vouch

Every number in your paper, vouched for by the code that produced it.

Full documentation: https://dlfelps.github.io/vouch/

Experiments record their results as they run. The paper cites them by key, \vouch{key}, and never contains a typed number. vouch check proves that every cited value exists, comes from a run whose code hasn't changed since, and that every claim still holds. When a cited value changes, vouch shows you the sentences to re-read. Any number traces back to the function, arguments, seeds, command and commit that produced it, from the terminal, a local web page, or by clicking it in the PDF.

It is built for people and for LLM agents writing papers: the correct number is always cheaper to use than a guessed one.

New here? Start with the tutorial (also browsable at https://dlfelps.github.io/vouch/tutorial/): a two-second experiment and a paper draft, taken step by step to a PDF whose values, table and figure all come from the code.

$ pip install -e .          # from a clone; Python ≥ 3.10, no dependencies
$ vouch init                # finds your paper, writes vouch.toml, copies vouch.sty

The base install is everything, the MCP server included, with no dependencies (only tomli on Python 3.10). Extras: .[pandas] to record pandas DataFrames; .[mcp] installs nothing more (vouch mcp needs nothing); .[test] for the test suite, which includes the official MCP SDK to test vouch mcp against, and matplotlib for the tutorial.

1. Record

Decorate the function that computes your results. Every call is recorded, keyed by the function and its arguments, together with the call itself: arguments, seeds, each seed's result, how long it took, and the exact code that ran.

import vouch

@vouch.track(over="seed")                 # calls that differ only in seed -> mean ± std
def evaluate(dataset: str, model: str, seed: int = 0) -> dict:
    ...
    return {"acc": acc, "loss": loss}

for model in ("resnet", "vit"):
    for seed in range(5):
        evaluate("cifar", model, seed=seed)
# -> evaluate.cifar.resnet.acc, evaluate.cifar.resnet.loss, evaluate.cifar.resnet.time, ...

Run the script as usual: python train.py. Formats and descriptions for whole families of keys go in vouch.toml:

[metrics]
"*.acc"  = { fmt = ".1pct", better = "higher", desc = "top-1 test accuracy" }
"*.loss" = { fmt = ".3f",   better = "lower",  desc = "test loss" }

Other ways to record, when a decorator doesn't fit:

No code changes at all	list the function in vouch.toml ([[track]] function = "train.py::evaluate") and run python -m vouch.exec train.py
A single number	vouch.record("cifar.n_test", 10000, desc="test images")
A dict or table of results	vouch.record_all(metrics, prefix="cifar.resnet"), vouch.table("main", rows, row_key="model")
A claim	vouch.claim("resnet_wins", vouch.gt(acc_r, acc_v), desc="ResNet beats ViT")
Another language	vouch run vit_jl --dep src/ -- julia train.jl (the program writes its values to $VOUCH_VALUES)
Results that already exist	vouch import results.json --run eval --producer eval.py (marked imported)

2. Find the key

$ vouch explore --open                  # a local page: script -> function -> keys; one click copies \vouch{...}
$ vouch search vit accuracy cifar       # ranked lookup from the terminal
$ vouch cite evaluate.cifar.vit.acc     # the exact snippet, what it renders as, its subfields
\vouch{evaluate.cifar.vit.acc}         →  90.6 ± 0.4%    (fmt .1pct)
\vouch[.2pct]{evaluate.cifar.vit.acc}  →  90.59 ± 0.44%  (fmt .2pct)
top-1 test accuracy · higher is better · fresh · run experiments.train
subfields: .mean 90.6% · .std 0.4% · .n 5 · .ci95 90.0-91.1% · .min 90.2% · .max 91.1%

.vouch/CATALOG.md, rewritten by every build, lists every citable key on one line each: the file an agent reads first.

Long generated keys can get short names in vouch_values.py: vouch.alias("resnet", "evaluate.cifar.resnet") makes \vouch{resnet.acc} work.

3. Cite

\usepackage{vouch}
...
ResNet reaches \vouch{evaluate.cifar.resnet.acc} top-1 accuracy
(\vouch[.3f]{evaluate.cifar.resnet.acc.mean} as a fraction).
\vouchclaim{resnet_wins}{ResNet outperforms ViT.}
\begin{tabular}{lr} \toprule Model & Acc \\ \midrule \vouchtable{main} \bottomrule \end{tabular}

Then build the generated values and compile as usual:

$ vouch build
$ latexmk -pdf paper/main.tex

In the draft, every number is a link to a "Value provenance" appendix: what the value is, the call that produced it (evaluate(dataset=cifar, model=resnet) over seed=0..4 (5 calls)), each seed's result, the run, the command and the commit. \usepackage[final]{vouch} removes all of it for submission.

4. Numbers computed from numbers

Differences, ratios, best-of and derived tables live in vouch_values.py, never in anyone's head. vouch build evaluates them and records exactly which values each one read.

import vouch

@vouch.derive("cifar.gap", fmt=".1f", unit="points", desc="ResNet minus ViT, points")
def gap(v):
    return 100 * (v["evaluate.cifar.resnet.acc.mean"] - v["evaluate.cifar.vit.acc.mean"])

@vouch.claim("cifar.big_gap", desc="ResNet leads by more than a point")
def big_gap(v):
    return vouch.gt(v["cifar.gap"], 1)          # "2.3 > 1 (margin 130%)"; thin margins warn

vouch compare A B does the arithmetic for you (difference, ratio, a Welch t-test for two means ± std) and prints the derive and claim that make it citable; --write adds them to vouch_values.py.

A number the paper needs before any run has produced it is a placeholder, never a guess:

vouch.expect("imagenet.vit.acc", desc="ViT top-1 on ImageNet", producer="python train.py --dataset imagenet")

The PDF shows [pending: imagenet.vit.acc], vouch todo lists what is owed, and the placeholder resolves once a run records the key.

5. Check

$ vouch check
vouch check: paper/main.tex · 16 citations · 1/1 cited runs fresh
✓ OK

vouch check never runs your code. It fails when:

• a cited key doesn't exist
• the code behind a cited run changed since the run (comments and formatting don't count)
• a claim no longer holds
• a figure is stale
• the generated files are out of date

It warns when a cited value changed since someone last read the sentences around it (vouch changes, then vouch ack KEY), and when the paper contains a typed number:

! bare-number    93.2% is typed by hand; it is evaluate.cifar.resnet.acc.mean: cite it as \vouch[.1pct]{...}
! no-source      97.1% matches no recorded value: record it where it is computed, or remove it

Converting a paper that already has typed numbers: vouch suggest shows, for each one, the key that prints it (or that nothing does); vouch suggest --apply rewrites the unambiguous ones.

Use it in a pre-commit hook (vouch hook install) and in CI:

# .github/workflows/paper.yml
name: paper
on: [push, pull_request]
jobs:
  vouch:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with: { python-version: "3.12" }
      - run: pip install -e path/to/vouch     # wherever vouch comes from
      - run: vouch check --strict

With Claude Code (or another agent)

$ vouch init --agents --yes

This installs a skill (.claude/skills/vouch/SKILL.md), which teaches the agent to record, find, cite, compare and wait for results; a short rules block in CLAUDE.md; and a hook that checks each .tex file the agent edits. A typed number, a mistyped key or a broken macro goes straight back to the agent in the same turn, with the fix. --stop-gate also stops the agent from finishing while vouch check --strict fails.

Any MCP client can use the same lookups as tools (search, get a value, cite, compare, pending, changes, check, trace) with vouch mcp; vouch init --agents mcp registers it in .mcp.json. Acknowledging changes stays a human action: there is no tool for it.

Commands

vouch init	set up vouch.toml, .vouch/ and paper/vouch.sty
vouch build	evaluate vouch_values.py, write the generated LaTeX, report changes
vouch check [--strict]	the gate (exit 0 pass, 1 fail, 2 could not check)
vouch explore	browse every recorded value; copy the LaTeX that cites it
vouch search / vouch cite	find a key by words / the exact snippet to paste
vouch compare A B [--write]	arithmetic and significance between two values, as citable code
vouch suggest [--apply]	turn numbers typed into the paper into citations
vouch todo	values the paper cites that no run has recorded yet
vouch ls / vouch trace / vouch status	list keys, see where a value came from, see which runs are stale
vouch changes / vouch ack / vouch review	cited values that moved since they were last read
vouch accept RUN --why ...	a reviewed staleness that doesn't affect the result
vouch run / vouch import	record runs of other languages; register existing results files
vouch sync [--strip]	write each cited value into a % vouch: comment on its line
vouch export --csv FILE	the provenance table
vouch catalog	rewrite .vouch/CATALOG.md
vouch init --agents	set up Claude Code: skill, rules, edit hook
vouch mcp	the lookups as MCP tools, over stdio

The full design is in SPEC.md. Browsable documentation: https://dlfelps.github.io/vouch/.