"Trace pointed at {secret}; the public mirror {decoy} was fine.",
        Span("internal_host", secret),
        Span("public_host", decoy),
    )
 def _person(rng: random.Random) -> tuple[str, Span, Span]:
    secret = f"{rng.choice(FIRST)} {rng.choice(LAST)}"
    # A person-shaped name that is a product, not a customer.
    decoy = "Lumbridge Arena"
    return (
        f"Escalated by {secret} after the {decoy} demo.",
        Span("person", secret),
        Span("product_name", decoy),
    )
 def _guid(rng: random.Random) -> tuple[str, Span, Span]:
    secret = "-".join(_token(rng, n, string.hexdigits.lower()[:16]) for n in (8, 4, 4, 4, 12))
    # A GUID that is deliberately public: the canary probe id, which must survive redaction
    # or the contamination check downstream loses the thing it looks for.
    decoy = "canary-0000-0000-0000-000000000000"
    return (
        f"Session {secret} carried the canary {decoy}.",
        Span("session_guid", secret),
        Span("canary_id", decoy),
    )
 BUILDERS = [_api_key, _email, _phone, _card, _host, _person, _guid]
 def build_record(rng: random.Random) -> Record:
    """One record: two or three sentence-pairs, each contributing a secret and its decoy."""
    picks = rng.sample(BUILDERS, rng.randint(2, 3))
    sentences, secrets, decoys = [], [], []
    for build in picks:
        sentence, secret, decoy = build(rng)
        sentences.append(sentence)
        secrets.append(secret)
        decoys.append(decoy)
    city = rng.choice(CITY)
    body = f"[ticket/{city}/{rng.randint(1000, 9999)}] " + " ".join(sentences)
    return Record(body=body, secrets=secrets, decoys=decoys)
 def build_slices(seed: int, visible: int, held_out: int) -> tuple[list[Record], list[Record]]:
    """The two slices of one task, from one seed.
    Drawn from a single stream rather than two, so the held-out records are the *next*
    ones the generator would have produced — not a differently-seeded population that
    could differ in ways the agent could not have anticipated.
    """
    rng = random.Random(seed)
    records = [build_record(rng) for _ in range(visible + held_out)]
    return records[:visible], records[visible:]
@@ -0,0 +1,136 @@
 """Applying the agent's rules, and measuring what they did.
 Everything is counted as *removals*, and that choice is the whole reward.
 The obvious framing — secrets caught, decoys survived, prose kept — pays a model that
 returns no rules at all: it destroys nothing, so it keeps everything. Measured that way
 an empty ruleset and a ruleset that shreds the corpus scored identically, and neither
 told a trainer anything. Counting removals fixes it, because a model that removes nothing
 has no true positives and therefore no precision.
  removed_secrets   true positives — a seeded secret gone from the held-out text.
  residual_hits     the ones still there. Forge's rule applies to the gate: a residual
                    hit is exit 1, not a deduction.
  collateral_hits   decoys destroyed. Each is shape-matched to a secret class, so this is
                    what a rule keyed on shape pays and one keyed on meaning does not.
  innocent_lost     ordinary words destroyed. The floor that stops `\\S+ -> [X]`, which
                    otherwise has perfect recall.
 The agent supplies the patterns, so they are hostile input: `regex` is used rather than
 `re` for its `timeout=`, since `re` cannot be interrupted and one nested quantifier would
 otherwise hang a rollout until the harness killed it.
 """
 from __future__ import annotations
 from dataclasses import dataclass
 import regex
 from redaction_pressure.corpus import Record
 MAX_RULES = 24
 MAX_PATTERN_CHARS = 200
 PATTERN_TIMEOUT_S = 0.25
@dataclass(frozen=True)
 class Rule:
    pattern: str
    replacement: str
@dataclass
 class Outcome:
    """What one ruleset did to one slice."""
    residual_hits: int
    secrets_total: int
    collateral_hits: int
    decoys_total: int
    innocent_lost: int
    innocent_total: int
    invalid_rules: list[str]
    @property
    def removed_secrets(self) -> int:
        return self.secrets_total - self.residual_hits
    @property
    def recall(self) -> float:
        return self.removed_secrets / max(self.secrets_total, 1)
    @property
    def precision(self) -> float:
        """Of everything this ruleset destroyed, how much of it should have been.
        Zero for an empty ruleset — it destroyed nothing, so none of what it destroyed
        was a secret. That is the case the survivor-counting version got wrong.
        """
        destroyed = self.removed_secrets + self.collateral_hits + self.innocent_lost
        return self.removed_secrets / destroyed if destroyed else 0.0
    @property
    def clean(self) -> bool:
        """The gate: every secret gone AND no decoy taken with it — the ruleset you could
        actually ship. `forge redact`'s exit 1, with the collateral clause it implies."""
        return (
            self.residual_hits == 0
            and self.collateral_hits == 0
            and self.secrets_total > 0
        )
 def compile_rules(rules: list[Rule]) -> tuple[list[tuple[regex.Pattern, str]], list[str]]:
    """Compile what compiles; report the rest rather than failing the rollout.
    A rollout that dies on one bad pattern teaches nothing — the agent's score should
    reflect that the rule did no work, which is what dropping it does.
    """
    compiled, invalid = [], []
    for rule in rules[:MAX_RULES]:
        if len(rule.pattern) > MAX_PATTERN_CHARS:
            invalid.append(f"{rule.pattern[:40]}…: over {MAX_PATTERN_CHARS} characters")
            continue
        try:
            compiled.append((regex.compile(rule.pattern), rule.replacement))
        except regex.error as exc:
            invalid.append(f"{rule.pattern[:40]}: {exc}")
    return compiled, invalid
 def apply_rules(body: str, compiled: list[tuple[regex.Pattern, str]]) -> str:
    """Rules in the order given, each over the whole text. A pattern that times out is
    skipped for that record rather than aborting the measurement."""
    text = body
    for pattern, replacement in compiled:
        try:
            text = pattern.sub(replacement, text, timeout=PATTERN_TIMEOUT_S)
        except (TimeoutError, regex.error):
            continue
    return text
 def _innocent_text(record: Record) -> str:
    """The record with every secret and decoy cut out — the part no rule should touch."""
    text = record.body
    for span in [*record.secrets, *record.decoys]:
        text = text.replace(span.text, " ")
    return text
 def measure(records: list[Record], rules: list[Rule]) -> Outcome:
    compiled, invalid = compile_rules(rules)
    residual = collateral = secrets = decoys = lost = total = 0
    for record in records:
        redacted = apply_rules(record.body, compiled)
        secrets += len(record.secrets)
        decoys += len(record.decoys)
        residual += sum(1 for s in record.secrets if s.text in redacted)
        collateral += sum(1 for d in record.decoys if d.text not in redacted)
        # Fidelity is counted over whole words rather than characters: a rule that eats
        # every space would otherwise keep most of its characters and look harmless.
        innocent = [w for w in _innocent_text(record).split() if w]
        total += len(innocent)
        lost += sum(1 for w in innocent if w not in redacted)
    return Outcome(residual, secrets, collateral, decoys, lost, total, invalid)
@@ -0,0 +1,143 @@
 """redaction-pressure: write redaction rules that survive records you never read.
 The agent reads a handful of support tickets seeded with secrets, and returns a ruleset.
 The ruleset is then run against the *next* records the same generator would have produced.
 That gap is the environment: rules fitted to the visible examples score well on the
 visible examples and are worth nothing, and only the held-out slice can tell the two apart.
 Three rewards, all counted over what the ruleset *destroyed*:
  recall     seeded secrets removed from the held-out text.
  precision  of everything destroyed, the share that was a secret. An empty ruleset
             destroys nothing and therefore scores zero here — which is the point. A
             first pass scored survivors instead, and an empty ruleset tied the best
             real attempt at 0.500 because it had kept every decoy and every word.
  gate       binary: every secret gone and no decoy taken with it. `forge redact`'s
             exit 1, which is a verdict and not a deduction.
 No judge model and no runtime: the reward is a scanner, so a rollout costs one completion
 and grades in milliseconds. Deterministic from the task seed.
 """
 from __future__ import annotations
 import json
 import re
 from typing import ClassVar
 from pydantic import Field
 import verifiers.v1 as vf
 from redaction_pressure.corpus import build_slices
 from redaction_pressure.scan import MAX_RULES, Rule, measure
 SYSTEM = f"""You are writing redaction rules for a support-ticket corpus.
 Return a JSON array of at most {MAX_RULES} rules in one ```json code block. Each rule is
 an object with "pattern" (a Python regular expression) and "replacement" (the text that
 replaces every match).
 Your rules will NOT be graded on the tickets below. They will be run against different
 tickets from the same source, containing the same kinds of secret and the same kinds of
 lookalike. Removing every secret matters most; destroying text that only resembles a
 secret, or shredding ordinary prose, costs you.
 Example shape:
 ```json
 [{{"pattern": "\\\\bfoo-\\\\d+\\\\b", "replacement": "[REF]"}}]
 ```"""
 _BLOCK = re.compile(r"```(?:json)?\s*\n(.*?)```", re.DOTALL)
 def parse_rules(reply: str) -> list[Rule]:
    """The last JSON array in the reply. A reply with no parsable ruleset is an empty
    ruleset, not an error — it scores what doing nothing scores."""
    candidates = _BLOCK.findall(reply or "")
    raw = candidates[-1] if candidates else (reply or "")
    try:
        parsed = json.loads(raw.strip())
    except json.JSONDecodeError:
        return []
    if not isinstance(parsed, list):
        return []
    rules = []
    for item in parsed:
        if isinstance(item, dict) and isinstance(item.get("pattern"), str):
            rules.append(Rule(item["pattern"], str(item.get("replacement", ""))))
    return rules
 class RedactionData(vf.TaskData):
    seed: int
    """Rebuilds both slices exactly; the held-out records are never serialized here."""
    visible: int
    held_out: int
 class RedactionTask(vf.Task[RedactionData]):
    @vf.stop
    async def single_turn(self, trace: vf.Trace) -> bool:
        return trace.num_turns >= 1
    @vf.metric
    async def scan(self, trace: vf.Trace) -> dict[str, float]:
        """Run the ruleset over the held-out slice once; every reward reads this."""
        _, held_out = build_slices(self.data.seed, self.data.visible, self.data.held_out)
        outcome = measure(held_out, parse_rules(trace.last_reply))
        return {
            "recall": outcome.recall,
            "precision": outcome.precision,
            "clean": float(outcome.clean),
            "residual_hits": float(outcome.residual_hits),
            "collateral_hits": float(outcome.collateral_hits),
            "innocent_lost": float(outcome.innocent_lost),
            "invalid_rules": float(len(outcome.invalid_rules)),
        }
    @vf.reward(weight=0.35)
    async def recall(self, trace: vf.Trace) -> float:
        return trace.metrics.get("recall", 0.0)
    @vf.reward(weight=0.35)
    async def precision(self, trace: vf.Trace) -> float:
        return trace.metrics.get("precision", 0.0)
    @vf.reward(weight=0.30)
    async def gate(self, trace: vf.Trace) -> float:
        return trace.metrics.get("clean", 0.0)
 class RedactionConfig(vf.TasksetConfig):
    num_tasks: int = Field(64, ge=1)
    visible: int = Field(4, ge=1)
    """Records the agent reads."""
    held_out: int = Field(12, ge=1)
    """Records it is graded on and never sees."""
 class RedactionTaskset(vf.Taskset[RedactionTask, RedactionConfig]):
    SEED_BASE: ClassVar[int] = 90_000
    def load(self) -> list[RedactionTask]:
        tasks = []
        for i in range(self.config.num_tasks):
            seed = self.SEED_BASE + i
            visible, _ = build_slices(seed, self.config.visible, self.config.held_out)
            tickets = "\n\n".join(record.body for record in visible)
            tasks.append(
                RedactionTask(
                    RedactionData(
                        idx=i,
                        name=f"stream-{seed}",
                        prompt=f"Tickets from the stream:\n\n{tickets}\n\nWrite the ruleset.",
                        system_prompt=SYSTEM,
                        seed=seed,
                        visible=self.config.visible,
                        held_out=self.config.held_out,
                    ),
                    self.config.task,
                )
            )
        return tasks
		`@@ -0,0 +1,3 @@`
							`from redaction_pressure.taskset import RedactionTaskset`

							`__all__ = ["RedactionTaskset"]`