Classes
class	FlatLeg
class	WalkLeg
class	Step
class	Composed

Functions
FlatLeg	_repeat_leg (int index)
FlatLeg	_perm_leg (str key)
dict[str, str]	permuted_criteria (str key)
Path	sandbox_root ()
str	slice_text (str node)
str	whole_policy (Path\|None root=None)
str\|None	node_state_text (str node, str leaf_state=DEFAULT_LEAF_STATE, str\|None policy_text=None)
Callable[[int, str, str], str]	node_instructions (str node)
dict[str, Any]	build_node_request (str node, Sequence[str] commands, bool with_policy=True, str leaf_state=DEFAULT_LEAF_STATE, str\|None policy_text=None)
str	node_of_request (Mapping[str, Any] body)
Composed	compose (Sequence[Step] steps, str remainder=DEFAULT_EARLY_STOP_REMAINDER)
dict[str, float]	_spread (str verdict, float probability, Mapping[str, float]\|None leaf_probs, str remainder_rule=DEFAULT_EARLY_STOP_REMAINDER)
dict[str, str]	gate_node_labels (str command, Path\|None root=None)
str	_gate_structure (str command, Any decision)
list[dict[str, Any]]	walk (list[dict[str, Any]] cases, bool with_policy, int batch=DEFAULT_BATCH, str\|None key=None, dict[str, Any]\|None usage=None, float timeout=DEFAULT_TIMEOUT, int retries=DEFAULT_RETRIES, Callable[..., Any]\|None opener=None, Callable[[float], None] sleep=time.sleep, Callable[[str], None]\|None progress=None, str leaf_state=DEFAULT_LEAF_STATE, str remainder=DEFAULT_EARLY_STOP_REMAINDER)
dict[str, Any]	_walked_row (dict[str, Any] case, list[Step] steps, float ms, int requests, str remainder=DEFAULT_EARLY_STOP_REMAINDER)
str	_composition_note (str remainder)
dict[str, Any]	run_leg (WalkLeg leg, list[dict[str, Any]] cases, int batch=DEFAULT_BATCH, str\|None key=None, float timeout=DEFAULT_TIMEOUT, int retries=DEFAULT_RETRIES, Callable[..., Any]\|None opener=None, Callable[[float], None] sleep=time.sleep, Callable[[str], None]\|None progress=None)
dict[str, Any]	run_flat_leg (FlatLeg leg, list[dict[str, Any]] cases, str\|None policy_text, int batch=DEFAULT_BATCH, str\|None key=None, float timeout=DEFAULT_TIMEOUT, int retries=DEFAULT_RETRIES, Callable[..., Any]\|None opener=None, Callable[[float], None] sleep=time.sleep, Callable[[str], None]\|None progress=None)
float\|None	_top_probability (Mapping[str, Any] row)
int	_agreement (Sequence[Mapping[str, Any]] rows)
int	_granted_more (Sequence[Mapping[str, Any]] rows)
dict[str, Any]	noise_band (Sequence[tuple[str, dict[str, Any]]] payloads)
dict[str, Any]	permutation_table (Sequence[tuple[str, dict[str, Any]]] payloads)
list[dict[str, Any]]	per_node_table (Sequence[Mapping[str, Any]] rows, Path\|None root=None)
str	render_noise_table (Mapping[str, Any] band)
str	render_permutation_table (Mapping[str, Any] table)
str	render_per_node_table (Sequence[Mapping[str, Any]] table)
list[dict[str, Any]]	leaf_control_table (Sequence[tuple[str, dict[str, Any]]] payloads, Path\|None root=None)
str	render_leaf_control_table (Sequence[Mapping[str, Any]] table)
dict[str, Any]	remainder_comparison (dict[str, Any] pinned, dict[str, Any] escalating, float threshold=DEFAULT_THRESHOLD)
list[dict[str, Any]]	pinned_rule_rows (Sequence[Mapping[str, Any]] rows)
str	render_remainder_table (Mapping[str, Any] result)
str	render_walk_cost_table (Sequence[tuple[str, dict[str, Any]]] payloads)
list[tuple[str, dict[str, Any]]]	_read_payloads (Path results_dir, Iterable[str] names)
int	_analyse (Path results_dir)
argparse.ArgumentParser	_build_parser ()
int	_run_flat (Sequence[FlatLeg] legs, Path base, Path out_dir, str key, argparse.Namespace args, dict[str, float] spent)
WalkLeg	_resolved_leg (WalkLeg leg, argparse.Namespace args)
int	_run_walks (Sequence[WalkLeg] legs, Path base, Path out_dir, str key, argparse.Namespace args, dict[str, float] spent)
int	main (list[str]\|None argv=None)

Variables
str	ROUTE = "typesafe-walk"
dict	PERMISSIVENESS = {"deny": 0, "confirm": 1, "allow": 2}
str	FALLBACK_LABEL = "deny"
tuple	LEAF_STATES = ("slice", "whole", "none")
str	DEFAULT_LEAF_STATE = "slice"
tuple	EARLY_STOP_REMAINDERS = ("none", "next")
str	DEFAULT_EARLY_STOP_REMAINDER = "none"
dict	NEXT_MOST_PERMISSIVE = {"deny": "confirm"}
float	SPEND_CAP_USD = 2.0
tuple	_STRUCTURE_SENTENCES
tuple	_CASING
tuple	_PROGRAM_SENTENCES
tuple	_QUOTED_METACHAR
tuple	_BARE
tuple	_UNKNOWN_OPTIONS
dict	POLICY_SLICES
dict	NODE_OPTIONS
tuple	NODE_ORDER
frozenset	LEAF_NODES = frozenset({"listing", "echo", "where", "pip"})
dict	STOP_VERDICTS
dict	NEXT_NODE
dict	PROGRAM_CLASSES
dict	NODE_CRITERIA
dict	NODE_VERBS
dict	CRITERIA_ORDERS
dict	WORD_CRITERIA = {label: label for label in LABELS}
dict	REPEAT_LEGS
dict	PERM_LEGS = {key: _perm_leg(key) for key in CRITERIA_ORDERS}
	name
	model
	case_file
	policy_file
	criteria
	note
dict	WALK_LEGS
tuple	PLAN_296_WALK_LEGS = ("hand", "hard", "hard_nopolicy")
tuple	PLAN_301_WALK_LEGS
tuple	CONTROL_LEGS = ("hard", "hard_rep1", "hard_whole", "hard_none")

Detailed Description

Jev as a walk of small typed questions, composed in code -- plus plan 296's
run-to-run noise band and its label-permutation legs.

Purpose
-------
Plan 293 measured Jev as ONE flat three-way choice with the whole 2207-character
policy in the state: 276 of 300 on the hard set, 7 errors granting more
permission than the gate. Two outside observations since then say that shape is
not neutral. Archer Hume's black-box analysis (2026-09-17) measured that adding
an irrelevant option shifts the log-odds between the existing options by about
0.28 and that an answer placed last among the options is chosen far more often
than the same answer placed first. And a developer on the TypeSafe Discord runs
Jev as a greedy walk over declared children with a per-node threshold and
composes the result in code -- which is the shape of the M8 gate itself:
metacharacter rule, then program class, then the per-program rule.

This module measures all three things the flat number cannot separate.

**The noise band (P1).** Plan 293's ``hard`` leg run twice more, unchanged, so
every later difference is read against how much the model moves when nothing
does.

**The label permutation (P2).** The same flat choice with the three criteria in
each of the six insertion orders, and one wording variant that keeps the
plan-293 order and reduces the criteria to the single words ``allow``,
``confirm`` and ``deny``. Order and wording move separately or they do not move
at all, and only running both says which.

**The walk (P3/P4).** A fixed three-deep tree, composed IN CODE. Node
``structure`` asks whether the command is clean, has a metacharacter outside
quotes, or is unterminated/NUL/empty. Node ``program`` asks which class the
program falls in. The four continuing classes each have one leaf asking that
class's own rule. Every node's state carries ONLY the policy sentences that
node needs -- pinned constants here, each a verbatim substring of
``policy.txt`` -- plus the batch of commands. The composition is a pure
function, and :mod:`test_296_walk` asserts it against ``classify_command``
itself on every leaf and every early stop.

That is a different trust shape from the flat choice. The model grounds atoms;
the gate's control flow stays in code where it can be read and tested.

**The slice control (plan 301 P1).** Plan 296 measured the ``where`` leaf at 8
of 13 with its slice and 12 of 13 with no policy text at all, and could not say
whether those three sentences are WRONG or whether that node simply does better
with less text. ``--leaf-state`` answers it by running the same walk with every
LEAF's state replaced by the whole of ``policy.txt`` and again with no policy
text, holding the two shared nodes at their slices -- plan 296 measured those
two as identical with and without, command for command, so moving them would put
a second variable inside a control. ``slice`` is the default and is plan 296's
request byte for byte.

**The remainder rule (plan 301 P2).** The composition places a path's leftover
belief by the leaf's own distribution, and on ``deny`` where the path never
reached a leaf. For an early stop that composes to ``deny`` that hands the
label the whole mass, so the row is filed at probability 1.0 however unsure the
node was and a WRONG early denial cannot escalate at any threshold.
``--early-stop-remainder next`` puts that remainder on the next most permissive
label instead. ``none`` is the default and is plan 296's arithmetic exactly.

Why the permutation and repeat legs live in this module rather than in a third
one: plan 296 P5 authorises exactly two baseline modules to change, this new
one and a helper extraction in :mod:`jev_direct`, so a separate
``jev_permute.py`` is not on the table. All three sets of legs share the
request, the post, the key and the row parse, which are imported rather than
copied.

Run it under the PROJECT venv from the subproject root, in a shell where the
operator has set ``TYPESAFE_API``::

python bench/argv_policy/baselines/jev_walk.py --all
python bench/argv_policy/baselines/jev_walk.py --control
python bench/argv_policy/baselines/jev_walk.py --analyse

The key is read from the process environment through :func:`jev_direct.api_key`
and from nowhere else. This module never opens a key file, never logs a key,
never writes one into a result file and never prints one.

Responsibilities
----------------
- :data:`NODE_OPTIONS` / :data:`NODE_CRITERIA` / :data:`POLICY_SLICES` -- the
tree, its typed options and the policy sentences each node sees.
- :func:`slice_text` / :func:`node_state_text` / :func:`build_node_request` /
:func:`node_of_request` -- one node's state and question under a given leaf
state, and the route back from a body to its node.
- :func:`compose` -- the pure composition: path to verdict and distribution,
under either remainder rule.
- :func:`gate_node_labels` -- what the gate's own decision implies each node
should have answered, which is what the per-node table is scored against.
- :func:`walk` / :func:`run_leg` -- a case set to scorable rows and a payload.
- :func:`run_flat_leg` -- one flat-choice leg (a P1 repeat or a P2 permutation).
- :func:`noise_band` / :func:`permutation_table` / :func:`per_node_table` --
the three analyses ``score.py`` cannot produce from a single file.
- :func:`leaf_control_table` / :func:`remainder_comparison` -- plan 301's two:
each leaf under each leaf state, and what the remainder rule moves.
- :func:`main` -- the CLI.

Diagnostics
-----------
Domain: OC_CHAT.MAIN
Levels:
L2 - run lifecycle (leg started, node requests sent, files written)
L3 - per-node routing, per-leg agreement, retries taken
L4 - module load

Contracts
---------
- Standard library only, exactly as the rest of this bench is, plus the gate
itself (:mod:`oc_chat.tools._argv_policy`), which is the oracle every label
on this bench already comes from. No dependency enters any ``pyproject.toml``.
- ``score.py`` is NOT changed and NOT imported: a walked row is the plan-288
row shape, so the unchanged scorer reads it. Two of the scorer's lines --
the top probability and the permissiveness order -- are restated here for
the analyses below, for the same reason :mod:`jev_direct` restates the first:
a baseline module stays runnable from the baselines directory on its own.
- Latency per walked decision is the SUM along its path of each node request's
wall time divided by that request's batch size, so it is comparable with the
flat legs' per-decision figure and counts every hop the walk actually made.
- A node the API does not answer produces a row with ``pred`` of ``None`` and
no probabilities rather than an invented verdict.
- Console output is ASCII only; commands are printed through :func:`ascii`.
- The only network host this module contacts is ``api.typesafe.ai``.

Dependencies
------------
- oc_diagnostics (structured debug logging via _dbg)
- oc_chat.tools._argv_policy (classify_command, the gate oracle each node's reference answer is derived from)
- baselines.jev_direct (the shared request builder, API key, post, answer parse, case reader, result writer and flat-leg evaluator)

Function Documentation

◆ _agreement()

int jev_walk._agreement ( Sequence[Mapping[str, Any]] rows )

protected

How many rows match the gate, restated from ``score.py``'s one line.

Definition at line 1214 of file jev_walk.py.

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◆ _analyse()

int jev_walk._analyse ( Path results_dir )

protected

Print P1's, P2's and P4's tables from the result files already on disk.

Definition at line 1661 of file jev_walk.py.

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◆ _build_parser()

argparse.ArgumentParser jev_walk._build_parser ( )

protected

Construct the CLI argument parser.

Definition at line 1728 of file jev_walk.py.

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◆ _composition_note()

str jev_walk._composition_note ( str remainder )

protected

The provenance sentence describing the arithmetic a leg actually used.

Two result files whose rows differ only by a composition rule have to be
told apart by something inside them, not by their file names.

Definition at line 1077 of file jev_walk.py.

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◆ _gate_structure()

str jev_walk._gate_structure	(	str	command,
		Any	decision )

protected

Node 1's reference answer, from the gate's pre-parse denials.

NUL is checked first because the gate reports it through the metacharacter
denial while the tree files it with the other unparseable inputs, which is
where a reader of the policy text would put it: the sentence that names NUL
says it denies "wherever it appears", quoted or not, and that is not the
outside-quotes rule the other branch asks about.

Definition at line 926 of file jev_walk.py.

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◆ _granted_more()

int jev_walk._granted_more ( Sequence[Mapping[str, Any]] rows )

protected

How many errors granted MORE permission than the gate.

The scorer's rule verbatim: an unrecognised prediction counts as granting
more, because understating this column is the one direction a bench must
never round in.

Definition at line 1220 of file jev_walk.py.

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◆ _perm_leg()

FlatLeg jev_walk._perm_leg ( str key )

protected

P2's permutation leg for insertion order *key*.

Definition at line 569 of file jev_walk.py.

◆ _read_payloads()

list[tuple[str, dict[str, Any]]] jev_walk._read_payloads	(	Path	results_dir,
		Iterable[str]	names )

protected

Load the named result files that are on disk, skipping those that are not.

Definition at line 1647 of file jev_walk.py.

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◆ _repeat_leg()

FlatLeg jev_walk._repeat_leg ( int index )

protected

P1's *index*-th repeat of plan 293's ``hard`` leg, byte-identical to it.

Definition at line 544 of file jev_walk.py.

◆ _resolved_leg()

WalkLeg jev_walk._resolved_leg	(	WalkLeg	leg,
		argparse.Namespace	args )

protected

*leg* under the CLI's levers, refusing a flag that contradicts a pinned one.

A leg that pins a lever is the reproducible object behind a plan-301 tag.
Running it under a contradicting flag would write a file whose model name
says one condition and whose rows are another, which is worse than not
running it at all -- so it raises rather than choosing a winner.

Definition at line 1820 of file jev_walk.py.

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◆ _run_flat()

int jev_walk._run_flat	(	Sequence[FlatLeg]	legs,
		Path	base,
		Path	out_dir,
		str	key,
		argparse.Namespace	args,
		dict[str, float]	spent )

protected

Run a set of flat legs, writing one result file each.

Definition at line 1784 of file jev_walk.py.

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◆ _run_walks()

int jev_walk._run_walks	(	Sequence[WalkLeg]	legs,
		Path	base,
		Path	out_dir,
		str	key,
		argparse.Namespace	args,
		dict[str, float]	spent )

protected

Run a set of walked legs, writing one result file each.

Definition at line 1851 of file jev_walk.py.

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◆ _spread()

dict[str, float] jev_walk._spread	(	str	verdict,
		float	probability,
		Mapping[str, float] \| None	leaf_probs,
		str	remainder_rule = DEFAULT_EARLY_STOP_REMAINDER )

protected

The three-label distribution for one composed verdict.

*leaf_probs* is absent exactly when the path stopped before a leaf, which
is the only case *remainder_rule* can reach: a leaf hands its own
distribution over and that is used under both rules.

Definition at line 868 of file jev_walk.py.

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◆ _top_probability()

float | None jev_walk._top_probability ( Mapping[str, Any] row )

protected

The row's top forecast probability, or ``None`` when it has none.

Definition at line 1204 of file jev_walk.py.

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◆ _walked_row()

dict[str, Any] jev_walk._walked_row	(	dict[str, Any]	case,
		list[Step]	steps,
		float	ms,
		int	requests,
		str	remainder = DEFAULT_EARLY_STOP_REMAINDER )

protected

One result row from one command's path through the tree.

Definition at line 1044 of file jev_walk.py.

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◆ build_node_request()

dict[str, Any] jev_walk.build_node_request	(	str	node,
		Sequence[str]	commands,
		bool	with_policy = True,
		str	leaf_state = DEFAULT_LEAF_STATE,
		str \| None	policy_text = None )

One request body asking *node*'s question about a batch of *commands*.

By default the state carries that node's slice and nothing else: not the
whole policy, not another node's sentences. That is the measurable
difference between this and the flat choice, so it is asserted by the
witnesses rather than merely intended here.

*leaf_state* is plan 301's control and moves the LEAVES only; see
:func:`node_state_text`. *with_policy* still wins over it, so plan 296's
withheld leg withholds from every node whatever the control says -- that
leg is a different measurement and this plan does not touch it.

Definition at line 743 of file jev_walk.py.

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◆ compose()

Composed jev_walk.compose	(	Sequence[Step]	steps,
		str	remainder = DEFAULT_EARLY_STOP_REMAINDER )

The pure composition: a path through the tree to one scorable verdict.

The verdict is the gate's own control flow applied to the nodes' answers.
Its probability is the PRODUCT of the chosen branch probabilities along the
path. The distribution the scorer reads puts that product on the composed
label and places the remainder by the leaf's own distribution where the
path reached a leaf, and on ``deny`` where it did not.

One consequence of that rule is worth stating plainly rather than leaving
to be discovered in a table: when the walk stops EARLY at ``deny`` -- a
metacharacter outside quotes, an unterminated quote, an unknown program --
there is no leaf distribution, so the remainder goes on ``deny`` as well
and the row is filed at probability 1.0 however unsure the node was. Those
rows carry no escalation signal at all. ``test_296_walk`` pins it and the
findings document reports what it costs.

*remainder* is plan 301 P2's lever over exactly that case. Under ``next``
an early stop composing to ``deny`` keeps the product on ``deny`` and puts
the leftover on ``confirm``, so an unsure early denial falls below an
escalation threshold instead of reading as certainty. Under the default
``none`` the arithmetic is plan 296's, unchanged. Nothing else moves: a
path that reached a leaf has a distribution and uses it under both rules,
and an early ``confirm`` at ``tier2`` keeps its fail-closed remainder.

A choice outside its node's options, or a path that never reaches a stop,
yields no verdict rather than a guess; a node that answered without
probabilities yields the verdict with no distribution, exactly as a
missing forecast is handled on the flat legs.

Definition at line 792 of file jev_walk.py.

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◆ gate_node_labels()

dict[str, str] jev_walk.gate_node_labels	(	str	command,
		Path \| None	root = None )

What the gate's own decision implies each node on its path should answer.

This is the reference the per-node table is scored against, and it is built
from what ``classify_command`` EXPOSES -- its decision, its parsed program
and its reason -- rather than from a second copy of the gate's internals.
The mapping is only defined for the nodes the gate's path actually visits,
so a command that stops at node 1 has one entry and not three.

Definition at line 900 of file jev_walk.py.

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◆ leaf_control_table()

list[dict[str, Any]] jev_walk.leaf_control_table	(	Sequence[tuple[str, dict[str, Any]]]	payloads,
		Path \| None	root = None )

P1: every leaf under every condition, one entry per leaf and file.

The four conditions are read side by side per LEAF rather than per file,
because the question is what one node does with more or less text and a
per-file table buries that under three nodes that did not move. The two
shared nodes are reported too, and they are the control's own control: if
they move between conditions, something other than the leaf state did.

Definition at line 1455 of file jev_walk.py.

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◆ main()

int jev_walk.main ( list[str] | None argv = None )

CLI entry point: run the pinned legs, or analyse what is on disk.

Definition at line 1886 of file jev_walk.py.

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◆ node_instructions()

Callable[[int, str, str], str] jev_walk.node_instructions ( str node )

The instruction writer for *node*, in :func:`jev_direct.build_request`'s shape.

The suffix is the builder's, so a withheld run loses the four words "under
the policy" here exactly as it does for the flat choice, and the withheld
walk cannot depend on a policy text it did not send.

Definition at line 725 of file jev_walk.py.

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◆ node_of_request()

str jev_walk.node_of_request ( Mapping[str, Any] body )

Which node built *body*, read back out of its instruction verb.

The criteria keys cannot do this: ``listing``, ``echo`` and ``where`` all
offer exactly ``allow`` and ``deny``, so an option set identifies three
nodes at once. The verb is distinct per node, and it is text the request
already carries for the model's sake -- which is better than adding a
routing field to a body whose shape is otherwise plan 293's.

Definition at line 773 of file jev_walk.py.

◆ node_state_text()

str \| None jev_walk.node_state_text	(	str	node,
		str	leaf_state = DEFAULT_LEAF_STATE,
		str \| None	policy_text = None )

The policy text *node* sees under *leaf_state*, or ``None`` for no text.

The two shared nodes keep their slices whatever the leaf state is. A leaf
gets its slice under ``slice``, the whole policy under ``whole``, and no
text at all under ``none``.

An unrecognised *leaf_state* raises rather than falling back to the default:
a typo that silently ran the default condition would produce a result file
whose name says ``whole`` and whose rows are ``slice``, which is the one
failure a control cannot survive.

*policy_text* is the whole policy when the caller already has it; the
fallback reads it from the bench directory so a witness can build one
request without threading the file through.

Definition at line 692 of file jev_walk.py.

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◆ noise_band()

dict[str, Any] jev_walk.noise_band ( Sequence[tuple[str, dict[str, Any]]] payloads )

P1: how far the same request moves across repeated runs.

Every later comparison in this plan is read against this. The files must
cover the same commands in the same order; anything else is a band that
looks fine and measures different commands, so it raises.

Definition at line 1240 of file jev_walk.py.

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◆ per_node_table()

list[dict[str, Any]] jev_walk.per_node_table	(	Sequence[Mapping[str, Any]]	rows,
		Path \| None	root = None )

P4: how many commands each node saw, stopped, and got right.

A node's agreement is against :func:`gate_node_labels` -- the answer the
gate's own decision and reason imply -- so it says whether the model got
the ATOM right, independently of whether the composed verdict came out
right. A walk can reach the correct verdict through a wrong program class,
and a per-node table is the only place that shows up.

``granted_more`` counts the rows that STOPPED at this node and whose
composed verdict grants more permission than the gate's. Plan 301 reads it
per leaf, where stopping is the same thing as arriving, so the column says
what a leaf's errors cost in the direction this bench is graded by rather
than only how many there were.

Definition at line 1336 of file jev_walk.py.

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◆ permutation_table()

dict[str, Any] jev_walk.permutation_table ( Sequence[tuple[str, dict[str, Any]]] payloads )

P2: what the label order costs, per order and per command.

Definition at line 1290 of file jev_walk.py.

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◆ permuted_criteria()

dict[str, str] jev_walk.permuted_criteria ( str key )

Plan 293's three criteria in insertion order *key*.

The VALUES are untouched -- this is a permutation of the mapping's key
order and of nothing else, which is the whole point of P2. A reworded
criterion would make the leg a wording experiment rather than an order one,
and the two are separated here by running each alone.

Definition at line 656 of file jev_walk.py.

◆ pinned_rule_rows()

list[dict[str, Any]] jev_walk.pinned_rule_rows ( Sequence[Mapping[str, Any]] rows )

*rows* from a ``next`` run, refiled as the pinned rule would have filed them.

This is what makes the remainder rule measurable rather than merely
reported. Two RUNS of the same request differ by the model's own movement,
which on this bench is worth about six commands, so a ``next`` file against
a ``slice`` file measures the rule plus the noise and cannot separate them.
The rule is a function of the composed row, though, so one run's answers can
be filed both ways: an early stop that composed to ``deny`` keeps its
verdict and takes the whole mass back, which is exactly what ``none`` does.
Everything else is already identical under both rules and is copied
unchanged.

The derivation runs in this direction only. A ``next`` row carries the
path's product on ``deny``; the ``none`` row it corresponds to carries 1.0
and has forgotten it, so a pinned file cannot be turned into an escalating
one.

Definition at line 1562 of file jev_walk.py.

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◆ remainder_comparison()

dict[str, Any] jev_walk.remainder_comparison	(	dict[str, Any]	pinned,
		dict[str, Any]	escalating,
		float	threshold = DEFAULT_THRESHOLD )

P2: what the remainder rule moves, counted on the rows it can reach.

The rule can only touch rows that stopped before a leaf, so the counts that
matter are over those rows and not over the file. ``wrong_now_escalating``
is the number the plan asks for by name: early-stopped commands whose
verdict disagrees with the gate and which the pinned rule filed at a
probability no threshold could catch.

The two files must cover the same commands in the same order; anything else
is a table that looks fine and compares different runs, so it raises.

Definition at line 1508 of file jev_walk.py.

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◆ render_leaf_control_table()

str jev_walk.render_leaf_control_table ( Sequence[Mapping[str, Any]] table )

P1's control table as markdown, grouped by node.

Definition at line 1492 of file jev_walk.py.

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◆ render_noise_table()

str jev_walk.render_noise_table ( Mapping[str, Any] band )

P1's table as markdown.

Definition at line 1391 of file jev_walk.py.

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◆ render_per_node_table()

str jev_walk.render_per_node_table ( Sequence[Mapping[str, Any]] table )

P4's per-node table as markdown.

Definition at line 1438 of file jev_walk.py.

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◆ render_permutation_table()

str jev_walk.render_permutation_table ( Mapping[str, Any] table )

P2's table as markdown.

Definition at line 1419 of file jev_walk.py.

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◆ render_remainder_table()

str jev_walk.render_remainder_table ( Mapping[str, Any] result )

P2's table as markdown.

Definition at line 1598 of file jev_walk.py.

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◆ render_walk_cost_table()

str jev_walk.render_walk_cost_table ( Sequence[tuple[str, dict[str, Any]]] payloads )

P4's request, token and cost columns, which the scorer does not carry.

Definition at line 1622 of file jev_walk.py.

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◆ run_flat_leg()

dict[str, Any] jev_walk.run_flat_leg	(	FlatLeg	leg,
		list[dict[str, Any]]	cases,
		str \| None	policy_text,
		int	batch = DEFAULT_BATCH,
		str \| None	key = None,
		float	timeout = DEFAULT_TIMEOUT,
		int	retries = DEFAULT_RETRIES,
		Callable[..., Any] \| None	opener = None,
		Callable[[float], None]	sleep = time.sleep,
		Callable[[str], None] \| None	progress = None )

Run one flat three-way choice leg -- a P1 repeat or a P2 permutation.

The evaluation itself is :func:`jev_direct.evaluate`, unchanged, reached
through its ``criteria`` seam. Only the provenance block is written here,
because it has to name plan 296, the criteria order actually sent and what
the leg was for, none of which plan 293's block can say.

Definition at line 1145 of file jev_walk.py.

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◆ run_leg()

dict[str, Any] jev_walk.run_leg	(	WalkLeg	leg,
		list[dict[str, Any]]	cases,
		int	batch = DEFAULT_BATCH,
		str \| None	key = None,
		float	timeout = DEFAULT_TIMEOUT,
		int	retries = DEFAULT_RETRIES,
		Callable[..., Any] \| None	opener = None,
		Callable[[float], None]	sleep = time.sleep,
		Callable[[str], None] \| None	progress = None )

Run one walked leg and build the payload the scorer reads.

Definition at line 1091 of file jev_walk.py.

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◆ sandbox_root()

Path jev_walk.sandbox_root ( )

The oc_chat subproject root: the root every gate label here is computed against.

Definition at line 668 of file jev_walk.py.

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◆ slice_text()

str jev_walk.slice_text ( str node )

The policy text *node* is given: its sentences, one per line.

Definition at line 674 of file jev_walk.py.

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◆ walk()

list[dict[str, Any]] jev_walk.walk	(	list[dict[str, Any]]	cases,
		bool	with_policy,
		int	batch = DEFAULT_BATCH,
		str \| None	key = None,
		dict[str, Any] \| None	usage = None,
		float	timeout = DEFAULT_TIMEOUT,
		int	retries = DEFAULT_RETRIES,
		Callable[..., Any] \| None	opener = None,
		Callable[[float], None]	sleep = time.sleep,
		Callable[[str], None] \| None	progress = None,
		str	leaf_state = DEFAULT_LEAF_STATE,
		str	remainder = DEFAULT_EARLY_STOP_REMAINDER )

Walk every case through the tree and return rows in the bench schema.

Commands are batched PER NODE, not per command: every command still at node
1 goes in one set of requests, then every command that reached node 2 goes
in the next, and so on down the four leaves. A node is therefore one
request per eight commands whatever the depth, and a command that stops at
node 1 costs exactly one request's share.

The row is plan 288's schema plus four keys the flat legs have no analogue
for: ``stopped_at``, ``requests``, ``path`` and ``node_confidence``.
``ms`` is the sum along the path, so it is the wall time one walked
decision actually took at a batch of *batch*.

*leaf_state* and *remainder* are plan 301's two levers and default to plan
296's behaviour. The whole policy is read once here rather than per request,
so a ``whole`` run sends one text it read one time.

Definition at line 950 of file jev_walk.py.

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◆ whole_policy()

str jev_walk.whole_policy ( Path | None root = None )

The whole of ``policy.txt``, read the way every runner on this bench reads it.

Stripped, because that is what :func:`jev_direct.read_policy` and the flat
legs send; a leaf under ``whole`` has to receive the same bytes the flat
choice receives or the control measures a second difference as well.

Definition at line 680 of file jev_walk.py.

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Variable Documentation

◆ _BARE

tuple jev_walk._BARE

protected

Initial value:

=  (
    "Bare \"pip install\" is confirm, bare \"python\"/\"pytest\"/\"oct\" are "
    "confirm, bare \"where\"/\"which\" deny, and bare \"echo\"/\"dir\"/\"ls\" allow."
)

Definition at line 277 of file jev_walk.py.

◆ _CASING

tuple jev_walk._CASING

protected

Initial value:

=  (
    # OCT-LINT: disable=no_hardcoded_secrets reason="plan 296: verbatim policy.txt casing sentence pinned as a node slice (witness W2 asserts it is a substring of the file); 4.58 bits/char of English prose with embedded quotes, no credential (operator ruling 2026-09-18)"
    "The PROGRAM name is case-insensitive and a .exe suffix is stripped, but "
    "pip SUBCOMMANDS are case-sensitive and must be lowercase (\"Pip List\" "
    "denies because \"List\" is not a known subcommand)."
)

Definition at line 248 of file jev_walk.py.

◆ _PROGRAM_SENTENCES

tuple jev_walk._PROGRAM_SENTENCES

protected

Initial value:

=  (
    "deny: everything else.",
    "Unknown programs (git, cat, type, grep, findstr, curl, wget, bash, sh, "
    "powershell, pwsh, node, del, copy, move, ren, md, rmdir, rm) are denied: "
    "reads go through the filesystem tools and git through oct_git_*.",
    _CASING,
)

Definition at line 255 of file jev_walk.py.

◆ _QUOTED_METACHAR

tuple jev_walk._QUOTED_METACHAR

protected

Initial value:

=  (
    "A metacharacter only inside quotes is allowed as data for the Tier-2 "
    "programs (python, python3, py, pytest, oct) but denies for everything "
    "else including echo, dir, ls."
)

Definition at line 270 of file jev_walk.py.

◆ _STRUCTURE_SENTENCES

tuple jev_walk._STRUCTURE_SENTENCES

protected

Initial value:

=  (
    "Any shell metacharacter | & ; < > $ backtick % or a newline outside "
    "quotes denies for every program, and NUL denies wherever it appears.",
    "An unterminated quote denies.",
    "An empty or whitespace-only command denies.",
)

Definition at line 236 of file jev_walk.py.

◆ _UNKNOWN_OPTIONS

tuple jev_walk._UNKNOWN_OPTIONS

protected

Initial value:

=  (
    "Unknown options anywhere deny (for example ls -R, dir /s, ls --sort, "
    "pip list -q)."
)

Definition at line 283 of file jev_walk.py.

◆ case_file

jev_walk.case_file

Definition at line 588 of file jev_walk.py.

◆ CONTROL_LEGS

tuple jev_walk.CONTROL_LEGS = ("hard", "hard_rep1", "hard_whole", "hard_none")

Definition at line 632 of file jev_walk.py.

◆ criteria

jev_walk.criteria

Definition at line 590 of file jev_walk.py.

◆ CRITERIA_ORDERS

dict jev_walk.CRITERIA_ORDERS

Initial value:

=  {
    "acd": ("allow", "confirm", "deny"),
    "adc": ("allow", "deny", "confirm"),
    "cad": ("confirm", "allow", "deny"),
    "cda": ("confirm", "deny", "allow"),
    "dac": ("deny", "allow", "confirm"),
    "dca": ("deny", "confirm", "allow"),
}

Definition at line 483 of file jev_walk.py.

◆ DEFAULT_EARLY_STOP_REMAINDER

str jev_walk.DEFAULT_EARLY_STOP_REMAINDER = "none"

Definition at line 209 of file jev_walk.py.

◆ DEFAULT_LEAF_STATE

str jev_walk.DEFAULT_LEAF_STATE = "slice"

Definition at line 202 of file jev_walk.py.

◆ EARLY_STOP_REMAINDERS

tuple jev_walk.EARLY_STOP_REMAINDERS = ("none", "next")

Definition at line 205 of file jev_walk.py.

◆ FALLBACK_LABEL

str jev_walk.FALLBACK_LABEL = "deny"

Definition at line 193 of file jev_walk.py.

◆ LEAF_NODES

frozenset jev_walk.LEAF_NODES = frozenset({"listing", "echo", "where", "pip"})

Definition at line 359 of file jev_walk.py.

◆ LEAF_STATES

tuple jev_walk.LEAF_STATES = ("slice", "whole", "none")

Definition at line 199 of file jev_walk.py.

◆ model

jev_walk.model

Definition at line 587 of file jev_walk.py.

◆ name

jev_walk.name

Definition at line 586 of file jev_walk.py.

◆ NEXT_MOST_PERMISSIVE

dict jev_walk.NEXT_MOST_PERMISSIVE = {"deny": "confirm"}

Definition at line 216 of file jev_walk.py.

◆ NEXT_NODE

dict jev_walk.NEXT_NODE

Initial value:

=  {
    ("structure", "clean"): "program",
    ("program", "tier1_listing"): "listing",
    ("program", "tier1_echo"): "echo",
    ("program", "tier1_where"): "where",
    ("program", "pip"): "pip",
}

Definition at line 381 of file jev_walk.py.

◆ NODE_CRITERIA

dict jev_walk.NODE_CRITERIA

Definition at line 404 of file jev_walk.py.

◆ NODE_OPTIONS

dict jev_walk.NODE_OPTIONS

Initial value:

=  {
    "structure": ("clean", "metachar_outside_quotes",
                  "unterminated_or_nul_or_empty"),
    "program": ("tier1_listing", "tier1_echo", "tier1_where", "pip", "tier2",
                "unknown"),
    "listing": ("allow", "deny"),
    "echo": ("allow", "deny"),
    "where": ("allow", "deny"),
    "pip": ("allow", "confirm", "deny"),
}

Definition at line 341 of file jev_walk.py.

◆ NODE_ORDER

tuple jev_walk.NODE_ORDER

Initial value:

1= ("structure", "program", "listing", "echo",

2 "where", "pip")

Definition at line 353 of file jev_walk.py.

◆ NODE_VERBS

dict jev_walk.NODE_VERBS

Initial value:

=  {
    "structure": "Classify the STRUCTURE of command",
    "program": "Classify the PROGRAM of command",
    "listing": "Classify the VERDICT of the dir/ls command",
    "echo": "Classify the VERDICT of the echo command",
    "where": "Classify the VERDICT of the where/which command",
    "pip": "Classify the VERDICT of the pip command",
}

Definition at line 466 of file jev_walk.py.

◆ note

jev_walk.note

Definition at line 591 of file jev_walk.py.

◆ PERM_LEGS

dict jev_walk.PERM_LEGS = {key: _perm_leg(key) for key in CRITERIA_ORDERS}

Definition at line 584 of file jev_walk.py.

◆ PERMISSIVENESS

dict jev_walk.PERMISSIVENESS = {"deny": 0, "confirm": 1, "allow": 2}

Definition at line 188 of file jev_walk.py.

◆ PLAN_296_WALK_LEGS

tuple jev_walk.PLAN_296_WALK_LEGS = ("hand", "hard", "hard_nopolicy")

Definition at line 623 of file jev_walk.py.

◆ PLAN_301_WALK_LEGS

tuple jev_walk.PLAN_301_WALK_LEGS

Initial value:

1= ("hard_rep1", "hard_whole", "hard_none",

2 "hard_next")

Definition at line 626 of file jev_walk.py.

◆ policy_file

jev_walk.policy_file

Definition at line 589 of file jev_walk.py.

◆ POLICY_SLICES

dict jev_walk.POLICY_SLICES

Definition at line 289 of file jev_walk.py.

◆ PROGRAM_CLASSES

dict jev_walk.PROGRAM_CLASSES

Initial value:

=  {
    "dir": "tier1_listing", "ls": "tier1_listing",
    "echo": "tier1_echo",
    "where": "tier1_where", "which": "tier1_where",
    "pip": "pip",
    "python": "tier2", "python3": "tier2", "py": "tier2",
    "pytest": "tier2", "oct": "tier2",
}

Definition at line 391 of file jev_walk.py.

◆ REPEAT_LEGS

dict jev_walk.REPEAT_LEGS

Initial value:

=  {
    "rep1": _repeat_leg(1),
    "rep2": _repeat_leg(2),
}

Definition at line 563 of file jev_walk.py.

◆ ROUTE

str jev_walk.ROUTE = "typesafe-walk"

Definition at line 184 of file jev_walk.py.

◆ SPEND_CAP_USD

float jev_walk.SPEND_CAP_USD = 2.0

Definition at line 219 of file jev_walk.py.

◆ STOP_VERDICTS

dict jev_walk.STOP_VERDICTS

Initial value:

=  {
    ("structure", "metachar_outside_quotes"): "deny",
    ("structure", "unterminated_or_nul_or_empty"): "deny",
    ("program", "unknown"): "deny",
    ("program", "tier2"): "confirm",
    ("listing", "allow"): "allow",
    ("listing", "deny"): "deny",
    ("echo", "allow"): "allow",
    ("echo", "deny"): "deny",
    ("where", "allow"): "allow",
    ("where", "deny"): "deny",
    ("pip", "allow"): "allow",
    ("pip", "confirm"): "confirm",
    ("pip", "deny"): "deny",
}

Definition at line 363 of file jev_walk.py.

◆ WALK_LEGS

dict jev_walk.WALK_LEGS

Initial value:

=  {
    "hand": WalkLeg("hand", "jev_walk_hand", "policy", "cases.json", True),
    "hard": WalkLeg("hard", "jev_walk_hard", "policy", "cases_hard.json", True),
    "hard_nopolicy": WalkLeg("hard_nopolicy", "jev_walk_hard_nopolicy",
                             "nopolicy", "cases_hard.json", False),
    "hard_rep1": WalkLeg("hard_rep1", "jev_walk_hard_rep1", "policy",
                         "cases_hard.json", True, leaf_state="slice",
                         plan="301"),
    "hard_whole": WalkLeg("hard_whole", "jev_walk_hard_whole", "whole",
                          "cases_hard.json", True, leaf_state="whole",
                          plan="301"),
    "hard_none": WalkLeg("hard_none", "jev_walk_hard_none", "none",
                         "cases_hard.json", True, leaf_state="none",
                         plan="301"),
    "hard_next": WalkLeg("hard_next", "jev_walk_hard_next", "next",
                         "cases_hard.json", True, leaf_state="slice",
                         early_stop_remainder="next", plan="301"),
}

Definition at line 600 of file jev_walk.py.

◆ WORD_CRITERIA

dict jev_walk.WORD_CRITERIA = {label: label for label in LABELS}

Definition at line 495 of file jev_walk.py.

Classes

Functions

Variables

Detailed Description

Function Documentation

◆ _agreement()

◆ _analyse()

◆ _build_parser()

◆ _composition_note()

◆ _gate_structure()

◆ _granted_more()

◆ _perm_leg()

◆ _read_payloads()

◆ _repeat_leg()

◆ _resolved_leg()

◆ _run_flat()

◆ _run_walks()

◆ _spread()

◆ _top_probability()

◆ _walked_row()

◆ build_node_request()

◆ compose()

◆ gate_node_labels()

◆ leaf_control_table()

◆ main()

◆ node_instructions()

◆ node_of_request()

◆ node_state_text()

◆ noise_band()

◆ per_node_table()

◆ permutation_table()

◆ permuted_criteria()

◆ pinned_rule_rows()

◆ remainder_comparison()

◆ render_leaf_control_table()

◆ render_noise_table()

◆ render_per_node_table()

◆ render_permutation_table()

◆ render_remainder_table()

◆ render_walk_cost_table()

◆ run_flat_leg()

◆ run_leg()

◆ sandbox_root()

◆ slice_text()

◆ walk()

◆ whole_policy()

Variable Documentation

◆ _BARE

◆ _CASING

◆ _PROGRAM_SENTENCES

◆ _QUOTED_METACHAR

◆ _STRUCTURE_SENTENCES

◆ _UNKNOWN_OPTIONS

◆ case_file

◆ CONTROL_LEGS

◆ criteria

◆ CRITERIA_ORDERS

◆ DEFAULT_EARLY_STOP_REMAINDER

◆ DEFAULT_LEAF_STATE

◆ EARLY_STOP_REMAINDERS

◆ FALLBACK_LABEL

◆ LEAF_NODES

◆ LEAF_STATES

◆ model

◆ name

◆ NEXT_MOST_PERMISSIVE

◆ NEXT_NODE

◆ NODE_CRITERIA

◆ NODE_OPTIONS

◆ NODE_ORDER

◆ NODE_VERBS

◆ note

◆ PERM_LEGS

◆ PERMISSIVENESS

◆ PLAN_296_WALK_LEGS

◆ PLAN_301_WALK_LEGS

◆ policy_file

◆ POLICY_SLICES

◆ PROGRAM_CLASSES

◆ REPEAT_LEGS