| --- |
| license: cc-by-4.0 |
| pretty_name: mssense Evaluation Benchmark — Closed-Vocabulary Action Trace Generation |
| language: |
| - en |
| - fr |
| task_categories: |
| - text-generation |
| - text2text-generation |
| tags: |
| - rpa |
| - robotic-process-automation |
| - action-trace |
| - closed-vocabulary |
| - structured-generation |
| - workflow |
| - benchmark |
| - evaluation |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/mssense_eval_benchmark_v1_1.jsonl |
| --- |
| |
| # mssense Evaluation Benchmark — Closed-Vocabulary Action Trace Generation |
|
|
| > **Canonical version / DOI:** archived on Zenodo at |
| > **https://doi.org/10.5281/zenodo.21105006** (CC-BY-4.0). This Hugging Face |
| > repository is a distribution mirror — please **cite the Zenodo DOI**. |
|
|
| An **evaluation-only** benchmark for closed-vocabulary action trace generation in |
| conversational Robotic Process Automation (RPA) authoring. Each sample pairs a |
| conversational request with the oracle labels needed to judge whether a generated |
| action trace is *executable* against a closed, typed, channel-specific action |
| catalogue — not merely schema-valid. |
|
|
| - **Version:** 1.1-eval |
| - **Samples:** 1865 (1772 seeds + 93 deterministic paraphrastic variants) |
| - **Task families (9):** clarification policy, LAT audit, semantic judgment, |
| workflow creation, business-rule extraction, visual grounding / governance, |
| modification intent, audit, interaction regression |
| - **Split:** none — the full file is the evaluation suite |
| - **License:** Creative Commons Attribution 4.0 International (CC-BY-4.0) |
|
|
| ## Terminology |
|
|
| A few names in this benchmark are specific to the platform it originates from. |
| They are kept verbatim because they are used throughout the samples and schema |
| (for example, in `sample_id` prefixes and `iris_*` field names) and changing them |
| would break reproducibility and the dataset's published identity. The acronyms |
| are platform-specific; the problems they instantiate are general and |
| platform-independent. |
|
|
| | Term | Meaning (community-standard concept) | |
| |---|---| |
| | **action trace** | an ordered sequence of typed, executable actions — the durable artefact the system must produce | |
| | **LAT** (*LeBrain Action Trace*) | the platform-specific instance of an action trace used in this benchmark; a list of typed steps. The field `lat_candidate` holds the candidate trace under analysis | |
| | **mssense** | the conversational intent-understanding and workflow-validation component evaluated by this benchmark (the *system under test*); also the benchmark's name | |
| | **LeBrain** | the automation / intelligence platform (Novelis) that connects applications and automates business processes; it defines the closed action catalogue and executes the traces | |
| | **IRIS** | LeBrain's Computer Use Agent; the `iris_*` fields (e.g., `iris_control_type`) describe executable UI steps targeted at IRIS | |
| | **Intentia** | the 2026 research programme of the Novelis R&D laboratory, within which `mssense` is developed | |
| | **channel** | an action category / connector — web, desktop, spreadsheet, email, database, API, file, control-flow | |
| | **oracle** | the per-sample ground-truth block (`expected_decision`, `expected_issue_types`, `required_checks`) used for scoring | |
|
|
| ## Contents |
|
|
| ``` |
| data/mssense_eval_benchmark_v1_1.jsonl the benchmark (one JSON object per line) |
| schema/evaluation_sample.v1_1.schema.json JSON Schema for a sample |
| docs/datasheet.md Datasheet for Datasets (Gebru et al., 2018) |
| docs/dataset_card.md dataset card |
| docs/evaluation_protocol.md metrics, splits, scoring conventions |
| docs/related_benchmarks_comparison.md property-by-property comparison of 16 public benchmarks |
| docs/why_new_benchmark.md one-page gap analysis |
| docs/statistical_power_analysis.md a priori power analysis per research question |
| docs/inter_annotator_agreement.md IAA disclosure and v1.2 roadmap |
| reports/CHANGELOG_v1.0_to_v1.1.md changes from v1.0 to v1.1 |
| LICENSE-DATA CC-BY-4.0 |
| CITATION.cff citation metadata |
| SHA256SUMS.txt integrity checksums |
| ``` |
|
|
| Verify integrity with `sha256sum -c SHA256SUMS.txt` (or `certutil -hashfile <file> SHA256` on Windows). |
|
|
| ## Sample format |
|
|
| One JSON object per line. Key fields include `sample_id`, `task_family`, |
| `channel_family`, `input_modality`, `difficulty`, `user_intent`, `input_payload`, |
| `lat_candidate`, `expected_decision`, `expected_issue_types`, `business_rules`, |
| and an `oracle` object with `required_checks`. See |
| `schema/evaluation_sample.v1_1.schema.json` and `docs/datasheet.md` for the full |
| specification. |
|
|
| The issue-type vocabulary is the platform's canonical set: |
| `MISSING_VALUE`, `UNRESOLVED_VARIABLE`, `AMBIGUOUS_SELECTOR`, |
| `MISSING_PRECONDITION`, `INCONSISTENT_FLOW`. |
|
|
| ```python |
| import json |
| samples = [json.loads(l) for l in open("data/mssense_eval_benchmark_v1_1.jsonl", encoding="utf-8")] |
| print(len(samples), "samples") |
| ``` |
|
|
| ## Provenance and license |
|
|
| The released benchmark comprises internally-authored audit, validation, and |
| generation cases, licensed under **CC-BY-4.0**. A WONDERBREAD-derived sub-corpus |
| is prepared in the release tree under a forward-looking attribution clause and is |
| **not** included in this v1.1 evaluation file pending adjudication; the clause |
| takes effect on its first integrated release. Full provenance is documented in |
| `docs/datasheet.md`. |
|
|
| **Privacy and sanitization.** This public release is privacy-sanitized: internal |
| authoring paths in the `input_payload.source_file` field were reduced to file |
| basenames, and incidental personal data that appeared as example form-fill values |
| in a few interaction scenarios were replaced with synthetic values. These are |
| metadata and scenario-input fields only; no oracle label (`expected_decision`, |
| `expected_issue_types`, `oracle`) was modified, so the evaluation is unaffected. |
|
|
| ## Associated publication |
|
|
| This benchmark supports the manuscript *Closed-Vocabulary Action Trace Generation |
| for Conversational RPA Authoring* (Yahaya Alassan, Ettifouri, Dahhane; Novelis), |
| submitted to the *Journal of Object Technology*. |
|
|
| ## Citation |
|
|
| Dataset DOI: **https://doi.org/10.5281/zenodo.21105006** (CC-BY-4.0). See |
| `CITATION.cff` for machine-readable citation metadata. |
