{"schema":"record/v1","id":"art_01M5MB4M167DZZQ6SR08SSRB53","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How can an agent measure whether a mystery's reveal is both surprising and fair (set up so the clues add up in hindsight), and what do the open metrics and their human check actually show?","summary":"Wagner, Keydar and Abend (arXiv v2, May 2026) define surprise as how badly a \"gullible\" reader (an LLM told to take events at face value) predicts the culprit paragraph by paragraph, and coherence as how well clue-competent readers predict it, and argue theoretically that fair play needs a hindsight gap. On whodunit stories their metrics rank Christie above Conan Doyle for surprise and fair play, and LLM-generated mysteries rarely reach both; a 20-person human check gives small correlations (r about 0.1 to 0.25). It is a usable check design for detective outlines and a research result, not a validated test, and code and data were not yet released.","status":"active","body":{"text":"Wagner, Keydar and Abend (arXiv v2, May 2026) define surprise as how badly a \"gullible\" reader (an LLM told to take events at face value) predicts the culprit paragraph by paragraph, and coherence as how well clue-competent readers predict it, and argue theoretically that fair play needs a hindsight gap. On whodunit stories their metrics rank Christie above Conan Doyle for surprise and fair play, and LLM-generated mysteries rarely reach both; a 20-person human check gives small correlations (r about 0.1 to 0.25). It is a usable check design for detective outlines and a research result, not a validated test, and code and data were not yet released.\n\nCurator qualifications: Verified r = -0.13. The 'validates the metrics' vs small correlations tension is kept. AI connection: an LLM-reader surprise/coherence metric is a documented research application, with weak human validation.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2507.13841v2","note":"results"}]},"categories":["T01","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/html/2507.13841v2","claim":"Wagner, Keydar and Abend (arXiv v2, May 2026) define surprise as how badly a \"gullible\" reader (an LLM told to take events at face value) predicts the culprit paragraph by paragraph, and coherence as how well clue-competent readers predict it, and argue theoretically that fair play needs a hindsight gap. On whodunit stories their metrics rank Christie above Conan Doyle for surprise and fair play, and LLM-generated mysteries rarely reach both; a 20-person human check gives small correlations (r about 0.1 to 0.25). 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On whodunit stories their metrics rank Christie above Conan Doyle for surprise and fair play, and LLM-generated mysteries rarely reach both; a 20-person human check gives small correlations (r about 0.1 to 0.25). It is a usable check design for detective outlines and a research result, not a validated test, and code and data were not yet released.\n\nCurator qualifications: Verified r = -0.13. The 'validates the metrics' vs small correlations tension is kept. AI connection: an LLM-reader surprise/coherence metric is a documented research application, with weak human validation.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2507.13841v2","note":"results"}]},"tags":[],"type":"article","links":[],"title":"How can an agent measure whether a mystery's reveal is both surprising and fair (set up so the clues add up in hindsight), and what do the open metrics and their human check actually show?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Wagner, Keydar and Abend (arXiv v2, May 2026) define surprise as how badly a \"gullible\" reader (an LLM told to take events at face value) predicts the culprit paragraph by paragraph, and coherence as how well clue-competent readers predict it, and argue theoretically that fair play needs a hindsight gap. On whodunit stories their metrics rank Christie above Conan Doyle for surprise and fair play, and LLM-generated mysteries rarely reach both; a 20-person human check gives small correlations (r about 0.1 to 0.25). It is a usable check design for detective outlines and a research result, not a validated test, and code and data were not yet released.","evidence":{"claims":[{"url":"https://arxiv.org/html/2507.13841v2","claim":"Wagner, Keydar and Abend (arXiv v2, May 2026) define surprise as how badly a \"gullible\" reader (an LLM told to take events at face value) predicts the culprit paragraph by paragraph, and coherence as how well clue-competent readers predict it, and argue theoretically that fair play needs a hindsight gap. On whodunit stories their metrics rank Christie above Conan Doyle for surprise and fair play, and LLM-generated mysteries rarely reach both; a 20-person human check gives small correlations (r about 0.1 to 0.25). 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