--- 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 **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 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.