--- dataset_info: - config_name: canonical features: - name: id dtype: string - name: language dtype: string - name: domain dtype: string - name: subdomain dtype: string - name: request dtype: string - name: state_json dtype: string - name: context_json dtype: string - name: candidate_actions_json dtype: string - name: gold_actions list: string - name: no_action dtype: bool - name: metadata_json dtype: string - name: source_dataset dtype: string - name: source_repo dtype: string - name: source_license dtype: string - name: source_url dtype: string - name: source_row_id dtype: string splits: - name: train num_bytes: 100878914 num_examples: 47800 - name: validation num_bytes: 12578426 num_examples: 5977 - name: test num_bytes: 12584997 num_examples: 5948 download_size: 33298784 dataset_size: 126042337 - config_name: pairs features: - name: group_id dtype: string - name: text_a dtype: string - name: text_b dtype: string - name: candidate_name dtype: string - name: label dtype: int64 - name: domain dtype: string - name: source_dataset dtype: string - name: is_no_action_task dtype: bool splits: - name: train num_bytes: 205269272 num_examples: 448596 - name: validation num_bytes: 25171533 num_examples: 56030 - name: test num_bytes: 25965211 num_examples: 55767 download_size: 37322179 dataset_size: 256406016 configs: - config_name: canonical data_files: - split: train path: canonical/train-* - split: validation path: canonical/validation-* - split: test path: canonical/test-* - config_name: pairs data_files: - split: train path: pairs/train-* - split: validation path: pairs/validation-* - split: test path: pairs/test-* --- # Enterprise Reflex Dataset **Repository:** `yasserrmd/enterprise-reflex-dataset` **Creator:** Mohamed Yasser **Language:** English **Task:** Enterprise action selection, dynamic action ranking, abstention, and System-2 routing **Status:** V0 Research Dataset ## Dataset Summary Enterprise Reflex Dataset is a structured dataset for training lightweight enterprise decision models that operate as a fast **System-1 layer** in front of larger reasoning models. The dataset is designed around a different problem from conventional intent classification. Instead of predicting from a fixed list of labels, a model receives: * a user or system request * optional enterprise state * optional contextual information * a runtime-defined set of candidate actions The model must determine which action best satisfies the request or select `NO_ACTION` when none of the available actions are appropriate. The dataset supports the development of models capable of: * dynamic action ranking * enterprise request-to-action matching * same-domain action discrimination * abstention * tool selection * workflow routing * System-1 / System-2 architectures ## Motivation Enterprise AI systems increasingly expose large numbers of APIs, tools, workflows, services, and agent actions. Sending every request directly to a large reasoning model can introduce: * unnecessary latency * higher inference cost * larger tool-selection spaces * inconsistent routing * unnecessary reasoning for simple decisions Enterprise Reflex explores an alternative architecture: ```text Request + State + Candidate Actions | v Enterprise Reflex | Action Ranking | +--------+--------+ | | Confident Uncertain | | Action System-2 LLM ``` The dataset was created to support this type of selective decision layer. ## Dataset Configurations The repository contains two primary configurations. ### `canonical` The reusable high-level representation of enterprise decision tasks. Typical structure: ```json { "id": "example-id", "language": "en", "domain": "procurement", "subdomain": "requisition", "request": "Create a new requisition for approved laptops.", "state": { "approval_status": "approved" }, "context": {}, "candidate_actions": [ { "name": "procurement.create_requisition", "description": "Create a new procurement requisition.", "family": "CREATE", "domain": "procurement" } ], "gold_actions": [ "procurement.create_requisition" ], "no_action": false, "metadata": {}, "provenance": { "source_dataset": "...", "source_repo": "...", "source_license": "...", "source_url": "...", "source_row_id": "..." } } ``` ### `pairs` Pairwise representation used to train Enterprise Reflex V0. Each request/state representation is paired with one candidate action and labelled according to whether that candidate is appropriate. Conceptually: ```text (request + state + context, candidate action) | v 0 / 1 ``` This allows candidate actions to be supplied dynamically instead of requiring a fixed classification head for every possible enterprise action. ## Data Construction The dataset combines normalized enterprise-oriented tasks and tool-use examples from multiple upstream datasets. Sources used during V0 construction include: * ODE Enterprise Use Cases * ServiceNow EnterpriseOps-Gym * Enterprise Worlds * ServiceNow task datasets * APIGen function-calling data * IT Support V2 Some datasets investigated during development were intentionally excluded from the primary training set because of access, redistribution, or licensing constraints. Examples include: * WorkArena instance data * CRMArena-Pro by default * gated EnterpriseBench data as training material ## Action Ontology Actions are represented using human-readable names and descriptions rather than fixed numeric class IDs. Examples: ```text procurement.create_requisition procurement.create_purchase_order procurement.request_approval itsm.create_incident itsm.create_problem itsm.create_change hr.update_employee hr.request_leave iam.reset_password ``` Broad action families include: ```text CREATE UPDATE DELETE SEARCH NOTIFY ROUTE EXECUTE OTHER ABSTAIN ``` `NO_ACTION` represents explicit abstention. ## NO_ACTION Generation `NO_ACTION` examples are not intended to mean simply "unsafe request." A major V0 construction strategy was to take an otherwise valid enterprise task and remove its valid action from the available candidate set. The correct answer therefore becomes: ```text NO_ACTION ``` because none of the currently available actions correctly satisfy the request. This allows the model to learn that it is not required to select a tool merely because tools are available. ## Hard Negatives The dataset contains negative candidate actions to teach action discrimination. V0 contains both: * cross-domain negatives * same-domain negatives Evaluation of V0 showed that future versions should contain a substantially higher proportion of difficult same-domain sibling actions. Examples: ```text procurement.create_requisition procurement.update_requisition procurement.create_purchase_order procurement.request_approval ``` and: ```text itsm.create_incident itsm.create_problem itsm.update_incident itsm.close_incident ``` ## Splits The dataset provides: ```text train validation test ``` Splitting is performed before pair expansion where possible to reduce leakage between related examples. ## Current Research Findings V0 evaluation indicates that the dataset is sufficient to train a strong request/action compatibility model. However, harder evaluation exposed areas where future dataset versions should improve: * same-domain sibling actions * counterfactual state pairs * enterprise policy constraints * semantic cross-domain collisions * calibrated abstention boundaries Future versions should place substantially greater emphasis on these cases rather than simply increasing the total number of generic examples. ## Recommended V1 Data Distribution A future hard-training extension may emphasize approximately: ```text 35% same-domain sibling discrimination 25% counterfactual state examples 15% policy and workflow constraints 10% semantic cross-domain collisions 8-10% NO_ACTION 5-7% ordinary/easier cases ``` These are design targets, not properties of the current V0 dataset. ## Intended Uses Appropriate research uses include: * enterprise action routing * agent tool selection * API selection * workflow routing * lightweight System-1 decision models * LLM tool-space reduction * abstention research * selective prediction * enterprise agent architectures ## Out-of-Scope Uses The dataset should not be interpreted as sufficient evidence for autonomous execution of high-impact enterprise operations. The dataset is not designed to independently authorize: * financial transactions * employee termination * security administration * destructive infrastructure operations * legal decisions * regulatory decisions * safety-critical actions Authorization should remain the responsibility of deterministic policy, workflow, access-control, and human-governance systems. ## Limitations ### English-first V0 is primarily English. Arabic and broader multilingual support are planned for later versions. ### Synthetic Transformation Some candidate sets, negative examples, normalized action descriptions, and `NO_ACTION` variants are generated or transformed from upstream tasks. They may not reproduce the exact distribution of a live enterprise environment. ### Domain Coverage Although broader than a single enterprise domain, the dataset does not represent every possible enterprise workflow, application, policy, or state transition. ### State Reasoning Current evaluation suggests models trained on V0 can rely more strongly on request/action semantics than on subtle changes in structured enterprise state. Future versions should contain substantially more counterfactual state pairs. ### NO_ACTION V0 models may over-predict abstention in some unfamiliar or weakly represented action areas. Future versions should improve the boundary between: ```text valid but unfamiliar action ``` and: ```text no valid action available ``` ## Licensing and Provenance This dataset was constructed from multiple upstream datasets with different licenses. Provenance information is retained where possible at the example level. Users are responsible for reviewing and complying with the licenses and terms associated with the original source datasets. The presence of derived data in this repository should not be interpreted as replacing or overriding upstream licenses. Known upstream sources used during construction include datasets released under licenses such as: * Apache-2.0 * MIT * CC-BY-4.0 Some investigated sources with restrictive or non-commercial terms were excluded from the default training pipeline. ## Reproducibility The `pairs` configuration preserves the training representation used for Enterprise Reflex V0. The `canonical` configuration is intended to remain the reusable representation for future dataset and model iterations. ## Version ```text Enterprise Reflex Dataset V0 September 2026 ``` ## Citation If this dataset is useful in your research or experiments, please reference the repository and creator: ```bibtex @misc{yasser2026enterprisereflexdataset, author = {Mohamed Yasser}, title = {Enterprise Reflex Dataset}, year = {2026}, publisher = {Hugging Face}, note = {Enterprise action selection and System-1 decision dataset} } ``` ## Disclaimer This is an experimental research dataset. Performance measured on this dataset or its associated benchmarks should not be interpreted as guaranteed performance in production enterprise environments.