{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:5VKYZ3ZPOKUBCX2RLYYWZGSKMV","short_pith_number":"pith:5VKYZ3ZP","canonical_record":{"source":{"id":"2606.24187","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T06:13:30Z","cross_cats_sorted":[],"title_canon_sha256":"37bdd990b23ed9840082601b863db815b8c3a4cd6f157e67723bc1e230f228e4","abstract_canon_sha256":"7021af0b7d386f42450f395d030062d45876b9acdd5e854d45c87a421717b61a"},"schema_version":"1.0"},"canonical_sha256":"ed558cef2f72a8115f515e316c9a4a6549f87313f356d5e7a5e25c7cf9879001","source":{"kind":"arxiv","id":"2606.24187","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.24187","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"arxiv_version","alias_value":"2606.24187v1","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24187","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"pith_short_12","alias_value":"5VKYZ3ZPOKUB","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"pith_short_16","alias_value":"5VKYZ3ZPOKUBCX2R","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"pith_short_8","alias_value":"5VKYZ3ZP","created_at":"2026-06-24T01:14:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:5VKYZ3ZPOKUBCX2RLYYWZGSKMV","target":"record","payload":{"canonical_record":{"source":{"id":"2606.24187","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T06:13:30Z","cross_cats_sorted":[],"title_canon_sha256":"37bdd990b23ed9840082601b863db815b8c3a4cd6f157e67723bc1e230f228e4","abstract_canon_sha256":"7021af0b7d386f42450f395d030062d45876b9acdd5e854d45c87a421717b61a"},"schema_version":"1.0"},"canonical_sha256":"ed558cef2f72a8115f515e316c9a4a6549f87313f356d5e7a5e25c7cf9879001","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:14:44.423436Z","signature_b64":"09Xl8PFqExHS1LRE7uGNMUZ0bYR319BeiUG/LVNYkLtsCPBMbuyIgwpy6mWZZTPBsNoNzg0e0EkVQpzskSvYCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ed558cef2f72a8115f515e316c9a4a6549f87313f356d5e7a5e25c7cf9879001","last_reissued_at":"2026-06-24T01:14:44.422707Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:14:44.422707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.24187","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-24T01:14:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QAMaSJEQPL/tP02EyMNVZH0JL554reeKP7qfxcXF2XLSueCeocUHJhcCF0JlI6BgV6P8R7bf0c15gyz0wUqdAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T03:05:01.270246Z"},"content_sha256":"f9da035913fe64df111f2a3d4c6d95ef4b0b2204ff24595ccce1833825b70c27","schema_version":"1.0","event_id":"sha256:f9da035913fe64df111f2a3d4c6d95ef4b0b2204ff24595ccce1833825b70c27"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:5VKYZ3ZPOKUBCX2RLYYWZGSKMV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Fast and Effective Long Video Understanding of Multimodal Large Language Models via Adaptive Quasi-Gaussian Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Baiyang Song, Chenxin Fang, Kun Zhang, Rongrong Ji, Tao Chen, Yiyi Zhou, Yunhang Shen","submitted_at":"2026-06-23T06:13:30Z","abstract_excerpt":"Long video understanding remains a daunting challenge for \\emph{Multimodal Large Language Models} (MLLMs) due to the excessive computation and memory footprint. Thus, \\emph{keyframe selection} is often adopted to mitigate this shortcoming, which however still suffers from low flexibility and high noise due to its hard sampling principle. In this paper, we define video frame selection as a problem of \\emph{Quasi-Gaussian Sampling}, and propose an adaptive and training-free approach termed \\textbf{\\emph{AdaQ}}. Inspired by the $3$-$\\sigma$ rule of Gaussian distribution, the objective of AdaQ is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24187","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/2606.24187/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":"2026-06-24T01:14:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4MFiNvdNmNVrgC/Rh7O3Ovn0ZVsALFVCZATlXZfhi/qSqORqbtHIz7jETe65WyOmlv3Z6fgMiVHi82F1uBVwCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T03:05:01.270879Z"},"content_sha256":"1fd9bf80bc830a85c8fe51330662784b3cabd5ab2f1aee1577397190a2a6a4a8","schema_version":"1.0","event_id":"sha256:1fd9bf80bc830a85c8fe51330662784b3cabd5ab2f1aee1577397190a2a6a4a8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5VKYZ3ZPOKUBCX2RLYYWZGSKMV/bundle.json","state_url":"https://pith.science/pith/5VKYZ3ZPOKUBCX2RLYYWZGSKMV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5VKYZ3ZPOKUBCX2RLYYWZGSKMV/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":"2026-06-28T03:05:01Z","links":{"resolver":"https://pith.science/pith/5VKYZ3ZPOKUBCX2RLYYWZGSKMV","bundle":"https://pith.science/pith/5VKYZ3ZPOKUBCX2RLYYWZGSKMV/bundle.json","state":"https://pith.science/pith/5VKYZ3ZPOKUBCX2RLYYWZGSKMV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5VKYZ3ZPOKUBCX2RLYYWZGSKMV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:5VKYZ3ZPOKUBCX2RLYYWZGSKMV","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":"7021af0b7d386f42450f395d030062d45876b9acdd5e854d45c87a421717b61a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T06:13:30Z","title_canon_sha256":"37bdd990b23ed9840082601b863db815b8c3a4cd6f157e67723bc1e230f228e4"},"schema_version":"1.0","source":{"id":"2606.24187","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.24187","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"arxiv_version","alias_value":"2606.24187v1","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24187","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"pith_short_12","alias_value":"5VKYZ3ZPOKUB","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"pith_short_16","alias_value":"5VKYZ3ZPOKUBCX2R","created_at":"2026-06-24T01:14:44Z"},{"alias_kind":"pith_short_8","alias_value":"5VKYZ3ZP","created_at":"2026-06-24T01:14:44Z"}],"graph_snapshots":[{"event_id":"sha256:1fd9bf80bc830a85c8fe51330662784b3cabd5ab2f1aee1577397190a2a6a4a8","target":"graph","created_at":"2026-06-24T01:14:44Z","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/2606.24187/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Long video understanding remains a daunting challenge for \\emph{Multimodal Large Language Models} (MLLMs) due to the excessive computation and memory footprint. Thus, \\emph{keyframe selection} is often adopted to mitigate this shortcoming, which however still suffers from low flexibility and high noise due to its hard sampling principle. In this paper, we define video frame selection as a problem of \\emph{Quasi-Gaussian Sampling}, and propose an adaptive and training-free approach termed \\textbf{\\emph{AdaQ}}. Inspired by the $3$-$\\sigma$ rule of Gaussian distribution, the objective of AdaQ is ","authors_text":"Baiyang Song, Chenxin Fang, Kun Zhang, Rongrong Ji, Tao Chen, Yiyi Zhou, Yunhang Shen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T06:13:30Z","title":"Towards Fast and Effective Long Video Understanding of Multimodal Large Language Models via Adaptive Quasi-Gaussian Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24187","kind":"arxiv","version":1},"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:f9da035913fe64df111f2a3d4c6d95ef4b0b2204ff24595ccce1833825b70c27","target":"record","created_at":"2026-06-24T01:14:44Z","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":"7021af0b7d386f42450f395d030062d45876b9acdd5e854d45c87a421717b61a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-23T06:13:30Z","title_canon_sha256":"37bdd990b23ed9840082601b863db815b8c3a4cd6f157e67723bc1e230f228e4"},"schema_version":"1.0","source":{"id":"2606.24187","kind":"arxiv","version":1}},"canonical_sha256":"ed558cef2f72a8115f515e316c9a4a6549f87313f356d5e7a5e25c7cf9879001","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ed558cef2f72a8115f515e316c9a4a6549f87313f356d5e7a5e25c7cf9879001","first_computed_at":"2026-06-24T01:14:44.422707Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-24T01:14:44.422707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"09Xl8PFqExHS1LRE7uGNMUZ0bYR319BeiUG/LVNYkLtsCPBMbuyIgwpy6mWZZTPBsNoNzg0e0EkVQpzskSvYCw==","signature_status":"signed_v1","signed_at":"2026-06-24T01:14:44.423436Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.24187","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f9da035913fe64df111f2a3d4c6d95ef4b0b2204ff24595ccce1833825b70c27","sha256:1fd9bf80bc830a85c8fe51330662784b3cabd5ab2f1aee1577397190a2a6a4a8"],"state_sha256":"5cf1dc78fb30e9230960c77ac69c32ea373d9d2b1b100d0771cdf3e067d7b3cd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pK4hY3o4g+rCoQNwrAXoiO9/9GbVHoMfcJA/x1SJu4aCZVNXGNuGJvYVfzTGUTG1pw1opysIsUTo5RnCz6f8CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T03:05:01.273922Z","bundle_sha256":"70efb8f5c0705d7a40143e7cbd4cd889a137081e23331a6e332f526bced87b06"}}