---
license: apache-2.0
language:
- en
tags:
- evaluation
- workflows
configs:
- config_name: cases
  default: true
  data_files:
  - split: test
    path: data/cases.parquet
- config_name: questions
  data_files:
  - split: test
    path: data/questions.parquet
- config_name: run_results
  data_files:
  - split: test
    path: data/run_results.parquet
---

# Invoice processing

Snapshot: 2026-09-28. 150 cases and 6,874 question instances.
Default reference: **consensus**. Labels are model-generated references.

## Data

Load configuration `cases`, `questions`, or `run_results`; all have a `test` split.

- **cases:** one row per `case_id`, with the complete input in `input_json`, descriptive
  `metadata_json`, and `openai`, `anthropic`, and `consensus` labelsets. Decisions are grouped
  by `policy_id` and contain `status`, `actions`, and `primary_action`.
- **questions:** one row per distinct case/node/question/input combination, identified by
  `question_instance_id`. `question_json` and `state_json` describe
  the model input. Each labelset contains `answer_json`, `probabilities`, `confidence`,
  `confidence_method`, and `expected_score` for score questions. `answer_json` is the modal
  answer (null for ties); the expected score is a separate numeric value.

Independent labels include the actual model, reasoning effort, question mode, and
`used_opus_fallback`. Missing labels have explicit status and null values; the case is retained.
Question status `not_answered` means that reference did not answer that question instance.

## Scoring

`dataset.json` lists policy IDs and the applicable comparison rules:

- `exact_actions`: compare complete action sets including arguments; ignore order and duplicates.
- `primary_action`: compare the designated primary action including its arguments.


## Run results

`run_results` contains 9 code-route runs selected by the final plots:
1,350 rows, one per `run_id` and `case_id`, with 61,866
recorded question answers. The `test` split contains every exported row; nothing is partitioned.

Each row includes the model's name, provider, reasoning effort, and question mode;
execution `status`, `cost_usd`, `wall_time_s`, `summed_call_time_s`, token counts, and call count;
and two nested lists:

- `questions`: `node_id`, `question_id`, `kind`, the actual `question_json` and `state_json`,
  `status`, `answer_json`, `probabilities`, `expected_score`, `confidence`, and `confidence_method`.
  Inputs are repeated so each result is self-contained. Answers are the recorded run values;
  `expected_score` is calculated from the distribution for score questions. Only recorded
  questions appear: branches not taken do not produce fabricated answers. Dynamic choice
  options are preserved exactly, including instances absent from the reference question table.
- `decisions`: `policy_id`, `status`, `actions`, `primary_action`, and `scores`. Each score has
  `metric_id` and a boolean `value` against the **consensus** reference (null when inapplicable).
  Metrics use only `exact_actions` or `primary_action` comparisons.

Runs use exactly the consensus scoring cases in `cases`. Prediction errors remain in scoring
and count as wrong. Measurements are per case, shared across policy decisions; missing
measurements remain null. Cost is recorded in USD using the basis named in each run summary.

`dataset.json` contains `runs`, keyed by `run_id`, with plotted aggregate scores, denominators,
mean cost/time, and measurement counts. The selected run for a model configuration is the one
used by the plot (highest primary accuracy), not necessarily its newest execution. Overall
plot points average workflows equally for configurations present in all four workflows.

## Consensus

Question-level consensus averages the available probability distributions for the same question
and input. `contributors` and `weights` specify 0.5 each for two sources or 1.0 for a single
source. Consensus confidence is the maximum blended probability.

Case-level consensus contains the final reference actions from applying the workflow to its
blended signals. These are the references used for evaluation. Distinct dynamic question
instances remain separate in the question table.

## Coverage

| Labelset | Cases with decisions | Opus fallback cases |
|---|---:|---:|
| openai | 150 | 0 |
| anthropic | 150 | 0 |
| consensus | 150 | 0 |

## Encoding

Fields ending in `_json` are JSON-encoded text; decode with `json.loads`.
Probability distributions are lists of `option`/`probability` pairs. Action arguments are
preserved in `arguments_json`. Descriptive metadata is separate from model-visible state.

## License

This dataset is licensed under the [Apache License 2.0](LICENSE).