{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:76ZQXBRQS3V3RSRIJQTO4F2KWD","short_pith_number":"pith:76ZQXBRQ","canonical_record":{"source":{"id":"2505.13860","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-20T03:12:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f4ab93b85ef6151fd8e356feb645c4ca25778a7c26725799c33278b3c0541f94","abstract_canon_sha256":"6e2c9c12cbdbf5829119b4eb716d4cee497ae1d0652f2d42e35f83d7a4a49eb9"},"schema_version":"1.0"},"canonical_sha256":"ffb30b863096ebb8ca284c26ee174ab0fe83ba3572104ae15d7d69a15de735cd","source":{"kind":"arxiv","id":"2505.13860","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.13860","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"arxiv_version","alias_value":"2505.13860v2","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13860","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"pith_short_12","alias_value":"76ZQXBRQS3V3","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"pith_short_16","alias_value":"76ZQXBRQS3V3RSRI","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"pith_short_8","alias_value":"76ZQXBRQ","created_at":"2026-07-05T11:32:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:76ZQXBRQS3V3RSRIJQTO4F2KWD","target":"record","payload":{"canonical_record":{"source":{"id":"2505.13860","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-20T03:12:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f4ab93b85ef6151fd8e356feb645c4ca25778a7c26725799c33278b3c0541f94","abstract_canon_sha256":"6e2c9c12cbdbf5829119b4eb716d4cee497ae1d0652f2d42e35f83d7a4a49eb9"},"schema_version":"1.0"},"canonical_sha256":"ffb30b863096ebb8ca284c26ee174ab0fe83ba3572104ae15d7d69a15de735cd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:29.462885Z","signature_b64":"U3d5jXMjWRTdH02VKAjzOtRCEqrwAl/AfuFFH1jSNrN0Z9Edm/3ebDpMDeoNKzVdlt2DGJxIdqvKGi35Y4i7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffb30b863096ebb8ca284c26ee174ab0fe83ba3572104ae15d7d69a15de735cd","last_reissued_at":"2026-07-05T11:32:29.462412Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:29.462412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.13860","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:32:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U/KbhCpnMcXCTugTQGfuU3tz34lESKEokL60zLnpos4OjXRRIRV461USzfIaHZn6opxUzf4NRkmte6QJsA2tBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T00:12:56.655032Z"},"content_sha256":"3872d22130857cec44a1bd5e44c654c5aa3c84f56aa870084c6fb02e1084d655","schema_version":"1.0","event_id":"sha256:3872d22130857cec44a1bd5e44c654c5aa3c84f56aa870084c6fb02e1084d655"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:76ZQXBRQS3V3RSRIJQTO4F2KWD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Domain Adaptation of VLM for Soccer Video Understanding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Henry Wang, Md Sirajus Salekin, Parmida Atighehchian, Shinan Zhang, Tiancheng Jiang","submitted_at":"2025-05-20T03:12:21Z","abstract_excerpt":"Vision Language Models (VLMs) have demonstrated strong performance in multi-modal tasks by effectively aligning visual and textual representations. However, most video understanding VLM research has been domain-agnostic, leaving the understanding of their transfer learning capability to specialized domains under-explored. In this work, we address this by exploring the adaptability of open-source VLMs to specific domains, and focusing on soccer as an initial case study. Our approach uses large-scale soccer datasets and LLM to create instruction-following data, and use them to iteratively fine-t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13860","kind":"arxiv","version":2},"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/2505.13860/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:32:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n2W4Ci8vHcr92n7okyLutnIImdS0+8j2Ql487NhDg6MAe92zg/pB8Fp7af7Ila3HwQJWi+mQkS6X5/xPG4VFBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T00:12:56.655425Z"},"content_sha256":"ee8d7bc36d12a2b68a42469993d9e5e03d997547ee9eab070651b47924c865ef","schema_version":"1.0","event_id":"sha256:ee8d7bc36d12a2b68a42469993d9e5e03d997547ee9eab070651b47924c865ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/76ZQXBRQS3V3RSRIJQTO4F2KWD/bundle.json","state_url":"https://pith.science/pith/76ZQXBRQS3V3RSRIJQTO4F2KWD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/76ZQXBRQS3V3RSRIJQTO4F2KWD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T00:12:56Z","links":{"resolver":"https://pith.science/pith/76ZQXBRQS3V3RSRIJQTO4F2KWD","bundle":"https://pith.science/pith/76ZQXBRQS3V3RSRIJQTO4F2KWD/bundle.json","state":"https://pith.science/pith/76ZQXBRQS3V3RSRIJQTO4F2KWD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/76ZQXBRQS3V3RSRIJQTO4F2KWD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:76ZQXBRQS3V3RSRIJQTO4F2KWD","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6e2c9c12cbdbf5829119b4eb716d4cee497ae1d0652f2d42e35f83d7a4a49eb9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-20T03:12:21Z","title_canon_sha256":"f4ab93b85ef6151fd8e356feb645c4ca25778a7c26725799c33278b3c0541f94"},"schema_version":"1.0","source":{"id":"2505.13860","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.13860","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"arxiv_version","alias_value":"2505.13860v2","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13860","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"pith_short_12","alias_value":"76ZQXBRQS3V3","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"pith_short_16","alias_value":"76ZQXBRQS3V3RSRI","created_at":"2026-07-05T11:32:29Z"},{"alias_kind":"pith_short_8","alias_value":"76ZQXBRQ","created_at":"2026-07-05T11:32:29Z"}],"graph_snapshots":[{"event_id":"sha256:ee8d7bc36d12a2b68a42469993d9e5e03d997547ee9eab070651b47924c865ef","target":"graph","created_at":"2026-07-05T11:32:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.13860/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Vision Language Models (VLMs) have demonstrated strong performance in multi-modal tasks by effectively aligning visual and textual representations. However, most video understanding VLM research has been domain-agnostic, leaving the understanding of their transfer learning capability to specialized domains under-explored. In this work, we address this by exploring the adaptability of open-source VLMs to specific domains, and focusing on soccer as an initial case study. Our approach uses large-scale soccer datasets and LLM to create instruction-following data, and use them to iteratively fine-t","authors_text":"Henry Wang, Md Sirajus Salekin, Parmida Atighehchian, Shinan Zhang, Tiancheng Jiang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-20T03:12:21Z","title":"Domain Adaptation of VLM for Soccer Video Understanding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13860","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3872d22130857cec44a1bd5e44c654c5aa3c84f56aa870084c6fb02e1084d655","target":"record","created_at":"2026-07-05T11:32:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6e2c9c12cbdbf5829119b4eb716d4cee497ae1d0652f2d42e35f83d7a4a49eb9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-20T03:12:21Z","title_canon_sha256":"f4ab93b85ef6151fd8e356feb645c4ca25778a7c26725799c33278b3c0541f94"},"schema_version":"1.0","source":{"id":"2505.13860","kind":"arxiv","version":2}},"canonical_sha256":"ffb30b863096ebb8ca284c26ee174ab0fe83ba3572104ae15d7d69a15de735cd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ffb30b863096ebb8ca284c26ee174ab0fe83ba3572104ae15d7d69a15de735cd","first_computed_at":"2026-07-05T11:32:29.462412Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:32:29.462412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U3d5jXMjWRTdH02VKAjzOtRCEqrwAl/AfuFFH1jSNrN0Z9Edm/3ebDpMDeoNKzVdlt2DGJxIdqvKGi35Y4i7Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T11:32:29.462885Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.13860","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3872d22130857cec44a1bd5e44c654c5aa3c84f56aa870084c6fb02e1084d655","sha256:ee8d7bc36d12a2b68a42469993d9e5e03d997547ee9eab070651b47924c865ef"],"state_sha256":"8feb6525525ffb7719cf74580548218d1d4abf7e033eb2f650bec58905bedfd5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zXee97Ib8//Axn5nwrhgKQTrAc+vW5eXJzv5EV9lSVcmns5xg3Nj5NFU1ffag5JlAWJvUirdODabNgvtldZyAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T00:12:56.657416Z","bundle_sha256":"70e8b1b248e339fb45f6ad53b9541b279f69251840c0f58a19af493cd9c2b7f2"}}