Datasets:
Related Public Benchmarks — Comparison
V5.15.h.3 artefact — JOT submission readiness.
This document compares mssense-eval-benchmark v1.1-eval against the most closely related public benchmarks and justifies, dataset-by-dataset, why no existing public corpus can substitute for it. It is the source material for the related-work paragraph and Section 8.1.x of the JOT manuscript (cf.
manuscript_section_2_6.md).
Comparison axes (the four required properties)
The mssense problem of closed-vocabulary action trace generation for conversational RPA authoring simultaneously requires four properties on the evaluation corpus:
- Closed-vocabulary actions tied to an executable RPA runtime. Each
action must belong to a fixed channel-specific catalogue with typed
parameters (e.g.
WEB_DOM.LeftClick(xpath, instance, tab_id),EXCEL.ReadWorksheetRange(workbook, sheet, range),CONTROL.ForLoop). A schema-only constraint is insufficient — the names and parameters must resolve in a real RPA runtime. - Conversational input. The user supplies natural language, possibly voice or screen recording, possibly multi-turn with implicit references ("yes, that one", "do the same for the other workflow"), possibly ambiguous. The benchmark must include samples that require a clarification or a verdict, not just a generation.
- Action trace as a durable artefact. The expected output is a multi-step typed action sequence that will be persisted and later interpreted by a workflow runtime. This is distinct from single-step tool calling (one tool per turn) and from generic structured output (one JSON object per call): correctness extends to multi-step dependencies that may span dozens of actions.
- Multi-modal input coverage. Real conversational RPA authoring blends text, voice, screenshot, and demonstration recording. A benchmark restricted to one modality cannot evaluate cross-modal robustness or modality-conditioned error rates (RQ3 of the manuscript).
A candidate public benchmark must satisfy all four to be a drop-in substitute. The table below shows that none does.
Comparison table
Legend: ✅ covered ; 🟠 partial ; ❌ absent.
| Benchmark | Year | (1) Closed-vocab RPA | (2) Conversational | (3) Multi-step trace | (4) Multi-modal | Verdict for mssense |
|---|---|---|---|---|---|---|
| mssense-eval-benchmark v1.1 | 2026 | ✅ 9 channels, ≥50 actions | ✅ multi-turn, ambiguity, clarification labels | ✅ LAT with control flow | ✅ text/audio/capture/demo/mixed | — |
| WONDERBREAD [Wornow et al. 2024] | 2024 | ❌ SOP texts, no RPA catalogue | 🟠 single-turn intent | 🟠 SOP is procedural text, not typed trace | ✅ demo + keyframes | Source for our creation block only; cannot substitute |
| WorkArena [Drouin et al. 2024] | 2024 | 🟠 ServiceNow web actions | ❌ single-turn agentic task | 🟠 web agent actions, not RPA traces | ❌ web-only | Web-only, agent-oriented |
| WorkArena++ [Boisvert et al. 2024] | 2024 | 🟠 ServiceNow composed tasks | ❌ single task spec | 🟠 multi-step on web | ❌ web-only | Long-horizon web, not RPA |
| Mind2Web [Deng et al. 2023] | 2023 | ❌ generic web HTML actions | ❌ task description | 🟠 multi-step web | ❌ web-only | Generic web agents |
| Multimodal-Mind2Web [Zheng et al. 2024] | 2024 | ❌ generic web | ❌ task description | 🟠 multi-step web | 🟠 web + screenshot | Web grounding, not RPA |
| WebArena [Zhou et al. 2024] | 2024 | ❌ web agent actions | ❌ single goal | ✅ multi-step interactive | ❌ web-only | Interactive agent execution, not authoring |
| OSWorld [Xie et al. 2024] | 2024 | ❌ OS-level interactions | ❌ task instruction | ✅ long horizons | 🟠 screenshot | OS agent execution, not authoring |
| MiniWoB++ [Shi et al. 2017, Liu et al. 2018] | 2018 | ❌ tiny synthetic web actions | ❌ short task | 🟠 short trajectories | ❌ web-only | Smoke-test scale |
| RICO / RICO-Semantics [Deka et al. 2017] | 2017 | ❌ no action vocabulary | ❌ no intent | ❌ no traces | 🟠 UI screenshots | UI dataset, not workflow |
| FlowMind [Zeng et al. 2024] | 2024 | 🟠 internal workflow lang | ❌ single instruction | ✅ workflow output | ❌ text-only | Closed-source corpus; not an open eval set |
| SmartFlow [Jain et al. 2024] | 2024 | 🟠 RPA workflows | ❌ single task | ✅ workflow output | 🟠 text + demo | Proprietary; no public eval suite released |
| Toolformer [Schick et al. 2023] | 2023 | ❌ generic tool calling | ❌ single turn | ❌ single tool call | ❌ text-only | Single-step tool use |
| ReAct (HotpotQA / ALFWorld variants) [Yao et al. 2023] | 2023 | ❌ generic reasoning + acting | 🟠 multi-turn | 🟠 episode level | ❌ text-only | Generic ReAct, not RPA |
| JSONSchemaBench [Geng et al. 2025] | 2025 | ❌ JSON schema fidelity only | ❌ no conversation | ❌ structured output, not trace | ❌ schema-only | Constrained decoding eval |
| XGrammar [Dong et al. 2025] | 2025 | ❌ grammar-constrained gen | ❌ no conversation | ❌ structured output | ❌ schema-only | Decoding engine, not benchmark |
| APIBench / API-Bank [Patil et al. 2023, Li et al. 2023] | 2023 | ❌ generic APIs | 🟠 short dialog | ❌ short tool sequence | ❌ text-only | API selection, not RPA |
| AgentBench [Liu et al. 2024] | 2024 | ❌ multi-domain agent | 🟠 multi-turn | 🟠 multi-step | 🟠 some modalities | Generic agent, not RPA authoring |
| SWE-bench [Jimenez et al. 2024] | 2024 | ❌ Python repo bugs | ❌ issue description | ❌ patch generation | ❌ text-only | Software engineering, unrelated |
| XCorpus [Dietrich et al. 2017] | 2017 | ❌ Java code corpus | ❌ no conversation | ❌ programs, not traces | ❌ code only | Cited for JOT methodology precedent only |
Why each candidate cannot substitute (per-dataset paragraph)
WONDERBREAD (Stanford, 2024)
Strengths. Largest public corpus of GUI demonstrations with intent + action trace + key frames + SOP, sourced from real human captures.
Why it does not substitute for mssense. WONDERBREAD's "action trace" is
a low-level GUI event sequence (clicks, keystrokes, scroll positions),
not an executable closed-vocabulary RPA trace. There is no fixed catalogue
of typed channel-specific actions to validate against. The SOPs are
free-form natural language, which is the input to the closed-vocabulary
generation problem, not its output. WONDERBREAD is therefore an
upstream source for the workflow_creation task family of mssense (we
extract metadata + intent + action_trace + SOP fields via an internal
extraction pipeline), but it cannot be used as
the evaluation target: a method that generates a WONDERBREAD-shaped output
is not testing what the manuscript claims to test.
Citation expected. Wornow, Riedel, Narayan, Mahbub, Ling, Yan, & Liang, "WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks," 2024.
WorkArena and WorkArena++ (ServiceNow, 2024)
Strengths. Real enterprise web tasks on ServiceNow, well-curated, with end-to-end goal satisfaction signals.
Why it does not substitute. Both are web-only (single channel family), single-turn task descriptions (no conversational clarification or implicit reference resolution), and agent-oriented (the agent acts on a live ServiceNow instance) rather than authoring-oriented (the system produces a durable workflow artefact). Closed-vocabulary RPA action traces are not the evaluation target.
Mind2Web and Multimodal-Mind2Web (2023–2024)
Strengths. Large-scale web task corpus across many sites, with trajectory annotations.
Why it does not substitute. Generic web HTML action vocabulary
(click, type, select) without RPA-platform binding, no closed
multi-channel catalogue (EXCEL / DESKTOP_UIA / CONTROL absent), no
conversational dialogue layer.
WebArena and OSWorld (2024)
Strengths. Realistic interactive environments for agent evaluation, WebArena on web, OSWorld on OS-level.
Why it does not substitute. Both evaluate agents that execute workflows live, not systems that author persistent workflow artefacts. A system can pass WebArena by completing a task without producing any durable trace; mssense requires the trace to be inspected post hoc.
MiniWoB++ (2017–2018)
Strengths. Reproducible micro-tasks.
Why it does not substitute. Toy synthetic web pages, single-turn tasks, no closed RPA vocabulary. Suitable as a smoke test, not as a publication-grade evaluation set.
FlowMind (JPMorgan, 2024) and SmartFlow (TCS, 2024)
Strengths. Both target RPA authoring with LLMs; FlowMind generates workflows, SmartFlow combines task descriptions and demonstrations.
Why neither substitutes. The evaluation corpora are not publicly released as open benchmarks. Both papers report results on proprietary internal datasets. A reviewer cannot reproduce mssense results by pointing to either, and no open distribution accompanies the original publications.
Constrained decoding (JSONSchemaBench, XGrammar)
Strengths. Important methodological foundations for the structured output stage of the proposed pipeline.
Why neither substitutes. Both target schema validity only. The
manuscript Section 1 explicitly argues that schema validity is necessary
but insufficient — vocabulary validity and workflow executability are
distinct, higher-level correctness criteria. A method that passes
JSONSchemaBench can still emit WEB_DOM.LeftClick parameters that
reference a non-existent variable or call a non-existent action.
Tool-augmented LLM agents (Toolformer, ReAct, APIBench, AgentBench)
Strengths. Define the tool calling paradigm that informs the action selection step of the pipeline.
Why none substitute. Tool calling concerns the runtime choice of one tool per turn. Action trace generation concerns the off-line authoring of a multi-step durable artefact whose actions remain valid after the conversation has ended. The benchmark must therefore expose multi-step compositional structure, which AgentBench partially exposes but without closed RPA vocabulary.
Java program corpora (XCorpus, DaCapo, Qualitas)
Strengths. Cited as methodology precedents for the JOT-publishable dataset-paper format (cf. XCorpus, JOT 2017, 76 programs).
Why none substitute. Unrelated domain (executable Java programs ≠ RPA action traces). Listed for the orienting comparison only.
Summary
The four required properties simultaneously isolate a gap that no single public benchmark fills. WONDERBREAD covers (4) demonstrations but not (1) closed RPA vocab; WorkArena covers (1)–(3) on web but not multi- modal or conversational; agent benchmarks cover execution but not authoring; constrained decoding benchmarks cover schema validity but not vocabulary or executability. mssense-eval-benchmark v1.1 is the first open evaluation corpus that simultaneously satisfies (1)–(4) and is therefore the only candidate consistent with the manuscript's claim ("closed-vocabulary action trace generation for conversational RPA authoring").
A pre-emptive paragraph for the manuscript is provided in
manuscript_section_2_6.md (Section 2.6 Why a New Benchmark?).