{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XGZE3NZ2HMWGRMH5YC3JLCWLPE","short_pith_number":"pith:XGZE3NZ2","schema_version":"1.0","canonical_sha256":"b9b24db73a3b2c68b0fdc0b6958acb7902223341d27e56439f2b18a9b47464d5","source":{"kind":"arxiv","id":"2606.01451","version":1},"attestation_state":"computed","paper":{"title":"Before and After Temperature: A Distributional View of Creative LLM Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aditi Kaushal, Harsha Ponnada, Sahiti Bulusu, Saiteja Dasari, S. Shria Parupudi, V. S. Raghu Parupudi","submitted_at":"2026-05-31T21:13:47Z","abstract_excerpt":"Reference-free evaluation of large language model (LLM) creativity relies on perplexity, entropy, and top-1 margin. We show that a much stronger signal lives one step earlier in the pipeline: in how sampling temperature \\emph{reshapes} the model's token distribution before the next token is drawn. On Llama-3.1-8B-Instruct generations of 500 open-ended creative prompts at $T \\in \\{0.3, 0.8, 1.5\\}$, a single per-token feature derived from this reshaping predicts the within-prompt creativity rank at Spearman $\\rho{=}0.918$ against an averaged gpt-4o\\,/\\,gemini-2.5-pro judge ($n{=}500$) and $\\rho{"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.01451","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-31T21:13:47Z","cross_cats_sorted":[],"title_canon_sha256":"4d87d422932b631f69300120e86f1464f036a8df81c4f67940f5ef86d65492c1","abstract_canon_sha256":"17a965fae0d4de2b3f0d33075f1439e3bf79013d4cac52d23d5063c4ef7be972"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:33.417857Z","signature_b64":"xYmhYYX6dp/yJyc6yQsVDk/95GkczJgMgRZYSFAzQa6Md+2BfzDmMFLweh2UIH3Sb4oktWbzWSlWlV+fr4iFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9b24db73a3b2c68b0fdc0b6958acb7902223341d27e56439f2b18a9b47464d5","last_reissued_at":"2026-06-02T02:04:33.417446Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:33.417446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Before and After Temperature: A Distributional View of Creative LLM Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aditi Kaushal, Harsha Ponnada, Sahiti Bulusu, Saiteja Dasari, S. Shria Parupudi, V. S. Raghu Parupudi","submitted_at":"2026-05-31T21:13:47Z","abstract_excerpt":"Reference-free evaluation of large language model (LLM) creativity relies on perplexity, entropy, and top-1 margin. We show that a much stronger signal lives one step earlier in the pipeline: in how sampling temperature \\emph{reshapes} the model's token distribution before the next token is drawn. On Llama-3.1-8B-Instruct generations of 500 open-ended creative prompts at $T \\in \\{0.3, 0.8, 1.5\\}$, a single per-token feature derived from this reshaping predicts the within-prompt creativity rank at Spearman $\\rho{=}0.918$ against an averaged gpt-4o\\,/\\,gemini-2.5-pro judge ($n{=}500$) and $\\rho{"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01451","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.01451/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.01451","created_at":"2026-06-02T02:04:33.417502+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.01451v1","created_at":"2026-06-02T02:04:33.417502+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01451","created_at":"2026-06-02T02:04:33.417502+00:00"},{"alias_kind":"pith_short_12","alias_value":"XGZE3NZ2HMWG","created_at":"2026-06-02T02:04:33.417502+00:00"},{"alias_kind":"pith_short_16","alias_value":"XGZE3NZ2HMWGRMH5","created_at":"2026-06-02T02:04:33.417502+00:00"},{"alias_kind":"pith_short_8","alias_value":"XGZE3NZ2","created_at":"2026-06-02T02:04:33.417502+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XGZE3NZ2HMWGRMH5YC3JLCWLPE","json":"https://pith.science/pith/XGZE3NZ2HMWGRMH5YC3JLCWLPE.json","graph_json":"https://pith.science/api/pith-number/XGZE3NZ2HMWGRMH5YC3JLCWLPE/graph.json","events_json":"https://pith.science/api/pith-number/XGZE3NZ2HMWGRMH5YC3JLCWLPE/events.json","paper":"https://pith.science/paper/XGZE3NZ2"},"agent_actions":{"view_html":"https://pith.science/pith/XGZE3NZ2HMWGRMH5YC3JLCWLPE","download_json":"https://pith.science/pith/XGZE3NZ2HMWGRMH5YC3JLCWLPE.json","view_paper":"https://pith.science/paper/XGZE3NZ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.01451&json=true","fetch_graph":"https://pith.science/api/pith-number/XGZE3NZ2HMWGRMH5YC3JLCWLPE/graph.json","fetch_events":"https://pith.science/api/pith-number/XGZE3NZ2HMWGRMH5YC3JLCWLPE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XGZE3NZ2HMWGRMH5YC3JLCWLPE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XGZE3NZ2HMWGRMH5YC3JLCWLPE/action/storage_attestation","attest_author":"https://pith.science/pith/XGZE3NZ2HMWGRMH5YC3JLCWLPE/action/author_attestation","sign_citation":"https://pith.science/pith/XGZE3NZ2HMWGRMH5YC3JLCWLPE/action/citation_signature","submit_replication":"https://pith.science/pith/XGZE3NZ2HMWGRMH5YC3JLCWLPE/action/replication_record"}},"created_at":"2026-06-02T02:04:33.417502+00:00","updated_at":"2026-06-02T02:04:33.417502+00:00"}