{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OUNPEGMO5N4YVIKMDMOZJASAB2","short_pith_number":"pith:OUNPEGMO","schema_version":"1.0","canonical_sha256":"751af2198eeb798aa14c1b1d9482400e8dce1b9ab54055b61a4373f9a5840a65","source":{"kind":"arxiv","id":"2606.31187","version":1},"attestation_state":"computed","paper":{"title":"Learning to Deny: Action Denial in Multimodal Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Raiyaan Abdullah, Shehreen Azad, Yogesh Singh Rawat","submitted_at":"2026-06-30T06:16:17Z","abstract_excerpt":"Multimodal large language models (MLLMs) have rapidly advanced video understanding, achieving strong zero-shot and few-shot recognition across standard benchmarks. Yet their ability to deny an action by recognizing when an activity is not happening despite strong contextual cues remains largely unexplored. We introduce UCF101-AD, a large-scale benchmark consisting of paired Action-Presence and Action-Denial clips, designed to evaluate this capacity for denial. Each negative video in UCF101-AD preserves the same contextual and motion cues, including persons, objects, and locations, as its posit"},"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":"2606.31187","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-30T06:16:17Z","cross_cats_sorted":[],"title_canon_sha256":"25b182babda2164af4af35b9b7779a72ea6b655ee7d9d222bea099713bda0c3e","abstract_canon_sha256":"af64bda37927414db8494b701ba08707e786dc6672b5e1ab3335ff6b96a70aae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T01:17:31.665186Z","signature_b64":"/XQB4mEJLo8O/uNscsmVXIXWlZxPAdS2qW5HYiF1IdVFD07sE1MLX1ovbn5JmIrpb/dDB3rFf/UY0D/u/YoBBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"751af2198eeb798aa14c1b1d9482400e8dce1b9ab54055b61a4373f9a5840a65","last_reissued_at":"2026-07-01T01:17:31.664771Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T01:17:31.664771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Deny: Action Denial in Multimodal Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Raiyaan Abdullah, Shehreen Azad, Yogesh Singh Rawat","submitted_at":"2026-06-30T06:16:17Z","abstract_excerpt":"Multimodal large language models (MLLMs) have rapidly advanced video understanding, achieving strong zero-shot and few-shot recognition across standard benchmarks. Yet their ability to deny an action by recognizing when an activity is not happening despite strong contextual cues remains largely unexplored. We introduce UCF101-AD, a large-scale benchmark consisting of paired Action-Presence and Action-Denial clips, designed to evaluate this capacity for denial. Each negative video in UCF101-AD preserves the same contextual and motion cues, including persons, objects, and locations, as its posit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.31187","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/2606.31187/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":"2606.31187","created_at":"2026-07-01T01:17:31.664827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.31187v1","created_at":"2026-07-01T01:17:31.664827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.31187","created_at":"2026-07-01T01:17:31.664827+00:00"},{"alias_kind":"pith_short_12","alias_value":"OUNPEGMO5N4Y","created_at":"2026-07-01T01:17:31.664827+00:00"},{"alias_kind":"pith_short_16","alias_value":"OUNPEGMO5N4YVIKM","created_at":"2026-07-01T01:17:31.664827+00:00"},{"alias_kind":"pith_short_8","alias_value":"OUNPEGMO","created_at":"2026-07-01T01:17:31.664827+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/OUNPEGMO5N4YVIKMDMOZJASAB2","json":"https://pith.science/pith/OUNPEGMO5N4YVIKMDMOZJASAB2.json","graph_json":"https://pith.science/api/pith-number/OUNPEGMO5N4YVIKMDMOZJASAB2/graph.json","events_json":"https://pith.science/api/pith-number/OUNPEGMO5N4YVIKMDMOZJASAB2/events.json","paper":"https://pith.science/paper/OUNPEGMO"},"agent_actions":{"view_html":"https://pith.science/pith/OUNPEGMO5N4YVIKMDMOZJASAB2","download_json":"https://pith.science/pith/OUNPEGMO5N4YVIKMDMOZJASAB2.json","view_paper":"https://pith.science/paper/OUNPEGMO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.31187&json=true","fetch_graph":"https://pith.science/api/pith-number/OUNPEGMO5N4YVIKMDMOZJASAB2/graph.json","fetch_events":"https://pith.science/api/pith-number/OUNPEGMO5N4YVIKMDMOZJASAB2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OUNPEGMO5N4YVIKMDMOZJASAB2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OUNPEGMO5N4YVIKMDMOZJASAB2/action/storage_attestation","attest_author":"https://pith.science/pith/OUNPEGMO5N4YVIKMDMOZJASAB2/action/author_attestation","sign_citation":"https://pith.science/pith/OUNPEGMO5N4YVIKMDMOZJASAB2/action/citation_signature","submit_replication":"https://pith.science/pith/OUNPEGMO5N4YVIKMDMOZJASAB2/action/replication_record"}},"created_at":"2026-07-01T01:17:31.664827+00:00","updated_at":"2026-07-01T01:17:31.664827+00:00"}