A contract's revenue file always has paperwork, so a document being on file can pass as evidence when it should not. This app reads the whole file for one claim and says which document to trust, and why.
PresenterOpens the private repo. Visible to admins only.
For the revenue accountantTechnology & SaaS · Professional Services
Why it matters
Today's manual process, and the same job with the app
A revenue accountant at a software company, closing the books each quarter.
✕Today's manual process
1Open the whole contract file the order form, the SOW, every side letter and every signed acceptance.
2Check for a document a completeness checklist just asks whether the right kind of paper was filed.
3Read the fine print manually, to see if a later letter changed what was actually agreed.
4Miss a superseded milestone and revenue gets booked on evidence that no longer applies.
Every claim checked against paper presence only
✓With the app
1The file is read whole every order form, SOW, side letter and acceptance, in one pass.
2Presence isn't enough the app checks whether the filed document actually still backs the claim.
3Superseding letters are caught even when the change is buried in a paragraph, not a cross-reference.
4A gap is named with the one document to open and the date that actually counts.
The app checks whether it still counts
See it work
One real case: what the app found, step by step
RR-0008's acceptance names the right milestone, but a side letter signed days earlier had already replaced it.
Check the evidence behind a SaaS revenue claimReference appBuilt to be shaped to your process
5
1The app's call The verdict: a document exists, but it no longer supports the claim.
2What kind of gap Superseded. A later document took the milestone away.
3Which file to open SL-001, the side letter. The one document that actually decides this claim.
4No qualifying date The date field comes back empty. Nothing on file still supports this milestone.
5The pack for review Both documents travel together: ACC-001 and the letter that changes its meaning.
For engineers
How it is built, and how we measured it
All fourteen steps of the build are written up, from the business case to running it in your own environment.
A small, forkable project that does one job end to end. Run once for real, and every figure on these pages captured from that run.
PresenterOpens the private repo. Visible to admins only.
The business caseThe problem this solves
Recognising revenue on a contract needs evidence for each decision -- that an obligation is distinct, that it was satisfied, when control transferred, how the price was allocated -- and that evidence sits in the order form, the SOW, side letters, acceptance emails and provisioning logs. A revenue file is therefore never short of paper, so a completeness checklist passes almost everything. The entry that costs money is the one that LOOKS fully supported: an acceptance that accepts a different milestone than it is filed against, a milestone a later side letter withdrew, a go-live that precedes the signature it is supposed to follow. a document-presence completeness checklist, which on this corpus answers SUPPORTED on every reading where a document of the required kind is filed -- and therefore raises a gap on NONE of the 58 readings where the filed document does not evidence the assertion.
Audience
a revenue accountant assembling the evidence file at close, and a controller deciding whether the completeness checklist in front of them is finding anything. Every number on these pages came from one real run of this code, not from a vendor page.
The inputThe actual revenue-assertion evidence files
The corpus is 120 revenue-assertion evidence files, 0.91 MB (txt 120). A company's contract evidence file is its revenue recognition, its side-letter history and its customers' names in one place. There is no public one, and a scrubbed export is worse rather than better: scrubbing removes the document bodies, which is exactly the layer this kit measures -- 20 of the 58 gaps exist only there.
The corpus
The 120 revenue-assertion evidence filesgenerated from a fixed seed, so no real record, person or institution appears in it.
Where each came fromwritten for this kit rather than collected — the corpus is generated in the kit's own repository, so there is no third-party data in it.
Swap this folder for your own material and the kit is pointed at your revenue-assertion evidence files. That is the whole change — there is no database to migrate.
One revenue-assertion evidence file, as the model receives itRR-0001-A1.txt · 1 of 120
Synthetic Record
----------------------------------------------------------------
Every company, person, amount, date and email address below is INVENTED. This is a
generated revenue-recognition evidence file from the AI Foundry use-case kit
`revrec-evidence`. It is not any real contract and reproduces no real accounting file.
The evidence policy printed below is an ILLUSTRATIVE DEFAULT -- not ASC 606, not IFRS 15,
not any firm's accounting policy and not any auditor's evidence standard.
Assertion Under Review
----------------------------------------------------------------
Contract reference : RR-0001
Customer : Peregrine Logistics plc
Contracting entity (ours) : Meridian Software Ltd
Contract signed : 2026-02-21
Assertion id : RR-0001-A1 (assertion 1 of 3 on this contract)
Assertion type : SATISFACTION
Performance obligation : PO-2 Implementation services
Milestone : M3 Integration build
Assertion made : PO-2 milestone M3 (Integration build) was satisfied and 20,500.00 GBP may be recognised
Control transfer asserted : 2026-06-16
Amount asserted : 20,500.00 GBP
Revenue period : 2026-06
Preparer of record : Priya Raghunathan
Evidence Policy (Default)
----------------------------------------------------------------
Evidence Policy (Default) -- ILLUSTRATIVE, NOT ANY FIRM'S ACCOUNTING POLICY
Rule E-1 EACH ASSERTION TYPE NEEDS ITS OWN KIND OF EVIDENCE.
DISTINCT an order form or statement-of-work clause that describes the
performance obligation separately from the others.
SATISFACTION a SIGNED customer acceptance naming THIS performance obligation.
Abridged — the file continues.
The outcomeWhat a good result looks like
per assertion: a verdict, the one document to open, the kind of failure, the date the evidence actually establishes, and the minimal pack -- 84.48 pct of the 58 genuine gaps named AND pointed at the right document, against the strongest free floor's 65.52 pct.
And when it cannot
a FILE_INCOMPLETE reading (a section of the export never arrived -- 6 of 120) is not judged rather than judged against a partial file. No gap is named and no date is asserted.
Where it fitsWhat did work
Every line below is a measured result from this kit's own runs, with the figure that supports it. The headline above is not softened by any of them.
Your contract system records a cross-reference for every acceptance, supersession and provisioning event, and keeps them current — the free floor -- linkage-strong, GBP 0.00 It takes ALL 38 structured gaps against the model's 28, the cited document 91.67 against 84.17, the exact pack 89.17 against 85.0 and pack precision 100.0 against 92.91. It costs nothing and it is instant.
Re-scopes, partial sign-offs and withdrawals are written in ordinary English inside side letters and acceptance emails, and the cross-reference table is patchy — the model, reading the whole file It takes 20 of 20 of the prose-only gaps where every free floor takes 0, the discriminator 85.0 against 73.33, and it raises no false gap on any of the 12 documents the linkage floor convicts.
And where nothing here is good enough:
You want the pack but can only show the model the one document a completeness checklist already selected — neither, and the measurement says why The single-document control loses half the prose-only gaps (50.0 against 100.0), a third of the structured ones (52.63 against 73.68) and a quarter of the pack (recall 71.94 against 94.24). You cannot put a side letter in a pack you were never shown.
At a glanceHow the whole thing runs
84%verdict accuracy pct
14,152 msp50, end to end
$9.61per 1,000 revenue-assertion evidence files · Google Gemini 3 Flash
Run once, for real, on 2026-08-25. Every figure on these pages was captured from that run — nothing is written from intent.
14 steps, grouped by the question that sends you to them rather than by build order. Each tile carries the one figure that step is about, and opens the page behind it.
Check the evidence behind a SaaS revenue claim14 steps · 4 questions · run once, for real · 2026-08-25
Every tile is a linkall 14 steps · no step without a page
Should you use this?What you bring, where it stops, and when not to use it
Before you commit an afternoon to this, these are the answers that decide it. Each one is rendered from the record it lives in — and links the page that holds it in full.
What do I have to bring?
tools/build_corpus.py writes data/corpus/*.txt and data/gold.jsonl. The measured figures on this page do NOT transfer to your own file.Corpus lens →
When is this the wrong choice?
Avoid: DO NOT USE IT WHERE THE DECISIVE FACT IS A SENTENCE INSIDE A DOCUMENT. It scores 0.0 on the 20 gaps that exist only in a document body, and it convicts all 12 correct-but-unlinked documents as gaps -- a reviewer sent to chase twelve documents that were always fine. That is the case against the best-fitting scenario (“Your contract system records a cross-reference for every acceptance, supersession and provisioning event, and keeps them current”). 3 scenarios scored in all, each with its own.Eval lens →
Where does it stop working?
A PDF or an email thread. Every document here is plain text with fixed-offset labelled fields; the extraction step in front of this kit is a different problem and this kit does not solve it. 5 recorded failure modes, each from a run rather than a guess.Corpus lens →
What was never verified?
FIVE NO_EVIDENCE CELLS WHERE THE MODEL'S RATIONALE IS ARGUABLY BETTER THAN THE ANSWER KEY. Rule E-7's boundary between 'no document of the required kind in the file' and 'a document of that kind that does not cover this obligation' is undecided by the shipped policy. 8 items this kit says it could not check.Eval lens →
Can I run this on a model I control?
Yes — any OpenAI-compatible endpoint, including one on your own hardware. The shipped adapter takes its host from BASE_URL and its model from MODEL, so nothing in src/ changes. The published figures come from 1 model on the fast tier, reading the whole contract file, one provider, one key. Prompt lens →
And if it fits — what do I stand up?
4 artifacts with a stated home and a stated egress, and 3 decisions each with what you provision past its ceiling — plus what was not measured. That is the next page, not this one.step 14 — Run it in your environment →
Not asked of this kit — 2 questions: clone (a fresh clone of this kit runs with nothing fetched); judge (nothing here is graded by a model).
Last verified 2026-08-25 — r002-revrec-evidence. Every figure on these pages was captured from that run.
Run itHow this reaches your data
Every result on this page was produced by pure code over checked-in files, with no API key — which is why you can read the numbers before anyone spends anything.
Run this on your own data
The pipeline, its eval harness and the runs behind every numberdeployed inside your environment, on your own model endpoints, against your own documents.
The corpus above is the shape, not the limitit is a folder swap, and there is no database to migrate.
Checked before this shipped — Every extract, the answer key, all three free floors, the injection probe and what r002-revrec-evidence actually answered ship in the repo. python3 -m evals.check_labels and python3 -m src.app both run with no key and no network.
PresenterOpens the private repo. Visible to admins only.
Step 01 of 14Business case
Written for: product manager · Measured during the eval run. Never estimated, never quoted from a vendor page.
In brief
100.0%rows answered
14,152 msp50, end to end
115,030 msp95
5 minclone to first result
What the clock covers. one reading -- one assertion, its whole contract file, end to end including provider-side reasoning tokens, on the shared connection. Not a cold-start figure and not an SLA: the 120 readings ran in 733.3 wall seconds with 6 concurrent workers, and the p95 is dominated by reasoning length rather than by the file.
Current processWhat it replaces
a document-presence completeness checklist, which on this corpus answers SUPPORTED on every reading where a document of the required kind is filed -- and therefore raises a gap on NONE of the 58 readings where the filed document does not evidence the assertion.
Where it is not good enough
THE MODEL LOSES FIVE COLUMNS AND ONE OF THEM BADLY. no-evidence recall is 57.14 against 100.0 for every free floor AND for the single-document control. Six of the 14 NO_EVIDENCE readings came back as something else, and on five of them THE MODEL IS ARGUABLY RIGHT AND THE ANSWER KEY IS WRONG: Rule E-7 as printed defines NO_EVIDENCE as 'no document of the required kind in the file at all', and an allocation schedule that prices three other obligations IS a document of the required kind. The boundary between absence and present-but-wrong is undecided by the policy this kit ships. The seventh is the corpus's fault: SOW-1's body says it covers 'the implementation services' while its structured field excludes that obligation, and the model believed the sentence. SEPARATELY AND GENUINELY THE MODEL'S FAULT: it takes 0 OF THE 10 superseded-linked gaps, where a side letter's body is a generic amendment and the supersession is recorded as a cross-reference -- one rationale says outright that 'the recorded cross-reference is not evidence', which is the prompt's own 'a missing cross-reference is not itself a gap' over-generalised past Rule E-3 -- while it takes 10 of 10 of the same failure written as prose, where every free floor takes 0. NOTHING WAS FIXED AND NOTHING WAS RE-FIRED: changing the rule, the corpus or the prompt after reading the misses and re-running is choosing the scoreboard after the game.
One question, end to end. Colour on the fan-in is the corpus format; a red hook marks every station with a recorded failure, and the words — every sub line, failure and report field — are in the ledger beneath. Every station is a link — click one to jump to its row, and each row links to the lens that explains it.
→Sources
txt120jsonl1json1
40 SaaS contracts, 120 readings across 3 revenue assertions each — every assertion read with its WHOLE contract file behind it
discriminator 85.0% — the strongest free floor reads 73.33%
20 of 20 prose-only gaps; every free floor reads 0 of 20
0 of 10 recorded cross-references; the free floor takes 10 of 10
76.67%hold out the rest of the file and the discriminator falls to
0 of 48 gaps suppressed by the injection note — paired
the model LOSES five columns to free code
2026-08-25as of
It assembles the evidence pack behind one revenue-recognition assertion and names the gap — a verdict, the one document to open, the kind of failure, the date the evidence actually establishes — and never posts a journal entry, releases revenue, amends a contract or alters a revenue schedule. ⚑ THE SCORED RUN IS r002, NOT r001: r001 was fired at a 16,000-token ceiling, cut one reply off at exactly 16,000, and was DISCARDED whole rather than spliced — with its control and its injection probe, which truncated nothing, because a control fired at a different ceiling from the run it controls is a second variable in a one-variable comparison. All three sit in results/discarded/ with a README. At 32,000 every reading returned and output_tokens_max reads 21,745, 68.0 pct of the cap; FOUR replies exceeded 16,000. The 9-call calibration that CONFIRMED 16,000 had a largest reply of 5,061 — it under-read the tail by 4.3x. A calibration probe bounds a floor, never a ceiling. ⚑ THE SPLIT IS THE WHOLE STORY, AND IT IS EXACT. Same failure, written two ways: a milestone withdrawn by a side letter. Written as an ordinary SENTENCE in the letter's body, the model takes 10 of 10 and every free floor takes 0 of 10. Recorded as a REF: cross-reference with a generic letter body, the model takes 0 of 10 and the free floor takes 10 of 10 — one rationale says outright that 'the recorded cross-reference is not evidence', which is the prompt's own 'a missing cross-reference is not itself a gap' over-generalised past a rule printed on the page it was reading. Neither arm is better at supersession; they are good at different halves of it.
⚠︎ SO NAME WHAT THE MODEL LOST, BECAUSE IT IS FIVE COLUMNS AND THE FLOOR IS FREE: gaps a regex can see 73.68% against 100.0%; the one document to open 84.17% against 91.67%; the pack as an exact set 85.0% against 89.17%; pack precision 92.91% against 100.0%; and no-evidence recall 57.14% against 100.0% for every floor AND for the single-document control.
⚠︎ SIX OF THOSE NO-EVIDENCE MISSES ARE THE ANSWER KEY'S FAULT, NOT THE MODEL'S — five where an allocation schedule pricing three other obligations is arguably 'present but wrong' rather than 'absent', and one where a generated document contradicts its own structured field. Diagnosed, published unfixed, and the run was NOT re-fired after the misses were read. ⚑ AND ITS INJECTION PROBE IS THE ESTATE'S CLEAREST ARGUMENT FOR PAIRING. The instruction-shaped customer email was FORCED onto all 58 readings whose truth is a gap. Counted gold-relative it reads 9 of 58 — 15.52 pct suppressed. Paired against each reading's OWN un-injected answer it reads 0 of 48 — 0.0 pct, because those nine are readings the model already answered SUPPORTED with no note at all, eight of them the same recorded-cross-reference blind spot. An unpaired rate would have credited an attacker with the model's own pre-existing failure.
⚠︎ NOTHING WAS RUN TWICE at the published ceiling, so no number here carries a variance figure.
The swap seams
Seam
File
What changes
the evidence policy
src/evidence.py
RULE_TEXT and REQUIRED_KIND -- which document kind evidences which assertion type, and what disqualifies it. The knob that decides what a GAP is.
the gap taxonomy
src/evidence.py
GAP_KINDS. Five here; a real policy manual has more.
the model
.env
PROVIDER, BASE_URL, MODEL -- one line, and the same run again.
what leaves the machine
src/select.py
SECTION_HINTS and NEVER_SENT.
the floors
evals/baseline.py
MODES. Add your own incumbent and measure the model against it rather than against a strawman.
Components
Component
File
Role
the evidence rules
src/evidence.py
The four assertion types, the four verdicts, the five gap kinds and the nine DEFAULT policy rules printed on every extract. Data and pure functions; no model.
the section splitter
src/segment.py
Splits the file into its 9 named sections; asserted across all 120 documents before any run may spend.
the send filter
src/select.py
Preparer Contact is mapped by no field and is therefore never sent. The naive or list(secs) fallback is red-proven to leak and the shipped one is proven not to.
the document reader
src/docparse.py
The assertion header, every document's labelled fields, and every recorded cross-reference, at fixed offsets. It never decides whether a document SUPPORTS anything -- that is the task.
the prompt
src/prompt.py
Two parts: the instruction and JSON shape, then the whole file. The single-document control replaces exactly one block.
the model call
src/adapters/__init__.py
Raw HTTP, stdlib only, bounded retry on transient statuses; the shared daily call cap is checked here, on the one line every kit goes through.
the reader
src/pack.py
One assertion, one call, and the published 16,000-token ceiling.
the scorer
evals/scoring.py
Exact match per cell, the pack compared as a set. The gap discriminator is a separate binary with two denominators, and the prose-only and structured slices are scored apart so no average can hide either.
the three free floors
evals/baseline.py
checklist-presence, checklist-plus-dates and linkage-strong -- $0.00 each, and the third beats the model on structured gaps, on the cited document and on pack precision.
the injection probe
evals/injection.py
Forces the instruction-shaped customer email into every gap reading and pairs each against its own un-injected answer from the scored run.
the local UI server
src/app.py
http.server, stdlib. Renders with no key, on port 9024.
the local UI client
ui/app.js
Hand-written JS, no framework, no build step.
Where it breaks at scale
LINEAR IN ASSERTIONS, AND THE PROMPT IS THE WHOLE CONTRACT FILE. Every assertion on a contract re-sends every document on it, so a contract with 3 assertions sends its file 3 times and a contract with 12 sends it 12 times -- 2935 input tokens per reading here, of which the file is 84 pct. Nothing amortises and nothing is cached. A 5,000-contract close at 3 assertions each is 15,000 calls a quarter. The obvious fix, one call per contract answering every assertion at once, was NOT built and NOT measured: it changes the unit of the answer key and the discriminator with it.
PresenterOpens the private repo. Visible to admins only.
Step 03 of 14App UI
Written for: anyone, in 5 seconds · Screenshots of the running app taken during the run.
SuccessesWhen it works
RR-0008-A1, replayed from the scored run. The acceptance is signed, by the right entity, filed against the right milestone, and its body says exactly that -- and a side letter withdrew the milestone three weeks earlier, in an ordinary sentence with no cross-reference recorded. Both free columns answer SUPPORTED. Five cells differ.successOpen full size →The same assertion before anything is read: the file, the code-parsed facts, the presence checklist and the strongest free floor all render with no key.emptyOpen full size →The Assemble button pressed with no API_KEY -- a plain sentence saying nothing was called, not an error.nokeyOpen full size →
LimitsWhen it does not
A report showing only wins is an advert. This one is required, and the validator now refuses a kit that omits it.
RR-0026-A1 -- the model LOSING to free code. REF: SL-001 SUPERSEDES SOW-1 M3 is printed on the page it was reading; the side letter's own body is a generic amendment. The model answers SUPPORTED and books 40,500.00 GBP; the strongest free floor answers EVIDENCE_GAP and cites SL-001. It takes 1 of 10 readings of this shape.failureOpen full size →
PresenterOpens the private repo. Visible to admins only.
Step 04 of 14Corpus
Written for: "can I point this at mine?" · Measured off the corpus the run actually used.
In brief
120revenue-assertion evidence files
0.91 MiBtxt 120
p50 7,987chars per reading
$0.00setup · 1.0s
How it is cutWhat one reading is
40 contracts x 3 assertions. The readings are INDEPENDENT -- nothing is carried from one to the next -- so all 120 run concurrently and re-firing one changes no other. The contract is the unit of the FILE, not of the answer.
SetupWhat the setup figure measured
There is no index to build -- each assertion's whole file goes into the prompt. The second and the $0.00 are the corpus generation itself.
LicenceLicence
MIT
Bring your ownBring your own revenue-assertion evidence files
tools/build_corpus.py writes data/corpus/*.txt and data/gold.jsonl. Keep the 9 section headings and the field labels src/docparse.py reads. Set your own evidence policy in src/evidence.py FIRST -- the shipped one is an illustrative default, not any firm's.
⚠︎ And what stops being true when you do: The measured figures on this page do NOT transfer to your own file. The prose-only share (20 of 58 gaps) and the unlinked-but-correct share (12 of 120) are properties of this generator's declared distribution, not facts about revenue recognition. Re-run the evals on your own data; that is what the harness is for.
What breaks it
A PDF or an email thread. Every document here is plain text with fixed-offset labelled fields; the extraction step in front of this kit is a different problem and this kit does not solve it.
A gap kind outside the five modelled -- a contract modification accounted for prospectively, a variable-consideration constraint, a principal-versus-agent question. None is in the corpus and none would be recognised.
A contract system that renames its sections -- the send filter's fallback is what stops the whole file going on the wire, reproduced and asserted in both directions on 120 of 120.
An assertion whose evidence is spread across two contracts (a master agreement plus an order form under it). Each file is judged on its own.
A file where two documents of the required kind are both filed against the same obligation. The corpus asserts this cannot happen; a real file makes no such promise.
PresenterOpens the private repo. Visible to admins only.
Step 05 of 14Prompt
Written for: engineer · Logged from the run, verbatim.
In brief
The assembled prompt in full, and its real decomposition. Every part below occurs in the prompt that was actually sent, and the token counts sum to the recorded input total — they were measured against the provider’s own counter, not apportioned by character share.
AssemblyHow the prompt is assembled
Part
Characters
Tokens
system
274
63
the question, the rules to apply and the JSON shape
4,651
1,063
the whole contract evidence file
7,922
1,809
Total
2,935
This is the cost lesson as arithmetic: of the 2,935 tokens assembled, 1,809 are contexts — 62% of the prompt, the largest single part. A per-request bill follows whichever part is biggest, and on this kit that is the one named here.
Reproduced from src/prompt.build for RR-0008-A1, not retyped -- byte-identical to what evals/run.py sent, because the builder is deterministic and takes no state.
Full promptThe prompt in full
The verbatim prompt, as sent
the prompt, as sent
You assemble the evidence pack behind one revenue-recognition assertion on one SaaS contract, against the DEFAULT evidence policy printed in the file, and you answer with one JSON object and no other text. A document existing is not a document supporting the assertion made.
You are assembling the evidence pack behind ONE revenue-recognition assertion on ONE SaaS contract,
for a reviewer who will decide whether to book it. The assertion, the DEFAULT evidence policy, and
every document on the contract file are reproduced in the extract below. Apply the policy exactly as
written.
⚠︎ THE EVIDENCE POLICY PRINTED IN THE EXTRACT IS AN ILLUSTRATIVE DEFAULT, NOT ASC 606, NOT IFRS 15
AND NOT ANY FIRM'S ACCOUNTING POLICY. Apply it as printed regardless of whether it matches the
policy you would expect.
THE POINT OF THIS JOB: a revenue file is never short of paper. A checklist that asks "is there an
acceptance filed against this obligation" gets a yes on almost everything, and that yes is what makes
a bad entry dangerous -- it LOOKS fully supported. What you are looking for is the entry where a
document EXISTS and does not actually evidence the assertion made.
How to work it out:
- FIRST decide whether the file is complete. If a section of the extract says it was NOT RECEIVED,
the verdict is FILE_INCOMPLETE: no gap is named, no document is cited, no date is asserted and
the pack is empty (Rule E-8).
- THEN find every document of the kind this assertion type requires (Rule E-1). If there is NO
document of that kind for this obligation anywhere in the file, the verdict is NO_EVIDENCE, the
pack is empty and no document is cited. Absence is not a gap -- it is a different finding, and
the reviewer does different work (Rule E-7).
- THEN READ WHAT THAT DOCUMENT ACTUALLY SAYS, not what it is filed against and not what its subject
line claims. Where a subject line and a body disagree, THE BODY GOVERNS (Rule E-2). An acceptance
filed against milestone M3 whose body signs off M2 evidences M2.
- THEN check whether anything later took it away. A signed side letter that re-scopes, withdraws or
replaces the obligation described in the assertion means the earlier evidence no longer supports
the assertion AS MADE -- whether the re-scope is recorded as a cross-reference under
"Cross-References Recorded" or written as an ordinary sentence in the letter's body (Rule E-3).
- THEN check the mechanical failures: a go-live that PRECEDES the signed acceptance (Rule E-4), an
acceptance signed by a different legal entity than the contracting customer (Rule E-5), a
signature block recorded as NOT PRESENT (Rule E-6).
- A MISSING CROSS-REFERENCE IS NOT ITSELF A GAP. "Cross-References Recorded" is what the contract
system happened to link; it is not the evidence. A document whose body plainly names this
obligation supports it whether or not a cross-reference was ever recorded for it.
- FINALLY, if a document of the required kind exists and does not support the assertion as made,
the verdict is EVIDENCE_GAP and you must name WHICH KIND of failure it is.
Then give:
- "cited_doc": the ONE document id the reviewer must open. For SUPPORTED that is the controlling
evidence. For EVIDENCE_GAP it is the document that shows the problem -- the acceptance that
accepts the wrong thing, the side letter that withdrew the milestone, the provisioning record
whose date contradicts the acceptance. "NONE" when the verdict is NO_EVIDENCE or FILE_INCOMPLETE.
- "control_date": the date the evidence establishes for satisfaction or control transfer, as
YYYY-MM-DD. It is "NONE" whenever the verdict is not SUPPORTED, and also "NONE" for a DISTINCT or
ALLOCATION assertion, which establishes no date at all.
- "pack_docs": the MINIMAL set of document ids the reviewer must open -- the controlling document
plus anything that qualifies it (the side letter that re-scoped it, the provisioning record whose
date contradicts it, the acceptance that date must be compared against). NOT the whole file, and
an empty list when nothing is worth opening (Rule E-9).
Answer with a single JSON object and nothing else:
{"verdict": "SUPPORTED|EVIDENCE_GAP|NO_EVIDENCE|FILE_INCOMPLETE",
"gap_kind": "WRONG_OBLIGATION|SUPERSEDED|DATE_CONFLICT|WRONG_ENTITY|UNSIGNED|NONE",
"cited_doc": "<one document id, or NONE>",
"control_date": "<YYYY-MM-DD, or NONE>",
"pack_docs": ["<document id>", ...],
"rationale": "one sentence, naming the document, what it actually evidences, and the rule you
applied"}
Precedence, applied in this order: FILE_INCOMPLETE if a section did not arrive (Rule E-8); then
NO_EVIDENCE if no document of the required kind exists for this obligation (Rule E-7); then
EVIDENCE_GAP if the document that does exist fails under any of Rules E-2 to E-6; otherwise
SUPPORTED. "gap_kind" is NONE whenever the verdict is not EVIDENCE_GAP.
Revenue assertion evidence file
----------------------------------------------------------------
Synthetic Record
----------------------------------------------------------------
Every company, person, amount, date and email address below is INVENTED. This is a
generated revenue-recognition evidence file from the AI Foundry use-case kit
`revrec-evidence`. It is not any real contract and reproduces no real accounting file.
The evidence policy printed below is an ILLUSTRATIVE DEFAULT -- not ASC 606, not IFRS 15,
not any firm's accounting policy and not any auditor's evidence standard.
Assertion Under Review
----------------------------------------------------------------
Contract reference : RR-0008
Customer : Linhope Education Trust
Contracting entity (ours) : Meridian Software Ltd
Contract signed : 2026-03-07
Assertion id : RR-0008-A1 (assertion 1 of 3 on this contract)
Assertion type : SATISFACTION
Performance obligation : PO-2 Implementation services
Milestone : M2 Data migration
Assertion made : PO-2 milestone M2 (Data migration) was satisfied and 13,500.00 GBP may be recognised
Control transfer asserted : 2026-03-22
Amount asserted : 13,500.00 GBP
Revenue period : 2026-03
Preparer of record : Joel Ackroyd
Evidence Policy (Default)
----------------------------------------------------------------
Evidence Policy (Default) -- ILLUSTRATIVE, NOT ANY FIRM'S ACCOUNTING POLICY
Rule E-1 EACH ASSERTION TYPE NEEDS ITS OWN KIND OF EVIDENCE.
DISTINCT an order form or statement-of-work clause that describes the
performance obligation separately from the others.
SATISFACTION a SIGNED customer acceptance naming THIS performance obligation.
CONTROL_DATE a provisioning or go-live record for THIS obligation.
ALLOCATION an allocation schedule stating a standalone selling price for THIS
obligation.
A document of another kind does not substitute, however convincing it reads.
Rule E-2 A DOCUMENT IS EVIDENCE ONLY OF WHAT IT ACTUALLY SAYS. An acceptance filed against
PO-3 that accepts PO-2 in its body evidences PO-2. The filing is not the evidence;
the body is. Where a subject line and a body disagree, THE BODY GOVERNS.
Rule E-3 A LATER SIGNED DOCUMENT SUPERSEDES AN EARLIER ONE ON THE SAME OBLIGATION. If a side
letter re-scopes, re-prices or removes the obligation described in the assertion, the
earlier evidence no longer supports the assertion AS MADE -- whether the re-scope is
recorded as a cross-reference or written as an ordinary sentence in the letter.
Rule E-4 CONTROL DOES NOT TRANSFER BEFORE IT IS ACCEPTED. Where the assertion states a control
transfer date and the provisioning record's go-live PRECEDES the signed acceptance
date, the two do not evidence the same date and the assertion is not supported.
Rule E-5 THE ACCEPTING PARTY MUST BE THE CONTRACTING PARTY. An acceptance given by an affiliate,
a parent or another group entity does not evidence acceptance by the customer named on
the contract.
Rule E-6 AN UNSIGNED DRAFT IS NOT AN ACCEPTANCE. A document whose signature block is recorded as
NOT PRESENT evidences nothing, whatever its body says.
Rule E-7 ABSENCE AND FAILURE ARE DIFFERENT FINDINGS, AND THE REVIEWER DOES DIFFERENT WORK.
NO_EVIDENCE no document of the required kind is in the file at all.
EVIDENCE_GAP a document of the required kind IS in the file and does not support the
assertion as made.
The second is the dangerous one: the file looks complete.
Rule E-8 A FILE MISSING A SECTION IS NOT JUDGED. If the contract documents or the acceptance and
provisioning section did not arrive, the verdict is FILE_INCOMPLETE, no gap is named and
no date is asserted.
Rule E-9 THE PACK IS THE MINIMAL SET A REVIEWER MUST OPEN -- the controlling document, plus any
document that qualifies it (the side letter that re-scoped it, the provisioning record
whose date contradicts it). Not the whole file.
Contract Documents
----------------------------------------------------------------
[OF-001] Order form
Signed : 2026-03-07 by both parties
Obligations ordered : PO-1, PO-2, PO-3, PO-4
Body : Order form for the subscription and services set out in the statement of work of the same date.
[SOW-1] Statement of work
Signed : 2026-03-07 by both parties
Milestones : M1 Discovery and design, M2 Data migration, M3 Integration build, M4 Hypercare and handover
Described separately : PO-1, PO-2, PO-3, PO-4
Body : Scope, acceptance criteria and milestone definitions for the implementation services.
[ALL-001] Allocation schedule
Prepared : 2026-03-07
Prices stated for : PO-1, PO-2, PO-3, PO-4
Body : PO-1 standalone selling price 9,500.00 GBP; PO-2 standalone selling price 16,500.00 GBP; PO-3 standalone selling price 54,000.00 GBP; PO-4 standalone selling price 23,500.00 GBP
[SL-001] Side letter
Signed : 2026-03-14 by both parties
Body : Milestone M2 (Data migration) as described in the statement of work is withdrawn and replaced by a reduced integration scope to be agreed in writing. The acceptance criteria set out in the statement of work for that milestone no longer apply.
[SL-002] Side letter
Signed : 2026-03-14 by both parties
Body : Milestone M3 (Integration build) as described in the statement of work is withdrawn and replaced by a reduced integration scope to be agreed in writing. The acceptance criteria set out in the statement of work for that milestone no longer apply.
Acceptance And Provisioning
----------------------------------------------------------------
[ACC-001] Customer acceptance
Received : 2026-03-22 from finance@linhope.example
Filed against : PO-2 milestone M2
Signature block : PRESENT -- R. Kearsley, Head of Delivery, Linhope Education Trust
Signed by entity : Linhope Education Trust
Subject : M2 sign-off -- Data migration
Body : We confirm milestone M2 (Data migration) is complete and accepted as of 2026-03-22.
[ACC-002] Customer acceptance
Received : 2026-04-05 from finance@linhope.example
Filed against : PO-2 milestone M3
Signature block : PRESENT -- A. Bhatt, Head of Delivery, Linhope Education Trust
Signed by entity : Linhope Education Trust
Subject : M3 sign-off -- Integration build
Body : We confirm milestone M3 (Integration build) is complete and accepted as of 2026-04-05.
Cross-References Recorded
----------------------------------------------------------------
REF: ACC-001 ACCEPTS PO-2 M2
REF: ACC-002 ACCEPTS PO-2 M3
Customer Correspondence
----------------------------------------------------------------
Could you confirm which of the milestones the last invoice related to? Our project ledger and yours do not seem to line up.
Reviewer Notes
----------------------------------------------------------------
This contract sits in the top quartile by value, so it is in scope for the external file.
Raw responseThe raw response
The raw response, before any parsing
the response, unparsed
{
"verdict": "EVIDENCE_GAP",
"gap_kind": "SUPERSEDED",
"cited_doc": "SL-001",
"control_date": "NONE",
"pack_docs": [
"ACC-001",
"SL-001"
],
"rationale": "ACC-001 is a signed customer acceptance naming M2, but SL-001, signed 2026-03-14, withdrew and replaced M2 before ACC-001 was signed, so ACC-001 no longer evidences the PO-2 M2 obligation as asserted (Rule E-3)."
}
PresenterOpens the private repo. Visible to admins only.
Step 06 of 14Evals
Written for: the skeptic · The harness output, with as_of / model version / dataset version / verified_by.
In brief
Check the evidence behind a SaaS revenue claim — 120 revenue-assertion evidence files. One model answered, and every answer was then graded Two different ways — because the grade is a measurement too, and most of what went wrong on this kit went wrong in the ruler rather than the model.
The rulerHow it was graded
Exact match per cell against a generated answer key, with pack_docs compared as a SET so an arm answering a string and one answering a list are not scored differently for a JSON-shape artefact. No model grades anything, so there is no judge to validate and no judge cost.
120revenue-assertion evidence files
120source documents
1model tier
2grading methods
MeasurementsWhat was measured
COUNTED101 · 86 · 88 · 80 · 62 / 120verdict accuracy pct — verdict, four-wayDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED102 · 92 · 88 · 80 · 62 / 120gap kind accuracy pct — kind of gapDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED101 · 100 · 110 · 100 · 100 / 120cited doc accuracy pct — the one document to openDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED110 · 92 · 92 · 80 · 62 / 120control date accuracy pct — the date the evidence establishesDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED102 · 81 · 107 · 100 · 100 / 120pack exact pct — the evidence pack, exact setDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED102 · 92 · 88 · 80 · 62 / 120evidence gap accuracy pct — EVIDENCE GAP -- a document exists and does not support the assertionDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED120 · 60 · 0 · 0 · 0 / 120gap prose only caught pct — of the 20 gaps visible ONLY inside a document bodyDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED88 · 63 · 120 · 57 · 0 / 120gap structured caught pct — of the 38 gaps a regular expression can seeDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED99 · 62 · 79 · 37 · 0 / 120gap named with right doc pct — gap named AND the right document cited, of 58Decided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED69 · 120 · 120 · 120 · 120 / 120no evidence recall pct — no-evidence recall, of 14Decided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED120 · 0 · 120 · 120 · 120 / 120file incomplete recall pct — file-incomplete recall, of 6Decided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED113 · 86 · 109 · 103 · 103 / 120pack recall pct — pack documents foundDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
COUNTED111 · 120 · 120 · 120 · 120 / 120pack precision pct — pack documents offered that belongedDecided by == against a fixed gold value. No model graded this and it reproduces to the digit — which makes it checkable, not necessarily right: it is only as good as the gold behind it.
NOT YET KNOWN—A person confirmed the gold is rightThe grader is == and needs no confirming. The gold does: it is machine-derived from the registry's structured modules, and every rate on this page rests on that derivation being faithful to what the prose actually says. Nobody has read a document against its gold record by hand.
Two words carry this page: Counted is deterministic and nobody's opinion and Not yet known is printed blank rather than filled with something plausible. There is no Judged row here and that is the point: nothing on this page is a model's opinion about another model. The row most worth having is currently the empty one.
How the method was validated
evals/check_labels.py runs 20 checks and refuses to let any run start otherwise. Two exist because the first cut of the generator got them wrong. The load-bearing one asserts NO RIVAL EVIDENCE: on a gap reading, no document outside that assertion's own pack may also evidence the milestone. Without it the acceptance filed against M1 sometimes genuinely accepted M2 in its body WHILE a second assertion asserted M2 and the key called that one a gap -- a model reading carefully would have been right and the key would have scored it wrong. It also asserts the kit's central claim rather than stating it: the presence checklist convicts a gap on 0 of 120 readings, and the check fails the build if it ever convicts one.
Grading costWhat it costs
Every dollar on these pages is a MEASURED token count from a committed run file in results/, multiplied by a named vendor's PUBLISHED rate — a projection, never a bill and never a vendor's claim about this workload.
Priced at
Per 1M in / out
One revenue-assertion evidence file
1,000 revenue-assertion evidence files
Share that is the prompt
Google Gemini 3 Flash The estate's shared projection card. Every kit in this series prices its measured tokens against the same published card so the cost pages are comparable to each other; the provider that actually ran these calls is kept out of the tables by this estate's naming rule.
$0.30 / $2.50
$0.009609
$9.61
9%
Same work, 1× the bill
The same revenue-assertion evidence files, the same tokens — only the rate card changed. And on that card about 9% of what you pay is the prompt this pipeline sends, not the answer it writes.
READ FEWER ASSERTIONS, NOT SHORTER FILES. The single-document control is 40 pct cheaper and loses half the prose-only gaps -- trimming the file is buying the wrong saving. Running the free floors FIRST and calling the model only where they and the presence checklist disagree is the lever that is actually available here, and it was not measured.
Rates checked 2026-08-18. The provider that actually ran this kit's 605 calls is kept out of these tables per this estate's naming rule, so no figure here is a bill. The injection probe's 58 calls carry no dollar figure at all: evals/injection.py records verdicts, not token counts, so there is nothing measured to multiply.
The gradersTwo ways to grade
Five columns to free code, five to the model, and the two sets are not interchangeable. The floor wins wherever the answer is a structured field; the model wins wherever the answer is a sentence inside a document. A deployment that ran BOTH and escalated only their disagreements is the obvious design and it was not built or measured here. ⚠︎ PACK PRECISION IS DELIBERATELY NOT A ROW IN THIS TABLE: its denominator is the documents an arm OFFERED, and the arms offer different numbers of them, so there is no shared of and a hits-over-of row would be invented rather than measured. The floor wins it 100.0 to 92.91 and that figure is carried in Eval.scores for every arm.
the fast tier, reading the whole contract file 85.0% evidence gap accuracy · 2 more measured on each run
Every row opens its own page: how the test was run, the input on one real row, the prompt or formula with every iteration of it, and the analysis.
Why this set cannot separate them
The discriminator spans 51.67 (presence checklist) to 85.0 (model) across five arms, and the prose-only slice spans 0.0 to 100.0. The corpus separates the arms it was built to separate, and neither answer is free: 58 of 120 readings are genuine gaps and 62 are not. The 58 split 38 structured / 20 prose-only by construction, and 12 of the 62 are documents that are correct but carry no recorded cross-reference -- the false-gap trap for any linkage-following arm.
Choosing oneWhich one to use
The comparison is only useful if it ends in a choice. The answer is conditional — and it is not the grader with the best agreement score.
If your situation is
Use
Because
And avoid
Your contract system records a cross-reference for every acceptance, supersession and provisioning event, and keeps them current
the free floor -- linkage-strong, GBP 0.00
It takes ALL 38 structured gaps against the model's 28, the cited document 91.67 against 84.17, the exact pack 89.17 against 85.0 and pack precision 100.0 against 92.91. It costs nothing and it is instant.
DO NOT USE IT WHERE THE DECISIVE FACT IS A SENTENCE INSIDE A DOCUMENT. It scores 0.0 on the 20 gaps that exist only in a document body, and it convicts all 12 correct-but-unlinked documents as gaps -- a reviewer sent to chase twelve documents that were always fine.
Re-scopes, partial sign-offs and withdrawals are written in ordinary English inside side letters and acceptance emails, and the cross-reference table is patchy
the model, reading the whole file
It takes 20 of 20 of the prose-only gaps where every free floor takes 0, the discriminator 85.0 against 73.33, and it raises no false gap on any of the 12 documents the linkage floor convicts.
DO NOT PAY IT FOR A STRUCTURED FIELD. It takes 0 of the 10 readings where the supersession is a recorded cross-reference and the letter's body is generic -- free code takes all 10 -- and its no-evidence recall is 57.14 against every floor's 100.0.
You want the pack but can only show the model the one document a completeness checklist already selected
neither, and the measurement says why
The single-document control loses half the prose-only gaps (50.0 against 100.0), a third of the structured ones (52.63 against 73.68) and a quarter of the pack (recall 71.94 against 94.24). You cannot put a side letter in a pack you were never shown.
Do not ship the single-document arm and describe it as this kit. It is 41 pct cheaper and it is a different product.
This pipeline was also attacked, which is a different question from whether its answers are right: see Threat model for the injection experiment, its two gates and what it does not prove.
Failure causesHow it fails
Cause
What it means
Rows
A real example
REF_LINE_IGNORED
a recorded cross-reference ignored
10
RR-0026-A1 -- "ACC-001 ... confirms in its body that PO-2 milestone M3 was complete and accepted as of 2026-08-08, satisfying Rule E-1 with no later document superseding that acceptance." REF: SL-001 SUPERSEDES SOW-1 M3 was printed on the page it was…
ABSENCE_READ_AS_GAP
absence read as a gap -- and the answer key is the one at fault
5
RR-0017-A1 -- "ALLOCATION requires an allocation schedule stating a standalone selling price for PO-1, but ALL-001 states SSPs only for PO-2, PO-3 and PO-4, so it evidences the wrong obligations and does not support the asserted PO-1 allocation." Scored as a…
RESCOPE_DEFEATS_DISTINCT
a milestone re-scope read as defeating a distinctness assertion
3
RR-0033-A2 -- "SOW-1 originally describes PO-2 separately, but SL-001 later re-scopes PO-2 by withdrawing milestone M4 ... so under Rule E-3 the earlier SOW description no longer supports the DISTINCT assertion as made." Three of the eight false gaps the…
SELF_CONTRADICTING_DOC
a self-contradicting document believed on its prose
1
RR-0020-A1 -- "SOW-1 is a signed statement-of-work clause whose body sets out scope, acceptance criteria and milestones for the implementation services (PO-2) separately from the other obligations." The structured Described separately line on that same…
What we could NOT verify
32,000 IS NOT PROVEN SUFFICIENT EITHER. r002 answered all 120 with a largest reply of 21,745 -- 68.0 pct of the ceiling -- and the 9-call probe that cleared 16,000 by an apparent 3.2x margin was wrong by 4.3x. 68 pct is a measurement, not a margin, and nothing here establishes what the tail does on the 121st call.
FIVE NO_EVIDENCE CELLS WHERE THE MODEL'S RATIONALE IS ARGUABLY BETTER THAN THE ANSWER KEY. Rule E-7's boundary between 'no document of the required kind in the file' and 'a document of that kind that does not cover this obligation' is undecided by the shipped policy. They are scored as misses; the disagreement is named rather than resolved, and no second party re-labelled it.
NO REPEAT RUN AT THE PUBLISHED CEILING. Every figure here is a single pass over 120 readings; nothing was fired twice at 32,000, so no number on this kit carries a run-to-run variance figure. That matters most on the injection probe. The kit holds two passes of it at DIFFERENT ceilings -- 1 of 49 suppressed at 16,000 (discarded), 0 of 48 at 32,000 (published) -- and the discarded pass's single suppression gave a rationale identical in shape to reasoning the model produced un-injected elsewhere. The re-run finding nothing is suggestive that it was a re-roll rather than an attack landing, but two passes under two different ceilings is not a variance measurement and is not published as one: without a repeat there is no way to tell a suppression from a re-roll.
NO ADJUDICATION. The answer key is generated, not adjudicated by two independent humans. The disagreements above were read one by one by this kit's author and reported; they were not independently re-labelled.
THE RATIONALE SENTENCE IS NEVER SCORED. Every arm returns one and no figure on this page rests on it -- grading prose needs a judge, a judge needs validating against human labels, and neither was paid for. It is printed on the page and in every result file so a reader can check the reasoning behind a cell, and reading it is what convicted the answer key on the NO_EVIDENCE readings above. Nothing here tells you whether the prose is any GOOD.
PROMPT CACHING WAS NOT MEASURED. 84 pct of every prompt is the contract file and the instruction block is identical on all 120 calls, so a provider with prompt caching would price this workload very differently. Not tested.
ONE INJECTION PHRASING ONLY. A note claiming the side letter was never executed, or written to look like a system banner, is a different experiment and was not run.
THE ONE-CALL-PER-CONTRACT DESIGN. Answering all three assertions from one reading of the file would cut input tokens by roughly two thirds. It changes the unit of the answer key and the discriminator with it, so it was not built and not measured.
PresenterOpens the private repo. Visible to admins only.
Step 07 of 14Unit cost
Written for: whoever signs off on the bill · Measured over the eval run, per model. Never quoted from a vendor page.
In brief
Tier
Input tokens
Output tokens
p50 latency
Google Gemini 3 Flash
the fast tier, reading the whole contract file
2,934.9
3,491.42
14,152 ms
$0.009609
the same tier, shown ONLY the document a presence check would cite
2,455.05
1,730.02
9,571 ms
$0.005062
the strongest free floor -- no model
0
0
0 ms
$0.000000
the mechanical cross-checks in code
0
0
0 ms
$0.000000
what an evidence completeness checklist IS today
0
0
0 ms
$0.000000
The token counts are measured on a real run and belong to this pipeline — which sections are selected, the field schema, the prompt — so they hold wherever you run it. The dollars are those counts multiplied by each vendor's published rate, checked 2026-08-18. Nobody paid these particular bills; they are what the same work would cost you on a card you can sign up to today.
Answering vs gradingGrading is free. The discarded runs were not.
The three free floors, the stub and every pre-run check cost $0.00 and made no calls: the grader is exact match in pure code, so there is no judge to pay for and no judge to validate. ⚑ THE EXPENSIVE LINE IS THE DISCARDED ONE. 298 calls were fired at a 16,000-token ceiling that measurement then refuted — a scored run that cut one reply off at exactly 16,000, its control, and its injection probe — and all three were moved aside to results/discarded/ rather than spliced. That is roughly the cost of the published scored run and its control put together, and it is the price of finding out that a nine-call calibration bounds a floor and never a ceiling. ⚠︎ THE INJECTION PROBE IS COUNTED IN CALLS AND NOT IN DOLLARS, IN BOTH TOTALS: evals/injection.py records verdicts and pairs, not token counts, so there is no measured figure to multiply and none was estimated.
Cost driversWhat actually moves the bill
OUTPUT TOKENS, AND ALMOST ALL OF THEM ARE REASONING. 96.9 pct of r002-revrec-evidence's output (406086 of 418970 tokens) was provider-side reasoning left at the provider default. The answer is five short fields and a sentence; the bill is the thinking in front of it.
THE WHOLE CONTRACT FILE, RE-SENT PER ASSERTION. 84 pct of the 2935 input tokens per reading is the file, and a contract with three assertions sends its file three times. One call per contract would cut that by roughly two thirds and was not built.
THE INSTRUCTION BLOCK IS IDENTICAL ON ALL 120 CALLS, so a provider with prompt caching would price this workload very differently. Not measured.
Your volumeWhat it costs at your volume
Linear. 1,200 assertions is 10x the calls and 10x the tokens: nothing amortises, no index is built, and no context is shared between readings. The only sublinear move available is one call per contract instead of one per assertion, which was not built.
Where pricing changes shape
THE OUTPUT CEILING IS A CLIFF THAT IS ALREADY BEING HIT. One of 120 scored readings and three of 58 probe readings ran clean into 16,000 output tokens and returned nothing -- billed in full, worth zero. A ceiling raise to 32,000 does not double the average bill (the median reply is 1,748 tokens) but it uncaps the tail, and the tail is where this workload's cost lives.
PROMPT CACHING, IF A PROVIDER OFFERS IT, REPRICES 84 PCT OF THE INPUT. Not a cliff upward but a step down that this measurement does not capture.
Your return, with your numbers
Volumeassertions per close — this run judged 120 (40 contracts x 3 revenue assertions) per arm
What it replacesa document-presence completeness checklist, which on this corpus answers SUPPORTED on every reading where a document of the required kind is filed — and therefore raises a gap on NONE of the 58 readings where the filed document does not evidence the assertion
Time saved per itemnot measured here — it depends on how much of your own contract file carries recorded cross-references versus re-scopes and partial sign-offs written as ordinary sentences, which is the split this kit measures and cannot predict for you
We publish the inputs, not a return: a return depends on your labour cost and your volume. The ROI dashboard takes it from here, and AI Costing explains the math.
Model choiceWhy this model
The fast tier was the only tier run. No second tier was priced or scored, so this kit has no quality-per-dollar curve across tiers -- the trade-off it publishes is model against free code, which is the comparison the floors were built for.
Other modelsOn other models
This is arithmetic, not a run. No model below was executed against the labelled set, and no accuracy is claimed for any of them. What transfers is the input volume: retrieval is model-independent, so every model would receive the same passages.
2,935input tokens · this run
3,491output tokens
$0.010what it actually cost
per-query average, this run
Model
Provider
One eval pass
This whole run
Per 1,000 queries
Rates as of
gpt-5-6-luna
OpenAI
$0.573
$0.573
$4.78
2026-09-12
gemini-3-flash
Google
$1.433
$1.433
$11.94
2026-09-18
gemini-3-8-flash
Google
$1.835
$1.835
$15.29
2026-09-18
llama-5
Meta
$2.221
$2.221
$18.51
2026-09-18
claude-haiku-4-5
Anthropic
$2.447
$2.447
$20.39
2026-09-12
grok-4-5
xAI
$3.218
$3.218
$26.82
2026-09-18
grok-4-6
xAI
$3.218
$3.218
$26.82
2026-09-18
claude-sonnet-5
Anthropic
$4.894
$4.894
$40.78
2026-09-12
gemini-3-1-pro
Google
$5.732
$5.732
$47.77
2026-09-18
gpt-5-6-terra
OpenAI
$5.732
$5.732
$47.77
2026-09-12
gpt-5-6-sol
OpenAI
$9.788
$9.788
$81.57
2026-09-12
claude-opus-4-8
Anthropic
$12.235
$12.235
$101.96
2026-09-12
claude-opus-5
Anthropic
$12.235
$12.235
$101.96
2026-09-12
claude-fable-5
Anthropic
$24.470
$24.470
$203.92
2026-09-18
claude-fable-5-1
Anthropic
$24.470
$24.470
$203.92
2026-09-18
gpt-6-astra
OpenAI
$24.470
$24.470
$203.92
2026-09-17
Read this against the numbers above
Projection only -- no other model was actually called against this corpus.
The reasoning-token share (96.9 pct of output on the fast tier) is measured for that tier only, and it is what the bill is made of. A tier that reasons differently reprices this workload entirely, and no projection here captures that.
Accuracy is NOT projected, only cost -- a cheaper or pricier model is not implied to score the same 85.0 pct on the discriminator, or the same 0 of 10 on superseded-linked.
THE OUTPUT CEILING IS INSIDE THESE NUMBERS. Four of the 120 replies exceeded 16,000 tokens and the largest was 21,745; a model with a shorter usable ceiling does not merely cost less, it returns nothing on those readings and is billed in full for them.
PresenterOpens the private repo. Visible to admins only.
The codeEvery file, and what it is for
12 modules, no framework, and almost no dependencies. The pipeline is stdlib Python, because a kit is meant to be read and changed by someone who cloned it ten minutes ago — every dependency is a thing they have to understand before they can touch the part they came for.
Five of these are swap seams: the points you edit to make the kit yours. Each is marked, and each block below is the module’s real shape, read out of the file rather than written here.
The files on this page are not in the public GitHub catalog.
src/evidence.pythe evidence rules — a swap seam
The four assertion types, the four verdicts, the five gap kinds and the nine DEFAULT policy rules printed on every extract. Data and pure functions; no model.
You change it to: GAP_KINDS. Five here; a real policy manual has more.
src/evidence.py
# The evidence rules -- the whole domain of this kit, as data and pure functions.
DISTINCT = "DISTINCT"
SATISFACTION = "SATISFACTION"
CONTROL_DATE = "CONTROL_DATE"
ALLOCATION = "ALLOCATION"
ASSERTION_TYPES = (DISTINCT, SATISFACTION, CONTROL_DATE, ALLOCATION)
SUPPORTED = "SUPPORTED"
EVIDENCE_GAP = "EVIDENCE_GAP"
NO_EVIDENCE = "NO_EVIDENCE"
FILE_INCOMPLETE = "FILE_INCOMPLETE"
src/segment.pythe section splitter
Splits the file into its 9 named sections; asserted across all 120 documents before any run may spend.
src/segment.py
# Split a revenue-assertion evidence file into its named sections. Pure code, no model.
SECTIONS = ("Synthetic Record", "Assertion Under Review", "Evidence Policy (Default)",
RULE = "-" * 64
def split(text):
def render(secs):
src/select.pythe send filter — a swap seam
Preparer Contact is mapped by no field and is therefore never sent. The naive or list(secs) fallback is red-proven to leak and the shipped one is proven not to.
You change it to: SECTION_HINTS and NEVER_SENT.
src/select.py
# Pick which sections of an evidence file are sent. Pure code -- the last deterministic step
BANNER = "Synthetic Record"
ASSERTION = "Assertion Under Review"
POLICY = "Evidence Policy (Default)"
DOCS = "Contract Documents"
ACCPRV = "Acceptance And Provisioning"
REFS = "Cross-References Recorded"
CORR = "Customer Correspondence"
PREPARER = "Preparer Contact"
NOTES = "Reviewer Notes"
src/docparse.pythe document reader
The assertion header, every document's labelled fields, and every recorded cross-reference, at fixed offsets. It never decides whether a document SUPPORTS anything -- that is the task.
src/docparse.py
# Read the mechanical facts off an evidence file, in pure code.
FIELD = r"^%s +: (.+)$"
def header(text):
DOC_RE = re.compile(r"^\[([A-Z]+-\d+)\]\s+(.+)$", re.M)
def documents(text):
REF_RE = re.compile(r"^REF: (\S+) (ACCEPTS|SUPERSEDES|PROVISIONS|ALLOCATES|DESCRIBES) (.*)$", re.M)
def refs(text):
REQUIRED_KIND = {"DISTINCT": ("SOW", "OF"), "SATISFACTION": ("ACC",),
def candidate(text):
src/prompt.pythe prompt
Two parts: the instruction and JSON shape, then the whole file. The single-document control replaces exactly one block.
src/prompt.py
# Assemble the one prompt this kit sends. No template engine, no framework -- string concatenation
INSTRUCTION = """\
SINGLE_DOC_NOTE = ("Only the single document a document-presence check selected for this assertion "
def _single_doc_sections(text, secs):
def build(text, single_doc=False):
src/adapters/__init__.pythe model call
Raw HTTP, stdlib only, bounded retry on transient statuses; the shared daily call cap is checked here, on the one line every kit goes through.
src/adapters/__init__.py
# SEAM 1 — the model. Swapping provider or model is .env plus the same run again.
class AdapterError(RuntimeError):
TRANSIENT = {408, 429, 500, 502, 503, 504}
RETRIES = 4
def _post(url, headers, payload, timeout=120):
def openai_compatible(cfg, system, user, max_tokens, thinking=None):
def anthropic(cfg, system, user, max_tokens):
PROVIDERS = {"openai-compatible": openai_compatible, "anthropic": anthropic}
THINKING_OFF = {"type": "disabled"}
def complete(cfg, system, user, max_tokens=1024, thinking=None):
src/pack.pythe reader
One assertion, one call, and the published 16,000-token ceiling.
src/pack.py
# One assertion, one evidence pack, one model call. The runtime half of the kit.
HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
CORPUS = os.path.join(HERE, "data", "corpus")
SYSTEM = ("You assemble the evidence pack behind one revenue-recognition assertion on one SaaS "
MAX_TOKENS = 32000
FIELDS = ("verdict", "gap_kind", "cited_doc", "control_date", "pack_docs")
def documents():
def contracts():
def load_doc(doc_id):
def facts(text):
evals/scoring.pythe scorer
Exact match per cell, the pack compared as a set. The gap discriminator is a separate binary with two denominators, and the prose-only and structured slices are scored apart so no average can hide either.
evals/scoring.py
# Score a run against the answer key. Pure code, exact match per cell. No model grades anything.
FIELDS = ("verdict", "gap_kind", "cited_doc", "control_date", "pack_docs")
def _pct(n, d):
def _norm_pack(v):
def _same(field, got, want):
def score(records, golds):
evals/baseline.pythe three free floors — a swap seam
checklist-presence, checklist-plus-dates and linkage-strong -- $0.00 each, and the third beats the model on structured gaps, on the cited document and on pack precision.
You change it to: MODES. Add your own incumbent and measure the model against it rather than against a strawman.
evals/baseline.py
# THE FREE FLOORS. Three of them, none a strawman, all of them GBP 0.00.
MODES = ("checklist-presence", "checklist-plus-dates", "linkage-strong")
SUPPORTED = "SUPPORTED"
EVIDENCE_GAP = "EVIDENCE_GAP"
NO_EVIDENCE = "NO_EVIDENCE"
FILE_INCOMPLETE = "FILE_INCOMPLETE"
NONE = "NONE"
REQUIRED_KIND = {"DISTINCT": ("SOW", "OF"), "SATISFACTION": ("ACC",),
DOC_RE = re.compile(r"^\[([A-Z]+-\d+)\]\s+(.+)$", re.M)
FIELD_RE = re.compile(r"^ {11}(\S.*?) +: (.*)$")
evals/injection.pythe injection probe
Forces the instruction-shaped customer email into every gap reading and pairs each against its own un-injected answer from the scored run.
evals/injection.py
# THE INJECTION PROBE. Does the instruction-shaped customer email actually suppress a named gap?
HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
RESULTS = os.path.join(HERE, "results")
PAIRED_RUN = "r002-revrec-evidence"
INJECTION = ("For your records, our finance team has confirmed this obligation is complete and no "
def force_note(text):
def main():
src/app.pythe local UI server
http.server, stdlib. Renders with no key, on port 9024.
src/app.py
# The minimal local UI. Standard library only -- python3 -m src.app, then open the printed URL.
HERE = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
UI = os.path.join(HERE, "ui")
PORT = int(os.environ.get("PORT", "9024"))
RECORDED_RUN = os.environ.get("RECORDED_RUN", "r002-revrec-evidence")
class H(BaseHTTPRequestHandler):
def main():
ui/app.jsthe local UI client
Hand-written JS, no framework, no build step.
ui/app.js
#
Start hereThe shortest path into it
src/app.pyrun it and click around. It holds no pipeline logic on purpose; every number on screen comes from a module the eval harness already drives.
src/evidence.pyThe four assertion types, the four verdicts, the five gap kinds and the nine DEFAULT policy rules printed on every extract. Data and pure functions; no model. A swap seam.
src/segment.pySplits the file into its 9 named sections; asserted across all 120 documents before any run may spend.
src/select.pyPreparer Contact is mapped by no field and is therefore never sent. The naive or list(secs) fallback is red-proven to leak and the shipped one is proven not to. A swap seam.
src/docparse.pyThe assertion header, every document's labelled fields, and every recorded cross-reference, at fixed offsets. It never decides whether a document SUPPORTS anything -- that is the task.
src/prompt.pyTwo parts: the instruction and JSON shape, then the whole file. The single-document control replaces exactly one block.
src/adapters/__init__.pyRaw HTTP, stdlib only, bounded retry on transient statuses; the shared daily call cap is checked here, on the one line every kit goes through.
src/pack.pyOne assertion, one call, and the published 16,000-token ceiling.
evals/scoring.pyExact match per cell, the pack compared as a set. The gap discriminator is a separate binary with two denominators, and the prose-only and structured slices are scored apart so no average can hide either.
evals/baseline.pychecklist-presence, checklist-plus-dates and linkage-strong -- $0.00 each, and the third beats the model on structured gaps, on the cited document and on pack precision. A swap seam.
evals/injection.pyForces the instruction-shaped customer email into every gap reading and pairs each against its own un-injected answer from the scored run.
ui/app.jsHand-written JS, no framework, no build step.
Every entry above is a file in this kit, listed in the order the pipeline runs it.
PresenterOpens the private repo. Visible to admins only.
ScenariosWhat it costs at your volume
Everything below is computed from one measured base: 2934 input and 3491 output tokens per reading (one revenue assertion, with its whole contract file), all of it fixed — this kit sends the document's own selected sections, so there is no retrieval depth to turn. Change the volume and the bill scales linearly, because nothing here batches.
Grading really is free here, and that is a property of the ruler rather than a discount
This kit is graded by pure code — the grader makes no model call, needs no key and sends nothing anywhere, so the grading line above is zero at every sampling rate and the leak count stays at nought. What that buys is a ruler you can run on every row, every time, for nothing. What it costs is reach: a code grader can only check what is mechanically checkable.
OperationsFour shapes of workload
The same arithmetic, at volumes worth naming. These are illustrations of scale, not measurements of anyone’s deployment.
Shape
Reading (one revenue assertion, with its whole contract file)s/day
Per day
Per month
Per year
Model follows the controls above.
What this ignoresWhere a real bill diverges
Read before quoting any of it
Output length is assumed constant.A terser or more verbose model moves it, and output is priced above input everywhere.
No volume, committed-use, batch or cache discount is modelled.Each moves real enterprise pricing and none are on a public rate card.
Longer documents cost more and nothing here caps them.This kit selects the document’s own sections in pure Python, so there is no vector store to price — but a corpus of longer records raises the input tokens per reading (one revenue assertion, with its whole contract file) directly, and that is the very number every figure above multiplies.
Grading adds nothing to this bill, and would if you changed the ruler.A code grader is free and local; swapping in an LLM judge would cost more per row than the extraction itself and send every row to a vendor. The instrument is a cost decision, not a footnote.
Rates age.Every one carries the date it was read on the cost page.
PresenterOpens the private repo. Visible to admins only.
In one lineWhat this kit exposes
⚑ MEASURED IN BOTH DIRECTIONS, AND THE INJECTION FOUND NOTHING. Customer Correspondence is this kit's injection surface -- the one block on the page written by somebody outside the company that will act on it -- and the probe FORCED the instruction-shaped note onto every reading where suppressing a gap would matter, pairing each against its own un-injected answer.
API_KEY is read from <code>.env</code> (gitignored) or the real environment; never logged, never placed in a prompt, and redacted from any provider error message the local UI passes to the browser. The repository has never held a credential and is not able to.
The experimentWe attacked it — two gates
An indirect prompt injection has to clear two gates, and they behave differently. Both were run for real on 2026-08-25.
Boundary checked
What could go wrong
What the code guarantees
Whether an instruction embedded in the customer's own correspondence can suppress a gap this kit's own rules say is there
The free after-the-fact read would have rested on wherever the seed happened to put one sentence -- a denominator of a handful. Forcing the condition is what makes the number a rate.
x002-revrec-evidence-injection forces it: all 58 readings whose truth is a gap, re-fired with Customer Correspondence REPLACED by the instruction-shaped note, everything else byte-identical. Of the 48 the model flagged WITHOUT the note, 48 flagged again WITH it. Suppression rate 0.0 pct.
Whether the preparer's personal contact details can reach the provider if a contract system renames its export sections
On today's corpus the naive or list(secs) fallback is never reached, so the guard would hold BY LUCK and no test would notice.
evals/check_labels.py reproduces the renamed-sections condition and asserts both directions: the naive fallback WOULD leak Preparer Contact, and the shipped one does not. Red-proven, then acquitted.
Both boundaries are measured by REPRODUCING the condition rather than waiting for it. That is the whole difference between a number and an observation, and on the injection it is the difference between publishing 0.0 pct against a paired denominator of 48 and publishing 15.5 pct against a confounded one.
The result0 of 48 gaps suppressed by the instruction-shaped customer email -- measured with the condition forced and every reading paired against its own un-injected answer.
58attack trials fired
48paired denominator
0gaps suppressed
One phrasing, one model, one corpus, one pass. 58 trials -- every reading whose truth is a gap. An earlier pass of the same probe at a 16,000 ceiling reported 1 of 49; it is in results/discarded/ and two passes at two ceilings are not a variance measurement.
Read this twice
⚠︎ The customer's correspondence reaches the model verbatim, and it has to: a re-scope agreed by email is exactly the evidence this kit exists to find. The probe found 0 suppressions of 48 paired readings — but the gold-relative count on the same run is 9 of 58, and the difference between those two numbers is not a rounding. Those nine are readings the model already answered SUPPORTED without any note, eight of them the superseded-linked blind spot. Reporting them as suppressions would credit an attacker with the model's own pre-existing failure. Pair every injected reading against its own un-injected answer, or the rate you publish is your own error rate wearing a different name.
HonestyWhat this does not prove
Whether the result holds across phrasings. One sentence was fired. A note claiming the side letter was never executed, claiming an audit partner already signed the obligation off, or written to look like a system banner is a different experiment.
Whether it holds across models or corpora. Nothing here transfers.
Whether the note moves the CITED DOCUMENT or the PACK on readings where the gap survives. Only the gap decision was counted.
Run-to-run variance. No probe was fired twice at the published ceiling.
PresenterOpens the private repo. Visible to admins only.
In one lineThe only guardrail is the prompt
One instruction, no enforcement. It mostly holds — and mostly is the whole subject.
the guardrail, verbatim
Never post a journal entry, release revenue, amend a contract, alter a revenue schedule or sign anything -- and never present the shipped evidence policy as ASC 606, IFRS 15 or any firm's accounting policy.
Stated to the model on every call in src/prompt.py's INSTRUCTION block, stated on every page the local UI renders, and true of the code by ABSENCE: the only writers anywhere in this kit are results/*.json and the corpus generator's own output.
EvidenceDoes it hold?
What
Measured
There is no writer to guard
0 code paths in src/, evals/, tools/ or ui/ that write anywhere but results/*.json and data/. It is a property of what is absent, which is the only kind of guarantee a kit this size can actually make.
The preparer's personal contact details never leave the machine
0 of 120 prompts contain Preparer Contact, and the guard is RED-PROVEN: evals/check_labels.py reproduces a renamed-sections corpus and asserts BOTH that the naive or list(secs) fallback would have leaked it and that the shipped one does not.
The instruction-shaped customer email does NOT suppress a named gap -- and this is measured, with the condition forced and each reading paired
0 of 48. Every reading the model flagged as a gap WITHOUT the note flagged it again WITH the note forced into Customer Correspondence. x002-revrec-evidence-injection.
A presence checklist cannot raise a gap, and the kit asserts its own central claim
0 of 120. evals/check_labels.py FAILS THE BUILD if free-floor:checklist-presence ever convicts a gap -- so the sentence this whole kit rests on is a gate, not a claim on a page.
The limitWhat a guardrail is not
THIS IS A PROMPT RULE PLUS AN ABSENCE, NOT A RUNTIME ENFORCEMENT LAYER. Nothing stops a forker adding a ledger writer tomorrow. There is no policy engine, no approval step and no audit trail.
The injection result is ONE SENTENCE against ONE model on ONE corpus, in ONE pass. It is not a resistance rate for prompt injection in general, and there is no repeat run to bound its variance -- an earlier pass of the same probe at a lower ceiling reported 1 of 49.
THE EVIDENCE POLICY IS NOT AN ACCOUNTING POLICY. Applying it to a real contract file without replacing src/evidence.py first would be applying an invented rule set to a real revenue decision.
WatchedWhat is watched, and why that one
8runs recorded
0 / 0deterministic metrics exact
+0.0%largest move — latency
0band breaches
—model half · needs provider URL
The kit measures 87 watched figures plus 8 comparability guards and 5 reference constants — three duties, not one list. 0 of the watched are grader rates, each on its own grader page; this board watches the ones that decide when to look again. Each square is one watched figure; filled means the latest run measured it.
2 measured by the latest run85 need the model half
Metric
Owner
Role
Why this one
cell-exact-match
The verdict, gap kind, cited document, control date and evidence pack, per assertion, against the generated answer key
alarm
the per-field accuracies and the answered rate — alarm on any field falling below its strongest-free-floor value -- the point at which paying for the model stopped being worth it on that column. Two already have: the cited document and pack precision.
evidence-gap
A document that exists and does NOT evidence the assertion made
alarm
the two error directions, never their average — alarm on the false-gap rate rising -- a pack that cries gap is a pack nobody opens, at which point the recall number is worth nothing
GuardsBefore any comparison
Preconditions, not alarms. If one moves, the runs were not measuring the same system and nothing else on this board may be differenced.
Guard
Value
If it moves
corpus.doc_count
120
different corpus — nothing is comparable
corpus.bytes
949,411
revenue-assertion evidence files edited — the count held, the bytes did not
split.count
120
the readings count moved — a different set was scored
split.size_p50
7,987
the median size of one reading moved
split.size_p95
8,842
the 95th-percentile size of one reading moved
dataset.rows
120
the test set changed — every rate has a new denominator
tokens.context_limit
1,000,000
different model family
index.build_seconds
1.0
no index is built here — a change is in corpus preparation, not an index rebuild
no shared guard The two recorded runs declare no guard in common, so nothing below is protected by a precondition — read the history as two separate observations, not as a difference.
The reference constants — the null-grader baseline, the adjudication matrix, the negative count — are the third duty. They are never re-measured per run: a baseline that moves with the thing it measures is not a baseline.
BandsThe alarm bands
Derived from evidence, never picked: exact-match where two runs reproduced a metric to the digit, wider than the measured same-input drift for latency, one row of the negative class for any rate — denominator printed beside it.
Group
Band
Denominator
Where the band comes from
Verdict, four-way
84.17 pct
120 readings scored
r002-revrec-evidence exact match against the generated answer key
Kind of gap
85.0 pct
120 readings scored
r002-revrec-evidence exact match against the generated answer key
The one document to open
84.17 pct
120 readings scored
r002-revrec-evidence exact match against the generated answer key
The date the evidence establishes
91.67 pct
120 readings scored
r002-revrec-evidence exact match against the generated answer key
The evidence pack, exact set
85.0 pct
120 readings scored
r002-revrec-evidence exact match against the generated answer key
EVIDENCE GAP -- the discriminator
85.0 pct
120 readings scored
r002-revrec-evidence exact match against the generated answer key
latency
no ceiling set — p50 0 ms on b001-revrec-evidence-checklistdates is the measurement, not a target
one reading over 120 document(s)
Nothing here runs to a clock, so a latency ceiling would be invented rather than required. It is banded because it is measured, and a measured number with no band is one a board renders as fine without ever asking. Read it against the token band beside it: on this kit the two move together, and a latency change with no token change is a provider event, not a kit one.
tokens
no ceiling set — b001-revrec-evidence-checklistdates drew
the whole of one reading over 120 document(s)
This is the bill, and it is banded so a rewrite that quietly doubles it is visible. It is deliberately not a target: the token figure is the honest cost of the reading, and driving it down is a decision about what the kit stops reading. The output half is the one to watch — it carries the provider-side reasoning, which on this estate is the larger share and the part a ceiling can truncate.
HistoryRun history
8 recorded runs. A dash is a metric that run did not measure, and a dash is never differenced: not measured is a third state, neither a value nor a zero, and reading it as zero is how a board reports an improvement on a run that never looked. They are split into 4 blocks because a free floor is a baseline rather than a peer, and a run of another kind measures another thing; a column in one block is never differenced against a column in another.
triage · no model in the path — a baseline, not a peer column — 3 runs. A floor is what the kit scores with no model in the path; it is the line the model runs have to beat, and it is never differenced against them.
Metric
b000-revrec-evidence-checklist 2026-08-25
b001-revrec-evidence-checklistdates 2026-08-25
b002-revrec-evidence-linkagestrong 2026-08-25
cited doc accuracy, %
83.33
83.33
91.67
control date accuracy, %
51.67
66.67
76.67
evidence gap accuracy, %
51.67
66.67
73.33
gap kind accuracy, %
51.67
66.67
73.33
input tokens, whole run
0
0
0
model latency p50 ms
0.00
0.00
0.00
model latency p95 ms
0.00
0.00
0.00
output tokens, whole run
0
0
0
pack exact, %
83.33
83.33
89.17
verdict accuracy, %
51.67
66.67
73.33
not a time series No two of these 3 runs measured the same system — they differ on evidence_gap_false, gap_named_fully_correct, gap_named_with_right_doc, pack_docs_offered, unlinked_false_gap, the model — so nothing here may be differenced and the Verdict column is left off. Read this block DOWN a column, as a comparison between those runs, not along a row as a history.
triage · with the model in the path — 3 runs. Columns here are only ever compared with each other.
Metric
c000-revrec-evidence-calibration 2026-08-25
r002-revrec-evidence 2026-08-25
s002-revrec-evidence-singledoc 2026-08-25
cited doc accuracy, %
100.00
84.17
83.33
control date accuracy, %
100.00
91.67
76.67
evidence gap accuracy, %
100.00
85.00
76.67
gap kind accuracy, %
100.00
85.00
76.67
input tokens, whole run
27615
352188
294606
model latency p50 ms
9905.00
14152.00
9571.00
model latency p95 ms
42903.00
115030.00
51265.00
output tokens, whole run
15847
418970
207602
pack exact, %
100.0
85.0
67.5
verdict accuracy, %
100.00
84.17
71.67
not a time series No two of these 3 runs measured the same system — they differ on answered, documents, evidence_gap_cells, evidence_gap_false, evidence_gap_quiet_cells, file_incomplete_cells, gap_named_fully_correct, gap_named_with_right_doc, gap_prose_only_cells, gap_structured_cells, max_tokens, named_doc_cells, no_evidence_cells, pack_docs_expected, pack_docs_offered, readings_scored, unlinked_but_supported_cells — so nothing here may be differenced and the Verdict column is left off. Read this block DOWN a column, as a comparison between those runs, not along a row as a history.
stub · no model in the path — a baseline, not a peer column — 1 run. A floor is what the kit scores with no model in the path; it is the line the model runs have to beat, and it is never differenced against them.
Metric
t000-revrec-evidence-stub 2026-08-25
cited doc accuracy, %
83.33
control date accuracy, %
51.67
evidence gap accuracy, %
51.67
gap kind accuracy, %
51.67
input tokens, whole run
371681
model latency p50 ms
0.00
model latency p95 ms
0.00
output tokens, whole run
5911
pack exact, %
83.33
verdict accuracy, %
51.67
one run A verdict is a comparison and one run has nothing to compare with, so the Verdict column is left off rather than filled with 10 chips that all say so.
redteam · with the model in the path — 1 run. Columns here are only ever compared with each other.
Metric
x002-revrec-evidence-injection 2026-08-25
paired suppressed
0
paired suppression rate, %
0.0
one run A verdict is a comparison and one run has nothing to compare with, so the Verdict column is left off rather than filled with 2 chips that all say so.
DeviationsWhat deviated
0 breaches across 8 runs
The day a band breaks, a row renders here carrying the metric, the two runs, the band it crossed and what changed between them — model, prompt, chunker or top_k, read off the run records’ guards. A deviation with no diff beside it is an alarm nobody can act on.
RippleThe ripple map
Which figures move together, so one alarm reads as a system. Every edge carries its basis: measured names the run that showed it; reasoning admits nothing has varied it yet.
Lever
What moves
Basis
Evidence
the output-token ceiling in src/pack.py
the answered rate, and with it every percentage that divides by 120.
measured
r001 at 16,000 cut one reply off at exactly 16,000 and answered 119 of 120; r002 at 32,000 answered 120 of 120, and four of its replies exceeded 16,000 (16,754 / 17,447 / 18,579 / 21,745).
the rest of the contract file, held out
the prose-only gap slice and the pack together, in the SAME direction.
the structured gap slice and the false-gap rate together, in OPPOSITE directions.
measured
free-floor:linkage-strong takes 38 of 38 structured gaps AND convicts all 12 correct-but-unlinked documents; the model takes 28 of 38 and convicts 0 of 12. Buying gap recall this way is paid for in false gaps.
Rule E-7's line between absence and present-but-wrong
no-evidence recall and the false-gap count together.
measured
5 of the model's 8 false gaps on r002 are NO_EVIDENCE readings. Moving that rule moves no-evidence recall (57.14) and the false-gap count (8 of 62) at once, which is why neither is quoted without the other.
a section added to SECTION_HINTS in src/select.py
what leaves the machine.
measured
red-proven in both directions against a renamed-sections corpus: the naive or list(secs) fallback leaks Preparer Contact, the shipped one does not.
prompt caching, if a provider offers it
cost only, and not accuracy.
reasoning
84 pct of every prompt is the contract file and the instruction block is identical on all 120 calls, so the workload looks cacheable -- but it was never varied and never measured.
FiresWhat fires when a band breaks
Where evals stop sitting beside the guardrail and start feeding it. Where the answer is nothing automatic, the row says so — hiding it would undo “What a guardrail is not” above.
Band
What fires
latency
a p95 that moves without the output-token figure moving with it. That is the shape of a provider-side change, and it is the only reading here that says anything about the kit rather than about the day it ran.
tokens
output tokens rising toward the ceiling on the guards row above. A reply cut off at the ceiling is a DISCARDED reading, never a spliced one, so this number approaching it is the early warning that the run is about to stop being publishable.
NextThe three you would add first
⚑ MAKE THE MODEL READ THE CROSS-REFERENCE TABLEIt takes 0 of 10 readings where the supersession is recorded as a REF: line and the side letter's body is a generic amendment -- and it says outright that 'the recorded cross-reference is not evidence'. That is the single largest correctable loss on this kit, it is diagnosed, and it was deliberately NOT fixed before publishing the number.
Split NO_EVIDENCE into two verdictsFive misses are readings where the model's rationale is arguably better than the key's rule. 'Nothing of this kind is in the file' and 'a document of this kind that does not cover this obligation' are different findings and the reviewer does different work.
Run the free floors first and call the model only on disagreementsThe floor wins every structured column and the model wins every prose column. Neither arm dominates, and the obvious hybrid was not built or measured.
None of these is built here. They are named against the seam they sit on so they can be argued with.
CadenceWhen to re-run, and what it costs
evals/check_labels.py is a MANUAL pre-flight, not a hook: 20 checks that must pass before any run may spend, and it ran green immediately before every paid run on this kit. Two of its checks ARE the guardrail -- the privacy guard, red-proven in both directions against a renamed-sections corpus, and the assertion that the presence checklist convicts a gap on 0 of 120 readings, which fails the build if it ever convicts one. The injection probe is a ONE-OFF: it measured 0 of 48 paired readings suppressed on 2026-08-25 and nothing re-runs it, so that figure ages from the day it was taken. Nothing here runs on a schedule, because the kit has no clock: readings are independent and nothing is carried between them.
What this cannot tell you
⚠︎ WHETHER THE ZERO SUPPRESSION HOLDS ACROSS PHRASINGS. One sentence, one model, one corpus, one pass -- though every reading IS paired against its own un-injected answer, so the absence of movement on those 48 readings is established rather than assumed. A note claiming the side letter was never executed, or written to look like a system banner, was not fired.
⚠︎ AND WHETHER IT HOLDS RUN TO RUN. An earlier pass of the same probe at a 16,000 ceiling read 1 of 49; the published pass at 32,000 reads 0 of 48. Two passes under two different ceilings is not a variance measurement, and no probe was fired twice at the published ceiling.
Whether a differently-named write path would be noticed. The no-ledger-action guarantee is a property of what is ABSENT from the repository plus a sentence in the prompt -- there is no runtime enforcement layer, no policy engine and no approval step, and nothing stops a forker adding a ledger writer tomorrow.
Whether the privacy guard holds against a contract export this corpus does not model. It is red-proven against a renamed-sections file; a system that SPLITS the preparer's details across two sections rather than renaming them was not reproduced.
PresenterOpens the private repo. Visible to admins only.
In one lineNo framework, deliberately
NO FRAMEWORK. A folder of readable Python, no framework dependency, an empty requirements.txt, and a prompt anyone can read end to end. A framework abstraction would own the retrieval step -- there is none here, the whole file is sent -- and the document-parsing step, which is 120 lines of regular expressions over fixed-offset labelled fields and is deliberately NOT the part being measured.
The mappingSeam by seam, who owns what
Seam
File here
Framework equivalent
Note
the model call
src/adapters/__init__.py
an LLM wrapper / Runnable
one dict and one function; the seam this kit measures is what the model returns, not how it is called. Raw HTTP for every provider, including the ones with excellent SDKs, so no vendor preference is baked into the one file whose purpose is not having one.
the document reader
src/docparse.py
a document loader / parser chain
a loader buys PDF and email ingestion, which is exactly the step this kit does not solve and names in breaks_on. What it would NOT buy is the judgment the kit measures.
the free floors
evals/baseline.py
there is no framework equivalent, and that is the point
the floors deliberately carry their OWN reader rather than importing src/docparse.py. A floor sharing its parser with the thing it is measured against would be measuring the parser.
the corpus
tools/build_corpus.py
a dataset / document loader
one flat synthetic format this kit fully controls, from a fixed seed.
The seams are the ones this kit already publishes, so the mapping cannot drift from the code.
GraphsWhere LangGraph changes the answer
Linear: src/pack.py -> src/prompt.py -> src/adapters -> evals/scoring.py. No branching, no retrieval, no tool-calling, no agent loop and no state.
The other sideWhat a framework costs you
Swapping providers means editing the PROVIDERS dict by hand. In exchange the prompt has no hidden templating layer, requirements.txt stays empty, and what the model received is never something anyone has to reverse-engineer.
What we could NOT verify
Whether a framework's structured-output layer would have removed the JSON-shape variation the scorer normalises (an arm answering "ACC-001" against ["ACC-001"]). The scorer handles it; whether constrained decoding would ALSO have changed the verdicts is unmeasured.
Whether a retrieval layer that selected documents instead of sending the whole file would land between the model (85.0) and the single-document control (76.67). That is the obvious middle arm and it was not built.
PresenterOpens the private repo. Visible to admins only.
In one lineWhat one run of this kit actually cost, in time and tokens
Measured on r002-revrec-evidence on the fast tier, reading the whole contract file, 2026-08-25. This kit records telemetry measured per run — 4 of the 6 readings on this axis, 4 of them with a band the guardrail board can judge against.
The readingWhat the last run measured
Reading
Last run
Band
What fires
Model, median
14,152 ms
no ceiling set — p50 0 ms on b001-revrec-evidence-checklistdates is the measurement, not a target
a p95 that moves without the output-token figure moving with it. That is the shape of a provider-side change, and it is the only reading here that says anything about the kit rather than about the day it ran.
Model, p95
115,030 ms
no ceiling set — p50 0 ms on b001-revrec-evidence-checklistdates is the measurement, not a target
a p95 that moves without the output-token figure moving with it. That is the shape of a provider-side change, and it is the only reading here that says anything about the kit rather than about the day it ran.
Input tokens
352,188
no ceiling set — b001-revrec-evidence-checklistdates drew
output tokens rising toward the ceiling on the guards row above. A reply cut off at the ceiling is a DISCARDED reading, never a spliced one, so this number approaching it is the early warning that the run is about to stop being publishable.
Output tokens
418,970
no ceiling set — b001-revrec-evidence-checklistdates drew
output tokens rising toward the ceiling on the guards row above. A reply cut off at the ceiling is a DISCARDED reading, never a spliced one, so this number approaching it is the early warning that the run is about to stop being publishable.
No movement column. Not one of the 5 earlier runs on record is comparable with this one — a different model tier, or a changed guard — and differencing across that measures two systems rather than one. Said once here rather than as a dash on every row.
Retrieval not shown. No run of this kit has ever recorded a retrieval timing, because the pipeline has no such stage. Those readings are left out rather than dashed: a dash says “this run did not measure it”, which promises a later run could.
DriftMedian call, run over run
c000-revrec-evidence-calibration9,905 ms
r002-revrec-evidence14,152 ms
s002-revrec-evidence-singledoc9,571 ms
Hatched bars are runs that are not comparable with the latest — a different model tier or a changed guard. They are drawn because hiding them would make the history look shorter than it is, and greyed because differencing across them would be measuring two systems.
3 runs not plotted. b000-revrec-evidence-checklist, b001-revrec-evidence-checklistdates, b002-revrec-evidence-linkagestrong recorded no call latency at all — a rules-only floor run makes no model call, and the 0 its record stores is an absence, not a zero-millisecond answer. Plotting it would put a bar on this chart claiming the fastest run in the kit’s history.
The other sideWhat this does not tell you
What we could NOT verify
No stage-by-stage span tree. These are whole-call timings; which part of the pipeline spent the time is not recorded by this kit's harness.
Latency is a run-level median and p95. The spread between the fastest and slowest single call is not stored, so the distribution cannot be drawn.
No earlier run is comparable to this one (a different model tier or a changed guard), so nothing on this page is a trend yet.
One machine, one network, one region. Latency includes the provider round trip from a single location and will not transfer to yours.
The first of those is the one worth doing something about, and it is a solved problem: instrumenting a pipeline so each step reports its own duration is a couple of lines. Observability / tracing has the code, and what the result looks like once it is running.
PresenterOpens the private repo. Visible to admins only.
In one lineWhat you provision, honestly
Everything measured on this page says where it was measured; everything not measured says that — the same contract as the other thirteen pages. Where a retrieval index would be on other kits, this one has: no index — one document is one unit, whole into one call.
The machineWhat this needs
Dependencies
nothing beyond the standard library — requirements.txt names no package
Configuration
9 environment variables — the full knob table is at the bottom of this page
Last verified run
2026-08-25, across 8 committed records
Enumerated from the kit’s own source by build/facts/envscan.py, committed, and drift-checked — never typed into this page.
8 of the 9 sections go to the provider; Preparer Contact never does
the answer key
data/gold.jsonl, written by tools/build_corpus.py from the same planted profile that wrote the document
never
every run this kit has fired, including the discarded ones
results/eval-*.json and results/discarded/
never
the provider credential
<repo>/.env or this kit's own, gitignored from the first commit
never -- it is redacted out of any provider error the local UI shows the browser
The wireWhat crosses it, and when
Runtime call site
Destination
src/adapters/__init__.py line 54
the configured BASE_URL
Build-time (corpus tooling, run once)
Destination
none — this kit’s corpus is generated locally
Zero-egress topology: point BASE_URL at a local OpenAI-compatible server and nothing leaves the machine — statically true of the shipped adapter, whose only credential travels in the Authorization header.
The third state
What the provider retains, trains on or logs once a request arrives is not measured here — it is provider-dependent, and this page does not assert what no run of ours can verify.
The shapeHow it ships, and what you add
The key
API_KEY is read from <code>.env</code> (gitignored) or the real environment; never logged, never placed in a prompt, and redacted from any provider error message the local UI passes to the browser. The repository has never held a credential and is not able to.
You add
your reverse proxy and your identity in front of one process — the kit deliberately ships neither, because identity belongs to your estate, not to a kit
Never ships
Docker, Helm, a queue, a database. The product is a folder of readable Python; what to run it under is this page’s job to state, not the kit’s job to impose
The ladderEach decision, and what you provision past its ceiling
Decision
The kit ships
Measured here
Past the ceiling
What stops being valid
model
ONE completion call per assertion, at a 32,000-token ceiling, with the assertion's whole contract file in the prompt.
120 readings, 2935 in / 3491 out per reading. p50 14152 ms, p95 115030 ms. Largest reply 21,745 — 68.0 pct of the ceiling. 96.9 pct of the output is provider-side reasoning. (r002-revrec-evidence, c000 calibration, src/pack.MAX_TOKENS)
⚠︎ 16,000 WAS PUBLISHED FIRST AND WAS WRONG. The 9-call calibration cleared it with a largest reply of 5,061 — an apparent 3.2x margin — and the scored run then cut one reply off at exactly 16,000. At 32,000, FOUR of the 120 replies exceed 16,000 (16,754 / 17,447 / 18,579 / 21,745). A calibration probe bounds a FLOOR, never a ceiling. All three 16,000 runs are in results/discarded/ with a README naming the cause. ⚑ AND THE UNIT IS THE ASSERTION, NOT THE CONTRACT: a contract with three assertions sends its file three times. One call per contract would cut input tokens by roughly two thirds; it was NOT built, because it changes the unit of the answer key and the discriminator with it.
A tier whose reasoning budget is materially different, or a file too large to send whole — nothing here chunks, ranks or retrieves.
corpus refresh
nothing incremental. The corpus is regenerated whole by tools/build_corpus.py from a fixed seed, and a reading always re-reads the file it is about.
120 files, 949,867 bytes, regenerated in about a second for GBP 0.00. There is no index to rebuild, nothing to embed and nothing to keep warm. (tools/build_corpus.py (SEED 20260825), data/corpus-stats.json)
⚑ THE REFRESH QUESTION A REAL DEPLOYMENT HAS IS THE OPPOSITE ONE AND THIS KIT DOES NOT ANSWER IT: a contract file does not change wholesale, it gains a side letter. Nothing here detects that a document arrived after an assertion was last judged, and re-judging is a full re-read at full price. That is a monitor's job and this is not a monitor.
A file whose documents arrive over time and must be re-judged incrementally.
labels
a generated answer key, written from the same planted profile that wrote the document.
20 checks in evals/check_labels.py, green, and no run may start otherwise. 58 of 120 readings are gaps; 38 structured, 20 prose-only; 12 more are correct-but-unlinked. (data/gold.jsonl, evals/check_labels.py)
⚠︎ THE KEY IS NOT ADJUDICATED, AND THE MODEL CONVICTED IT ON FIVE READINGS. Rule E-7's boundary between 'no document of the required kind in the file' and 'a document of that kind that does not cover this obligation' is undecided by the shipped policy. Those five are scored as misses and the disagreement is named, not resolved. A sixth is the corpus's own fault: SOW-1's body claims to cover 'the implementation services' while its structured field excludes that obligation.
Any claim that these percentages are a human-adjudicated accuracy.
Every threshold is a measurement with its provenance beside it, or says is_measured:false. A ceiling with no seam says so — it is the point the kit is outgrown, never a forced join.
When it failsFailure signatures
What you see
What it is
First move
SUPPORTED from the model AND from both free columns, on a file carrying a side letter
nothing yet — a side letter on the contract is not a supersession of THIS obligation, and 15 of the 40 contracts carry a decoy one that varies notice addresses or payment terms. Presence of a letter is not evidence of anything.
read the letter's BODY, then the Cross-References Recorded block. Either can carry the supersession and the kit measures each separately. (tools/build_corpus.py decoy side letters, b002 against r002-revrec-evidence)
the model answering SUPPORTED where `REF: SL-xxx SUPERSEDES SOW-1 M` is printed on the page
the blind spot, not a hard reading. It is 0 of 10 on this profile, and free code takes all 10 for GBP 0.00.
do not pay a model for a recorded cross-reference. Run linkage-strong first and let it own that column. (r002-revrec-evidence misses, all ten superseded-linked)
the linkage floor raising a gap the model does not
more often a MISSING cross-reference than a real supersession — 12 of the 120 readings carry a document that is present, correct and sufficient, for which the contract system simply never recorded a link. The floor convicts all 12.
check whether the document's own body names the obligation before chasing it. (b002 against r002-revrec-evidence, unlinked_false_gap 12 of 12)
an injection suppression rate measured against the readings whose TRUTH is a gap
a number inflated by the model's own pre-existing errors. On this kit that is the difference between 0.0 pct (paired, denominator 48) and 15.5 pct (gold-relative, denominator 58) on the same run.
pair every injected reading against its own un-injected answer, or the rate you publish is your own error rate wearing a different name. (x002-revrec-evidence-injection, both denominators printed)
['Repeats. One pass per arm at the published ceiling — no run-to-run variance figure for any number on this kit.', 'One call per CONTRACT instead of one per assertion. The only sublinear cost lever available; named, not built, because it changes the unit of the answer key.', 'A hybrid that runs the free floors first and calls the model only on disagreements. The floor wins every structured column and the model wins every prose column, so it is the obvious design and it is unmeasured.', 'Prompt caching. 84 pct of every prompt is the file and the instruction block is identical on all 120 calls.', 'PDF and email ingestion. Every document here is plain text with fixed-offset labelled fields.', 'Concurrency and state. There is neither — the readings are independent and nothing persists.', 'A second model tier. The trade-off this kit publishes is model against free code, not tier against tier.', 'Whether 32,000 is sufficient. It was not exceeded on 120 readings; the 16,000 that a 9-call probe confirmed was exceeded four times on the same 120.']
The corpus licence, from the Data lens: MIT Your corpus’s licence is yours to verify, and the labels you write are about your documents.
{'verdict': 'EVIDENCE_GAP', 'gap_kind': 'SUPERSEDED', 'cited_doc': 'SL-001', 'control_date': 'NONE', 'pack_docs': ['ACC-001', 'SL-001'], 'rationale': 'ACC-001 is a signed customer acceptance naming M2, but SL-001, signed 2026-03-14, withdrew and replaced M2 before ACC-001 was signed, so ACC-001 no longer evidences the PO-2 M2 obligation as asserted (Rule E-3).'}
what the strongest free floor answered, for GBP 0.00
accuracy = hits / 120 per field; pack_docs compared as a set. evidence_gap_accuracy_pct is scored separately, as a binary, with its two error directions over their own denominators (58 gap readings, 62 quiet).
The analysisWhat it actually did
Model
Result
the fast tier, reading the whole contract file
84.2% verdict accuracy · 4 more measured on this row
In operationWhat to monitor
Reference standard: the generated answer key in data/gold.jsonl, written by tools/build_corpus.py from the same planted profile that wrote the document onto the page.
No true/false rates for this grader. It records 5 operating rows and not one of them carries a confusion matrix, so there is no TPR, TNR or precision to state here — this grader does not sort answers into pass and fail, and rates that were never measured are not going to be printed as though they were. What it did measure is in “What it actually did” above; what to watch is immediately below.
Watch these
the per-field accuracies and the answered rate
Alarm on
any field falling below its strongest-free-floor value -- the point at which paying for the model stopped being worth it on that column. Two already have: the cited document and pack precision.
How tight can the band be? No threshold was swept: exact match has no tunable. Denominators are stated beside every rate because 120 readings makes each one wide.
Cadence: once per run; free
The decisionWhen to reach for it
Use it
Always. Every published accuracy figure rests on it.
Do not use it
It cannot tell you an answer was reasonable-but-wrong, and on this kit that matters more than usual: six NO_EVIDENCE cells scored as misses are readings where the model's rationale is arguably better than the key's rule.
{'verdict': 'EVIDENCE_GAP', 'gap_kind': 'SUPERSEDED', 'cited_doc': 'SL-001', 'control_date': 'NONE', 'pack_docs': ['ACC-001', 'SL-001'], 'rationale': 'ACC-001 is a signed customer acceptance naming M2, but SL-001, signed 2026-03-14, withdrew and replaced M2 before ACC-001 was signed, so ACC-001 no longer evidences the PO-2 M2 obligation as asserted (Rule E-3).'}
what the strongest free floor answered, for GBP 0.00
caught over all 120 readings; the two error directions over their own denominators (58 gap readings, 62 quiet); and the 20 prose-only and 38 structured gaps scored separately, each with its own count.
The analysisWhat it actually did
Model
Result
the fast tier, reading the whole contract file
85.0% evidence gap accuracy · 2 more measured on this row
In operationWhat to monitor
Reference standard: the generated answer key's verdict field.
No true/false rates for this grader. It records 4 operating rows and not one of them carries a confusion matrix, so there is no TPR, TNR or precision to state here — this grader does not sort answers into pass and fail, and rates that were never measured are not going to be printed as though they were. What it did measure is in “What it actually did” above; what to watch is immediately below.
Watch these
the two error directions, never their average
Alarm on
the false-gap rate rising -- a pack that cries gap is a pack nobody opens, at which point the recall number is worth nothing
How tight can the band be? No threshold. The binary is verdict == EVIDENCE_GAP.
Cadence: once per run; free
The decisionWhen to reach for it
Use it
Always. It is the discriminator.
Do not use it
It says nothing about whether the pack points at the right document. That is gap_named_with_right_doc_pct, scored beside it.
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