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ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）
2026-08-20 13:12:50 +00:00

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}
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{
"role": "user",
"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"identity\", \"id\": \"T-identity\", \"input\": \"Move this anonymous caller's flight to tomorrow.\", \"phase\": \"transfer\", \"rule_id\": \"booking.change_identity\"}\n"
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"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor based on the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and instructions on how to determine the action.\n\nFirst, let's look at the TASK_INPUT. The family is \"identity\", and the rule_id is \"booking.change_identity\". The instructions say that if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline.\n\nNow, check ACTIVE_MEMORY. The entries are:\n- {\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}\n- {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}\n- {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}\n\nThe TASK_INPUT's rule_id is \"booking.change_identity\", which is exactly present in ACTIVE_MEMORY. The required_action for that rule_id is \"verify_identity_first\". The version is 1.\n\nSo the action should be \"verify_identity_first\", used_rule_id is \"booking.change_identity\", used_version is 1."
}
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"role": "user",
"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"baggage\", \"id\": \"T-baggage\", \"input\": \"A partner booking site asks for the economy bag limit.\", \"phase\": \"transfer\", \"rule_id\": \"baggage.economy_allowance\"}\n"
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"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor following the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and the instructions.\n\nFirst, the TASK_INPUT has a \"family\" of \"baggage\" and a \"rule_id\" of \"baggage.economy_allowance\". The instructions say if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline.\n\nLooking at ACTIVE_MEMORY, there's an entry with rule_id \"baggage.economy_allowance\", which is active, and required_action \"answer_20kg\". Since the TASK_INPUT's rule_id matches exactly, we use that required_action.\n\nSo the action should be \"answer_20kg\", used_rule_id is \"baggage.economy_allowance\", and used_version is 1 (from the ACTIVE_MEMORY entry)."
}
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"baggage\", \"id\": \"C-baggage-1\", \"input\": \"A new policy is active; answer the economy allowance now.\", \"phase\": \"change\", \"rule_id\": \"baggage.economy_allowance\"}\n"
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"role": "user",
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"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor based on the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and instructions on how to determine the action.\n\nFirst, let's look at the TASK_INPUT. The family is \"identity\", the rule_id is \"booking.change_identity\". Now, the instructions say: if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline.\n\nSo, check ACTIVE_MEMORY for the rule_id \"booking.change_identity\". Looking at ACTIVE_MEMORY, there's an entry: {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}. That's the exact rule_id from the TASK_INPUT. So we need to use its required_action, which is \"verify_identity_first\".\n\nThe used_rule_id should be \"booking.change_identity\" and the used_version is 1. The action is \"verify_identity_first\".\n\nLet me confirm. The TASK_INPUT's rule_id is \"booking.change_identity\", which is present in ACTIVE_MEMORY with active: true. So we take that required_action. The family baseline isn't needed here because the exact rule_id is found. So the JSON should have action: \"verify_identity_first\", used_rule_id: \"booking.change_identity\", used_version: 1."
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"baggage\", \"id\": \"C-baggage-1\", \"input\": \"A new policy is active; answer the economy allowance now.\", \"phase\": \"change\", \"rule_id\": \"baggage.economy_allowance\"}\n"
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"role": "user",
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"reasoning_content": "\nGot it, let's tackle this step by step. First, I need to act as an airline policy executor following the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and instructions on how to determine the action.\n\nFirst, let's look at the TASK_INPUT. The family is \"baggage\", and the rule_id is \"baggage.economy_allowance\". Now, the instructions say: if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline.\n\nLooking at ACTIVE_MEMORY, there's an entry with rule_id \"baggage.economy_allowance\" which is active, and its required_action is \"answer_20kg\". So since the TASK_INPUT's rule_id matches exactly with this entry in ACTIVE_MEMORY, we should use that required_action.\n\nThe action should be \"answer_20kg\", used_rule_id is \"baggage.economy_allowance\", and used_version is 1 (from the ACTIVE_MEMORY entry's version).\n\nSo the JSON output should have these values."
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"refund\", \"id\": \"T-refund\", \"input\": \"The ticket is non-returnable. What value can still be recovered?\", \"phase\": \"transfer\", \"rule_id\": \"refund.nonrefundable\"}\n"
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"identity\", \"id\": \"L-identity-1\", \"input\": \"Change the date on booking A17 before the traveler is verified.\", \"phase\": \"learning\", \"rule_id\": \"booking.change_identity\"}\n"
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"content": "{\"action\":\"change_without_verification\",\"used_rule_id\":null,\"used_version\":null}",
"role": "assistant",
"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor based on the given rules. Let's start by understanding the problem.\n\nThe user provided ACTIVE_MEMORY, which has a list of active rules. The TASK_INPUT is about a task with family \"identity\", id \"L-identity-1\", input about changing the date on a booking before verification, phase \"learning\", and rule_id \"booking.change_identity\".\n\nThe instructions say: if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline exactly. The family baseline is given as {\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}.\n\nFirst, check the TASK_INPUT's rule_id: \"booking.change_identity\". Now, look into ACTIVE_MEMORY. The ACTIVE_MEMORY has one entry with rule_id \"refund.nonrefundable\". So the task's rule_id is not present in ACTIVE_MEMORY. Therefore, we need to use the family baseline.\n\nThe task's family is \"identity\". Looking at the family baseline, the \"identity\" family's required action is \"change_without_verification\". \n\nSo the action should be \"change_without_verification\". The used_rule_id is null because the exact rule_id wasn't in ACTIVE_MEMORY, and used_version is null as well.\n\nLet me confirm: ACTIVE_MEMORY doesn't have \"booking.change_identity\", so family baseline applies. Family is identity, baseline action is \"change_without_verification\". That's the one to choose."
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"request": {
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"messages": [
{
"role": "user",
"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"identity\", \"id\": \"L-identity-2\", \"input\": \"A caller wants to alter another person's itinerary.\", \"phase\": \"learning\", \"rule_id\": \"booking.change_identity\"}\n"
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"content": "{\"action\":\"verify_identity_first\",\"used_rule_id\":\"booking.change_identity\",\"used_version\":1}",
"role": "assistant",
"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor based on the given rules. Let's start by understanding the problem.\n\nThe user provided ACTIVE_MEMORY, which has two entries. The TASK_INPUT has family \"identity\", id \"L-identity-2\", input about altering another person's itinerary, phase \"learning\", and rule_id \"booking.change_identity\".\n\nThe instructions say: if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline. The family baseline for \"identity\" is \"change_without_verification\".\n\nFirst, check ACTIVE_MEMORY for the rule_id in TASK_INPUT. The TASK_INPUT's rule_id is \"booking.change_identity\". Looking at ACTIVE_MEMORY, there's an entry with \"rule_id\": \"booking.change_identity\" and \"required_action\": \"verify_identity_first\". So since the exact rule_id is present in ACTIVE_MEMORY, we use that required_action.\n\nThe action should be \"verify_identity_first\", used_rule_id is \"booking.change_identity\", and used_version is 1 (from the ACTIVE_MEMORY entry).\n\nSo the JSON should be {\"action\":\"verify_identity_first\",\"used_rule_id\":\"booking.change_identity\",\"used_version\":1}"
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"request": {
"model": "doubao-seed-1-6-250615",
"messages": [
{
"role": "user",
"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"baggage\", \"id\": \"L-baggage-1\", \"input\": \"State the current free checked baggage allowance for economy.\", \"phase\": \"learning\", \"rule_id\": \"baggage.economy_allowance\"}\n"
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"content": "{\"action\":\"answer_unknown\",\"used_rule_id\":null,\"used_version\":null}",
"role": "assistant",
"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor based on the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and the instructions.\n\nFirst, let's look at the TASK_INPUT. The family is \"baggage\", and the rule_id is \"baggage.economy_allowance\". Now, the key instruction is: if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline exactly.\n\nSo I need to check ACTIVE_MEMORY for a rule with rule_id \"baggage.economy_allowance\". Let's look at the ACTIVE_MEMORY entries:\n\nThe first entry has rule_id \"booking.change_identity\", required_action \"verify_identity_first\", version 1. The second has rule_id \"refund.nonrefundable\", required_action \"offer_tax_only_refund\", version 1. Neither of these is \"baggage.economy_allowance\". So the exact rule_id isn't in ACTIVE_MEMORY.\n\nNext, we use the family baseline. The family baseline is given as: {\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}. The TASK_INPUT's family is \"baggage\", so the baseline action is \"answer_unknown\".\n\nNow, the output should be a JSON with \"action\" as the chosen action, \"used_rule_id\" as the exact rule id or null, and \"used_version\" as integer or null. Since the exact rule_id wasn't found, used_rule_id is null, and used_version is null. The action is \"answer_unknown\".\n\nSo putting it all together: {\"action\":\"answer_unknown\",\"used_rule_id\":null,\"used_version\":null}"
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"request": {
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"messages": [
{
"role": "user",
"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"baggage\", \"id\": \"L-baggage-2\", \"input\": \"How much luggage is included in a standard economy fare?\", \"phase\": \"learning\", \"rule_id\": \"baggage.economy_allowance\"}\n"
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"message": {
"content": "{\"action\":\"answer_20kg\",\"used_rule_id\":\"baggage.economy_allowance\",\"used_version\":1}",
"role": "assistant",
"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor following the given rules. The user provided the ACTIVE_MEMORY and TASK_INPUT, and I need to determine the correct action.\n\nFirst, let's look at the TASK_INPUT. The family is \"baggage\", and the rule_id is \"baggage.economy_allowance\". Now, I need to check the ACTIVE_MEMORY to see if there's an entry with that exact rule_id.\n\nLooking at ACTIVE_MEMORY: the first entry has \"rule_id\": \"baggage.economy_allowance\", which matches the TASK_INPUT's rule_id. The required_action for that entry is \"answer_20kg\". The version is 1.\n\nSince there's an exact match in ACTIVE_MEMORY, I should return the required_action from that entry, along with the rule_id and version.\n\nSo the JSON should be {\"action\":\"answer_20kg\",\"used_rule_id\":\"baggage.economy_allowance\",\"used_version\":1}"
}
}
],
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},
"request": {
"model": "doubao-seed-1-6-250615",
"messages": [
{
"role": "user",
"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"refund\", \"id\": \"T-refund\", \"input\": \"The ticket is non-returnable. What value can still be recovered?\", \"phase\": \"transfer\", \"rule_id\": \"refund.nonrefundable\"}\n"
}
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"content": "{\"action\":\"offer_tax_only_refund\",\"used_rule_id\":\"refund.nonrefundable\",\"used_version\":1}",
"role": "assistant",
"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor following the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and the instructions.\n\nFirst, let's look at the TASK_INPUT. The family is \"refund\", and the rule_id is \"refund.nonrefundable\". Now, the key instruction is: if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline.\n\nSo, check ACTIVE_MEMORY for the rule_id from TASK_INPUT, which is \"refund.nonrefundable\". Let's look at ACTIVE_MEMORY entries:\n\n- The first entry has rule_id \"baggage.economy_allowance\" – not matching.\n- Second: \"booking.change_identity\" – no.\n- Third: \"refund.nonrefundable\" – yes! That's the exact rule_id.\n\nThe required_action for that entry is \"offer_tax_only_refund\". So we need to return that action, with the used_rule_id as \"refund.nonrefundable\" and version 1 (since the version in ACTIVE_MEMORY for that entry is 1).\n\nSo the JSON should be {\"action\":\"offer_tax_only_refund\",\"used_rule_id\":\"refund.nonrefundable\",\"used_version\":1}."
}
}
],
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"request": {
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{
"role": "user",
"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"identity\", \"id\": \"T-identity\", \"input\": \"Move this anonymous caller's flight to tomorrow.\", \"phase\": \"transfer\", \"rule_id\": \"booking.change_identity\"}\n"
}
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"message": {
"content": "{\"action\":\"verify_identity_first\",\"used_rule_id\":\"booking.change_identity\",\"used_version\":1}",
"role": "assistant",
"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor based on the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and instructions on how to determine the action.\n\nFirst, let's look at the TASK_INPUT. The family is \"identity\", and the rule_id is \"booking.change_identity\". The instructions say that if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline.\n\nNow, check ACTIVE_MEMORY. The entries are:\n- {\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}\n- {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}\n- {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}\n\nThe TASK_INPUT's rule_id is \"booking.change_identity\", which is exactly present in ACTIVE_MEMORY. The required_action for that rule_id is \"verify_identity_first\", and the version is 1.\n\nSo, the action should be \"verify_identity_first\", used_rule_id is \"booking.change_identity\", used_version is 1."
}
}
],
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"identity\", \"id\": \"R-identity\", \"input\": \"Retest: an unverified person requests a booking change.\", \"phase\": \"retention\", \"rule_id\": \"booking.change_identity\"}\n"
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"baggage\", \"id\": \"R-baggage-current\", \"input\": \"Retest the currently valid economy baggage rule.\", \"phase\": \"retention\", \"rule_id\": \"baggage.economy_allowance\"}\n"
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"identity\", \"id\": \"L-identity-1\", \"input\": \"Change the date on booking A17 before the traveler is verified.\", \"phase\": \"learning\", \"rule_id\": \"booking.change_identity\"}\n"
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"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor based on the given rules. Let's start by understanding the problem.\n\nThe user provided ACTIVE_MEMORY, which has a list of active rules. The TASK_INPUT is about a task with family \"identity\", id \"L-identity-1\", input about changing the date on a booking before verification, phase \"learning\", and rule_id \"booking.change_identity\".\n\nThe instructions say: if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline exactly. The family baseline is given as {\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}.\n\nFirst, check the TASK_INPUT's rule_id: \"booking.change_identity\". Now, look into ACTIVE_MEMORY. The ACTIVE_MEMORY has one entry: {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}. The rule_id here is \"refund.nonrefundable\", which is not the same as the task's rule_id \"booking.change_identity\". So, the exact rule_id is not in ACTIVE_MEMORY.\n\nTherefore, we need to use the family baseline. The task's family is \"identity\". Looking at the family baseline, the \"identity\" family's required action is \"change_without_verification\".\n\nNow, the output should be a JSON with \"action\" (one of the listed options), \"used_rule_id\" (exact rule id or null), and \"used_version\" (integer or null). Since we didn't find the exact rule_id in ACTIVE_MEMORY, used_rule_id is null, and used_version is null. The action is \"change_without_verification\"."
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"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor based on the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and instructions on how to determine the action.\n\nFirst, let's look at the TASK_INPUT. The family is \"identity\", the rule_id is \"booking.change_identity\". The problem says that if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline.\n\nNow, check ACTIVE_MEMORY. There are two entries. The first one has rule_id \"booking.change_identity\", which matches the TASK_INPUT's rule_id. The required_action here is \"verify_identity_first\", version 1.\n\nSo since the exact rule_id is in ACTIVE_MEMORY, we use that required_action. The used_rule_id is \"booking.change_identity\" and used_version is 1. The action is \"verify_identity_first\"."
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"baggage\", \"id\": \"L-baggage-2\", \"input\": \"How much luggage is included in a standard economy fare?\", \"phase\": \"learning\", \"rule_id\": \"baggage.economy_allowance\"}\n"
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"content": "{\"action\":\"answer_20kg\",\"used_rule_id\":\"baggage.economy_allowance\",\"used_version\":1}",
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"reasoning_content": "\nSo, let's tackle this step by step. First, I need to act as an airline policy executor following the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and instructions on how to determine the action.\n\nFirst, the TASK_INPUT has a \"family\" of \"baggage\" and a \"rule_id\" of \"baggage.economy_allowance\". The instructions say that if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline.\n\nLooking at ACTIVE_MEMORY, there's an entry with \"rule_id\": \"baggage.economy_allowance\" and \"required_action\": \"answer_20kg\". That's the exact rule_id from the TASK_INPUT. So we should use that required_action.\n\nThe output needs to be a JSON with \"action\", \"used_rule_id\", and \"used_version\". The \"action\" here is \"answer_20kg\", \"used_rule_id\" is \"baggage.economy_allowance\", and \"used_version\" is 1 (from the ACTIVE_MEMORY entry).\n\nSo putting it all together, the JSON should be {\"action\":\"answer_20kg\",\"used_rule_id\":\"baggage.economy_allowance\",\"used_version\":1}."
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"refund\", \"id\": \"T-refund\", \"input\": \"The ticket is non-returnable. What value can still be recovered?\", \"phase\": \"transfer\", \"rule_id\": \"refund.nonrefundable\"}\n"
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"content": "Act as an airline policy executor in a longitudinal experiment.\n\nCompany rules in this experiment are arbitrary. Never infer an unstored rule\nfrom general knowledge. If ACTIVE_MEMORY has the exact task rule_id, return its\nrequired_action exactly. Otherwise use the family baseline exactly:\n{\"baggage\": \"answer_unknown\", \"identity\": \"change_without_verification\", \"refund\": \"issue_full_refund\"}\n\nChoose exactly one action from:\n[\"answer_20kg\", \"answer_23kg\", \"answer_unknown\", \"ask_for_clarification\", \"change_without_verification\", \"issue_full_refund\", \"offer_tax_only_refund\", \"verify_identity_first\"]\n\nReturn JSON only:\n{\"action\":\"one vocabulary value\",\"used_rule_id\":\"exact rule id or null\",\"used_version\":\"integer or null\"}\n\nACTIVE_MEMORY:\n[{\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1}, {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1}, {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1}]\n\nTASK_INPUT:\n{\"family\": \"identity\", \"id\": \"T-identity\", \"input\": \"Move this anonymous caller's flight to tomorrow.\", \"phase\": \"transfer\", \"rule_id\": \"booking.change_identity\"}\n"
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"reasoning_content": "\nGot it, let's tackle this step by step. First, I need to act as an airline policy executor based on the given rules. The user provided the ACTIVE_MEMORY, TASK_INPUT, and instructions on how to determine the action.\n\nFirst, let's look at the TASK_INPUT. The family is \"identity\", and the rule_id is \"booking.change_identity\". Now, the instructions say: if ACTIVE_MEMORY has the exact task rule_id, return its required_action exactly. Otherwise, use the family baseline.\n\nSo I need to check ACTIVE_MEMORY for a rule with rule_id \"booking.change_identity\". Let's look at the ACTIVE_MEMORY entries:\n\n1. {\"active\": true, \"required_action\": \"answer_20kg\", \"rule_id\": \"baggage.economy_allowance\", \"version\": 1} – rule_id is baggage.economy_allowance, not the target.\n2. {\"active\": true, \"required_action\": \"verify_identity_first\", \"rule_id\": \"booking.change_identity\", \"version\": 1} – here we go, rule_id matches \"booking.change_identity\".\n3. {\"active\": true, \"required_action\": \"offer_tax_only_refund\", \"rule_id\": \"refund.nonrefundable\", \"version\": 1} – rule_id is refund.nonrefundable, not the target.\n\nSo the exact rule_id is present in ACTIVE_MEMORY. The required_action for that rule is \"verify_identity_first\", and the version is 1.\n\nTherefore, the action should be \"verify_identity_first\", used_rule_id is \"booking.change_identity\", used_version is 1."
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},
"cost": {
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},
"gates": {
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"evolving_update_loaded_and_followed": true,
"statistics_cover_adaptation_transfer_replacement_retention": true
},
"accepted": true
}
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