{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5DHH6GFBWCVGYJ4253F4HDBRHL","short_pith_number":"pith:5DHH6GFB","schema_version":"1.0","canonical_sha256":"e8ce7f18a1b0aa6c279aeecbc38c313af25171209893d7e7a96553960678948a","source":{"kind":"arxiv","id":"2605.24500","version":1},"attestation_state":"computed","paper":{"title":"EgoAdapt: A Multi-Scene Egocentric Adaptation Method for CVPR 2026 HD-EPIC VQA Challenge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guozhi Qiu, Liqiang Nie, Weili Guan, Yupeng Hu, Zhiheng Fu, Zhiwei Chen, Zixu Li","submitted_at":"2026-05-23T10:16:51Z","abstract_excerpt":"This technical report presents our solution, EgoAdapt (Egocentric Adaptation via Category, Calibration, and Consistency), to the CVPR 2026 HD-EPIC VQA challenge. HD-EPIC evaluates whether a vision-language model can reason over realistic first-person kitchen videos, where the evidence for an answer may be a short hand-object interaction, a long recipe trajectory, a spatial relation to a fixture, or a subtle gaze cue. The benchmark contains 26K multiple-choice questions across seven macro-categories: recipe, ingredient, nutrition, fine-grained action, 3D perception, object motion, and gaze. We "},"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":"2605.24500","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-23T10:16:51Z","cross_cats_sorted":[],"title_canon_sha256":"6927f393cb9e98ab981749e1d9e8e2574d2bdbcc2c0327085fb4f66b066f2110","abstract_canon_sha256":"f9fcb00031ea860d1b46d4ee60962198c0d38dc490a5b5b583ae664cde9e72a0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:43.204596Z","signature_b64":"CyTJ416sCTdEhBvtpqA49enIwlCKIO5rwVWKX1eQCi5qYXNzp6ODj+DAn15SzGZ/4eDQ3FthWxmNvxQCma+XBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8ce7f18a1b0aa6c279aeecbc38c313af25171209893d7e7a96553960678948a","last_reissued_at":"2026-05-26T01:03:43.203921Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:43.203921Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EgoAdapt: A Multi-Scene Egocentric Adaptation Method for CVPR 2026 HD-EPIC VQA Challenge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guozhi Qiu, Liqiang Nie, Weili Guan, Yupeng Hu, Zhiheng Fu, Zhiwei Chen, Zixu Li","submitted_at":"2026-05-23T10:16:51Z","abstract_excerpt":"This technical report presents our solution, EgoAdapt (Egocentric Adaptation via Category, Calibration, and Consistency), to the CVPR 2026 HD-EPIC VQA challenge. HD-EPIC evaluates whether a vision-language model can reason over realistic first-person kitchen videos, where the evidence for an answer may be a short hand-object interaction, a long recipe trajectory, a spatial relation to a fixture, or a subtle gaze cue. The benchmark contains 26K multiple-choice questions across seven macro-categories: recipe, ingredient, nutrition, fine-grained action, 3D perception, object motion, and gaze. We "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.24500","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/2605.24500/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":"2605.24500","created_at":"2026-05-26T01:03:43.204023+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.24500v1","created_at":"2026-05-26T01:03:43.204023+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.24500","created_at":"2026-05-26T01:03:43.204023+00:00"},{"alias_kind":"pith_short_12","alias_value":"5DHH6GFBWCVG","created_at":"2026-05-26T01:03:43.204023+00:00"},{"alias_kind":"pith_short_16","alias_value":"5DHH6GFBWCVGYJ42","created_at":"2026-05-26T01:03:43.204023+00:00"},{"alias_kind":"pith_short_8","alias_value":"5DHH6GFB","created_at":"2026-05-26T01:03:43.204023+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/5DHH6GFBWCVGYJ4253F4HDBRHL","json":"https://pith.science/pith/5DHH6GFBWCVGYJ4253F4HDBRHL.json","graph_json":"https://pith.science/api/pith-number/5DHH6GFBWCVGYJ4253F4HDBRHL/graph.json","events_json":"https://pith.science/api/pith-number/5DHH6GFBWCVGYJ4253F4HDBRHL/events.json","paper":"https://pith.science/paper/5DHH6GFB"},"agent_actions":{"view_html":"https://pith.science/pith/5DHH6GFBWCVGYJ4253F4HDBRHL","download_json":"https://pith.science/pith/5DHH6GFBWCVGYJ4253F4HDBRHL.json","view_paper":"https://pith.science/paper/5DHH6GFB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.24500&json=true","fetch_graph":"https://pith.science/api/pith-number/5DHH6GFBWCVGYJ4253F4HDBRHL/graph.json","fetch_events":"https://pith.science/api/pith-number/5DHH6GFBWCVGYJ4253F4HDBRHL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5DHH6GFBWCVGYJ4253F4HDBRHL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5DHH6GFBWCVGYJ4253F4HDBRHL/action/storage_attestation","attest_author":"https://pith.science/pith/5DHH6GFBWCVGYJ4253F4HDBRHL/action/author_attestation","sign_citation":"https://pith.science/pith/5DHH6GFBWCVGYJ4253F4HDBRHL/action/citation_signature","submit_replication":"https://pith.science/pith/5DHH6GFBWCVGYJ4253F4HDBRHL/action/replication_record"}},"created_at":"2026-05-26T01:03:43.204023+00:00","updated_at":"2026-05-26T01:03:43.204023+00:00"}