Datasets:

ryan-superman
/

bio-agent-trajectories

"""The VERA agent loop: bounded state instead of transcript replay.
The one structural difference from `bmb.agent`, and the reason this module exists: the
prompt is rebuilt each step from a **bounded rendering of the analysis graph** plus the
single most recent tool output, rather than from the whole transcript. Measured on
BioMysteryBench, transcript replay costs 9,267 tokens per step to deliver ~400 tokens of
new content; at a 4k state budget the same token spend buys roughly four times the steps.
Everything else is deliberately reused from the existing harness -- workspace staging,
`run_bash`, the `Attempt` record and its status taxonomy -- so a VERA row and a baseline
row differ in the loop and nothing else, and remain directly comparable.
"""

from __future__ import annotations

import json
import re
import time
import urllib.error
import urllib.request
from typing import Sequence
from pathlib import Path

from bmb.codex_backend import Attempt, emission_is_malformed, extract_answer, strip_reasoning
from bmb.problems import Problem, RULES
from bmb.sandbox import Workspace, run_bash
from vera.checks import rho
from vera.protocol import parse_emission, run_checks
from vera.state import AnalysisState, Node

PROTOCOL = """Work in your current directory. Each turn, emit exactly one block:
<command>SHELL COMMAND TO RUN</command>
When a command has established a finding, add a NODE line and its CHECK lines:
NODE kind=KIND summary="WHAT YOU ESTABLISHED" artifact=FILE parents=n1,n2
CHECK CHECKKIND "WHAT MUST BE TRUE" :: ONE-LINE COMMAND PRINTING True OR False
kind is one of: load, qc, transform, test, interpret, answer.
CHECKKIND is one of: conservation, range, invariance, type.
Everything after :: is your check command, taken verbatim to the end of the line, so
quotes, < and > inside it are safe. Each CHECK must be ONE line -- if you need a
multi-line script, write it to a file in <command> and have the CHECK run that file.
CHECKS ARE RUN BY THE HARNESS, NOT TAKEN ON YOUR WORD. A check must print exactly True
or False as its LAST line. Printing a number, a sequence or a path is malformed and will
be rejected -- write a comparison, not a measurement:
  WRONG   CHECK range "n reads" :: grep -c '^>' reads.fasta
  RIGHT   CHECK range "n reads" :: test $(grep -c '^>' reads.fasta) -eq 1042 && echo True || echo False
  WRONG   CHECK type "sequence" :: python3 -c "print(seq[:50])"
  RIGHT   CHECK type "starts with ATG" :: python3 -c "print(open('g.fa').readlines()[1].startswith('ATG'))"
Use REAL filenames from your working directory -- never the placeholder word "file".
A STRONG check states an invariant that would FAIL if your analysis were wrong: a total
preserved across a filter, an estimate stable across two subsamples, a value inside its
admissible range. A check that passes no matter what the data says (a file existing, a
tool being installed) is worth nothing and will be scored as worthless.
Your summary is the ONLY thing you will see about this step later -- state the finding
("14,203 of 34,460 cells pass QC"), not the activity ("ran QC"). Do not re-declare a
node you have already recorded.
When you are certain, end a turn with:
FINAL ANSWER: <answer>"""


_COMMANDISH = re.compile(
    r"^\s*(?:\$\s*)?(?:ls|cd|cat|head|tail|wc|awk|sed|grep|zcat|gzip|sort|uniq|cut|echo"
    r"|python3?|Rscript|conda|pip|samtools|bcftools|blastn|makeblastdb|find|for|while|if)\b"
    r"|[|>]\s*\S|&&|\$\(", re.I)


def looks_like_a_command(text: str) -> bool:
    """True if `text` is a shell command rather than an answer to the question.
    The forced-answer phase made this necessary. Asked to commit, the model sometimes
    replies with the next command it wanted to run, and a naive extractor files that as
    the final answer -- turning a run that would have been *excluded* into one that is
    *confidently wrong*, which is strictly worse. Observed verbatim:
    ``awk -F'\t' 'NR>1{print $2}' alcohol.txt | head -5``.
    """
    return bool(_COMMANDISH.search(text or ""))


# Fragments that are the scaffolding of an answer rather than an answer. Seen when a
# prefilled continuation comes back empty: the recorded answer was the literal string
# "FINAL ANSWER:", which passed a naive length check and would have been graded as a
# wrong answer instead of an honest exclusion.
_NON_ANSWERS = {"final answer", "final answer:", "answer", "answer:", "n/a", "na",
                "unknown", "none", "tbd", "...", "-"}


_TRAILING_MARKUP = re.compile(r"(?:</?(?:command|node|check|think|tool_call)\s*/?>|```+)\s*$",
                              re.I)


def strip_markup(text: str) -> str:
    """Remove protocol tags and fences the model leaves attached to an answer.
    Observed in a real run: the graded answer was ``'Norovirus GII</command>'``. That one
    happened to be judged correct, but only by luck -- an answer carrying a closing tag is
    a string the grader was never meant to see, and for an exact-match rubric it would
    fail. Applied repeatedly, since models stack them (``X</command>```).
    """
    out = (text or "").strip()
    for _ in range(4):
        new = _TRAILING_MARKUP.sub("", out).strip()
        if new == out:
            break
        out = new
    return out


def answer_is_usable(text: str) -> bool:
    """Whether an extracted answer may be recorded as the run's conclusion.
    Rejects three things, each observed in a real run: a shell command (the model replied
    with its next action), an empty or stub emission, and the answer *prefix* itself left
    over from a prefill whose continuation was empty.
    """
    text = strip_markup(text)
    if len(text) < 3 or emission_is_malformed(text):
        return False
    stripped = text.lower().strip().rstrip(":.").strip()
    if stripped in _NON_ANSWERS or not any(c.isalnum() for c in text):
        return False
    # A prefilled emission that only echoes the prefix carries no content.
    if stripped.startswith("final answer") and len(stripped) < len("final answer") + 3:
        return False
    return not looks_like_a_command(text)


ANSWER_PREFIX = "FINAL ANSWER:"


def _chat(base_url: str, model: str, prompt: str, *, timeout: int, max_tokens: int = 2048,
          prefill: str = "") -> str:
    """Call an OpenAI-compatible /chat/completions endpoint and return the message text.
    Args:
        base_url: Endpoint root, e.g. ``http://host:8404/v1``.
        model: Served model id.
        prompt: The user turn.
        timeout: Wall clock, seconds.
        max_tokens: Completion cap.
        prefill: If given, the assistant turn is *started* with this text and the model
            continues it. This is not a stylistic preference. Asked in plain language to
            reply with a single ``FINAL ANSWER:`` line, BigBang-v1 instead emitted
            ``I need to identify the bacterium ... <tool_call> </think>`` -- it kept
            trying to act, so nothing was extractable and the run was recorded as
            max_steps with no answer. Prefilling removes the need for compliance: the
            model has already "said" the prefix and can only continue it.
    Returns:
        The message text, with `prefill` prepended so callers see a complete emission.
    Raises:
        RuntimeError: On a transport error or a malformed response, so the caller can
            record a `provider` status rather than mistaking it for a bad answer.
    """
    messages = [{"role": "user", "content": prompt}]
    payload = {
        "model": model,
        "messages": messages,
        "max_tokens": max_tokens,
        "temperature": 0.0,
    }
    if prefill:
        messages.append({"role": "assistant", "content": prefill})
        # vLLM extensions: continue the assistant turn rather than starting a new one.
        payload["continue_final_message"] = True
        payload["add_generation_prompt"] = False
    body = json.dumps(payload).encode()
    req = urllib.request.Request(
        base_url.rstrip("/") + "/chat/completions", data=body,
        headers={"Content-Type": "application/json", "Authorization": "Bearer local"},
    )
    try:
        with urllib.request.urlopen(req, timeout=timeout) as resp:
            payload_out = json.loads(resp.read().decode(errors="replace"))
    except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc:
        raise RuntimeError(f"chat call failed: {exc}") from exc
    try:
        text = payload_out["choices"][0]["message"]["content"] or ""
    except (KeyError, IndexError) as exc:
        raise RuntimeError(f"malformed chat response: {str(payload_out)[:200]}") from exc
    return (prefill + text) if prefill else text


FORCE_ANSWER_RESERVE = 3


def build_forced_prompt(problem: Problem, state: AnalysisState, *, state_budget: int,
                        reason: str) -> str:
    """Demand a final answer from the analysis so far, with no further commands.
    Exists because of the single largest measured loss: on the 73-problem baseline,
    **22 of 73 problems (30%) produced no usable answer at all** -- 12 excluded for
    exhausting the budget and 10 graded on an empty or stub emission. Those runs had done
    the analysis; they simply never committed to a conclusion. A budget that ends without
    asking for one converts work already done into a zero.
    """
    return "\n".join([
        problem.question, "",
        "=== YOUR ANALYSIS ===", state.render(state_budget), "",
        f"STOP INVESTIGATING. {reason}",
        "Give your single best answer from the analysis above, even if you are not "
        "certain -- an uncertain answer scores better than none, and no further commands "
        "will be run.",
        "",
        "Reply with exactly one line and nothing else:",
        "FINAL ANSWER: <answer>",
    ])


RECENT_WINDOW = 4
# Total characters the recent window may occupy. Without a cap the window carried four
# tool outputs at `sandbox.MAX_OUTPUT_CHARS` (20k) each, so a step prompt could reach
# ~324 KB -- large enough that the CLI shim returned HTTP 500 (the prompt is passed as a
# command-line argument) and 68 of 70 problems died as provider timeouts. A method whose
# claim is bounded context has no business sending unbounded prompts.
WINDOW_CHARS = 12_000


def _clip_middle(text: str, budget: int) -> str:
    """Trim to `budget` chars keeping both ends.
    The head carries what a command produced; the tail carries any traceback. Cutting
    either loses the reason the step happened.
    """
    text = text or ""
    if budget <= 0:
        return ""
    if len(text) <= budget:
        return text
    half = max(1, (budget - 40) // 2)
    return f"{text[:half]}\n...[{len(text) - 2 * half} chars elided]...\n{text[-half:]}"


def build_step_prompt(problem: Problem, workspace: Path, state: AnalysisState,
                      recent: Sequence[tuple[str, str]], *, state_budget: int,
                      nudge: str = "", window: int = RECENT_WINDOW) -> str:
    """Assemble one step's prompt: task, bounded state, and a window of recent history.
    Cost is still O(B + window) rather than O(T), so the quadratic blow-up is gone --
    but the window is not optional. A first version passed only the state graph plus the
    single latest tool output, and it scored 17.6% against the baseline's 37.7%: the
    model repeatedly answered "cannot determine from the provided analysis logs", because
    node summaries written by the model are lossy and drop the concrete handles
    (filenames, column labels, sample ids) the next step needs.
    The lesson is that summarisation is not free. Compressing *old* context is affordable;
    compressing recent context destroys the analysis.
    """
    listing = "\n".join(f"- {Path(n).name}" for n in problem.data_members)
    parts = [
        problem.question, "", RULES, "",
        f"Data files in {workspace}:\n{listing}", "",
        PROTOCOL, "",
        "=== ANALYSIS SO FAR ===", state.render(state_budget), "",
    ]
    tail = list(recent)[-window:]
    if tail:
        parts.append("=== YOUR LAST %d COMMANDS AND THEIR OUTPUT ===" % len(tail))
        # Spend the window budget newest-first: the latest output is what the next action
        # builds on, and older ones are already summarised in the analysis graph.
        rendered: list[str] = []
        left = WINDOW_CHARS
        for cmd, out in reversed(tail):
            share = max(600, left // 2) if len(rendered) < 2 * (len(tail) - 1) else left
            block = f"$ {cmd}\n{_clip_middle(out, min(share, left))}\n"
            if left <= 0:
                break
            rendered.append(block[:left])
            left -= len(block)
        parts += list(reversed(rendered))
    if nudge:
        parts += [f"NOTE: {nudge}", ""]
    parts.append("Emit your next block.")
    return "\n".join(parts)


def run_vera_attempt(
    problem: Problem,
    model: str,
    base_url: str,
    *,
    max_steps: int = 50,
    state_budget: int = 4000,
    step_timeout: int = 900,
    chat_timeout: int = 600,
    workspace_root: Path | None = None,
    transcript_dir: Path | None = None,
) -> Attempt:
    """Run one attempt under VERA and return it ungraded.
    Args:
        problem: The problem to pose.
        model: Model id served at `base_url`.
        base_url: OpenAI-compatible endpoint, e.g. ``http://host:8404/v1``.
        max_steps: Step ceiling. Defaults to 50 rather than 25 because the point of the
            bounded state is to make that affordable.
        state_budget: Tokens allowed for the rendered analysis graph.
        step_timeout: Per-shell-command wall clock, seconds.
        chat_timeout: Per-model-call wall clock, seconds.
        workspace_root: Parent for the scratch workspace.
        transcript_dir: Where to persist the run, including the final graph.
    Returns:
        An `Attempt` with the baseline status taxonomy, plus `extra` carrying the
        cumulative verification reward, the node count and the mean node rho.
    """
    started = time.time()
    att = Attempt(problem_id=problem.id, model=f"vera:{model}", status="ok",
                  human_solvable=problem.human_solvable)
    state = AnalysisState()
    total_reward = 0.0
    log: list[str] = []

    with Workspace(problem, root=workspace_root, keep=False) as ws:
        assert ws.path is not None
        out_dir = Path(transcript_dir).resolve() if transcript_dir else None
        if out_dir:
            out_dir.mkdir(parents=True, exist_ok=True)
            att.transcript_dir = str(out_dir)

        recent: list[tuple[str, str]] = []
        nudge, step = "", 0
        rejections: dict[str, int] = {}
        while step < max_steps:
            step += 1
            forcing = step > max_steps - FORCE_ANSWER_RESERVE
            if forcing:
                prompt = build_forced_prompt(
                    problem, state, state_budget=state_budget,
                    reason=f"You have used {step - 1} of {max_steps} steps.")
            else:
                prompt = build_step_prompt(problem, ws.path, state, recent,
                                           state_budget=state_budget, nudge=nudge)
            nudge = ""
            try:
                reply = _chat(base_url, model, prompt, timeout=chat_timeout,
                              prefill=ANSWER_PREFIX + " " if forcing else "")
            except RuntimeError as exc:
                att.status, att.error = "provider", str(exc)
                break
            log.append(f"--- step {step} ---\n{reply}\n")

            if forcing:
                # No commands, no nodes -- only a commitment. Reasoning is stripped first
                # because a model that writes "FINAL ANSWER:" inside a think block would
                # otherwise have its reasoning mined as the answer.
                candidate = extract_answer(strip_reasoning(reply))
                if answer_is_usable(candidate):
                    att.answer, att.status = strip_markup(candidate), "ok"
                    break
                nudge = ("your reply was not an answer. Do not emit a shell command. "
                         "Name the organism, gene, cell type or value the question asks for.")
                continue

            emission = parse_emission(reply)
            if emission.error:
                nudge = f"your last turn was rejected: {emission.error}"
                last_output = ""
                continue

            if emission.command:
                res = run_bash(ws.path, emission.command, timeout=step_timeout)
                out = res.as_tool_output()
                recent.append((emission.command, out))
                log.append(f"$ {emission.command}\n{out}\n")

            if emission.declares_node:
                results = run_checks(emission.checks, ws.path, run_bash)
                malformed = [r for r in results if "no boolean verdict" in r.detail]
                # A check that printed a measurement rather than a verdict is a protocol
                # error, not evidence about the analysis. Scoring it would make rho a
                # compliance metric. Ask once for a rewrite; if the model repeats itself,
                # accept the failure so a non-complying model cannot stall the run.
                if malformed and rejections.get(emission.summary, 0) < 1:
                    rejections[emission.summary] = rejections.get(emission.summary, 0) + 1
                    bad = malformed[0]
                    nudge = (f"check {bad.name!r} printed a measurement, not a verdict "
                             f"({bad.detail}). Rewrite it as a comparison ending in "
                             f"True or False, using real filenames, then re-declare the node.")
                    continue
                node = Node(
                    id=f"n{len(state) + 1}", kind=emission.kind,
                    summary=emission.summary or "(no summary given)",
                    artifact=emission.artifact,
                    parents=tuple(p for p in emission.parents if p in state),
                    command=emission.command, checks=results,
                )
                state.add(node)
                total_reward += state.reward([node])
                log.append(f"[node {node.id}] rho={node.rho:.2f} "
                           f"({sum(c.passed for c in results)}/{len(results)} checks)\n")

            if emission.answer:
                # Emission gate: 29% of baseline errors were emission failures, so an
                # answer is only released if the analysis behind it verified.
                leaves = state.leaves()
                gate = rho([c for n in leaves for c in n.checks]) if leaves else 0.0
                if gate == 0.0 and step <= max_steps - FORCE_ANSWER_RESERVE and state:
                    nudge = ("your answer was withheld: no leaf node has a passing check. "
                             "Add a check that verifies the result you are about to report.")
                    continue
                att.answer = strip_markup(emission.answer)
                att.status = "ok"
                break
        else:
            # The budget ran out without a commitment. Ask once more, outside the loop:
            # this is exactly the case that produced 12 of the 73 baseline exclusions.
            try:
                reply = _chat(base_url, model, build_forced_prompt(
                    problem, state, state_budget=state_budget,
                    reason="Your step budget is exhausted."), timeout=chat_timeout,
                    prefill=ANSWER_PREFIX + " ")
                candidate = extract_answer(strip_reasoning(reply))
            except RuntimeError:
                candidate = ""
            if answer_is_usable(candidate):
                att.answer, att.status = strip_markup(candidate), "ok"
                att.extra["answer_forced"] = True
            else:
                att.status = "max_steps"
                att.error = f"no answer within {max_steps} steps"

        att.n_steps = step
        nodes = state.nodes
        att.extra.update({
            "verification_reward": round(total_reward, 3),
            "n_nodes": len(nodes),
            "mean_rho": round(sum(n.rho for n in nodes) / len(nodes), 3) if nodes else 0.0,
            "n_checks": sum(len(n.checks) for n in nodes),
        })
        if out_dir:
            (out_dir / "transcript.txt").write_text("\n".join(log))
            (out_dir / "graph.txt").write_text(state.render(1_000_000))
            (out_dir / "final_answer.txt").write_text(att.answer or "")

    if att.status == "ok" and not att.answer:
        att.status, att.error = "format", "loop ended with no answer"
    att.wall_s = time.time() - started
    return att

	"""The VERA agent loop: bounded state instead of transcript replay.

	The one structural difference from `bmb.agent`, and the reason this module exists: the
	prompt is rebuilt each step from a bounded rendering of the analysis graph plus the
	single most recent tool output, rather than from the whole transcript. Measured on
	BioMysteryBench, transcript replay costs 9,267 tokens per step to deliver ~400 tokens of
	new content; at a 4k state budget the same token spend buys roughly four times the steps.

	Everything else is deliberately reused from the existing harness -- workspace staging,
	`run_bash`, the `Attempt` record and its status taxonomy -- so a VERA row and a baseline
	row differ in the loop and nothing else, and remain directly comparable.
	"""

	from __future__ import annotations

	import json
	import re
	import time
	import urllib.error
	import urllib.request
	from typing import Sequence
	from pathlib import Path

	from bmb.codex_backend import Attempt, emission_is_malformed, extract_answer, strip_reasoning
	from bmb.problems import Problem, RULES
	from bmb.sandbox import Workspace, run_bash
	from vera.checks import rho
	from vera.protocol import parse_emission, run_checks
	from vera.state import AnalysisState, Node

	PROTOCOL = """Work in your current directory. Each turn, emit exactly one block:

	<command>SHELL COMMAND TO RUN</command>

	When a command has established a finding, add a NODE line and its CHECK lines:

	NODE kind=KIND summary="WHAT YOU ESTABLISHED" artifact=FILE parents=n1,n2
	CHECK CHECKKIND "WHAT MUST BE TRUE" :: ONE-LINE COMMAND PRINTING True OR False

	kind is one of: load, qc, transform, test, interpret, answer.
	CHECKKIND is one of: conservation, range, invariance, type.

	Everything after :: is your check command, taken verbatim to the end of the line, so
	quotes, < and > inside it are safe. Each CHECK must be ONE line -- if you need a
	multi-line script, write it to a file in <command> and have the CHECK run that file.

	CHECKS ARE RUN BY THE HARNESS, NOT TAKEN ON YOUR WORD. A check must print exactly True
	or False as its LAST line. Printing a number, a sequence or a path is malformed and will
	be rejected -- write a comparison, not a measurement:

	WRONG CHECK range "n reads" :: grep -c '^>' reads.fasta
	RIGHT CHECK range "n reads" :: test $(grep -c '^>' reads.fasta) -eq 1042 && echo True \|\| echo False

	WRONG CHECK type "sequence" :: python3 -c "print(seq[:50])"
	RIGHT CHECK type "starts with ATG" :: python3 -c "print(open('g.fa').readlines()[1].startswith('ATG'))"

	Use REAL filenames from your working directory -- never the placeholder word "file".
	A STRONG check states an invariant that would FAIL if your analysis were wrong: a total
	preserved across a filter, an estimate stable across two subsamples, a value inside its
	admissible range. A check that passes no matter what the data says (a file existing, a
	tool being installed) is worth nothing and will be scored as worthless.

	Your summary is the ONLY thing you will see about this step later -- state the finding
	("14,203 of 34,460 cells pass QC"), not the activity ("ran QC"). Do not re-declare a
	node you have already recorded.

	When you are certain, end a turn with:
	FINAL ANSWER: <answer>"""


	_COMMANDISH = re.compile(
	r"^\s(?:\$\s)?(?:ls\|cd\|cat\|head\|tail\|wc\|awk\|sed\|grep\|zcat\|gzip\|sort\|uniq\|cut\|echo"
	r"\|python3?\|Rscript\|conda\|pip\|samtools\|bcftools\|blastn\|makeblastdb\|find\|for\|while\|if)\b"
	r"\|[\|>]\s*\S\|&&\|\$\(", re.I)


	def looks_like_a_command(text: str) -> bool:
	"""True if `text` is a shell command rather than an answer to the question.

	The forced-answer phase made this necessary. Asked to commit, the model sometimes
	replies with the next command it wanted to run, and a naive extractor files that as
	the final answer -- turning a run that would have been excluded into one that is
	confidently wrong, which is strictly worse. Observed verbatim:
	``awk -F'\t' 'NR>1{print $2}' alcohol.txt \| head -5``.
	"""
	return bool(_COMMANDISH.search(text or ""))


	# Fragments that are the scaffolding of an answer rather than an answer. Seen when a
	# prefilled continuation comes back empty: the recorded answer was the literal string
	# "FINAL ANSWER:", which passed a naive length check and would have been graded as a
	# wrong answer instead of an honest exclusion.
	_NON_ANSWERS = {"final answer", "final answer:", "answer", "answer:", "n/a", "na",
	"unknown", "none", "tbd", "...", "-"}


	_TRAILING_MARKUP = re.compile(r"(?:</?(?:command\|node\|check\|think\|tool_call)\s/?>\|```+)\s$",
	re.I)


	def strip_markup(text: str) -> str:
	"""Remove protocol tags and fences the model leaves attached to an answer.

	Observed in a real run: the graded answer was ``'Norovirus GII</command>'``. That one
	happened to be judged correct, but only by luck -- an answer carrying a closing tag is
	a string the grader was never meant to see, and for an exact-match rubric it would
	fail. Applied repeatedly, since models stack them (``X</command>```).
	"""
	out = (text or "").strip()
	for _ in range(4):
	new = _TRAILING_MARKUP.sub("", out).strip()
	if new == out:
	break
	out = new
	return out


	def answer_is_usable(text: str) -> bool:
	"""Whether an extracted answer may be recorded as the run's conclusion.

	Rejects three things, each observed in a real run: a shell command (the model replied
	with its next action), an empty or stub emission, and the answer prefix itself left
	over from a prefill whose continuation was empty.
	"""
	text = strip_markup(text)
	if len(text) < 3 or emission_is_malformed(text):
	return False
	stripped = text.lower().strip().rstrip(":.").strip()
	if stripped in _NON_ANSWERS or not any(c.isalnum() for c in text):
	return False
	# A prefilled emission that only echoes the prefix carries no content.
	if stripped.startswith("final answer") and len(stripped) < len("final answer") + 3:
	return False
	return not looks_like_a_command(text)


	ANSWER_PREFIX = "FINAL ANSWER:"


	def _chat(base_url: str, model: str, prompt: str, *, timeout: int, max_tokens: int = 2048,
	prefill: str = "") -> str:
	"""Call an OpenAI-compatible /chat/completions endpoint and return the message text.

	Args:
	base_url: Endpoint root, e.g. ``http://host:8404/v1``.
	model: Served model id.
	prompt: The user turn.
	timeout: Wall clock, seconds.
	max_tokens: Completion cap.
	prefill: If given, the assistant turn is started with this text and the model
	continues it. This is not a stylistic preference. Asked in plain language to
	reply with a single ``FINAL ANSWER:`` line, BigBang-v1 instead emitted
	``I need to identify the bacterium ... <tool_call> </think>`` -- it kept
	trying to act, so nothing was extractable and the run was recorded as
	max_steps with no answer. Prefilling removes the need for compliance: the
	model has already "said" the prefix and can only continue it.

	Returns:
	The message text, with `prefill` prepended so callers see a complete emission.

	Raises:
	RuntimeError: On a transport error or a malformed response, so the caller can
	record a `provider` status rather than mistaking it for a bad answer.
	"""
	messages = [{"role": "user", "content": prompt}]
	payload = {
	"model": model,
	"messages": messages,
	"max_tokens": max_tokens,
	"temperature": 0.0,
	}
	if prefill:
	messages.append({"role": "assistant", "content": prefill})
	# vLLM extensions: continue the assistant turn rather than starting a new one.
	payload["continue_final_message"] = True
	payload["add_generation_prompt"] = False
	body = json.dumps(payload).encode()
	req = urllib.request.Request(
	base_url.rstrip("/") + "/chat/completions", data=body,
	headers={"Content-Type": "application/json", "Authorization": "Bearer local"},
	)
	try:
	with urllib.request.urlopen(req, timeout=timeout) as resp:
	payload_out = json.loads(resp.read().decode(errors="replace"))
	except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc:
	raise RuntimeError(f"chat call failed: {exc}") from exc
	try:
	text = payload_out["choices"][0]["message"]["content"] or ""
	except (KeyError, IndexError) as exc:
	raise RuntimeError(f"malformed chat response: {str(payload_out)[:200]}") from exc
	return (prefill + text) if prefill else text


	FORCE_ANSWER_RESERVE = 3


	def build_forced_prompt(problem: Problem, state: AnalysisState, *, state_budget: int,
	reason: str) -> str:
	"""Demand a final answer from the analysis so far, with no further commands.

	Exists because of the single largest measured loss: on the 73-problem baseline,
	22 of 73 problems (30%) produced no usable answer at all -- 12 excluded for
	exhausting the budget and 10 graded on an empty or stub emission. Those runs had done
	the analysis; they simply never committed to a conclusion. A budget that ends without
	asking for one converts work already done into a zero.
	"""
	return "\n".join([
	problem.question, "",
	"=== YOUR ANALYSIS ===", state.render(state_budget), "",
	f"STOP INVESTIGATING. {reason}",
	"Give your single best answer from the analysis above, even if you are not "
	"certain -- an uncertain answer scores better than none, and no further commands "
	"will be run.",
	"",
	"Reply with exactly one line and nothing else:",
	"FINAL ANSWER: <answer>",
	])


	RECENT_WINDOW = 4
	# Total characters the recent window may occupy. Without a cap the window carried four
	# tool outputs at `sandbox.MAX_OUTPUT_CHARS` (20k) each, so a step prompt could reach
	# ~324 KB -- large enough that the CLI shim returned HTTP 500 (the prompt is passed as a
	# command-line argument) and 68 of 70 problems died as provider timeouts. A method whose
	# claim is bounded context has no business sending unbounded prompts.
	WINDOW_CHARS = 12_000


	def _clip_middle(text: str, budget: int) -> str:
	"""Trim to `budget` chars keeping both ends.

	The head carries what a command produced; the tail carries any traceback. Cutting
	either loses the reason the step happened.
	"""
	text = text or ""
	if budget <= 0:
	return ""
	if len(text) <= budget:
	return text
	half = max(1, (budget - 40) // 2)
	return f"{text[:half]}\n...[{len(text) - 2 * half} chars elided]...\n{text[-half:]}"


	def build_step_prompt(problem: Problem, workspace: Path, state: AnalysisState,
	recent: Sequence[tuple[str, str]], *, state_budget: int,
	nudge: str = "", window: int = RECENT_WINDOW) -> str:
	"""Assemble one step's prompt: task, bounded state, and a window of recent history.

	Cost is still O(B + window) rather than O(T), so the quadratic blow-up is gone --
	but the window is not optional. A first version passed only the state graph plus the
	single latest tool output, and it scored 17.6% against the baseline's 37.7%: the
	model repeatedly answered "cannot determine from the provided analysis logs", because
	node summaries written by the model are lossy and drop the concrete handles
	(filenames, column labels, sample ids) the next step needs.

	The lesson is that summarisation is not free. Compressing old context is affordable;
	compressing recent context destroys the analysis.
	"""
	listing = "\n".join(f"- {Path(n).name}" for n in problem.data_members)
	parts = [
	problem.question, "", RULES, "",
	f"Data files in {workspace}:\n{listing}", "",
	PROTOCOL, "",
	"=== ANALYSIS SO FAR ===", state.render(state_budget), "",
	]
	tail = list(recent)[-window:]
	if tail:
	parts.append("=== YOUR LAST %d COMMANDS AND THEIR OUTPUT ===" % len(tail))
	# Spend the window budget newest-first: the latest output is what the next action
	# builds on, and older ones are already summarised in the analysis graph.
	rendered: list[str] = []
	left = WINDOW_CHARS
	for cmd, out in reversed(tail):
	share = max(600, left // 2) if len(rendered) < 2 * (len(tail) - 1) else left
	block = f"$ {cmd}\n{_clip_middle(out, min(share, left))}\n"
	if left <= 0:
	break
	rendered.append(block[:left])
	left -= len(block)
	parts += list(reversed(rendered))
	if nudge:
	parts += [f"NOTE: {nudge}", ""]
	parts.append("Emit your next block.")
	return "\n".join(parts)


	def run_vera_attempt(
	problem: Problem,
	model: str,
	base_url: str,
	*,
	max_steps: int = 50,
	state_budget: int = 4000,
	step_timeout: int = 900,
	chat_timeout: int = 600,
	workspace_root: Path \| None = None,
	transcript_dir: Path \| None = None,
	) -> Attempt:
	"""Run one attempt under VERA and return it ungraded.

	Args:
	problem: The problem to pose.
	model: Model id served at `base_url`.
	base_url: OpenAI-compatible endpoint, e.g. ``http://host:8404/v1``.
	max_steps: Step ceiling. Defaults to 50 rather than 25 because the point of the
	bounded state is to make that affordable.
	state_budget: Tokens allowed for the rendered analysis graph.
	step_timeout: Per-shell-command wall clock, seconds.
	chat_timeout: Per-model-call wall clock, seconds.
	workspace_root: Parent for the scratch workspace.
	transcript_dir: Where to persist the run, including the final graph.

	Returns:
	An `Attempt` with the baseline status taxonomy, plus `extra` carrying the
	cumulative verification reward, the node count and the mean node rho.
	"""
	started = time.time()
	att = Attempt(problem_id=problem.id, model=f"vera:{model}", status="ok",
	human_solvable=problem.human_solvable)
	state = AnalysisState()
	total_reward = 0.0
	log: list[str] = []

	with Workspace(problem, root=workspace_root, keep=False) as ws:
	assert ws.path is not None
	out_dir = Path(transcript_dir).resolve() if transcript_dir else None
	if out_dir:
	out_dir.mkdir(parents=True, exist_ok=True)
	att.transcript_dir = str(out_dir)

	recent: list[tuple[str, str]] = []
	nudge, step = "", 0
	rejections: dict[str, int] = {}
	while step < max_steps:
	step += 1
	forcing = step > max_steps - FORCE_ANSWER_RESERVE
	if forcing:
	prompt = build_forced_prompt(
	problem, state, state_budget=state_budget,
	reason=f"You have used {step - 1} of {max_steps} steps.")
	else:
	prompt = build_step_prompt(problem, ws.path, state, recent,
	state_budget=state_budget, nudge=nudge)
	nudge = ""
	try:
	reply = _chat(base_url, model, prompt, timeout=chat_timeout,
	prefill=ANSWER_PREFIX + " " if forcing else "")
	except RuntimeError as exc:
	att.status, att.error = "provider", str(exc)
	break
	log.append(f"--- step {step} ---\n{reply}\n")

	if forcing:
	# No commands, no nodes -- only a commitment. Reasoning is stripped first
	# because a model that writes "FINAL ANSWER:" inside a think block would
	# otherwise have its reasoning mined as the answer.
	candidate = extract_answer(strip_reasoning(reply))
	if answer_is_usable(candidate):
	att.answer, att.status = strip_markup(candidate), "ok"
	break
	nudge = ("your reply was not an answer. Do not emit a shell command. "
	"Name the organism, gene, cell type or value the question asks for.")
	continue

	emission = parse_emission(reply)
	if emission.error:
	nudge = f"your last turn was rejected: {emission.error}"
	last_output = ""
	continue

	if emission.command:
	res = run_bash(ws.path, emission.command, timeout=step_timeout)
	out = res.as_tool_output()
	recent.append((emission.command, out))
	log.append(f"$ {emission.command}\n{out}\n")

	if emission.declares_node:
	results = run_checks(emission.checks, ws.path, run_bash)
	malformed = [r for r in results if "no boolean verdict" in r.detail]
	# A check that printed a measurement rather than a verdict is a protocol
	# error, not evidence about the analysis. Scoring it would make rho a
	# compliance metric. Ask once for a rewrite; if the model repeats itself,
	# accept the failure so a non-complying model cannot stall the run.
	if malformed and rejections.get(emission.summary, 0) < 1:
	rejections[emission.summary] = rejections.get(emission.summary, 0) + 1
	bad = malformed[0]
	nudge = (f"check {bad.name!r} printed a measurement, not a verdict "
	f"({bad.detail}). Rewrite it as a comparison ending in "
	f"True or False, using real filenames, then re-declare the node.")
	continue
	node = Node(
	id=f"n{len(state) + 1}", kind=emission.kind,
	summary=emission.summary or "(no summary given)",
	artifact=emission.artifact,
	parents=tuple(p for p in emission.parents if p in state),
	command=emission.command, checks=results,
	)
	state.add(node)
	total_reward += state.reward([node])
	log.append(f"[node {node.id}] rho={node.rho:.2f} "
	f"({sum(c.passed for c in results)}/{len(results)} checks)\n")

	if emission.answer:
	# Emission gate: 29% of baseline errors were emission failures, so an
	# answer is only released if the analysis behind it verified.
	leaves = state.leaves()
	gate = rho([c for n in leaves for c in n.checks]) if leaves else 0.0
	if gate == 0.0 and step <= max_steps - FORCE_ANSWER_RESERVE and state:
	nudge = ("your answer was withheld: no leaf node has a passing check. "
	"Add a check that verifies the result you are about to report.")
	continue
	att.answer = strip_markup(emission.answer)
	att.status = "ok"
	break
	else:
	# The budget ran out without a commitment. Ask once more, outside the loop:
	# this is exactly the case that produced 12 of the 73 baseline exclusions.
	try:
	reply = _chat(base_url, model, build_forced_prompt(
	problem, state, state_budget=state_budget,
	reason="Your step budget is exhausted."), timeout=chat_timeout,
	prefill=ANSWER_PREFIX + " ")
	candidate = extract_answer(strip_reasoning(reply))
	except RuntimeError:
	candidate = ""
	if answer_is_usable(candidate):
	att.answer, att.status = strip_markup(candidate), "ok"
	att.extra["answer_forced"] = True
	else:
	att.status = "max_steps"
	att.error = f"no answer within {max_steps} steps"

	att.n_steps = step
	nodes = state.nodes
	att.extra.update({
	"verification_reward": round(total_reward, 3),
	"n_nodes": len(nodes),
	"mean_rho": round(sum(n.rho for n in nodes) / len(nodes), 3) if nodes else 0.0,
	"n_checks": sum(len(n.checks) for n in nodes),
	})
	if out_dir:
	(out_dir / "transcript.txt").write_text("\n".join(log))
	(out_dir / "graph.txt").write_text(state.render(1_000_000))
	(out_dir / "final_answer.txt").write_text(att.answer or "")

	if att.status == "ok" and not att.answer:
	att.status, att.error = "format", "loop ended with no answer"
	att.wall_s = time.time() - started
	return att