{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IJ7K5J56AZJYFQ4QOGUUT5E5QG","short_pith_number":"pith:IJ7K5J56","schema_version":"1.0","canonical_sha256":"427eaea7be065382c39071a949f49d818596ea1532ed8bb6a848a4a6ddc99a55","source":{"kind":"arxiv","id":"2502.09573","version":3},"attestation_state":"computed","paper":{"title":"Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jiachen Sun, Madhura Raju, Mark Beliaev, Victor Yang, Xinghai Hu","submitted_at":"2025-02-13T18:31:17Z","abstract_excerpt":"In this study, we tackle industry challenges in video content classification by exploring and optimizing GPT-based models for zero-shot classification across seven critical categories of video quality. We contribute a novel approach to improving GPT's performance through prompt optimization and policy refinement, demonstrating that simplifying complex policies significantly reduces false negatives. Additionally, we introduce a new decomposition-aggregation-based prompt engineering technique, which outperforms traditional single-prompt methods. These experiments, conducted on real industry prob"},"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":"2502.09573","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-13T18:31:17Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"e25e96a4f14270395045a6cea410e842b1317d1940074eb0de2f69421b8a0fd3","abstract_canon_sha256":"bd0c845bcf22c8b7769e24a1d648a5967fdd3d2032915021f3419add128fed3a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:15.198739Z","signature_b64":"vtsagOE/AZYkVJKnjwbqUaH4q2nVret9xYlMJuDCgt5ShjupvRZoBwO8MQEbCoUY87f9ggje//ujrFKQSomRBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"427eaea7be065382c39071a949f49d818596ea1532ed8bb6a848a4a6ddc99a55","last_reissued_at":"2026-07-05T10:54:15.198244Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:15.198244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jiachen Sun, Madhura Raju, Mark Beliaev, Victor Yang, Xinghai Hu","submitted_at":"2025-02-13T18:31:17Z","abstract_excerpt":"In this study, we tackle industry challenges in video content classification by exploring and optimizing GPT-based models for zero-shot classification across seven critical categories of video quality. We contribute a novel approach to improving GPT's performance through prompt optimization and policy refinement, demonstrating that simplifying complex policies significantly reduces false negatives. Additionally, we introduce a new decomposition-aggregation-based prompt engineering technique, which outperforms traditional single-prompt methods. These experiments, conducted on real industry prob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09573","kind":"arxiv","version":3},"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/2502.09573/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":"2502.09573","created_at":"2026-07-05T10:54:15.198304+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.09573v3","created_at":"2026-07-05T10:54:15.198304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09573","created_at":"2026-07-05T10:54:15.198304+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJ7K5J56AZJY","created_at":"2026-07-05T10:54:15.198304+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJ7K5J56AZJYFQ4Q","created_at":"2026-07-05T10:54:15.198304+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJ7K5J56","created_at":"2026-07-05T10:54:15.198304+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/IJ7K5J56AZJYFQ4QOGUUT5E5QG","json":"https://pith.science/pith/IJ7K5J56AZJYFQ4QOGUUT5E5QG.json","graph_json":"https://pith.science/api/pith-number/IJ7K5J56AZJYFQ4QOGUUT5E5QG/graph.json","events_json":"https://pith.science/api/pith-number/IJ7K5J56AZJYFQ4QOGUUT5E5QG/events.json","paper":"https://pith.science/paper/IJ7K5J56"},"agent_actions":{"view_html":"https://pith.science/pith/IJ7K5J56AZJYFQ4QOGUUT5E5QG","download_json":"https://pith.science/pith/IJ7K5J56AZJYFQ4QOGUUT5E5QG.json","view_paper":"https://pith.science/paper/IJ7K5J56","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.09573&json=true","fetch_graph":"https://pith.science/api/pith-number/IJ7K5J56AZJYFQ4QOGUUT5E5QG/graph.json","fetch_events":"https://pith.science/api/pith-number/IJ7K5J56AZJYFQ4QOGUUT5E5QG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJ7K5J56AZJYFQ4QOGUUT5E5QG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJ7K5J56AZJYFQ4QOGUUT5E5QG/action/storage_attestation","attest_author":"https://pith.science/pith/IJ7K5J56AZJYFQ4QOGUUT5E5QG/action/author_attestation","sign_citation":"https://pith.science/pith/IJ7K5J56AZJYFQ4QOGUUT5E5QG/action/citation_signature","submit_replication":"https://pith.science/pith/IJ7K5J56AZJYFQ4QOGUUT5E5QG/action/replication_record"}},"created_at":"2026-07-05T10:54:15.198304+00:00","updated_at":"2026-07-05T10:54:15.198304+00:00"}