---
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
---

# finance-data — DHR / ILMN M&A Analysis Tasks

Investment-banking analysis tasks set in a (fictional) strategic M&A project
where Danaher (DHR) explores acquiring Illumina (ILMN). Each task gives an
agent a prompt plus a full deal-room of reference materials (financial models,
spreadsheets, PDFs, research) and grades the answer against a per-criterion
rubric. Tasks that require modifying a workbook also ship the expert's gold
workbook as a deliverable file.

## Layout

```
data/                 # the task table (parquet, 3 rows)
reference_folder/     # the shared deal-room every task works from
├── filesystem/       #   models, filings, research, deal docs (xlsx/pdf/pptx/…)
└── .apps_data/       #   app state (mail, calendar, chat) for the simulated environment
deliverable_files/    # gold deliverable workbooks, one subfolder per task
```

All tasks share **one reference folder** — they are set in the same deal. The
`reference_folder` column holds its repo-relative path.

## Columns

| Column | Type | Description |
|---|---|---|
| `task_id` | string | e.g. `task_helix_dhr_ilmn_06` |
| `sector` | string | `Finance and Insurance` |
| `occupation` | string | `Investment Banking Analyst` |
| `prompt` | string | Self-contained task instructions (includes scenario framing) |
| `reference_folder` | string | Repo-relative path to the shared deal-room (`reference_folder`) |
| `deliverable_files` | list[string] | Repo-relative paths to the gold deliverable workbook(s); empty for answer-only tasks |
| `gold_response` | string | The expert gold answer (text) |
| `rubric_pretty` | string | Human-readable rubric |
| `rubric_json` | string | JSON-encoded rubric (see below) |

## Rubric format (`rubric_json`)

A JSON array; each entry is one grading criterion with fields:

- `verifier_id`, `index` — identity/ordering
- `criteria` — the criterion text
- `criteria_explanation` — step-by-step reference reasoning for the grader
- `weight` — contribution to the weighted task score
- `is_primary_objective` — a task passes only if **all** primary criteria pass
- `tolerance.type` / `tolerance.low` / `tolerance.high` / `tolerance.units` —
  numeric acceptance band (e.g. exact, absolute_range)
- `dependencies` — criteria that must pass for this one to be meaningful
- `criterion_type`, `evaluation_scope`, `expected_file_type`, `human_rating`, `tags`, `universal`

## Intended harness

Populate a sandboxed environment from `reference_folder`, run the agent with
file/spreadsheet/PDF tools, and grade the final answer with an LLM judge that
applies each rubric criterion within its stated tolerance. A task passes iff
every primary-objective criterion passes. For tasks with `deliverable_files`,
the gold workbook shows the expected end-state of the modified model.