Add Cascade Bench (37 workflow trajectories)

README.md

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+---
+license: other
+language:
+- en
+tags:
+- servicenow
+- workflows
+- business-rules
+- agents
+- benchmark
+pretty_name: Cascade Bench
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train.jsonl
+---
+
+# Cascade Bench
+
+A benchmark of ServiceNow workflow trajectories ("World of Workflows++"). Each row is a
+single tool action applied to a domain, together with the seed/supporting data, the table
+schemas, the business rules that can fire, and the resulting cascade of execution logs and
+audit records.
+
+## Summary
+
+- **37 samples**, one per workflow domain (e.g. `accounts_payable_processing`,
+  `incident_escalation`, `change_management`).
+- One split: `train`.
+
+## Columns
+
+| Column | Type | Description |
+|---|---|---|
+| `domain` | string | Workflow domain / sample id |
+| `topology` | string | Cascade topology (e.g. `linear`) |
+| `total_brs_fired` | int | Business rules that actually fired |
+| `expected_br_count` | int | Expected number of business rules |
+| `audit_count` | int | Number of deduped audit records |
+| `raw_audit_count` | int | Number of raw audit records |
+| `tool_name` | string | The action invoked |
+| `parameters` | string (JSON) | `{table_name, operation, fields}` — `fields` keys are domain-specific |
+| `seed_data` | string (JSON) | Initial record state for the target table |
+| `supporting_data` | string (JSON) | Map of related table name → rows |
+| `schema` | string (JSON) | Map of table name → column schema |
+| `business_rules` | list[struct] | `name, fires_on_table, filter_condition, trigger_sequence, trigger_type, order, script` |
+| `ewm_logs` | list[struct] | Execution-log entries (`u_br_name`, `u_table_name`, `u_field_name`, `u_old_value`, `u_new_value`, …) |
+| `audits` | list[struct] | Deduped field-level audit (`tablename, fieldname, oldvalue, newvalue, documentkey`) |
+| `raw_audits` | list[struct] | Raw field-level audit, same shape as `audits` |
+
+> The four JSON-string columns (`parameters`, `seed_data`, `supporting_data`, `schema`)
+> hold dicts whose keys (table/field names) differ per domain, so they are stored as
+> serialized JSON to keep a stable Arrow schema. Call `json.loads()` to use them.
+
+## Usage
+
+```python
+from datasets import load_dataset
+import json
+
+ds = load_dataset("ServiceNow-AI/cascade_bench", split="train")
+
+row = ds[0]
+print(row["domain"], row["tool_name"])
+schema = json.loads(row["schema"])          # variable-key dict
+params = json.loads(row["parameters"])
+brs = row["business_rules"]                  # native list of structs
+```