"""Editable, illustrative System One requests; these are not benchmark results.""" MODEL = 'apertus-decide' def request(state, **questions): return {'model': MODEL, 'state': state, 'questions': questions} def choice(instructions, **criteria): return {'type': 'choice', 'instructions': instructions, 'criteria': criteria} def score(instructions, *criteria): return {'type': 'score', 'instructions': instructions, 'criteria': list(criteria)} def noul(instructions, criteria=None): question = {'type': 'noul', 'instructions': instructions} if criteria is not None: question['criteria'] = criteria return question EXAMPLES = { 'Customer support · combined decisions': request( 'My order arrived with a cracked screen. I would like my money back, please.', intent=choice('What is the primary customer intent?', refund='Request a refund for a damaged item', tracking='Track a delayed parcel', information='Ask about product specifications'), urgency=score('How urgent is the customer request?', 'Routine product enquiry', 'Customer needs help with a damaged purchase', 'Critical service outage affecting many customers'), refund_requested=noul('Is the customer asking for a refund?')), 'Agent routing · Choice': request( 'Find the opening hours on the museum website and include a source link.', route=choice('Which capability is most appropriate for this request?', web_search='Find current information on websites and cite sources', calculator='Perform numerical calculations', document_search='Search only the uploaded private documents', direct_answer='Answer a general question without external evidence')), 'Incident triage · ordered Score': request( {'service': 'checkout', 'symptom': 'All payment attempts fail', 'scope': 'All customers', 'workaround': 'None available'}, severity=score('Rate the operational severity of this incident.', 'Cosmetic issue with no functional impact', 'Minor issue with a working workaround', 'Major feature impaired for some customers', 'Critical outage blocking all purchases')), 'Retrieval gate · Noul': request( 'According to the uploaded annual report, what were the operating expenses in 2025?', needs_documents=noul('Does answering this request require consulting the uploaded document?')), 'Evidence check · explicit false / true': request( {'claim': 'The invoice has been paid.', 'evidence': 'Invoice 1042 is marked overdue. No payment is recorded.'}, supported=noul('Does the evidence support the claim?', {'false': 'The evidence does not support the claim', 'true': 'The evidence supports the claim'})), 'Inbox routing · structured object': request( {'subject': 'Cannot sign in', 'message': 'My password reset link has expired.', 'channel': 'email'}, team=choice('Which team should handle this message?', account='Login, passwords and account access', billing='Invoices, subscriptions and payments', shipping='Parcel tracking and delivery')), 'Review analysis · mixed questions': request( 'The battery lasts all day and the camera is excellent, but the case scratches easily.', sentiment=choice('What is the overall sentiment?', positive='Mostly satisfied with the product', negative='Mostly dissatisfied', neutral='No clear positive or negative opinion'), satisfaction=score('Rate the expressed product satisfaction.', 'Very dissatisfied', 'Dissatisfied', 'Mixed', 'Satisfied', 'Very satisfied'), durability_issue=noul('Does the review mention a durability problem?')), 'Conversation handoff · array state': request( [{'role': 'user', 'content': 'I cannot access my account.'}, {'role': 'agent', 'content': 'Please try the password reset link.'}, {'role': 'user', 'content': 'I tried twice. Can I speak with a person?'}], handoff=noul('Is the user explicitly requesting a human agent?'), next_action=choice('What should happen next?', human='Offer a handoff to a human support agent', repeat='Send the same password reset instructions again', close='Close the conversation as resolved')), 'Content operations · nullable descriptions': request( 'A practical tutorial showing how to install a Python package and run a first script.', category=choice('Choose the best content category.', tutorial=None, announcement=None, opinion=None)), 'Answer quality · five-level rubric': request( {'question': 'What is photosynthesis?', 'answer': 'Plants use light energy to convert carbon dioxide and water into sugars, releasing oxygen.'}, quality=score('Rate how well the answer addresses the question.', 'Irrelevant or incorrect', 'Major factual errors', 'Partly correct but incomplete', 'Correct and relevant', 'Correct, relevant and clearly explained')), 'Document analysis · several Choice questions': request( 'This research article presents a new text encoder and compares it with existing models on retrieval tasks.', document_type=choice('What type of document is this?', research='Academic research article', manual='User manual', news='News report'), subject=choice('What is the main subject?', machine_learning='Text representation and information retrieval', economics='Markets and economic policy', ecology='Ecosystems and biodiversity')), 'Evidence review · several Noul questions': request( {'note': 'The package arrived on Tuesday. The outer box was damaged, but the contents were intact.'}, delivered=noul('Has the package been delivered?'), packaging_damaged=noul('Was the outer packaging damaged?'), contents_damaged=noul('Were the contents damaged?')), 'Writing assessment · several Score questions': request( 'The application stores files locally. To export a file, select Export from the File menu.', clarity=score('How clearly is the procedure explained?', 'Unclear', 'Partially clear', 'Clear', 'Very clear'), detail=score('How detailed is the procedure?', 'No actionable detail', 'Basic instructions', 'Detailed instructions', 'Comprehensive instructions')), 'Research screening · nested object state': request( {'study': {'title': 'Evaluating sentence embeddings for document search', 'method': 'Comparison of dense retrieval models', 'evaluation': {'datasets': 'Three public retrieval collections', 'metric': 'nDCG@10'}}}, relevant=noul('Is the study relevant to embedding-based information retrieval?'), method=choice('Which methodological category best describes the study?', empirical='Empirical comparison using benchmark datasets', theoretical='Theoretical analysis without experiments', review='Literature review without original evaluation')), 'Meeting records · array of observations': request( ['The team agreed to update the documentation.', 'No owner was assigned.', 'The completion date remains open.'], action_exists=noul('Was a follow-up action agreed?'), owner_assigned=noul('Was a responsible person assigned?'), completeness=score('How complete is the action record?', 'No action recorded', 'Action only', 'Action and owner', 'Action, owner and deadline')), 'Document relationship · structured instructions': request( {'document_a': 'The service is available Monday to Friday.', 'document_b': 'The service is closed on Saturdays and Sundays.'}, relation={'type': 'choice', 'instructions': {'task': 'Classify the relationship between the two statements.', 'scope': 'Consider only their stated availability.'}, 'criteria': {'consistent': 'The statements can both be true', 'contradictory': 'The statements cannot both be true', 'unrelated': 'The statements address unrelated subjects'}}), 'Workflow routing · structured option descriptions': request( {'message': 'Please correct the spelling in the introduction.', 'attachment': 'draft.txt'}, workflow={'type': 'choice', 'instructions': 'Select the appropriate workflow.', 'criteria': {'copy_edit': {'purpose': 'Correct spelling, grammar and punctuation'}, 'summarize': {'purpose': 'Produce a shorter account of the content'}, 'translate': {'purpose': 'Convert the text to another language'}}}), 'Rubric evaluation · structured Score levels': request( 'The release note identifies the issue, affected version and available workaround.', completeness={'type': 'score', 'instructions': 'Evaluate the completeness of the release note.', 'criteria': [{'level': 'Insufficient', 'description': 'No issue or affected version'}, {'level': 'Partial', 'description': 'Issue described but no workaround'}, {'level': 'Complete', 'description': 'Issue, affected version and workaround included'}]}), 'Binary classification · two-option Choice': request( 'The service stopped responding immediately after the configuration update.', category=choice('Does this message report an operational incident?', incident='A failure or interruption of a service', other='An informational request without a reported failure')), 'Schema variation · model alias omitted': { 'state': 'The article describes how to configure a document search index.', 'questions': {'technical': noul('Is this technical documentation?')}}, 'Schema variation · full model identifier': { **request('The user wants to locate a passage in an uploaded report.', route=choice('Which capability should be used?', retrieval='Search the uploaded documents', calculation='Perform an arithmetic calculation')), 'model': 'andreasmartin/apertus-v1.1-swiss-decide-0.4b-bidir'}, 'Content review · uncertainty as a Choice option': request( 'The note states that a delivery is scheduled but does not report whether it arrived.', delivery_status=choice('What delivery status is supported by the note?', delivered='The note explicitly confirms arrival', not_delivered='The note explicitly confirms a failed delivery', insufficient_evidence='The note does not establish whether delivery occurred')), } # The actions below are candidate decisions, not operations executed by this service. APPLICATION_EXAMPLES = { 'Email triage': request('I was charged twice.', queue=choice('Which queue should handle this email?', billing='Charges, invoices and payments', account='Login and account access', shipping='Orders and delivery'), urgency=score('How urgently should this email be reviewed?', 'Routine informational inquiry', 'Time-sensitive customer billing problem', 'Critical widespread service outage'), next_action=choice('Which follow-up is most appropriate?', billing_queue='Route to the billing queue for review', documentation='Send general product documentation', order_lookup='Look up the delivery status')), 'Support routing': request('Nobody can log in. All employees receive a service unavailable error.', team=choice('Which team should investigate?', it_operations='Widespread service availability and authentication incidents', billing='Payment and invoice queries', sales='Product demonstrations and pricing'), severity=score('How severe is the reported incident?', 'Minor inconvenience', 'Some users affected', 'Critical outage affecting all users'), next_action=choice('Select an appropriate response.', on_call='Assign the on-call operations team', routine_queue='Add to the routine information queue', close='Close as resolved')), 'Lead scoring': request('Can we book a demo? Our team is evaluating your product for rollout next month.', purchase_intent=score('How strong is the expressed purchase intent?', 'No commercial interest', 'General exploration', 'Active evaluation', 'High intent with a concrete purchasing timeframe'), demo_requested=noul('Is a product demonstration explicitly requested?'), next_action=choice('Which follow-up fits this inquiry?', sales_follow_up='Prioritize a sales follow-up and arrange a demo', newsletter='Offer a general newsletter subscription', technical_incident='Assign to incident response')), 'Model routing': request({'task': 'Summarize a short FAQ', 'content': 'Office hours are 9 to 5. Support is available by email. The FAQ contains six short entries.'}, model_tier=choice('Which model tier is proportionate to this task?', small='Small model for short, straightforward text transformations', large='Large model for complex multi-step synthesis across long documents', specialized='Specialized model for image or audio analysis'), next_action=choice('Which dispatch decision follows from the task?', small_model='Dispatch to the configured small text model', vision_model='Dispatch to an image analysis model', request_image='Ask the user to upload an image')), 'Tool selection': request('Where is order #482?', tool=choice('Which available capability best answers this request?', order_lookup='Retrieve order status and tracking by order number', calculator='Calculate numerical expressions', product_search='Find product descriptions'), next_action=choice('Which action should the calling application consider?', order_status_api='Call the order-status API using the provided order number', calculate='Perform an arithmetic calculation', product_catalog='Search the product catalog')), 'Safety gate': request({'request': 'Delete the customer records from the archive.', 'policy': 'Deleting records requires explicit approval by a human operator.', 'approval': 'No human approval has been recorded.'}, approval_required=noul('According to the supplied policy, does this request require human approval?'), next_action=choice('Which next action is consistent with the supplied policy and approval status?', pause='Pause and request human approval', proceed='Delete the records immediately', report_complete='Report that deletion is complete')), 'Answer evaluation': request({'source': 'The standard warranty lasts one year.', 'draft_answer': 'The product has a five-year standard warranty.'}, support=choice('How does the draft answer relate to the source?', supported='The draft is supported by the source', contradicted='The draft conflicts with the source', insufficient_evidence='The source does not address the claim'), next_action=choice('Which next step should the application consider?', regenerate='Retry answer generation using the source', accept='Accept the draft unchanged', calculate='Use a calculator')), 'Agent verification': request({'task': 'Export the report to a file.', 'agent_message': 'The export is complete.', 'observations': {'file_exists': False, 'export_log': 'Export failed: destination unavailable.'}}, completion=choice('Does the observed state establish task completion?', complete='The required export file exists and the export succeeded', incomplete='The required export file is missing or export failed', unknown='There is no observation of the export outcome'), next_action=choice('Which next step follows from the observations?', retry_export='Retry the export step after addressing the destination error', deliver='Deliver the successfully exported file', close='Close the task as completed')), 'RAG grounding': request({'question': 'What were operating expenses in 2025?', 'retrieved_passages': ['The company opened a new office in 2025.', 'The annual report includes a financial statements section.']}, evidence=choice('Do the retrieved passages support answering the question?', sufficient='The passages state the requested operating expenses', insufficient='The passages do not provide the requested operating expenses', conflicting='The passages give contradictory expense figures'), next_action=choice('Which next step is justified by the available evidence?', retrieve_more='Retrieve more relevant financial passages', answer='Answer with an expense figure from the supplied passages', calculate='Calculate the expenses from numeric values in the passages')), } EXAMPLES = {**APPLICATION_EXAMPLES, **EXAMPLES}