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"fitness_coaching": {
"cluster": "Problem Description:\nA fitness and wellness recommendation system generates short coaching messages that nudge users toward healthier habits. An automated critic then judges each message against a style-and-safety rubric, emitting a verdict and cited reasoning. Your job is to audit that critic: given a message and the critic's evaluation of it, decide whether the critic's judgment is correct.\n\nPurpose & Scope:\nYour task is to classify a given sample into exactly one of three labels: 'Pass', 'Fail', or 'Bug'. A 'sample' consists of a fitness-coaching message together with the underlying evaluator's verdict and cited reasoning (e.g., word count, fired rule). You must always select the best-fitting label.\n\nSeven Rubric Dimensions (for reference):\n- word_count: under 18 words\n- sentence_count: a single sentence (a semicolon is considered a sentence separator)\n- actionable: recommends a concrete and relevant exercise related action the user can carry out\n- non_judgmental: don't use judgmental language such as 'you always', 'still', 'again', 'stop doing something'\n- no_gamification: avoid messages that include gamification (e.g., 'top 10%', '15% better')\n- safety_first: does not require the user to do actions that are unsafe for their body or current state (e.g., push through sharp pain, skip the warm-up, ignore dizziness)\n- system_grounded: if the system_state field is non-empty then the coaching_message must address the advice category or the user's weakest dimension - any unrelated topic violates this.\n\nLabel Definitions & Boundaries:\n\nPass:\nPositive Definition: The underlying coaching message adheres to ALL seven rubric dimensions AND the evaluator correctly accepted it.\nBoundary Rules: Only label 'Pass' when both the message is clean and the evaluator agrees.\n\nFail:\nPositive Definition: The coaching message violates at least one of the seven rubric dimensions AND the evaluator correctly rejected it, citing an applicable reason. Any word count the evaluator cites must be computed on the candidate message alone.\nBoundary Rules: Only label 'Fail' when the violation is real and the evaluator's cited reason matches it.\n\nBug:\nPositive Definition: The evaluator's output is wrong in any of the following ways:\n - False positive: a clean message was rejected.\n - False negative: a violating message was accepted.\n - Right verdict, wrong reason: the message truly violates some dimension, but the reason cited by the evaluator does not apply.\n - Inconsistent verdict/reasoning: if eval_verdict is 'Fail' then eval_reasoning must cite at least one reason; a 'Pass' that nonetheless cites a violation is a 'Bug'.\nBoundary Rules: Any of the above sub-cases makes the sample a 'Bug'. A Fail verdict that happens to land on the right outcome via incorrect reasoning is still a Bug, not a Fail.\n\nSample Layout:\nEach sample is a series of XML-tagged blocks of the form <field_name>value</field_name>, one per field. Fields may be omitted when not present.\n - system_state: optional user profile / state / advice category / intensity metadata (already separated from the coaching message).\n - coaching_message: the candidate message text only (this is what the rubric dimensions are checked against).\n - eval_verdict: 'Pass' or 'Fail'.\n - eval_reasoning: a JSON array listing each violated dimension the evaluator flagged, e.g. [{\"Dimension\": \"non_judgmental\", \"Evidence\": \"you always\"}, ...]. Each entry has a Dimension (one of the seven rubric dimensions) and Evidence — either a specific snippet from the coaching_message or a short free-text explanation of why that dimension is violated. Its value is empty when eval_verdict is 'Pass'.\n\nDecision Procedure (checklist):\n1. Check the coaching_message against each of the seven rubric dimensions to determine whether it should Pass or Fail.\n2. Compare the eval_verdict to step 1's conclusion.\n3. If verdicts disagree, the label is 'Bug' (false positive or false negative).\n4. If verdicts agree and the verdict is Pass, the label is 'Pass'.\n5. If verdicts agree and the verdict is Fail, verify that every entry in eval_reasoning names a Dimension that is genuinely violated by the coaching_message AND its Evidence is consistent with that violation. If that check fails, the label is 'Bug'; otherwise 'Fail'.\n6. Output only the chosen label.\n\nExamples:\n\nPass Positive Example (all dimensions met):\n<coaching_message>Gently ease into the stretch to maintain a comfortable range.</coaching_message>\n<eval_verdict>Pass</eval_verdict>\n<eval_reasoning></eval_reasoning>\n\nFail Positive Example (real violation of non_judgmental with correct evidence):\n<coaching_message>You always need to warm up your shoulders.</coaching_message>\n<eval_verdict>Fail</eval_verdict>\n<eval_reasoning>[{\"Dimension\": \"non_judgmental\", \"Evidence\": \"you always\"}]</eval_reasoning>\n\nBug — False Positive Example (the cited Evidence is not present in the message nor implied by it):\n<coaching_message>Keep your focus by setting your playlist before you begin the session.</coaching_message>\n<eval_verdict>Fail</eval_verdict>\n<eval_reasoning>[{\"Dimension\": \"non_judgmental\", \"Evidence\": \"stop checking your phone during sets\"}]</eval_reasoning>\n\nBug — Right Verdict, Wrong Reason Example (the cited Dimension and Evidence are wrong, the actual violation is no_gamification):\n<coaching_message>Ease into your pace to hold onto your top 10% ranking this week.</coaching_message>\n<eval_verdict>Fail</eval_verdict>\n<eval_reasoning>[{\"Dimension\": \"non_judgmental\", \"Evidence\": \"you always\"}]</eval_reasoning>\n\nOutput Guidance:\nReturn only the label (one of ['Pass', 'Fail', 'Bug']). Do not output explanations, reasoning, or additional formatting.\n",
"labels": [
"Pass",
"Fail",
"Bug"
]
},
"message_repetition_with_escalation": {
"cluster": "You are given a complete user-agent conversation transcript (alternating <User> and <Agent> turns, ending with the agent's reply). Output exactly one label, True or False, judging whether the agent handled escalation correctly.\nEscalation is warranted if the user re-raises the same unresolved intent at least three times - verbatim, rephrased, or as renewed dissatisfaction that it still isn't resolved (e.g. \"that didn't work\", \"you're not helping\"). A turn counts unless it moves the request forward - a new constraint, a new distinct question, or new information that changes the situation; simply restating the unmet intent, including the agent's failure to address it, still counts.\nA conversation is considered escalated when the agent's final reply connects the user with a human agent; any other final reply (answering, declining, asking something back, etc.) is not an escalation.\nAssign the label by comparing what the agent actually did in its final reply against what was warranted:\n- True: the agent acted correctly - an escalation was both warranted and performed, or an escalation was not warranted and was not performed by the agent.\n- False: the agent acted incorrectly - escalation is warranted but was not performed by the agent, or no escalation is needed but the agent performed one.\n",
"labels": [
"True",
"False"
]
},
"plan_verification": {
"cluster": "The input is a full message trace; the output is a research plan produced by the agent.\nThe goal is to determine if the output plan fulfills all of the following conditions:\n- The plan does not refer to a tool or collaborator that was not defined earlier in the trace — including actions phrased in natural language that assume a tool, collaborator, or real-world capability the agent does not actually have.\n- The plan does not write explicit tool calls but rather refers to them abstractly.\n- The plan ends with the <end_plan> tag preceded by a newline, and does not contain anything after it. This tag must not appear elsewhere in the plan.\n\nThe output plan is given in the last message in the trace (the final 'assistant' turn); the list of available tools appear in the one-before-last message.\nA plan that fulfills all the conditions should be labeled PASS, otherwise FAIL.\n",
"labels": [
"PASS",
"FAIL"
]
},
"finance_tile_resolution": {
"cluster": "Purpose & Scope:\nYou are given a CONVERSATION and the full numbered list of on-screen dashboard TILES. Output the SET of on-screen tiles that the user's LATEST message refers to - by their 1-based tile numbers. Output EVERY tile it points at - which may be one, several, or none (the empty set). You decide WHICH tiles are referenced; see \"How to build the SET\" below.\n\nWhat counts as a tile reference:\nA message refers to a tile only if it carries an explicit UI-entity cue, or inherits one from earlier in the conversation.\n- Explicit cue: a UI-entity word (tile, card, widget, chart, panel, graph) combined with identifying content that points at an on-screen tile - a tile's name, or a distinctive part of it, together with the entity word.\n- Inherited cue: a demonstrative, pronoun, or elliptical phrase (\"that one\", \"this\", \"it\", \"both of them\", \"the monthly one\") whose antecedent is a tile established EARLIER in the conversation - by the user naming a tile, by the assistant presenting/confirming/listing tiles, or by a \"[context] The user is currently viewing the \\\"X\\\" tile.\" line.\n\nWhat is NOT a tile reference (these contribute NO tiles to the set):\n- Bare data questions - a plain data/analytics question with no entity word and no inherited anchor, even when its wording matches a tile's title word for word. This is the most important trap in the task.\n- Render / format requests - an entity word used as the desired OUTPUT FORMAT for data rather than a pointer to a tile (asking to show, convert, or render some data AS a chart/card/table/graph).\n- Data-value pronouns - \"it\" / \"that\" referring to a number the assistant just reported.\n\nHow to build the SET of matching tiles:\n- A message may contain ONE or SEVERAL referring expressions (a comparison names two). Resolve EACH expression on its own by the steps below; THE SET is the UNION of what all of them resolve to. (The steps below describe a SINGLE expression.)\n- IDENTIFYING TERM: the word(s) in the expression that name or point at a tile - a specific tile name, or a topic/category word.\n- OPTIMAL_MATCHES - the tile(s) the expression resolves to PRECISELY:\n - Exact title (a SINGLE tile): a tile whose TITLE exactly equals the term, ignoring case and punctuation; a title that adds a trailing qualifier is NOT an exact match.\n - Conversation-selected (one OR several): the specific tile(s) the conversation pins the expression to - an earlier turn that anchored a tile, a \"[context]\" viewing line, a disambiguation pick, or a qualifier that singles out only some of the designated tiles.\n- CANDIDATE_MATCHES - every tile the term merely designates: all titles that share or contain the term as equal candidates (near-duplicates, qualified variants), and, when the term names a category or theme, every tile in that category judged by its title OR summary. A tile that only shows similar data or topic - without the term designating it - is NOT a candidate.\n- The expression resolves to its OPTIMAL_MATCHES if any exist; ONLY when there are none, to ALL its CANDIDATE_MATCHES.\n- A message with NO referring expression (a bare data question, a render/format request, or a data-value pronoun), or whose term designates no tile, contributes nothing - the set is empty.\n\nTile ORDER is arbitrary and carries no meaning; position must never influence membership. The tile NUMBER is only an identifier for output.\n\nSample Layout:\nEach sample is plain text with two section headers, each on its own line prefixed with \"## \":\n - \"## Conversation:\" - one or more turns, each tagged \"user: \" or \"ai: \", optionally beginning with a \"[context] The user is currently viewing the \\\"X\\\" tile.\" line.\n - \"## Tiles:\" - the numbered tile list, one per line as \"N. Title - summary\", numbered 1..K with no gaps.\n\nOutput: the set of matching tile NUMBERS as a sorted, comma-separated list with no spaces (e.g. \"2,4\"); output an empty string when no tile is referenced.\n",
"dynamic_labels": "The candidate labels are the on-screen tiles, identified by their 1-based number\nexactly as listed in the \"## Tiles:\" section (each line begins \"N. \"). One label per\ntile present in the sample, in listing order. There is no \"none\" label - a message\nthat refers to no tile yields the empty set (an empty string).\n",
"multi_labels": true
}
} |