{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:KZB36Y4WDZHEGCX74XWNC3NVDH","short_pith_number":"pith:KZB36Y4W","canonical_record":{"source":{"id":"2505.02441","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-05T08:10:22Z","cross_cats_sorted":[],"title_canon_sha256":"82c09e3f0ef148bdfe30a082254dd320a81d7c070949f0ad2841abe675f507fa","abstract_canon_sha256":"d4841ec6ba3c5a7b3137b0a0b9e963399c15f5df6aca29e99e9ec81a0b8b6088"},"schema_version":"1.0"},"canonical_sha256":"5643bf63961e4e430affe5ecd16db519e62924ba630dfa48bbf0940300baae85","source":{"kind":"arxiv","id":"2505.02441","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.02441","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"arxiv_version","alias_value":"2505.02441v1","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.02441","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"pith_short_12","alias_value":"KZB36Y4WDZHE","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"pith_short_16","alias_value":"KZB36Y4WDZHEGCX7","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"pith_short_8","alias_value":"KZB36Y4W","created_at":"2026-07-05T10:58:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:KZB36Y4WDZHEGCX74XWNC3NVDH","target":"record","payload":{"canonical_record":{"source":{"id":"2505.02441","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-05T08:10:22Z","cross_cats_sorted":[],"title_canon_sha256":"82c09e3f0ef148bdfe30a082254dd320a81d7c070949f0ad2841abe675f507fa","abstract_canon_sha256":"d4841ec6ba3c5a7b3137b0a0b9e963399c15f5df6aca29e99e9ec81a0b8b6088"},"schema_version":"1.0"},"canonical_sha256":"5643bf63961e4e430affe5ecd16db519e62924ba630dfa48bbf0940300baae85","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:58:37.115882Z","signature_b64":"r4Mc55tYNM3r9ILWel8j+71quFU3GO3MY2zcLGFtEeG8AVOfEc25OK09foYtOLm7WgCTxBLhdLrlTSweKoGPAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5643bf63961e4e430affe5ecd16db519e62924ba630dfa48bbf0940300baae85","last_reissued_at":"2026-07-05T10:58:37.115308Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:58:37.115308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.02441","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":"2026-07-05T10:58:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+gKlW7NWZqEqbPlOR0DVilgjhryAFCYMzAYWC10PqlbW8Ii6EobKJEEC9oFtiIFsYkfXpT8oNFuZeI7qAnXGCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T17:09:21.613496Z"},"content_sha256":"c6a7934f1f792476ddbc333fd294853da6e320e207e9d80c97435dfd1757e19e","schema_version":"1.0","event_id":"sha256:c6a7934f1f792476ddbc333fd294853da6e320e207e9d80c97435dfd1757e19e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:KZB36Y4WDZHEGCX74XWNC3NVDH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Jiaqi Zhang, Kejian Yu, Zhuodong Liu","submitted_at":"2025-05-05T08:10:22Z","abstract_excerpt":"Accurate identification of agricultural pests is essential for crop protection but remains challenging due to the large intra-class variance and fine-grained differences among pest species. While deep learning has advanced pest detection, most existing approaches rely solely on low-level visual features and lack effective multi-modal integration, leading to limited accuracy and poor interpretability. Moreover, the scarcity of high-quality multi-modal agricultural datasets further restricts progress in this field. To address these issues, we construct two novel multi-modal benchmarks-CTIP102 an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.02441","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/2505.02441/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":"2026-07-05T10:58:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gcg2t+mpB/6Wzz6wGgufDGm3V4KuwKqwLb0ZUrgMPSdgJg4jMWAdlX/r2B241i/sD7Z6iUiaAcSIRJGMcylVDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T17:09:21.613995Z"},"content_sha256":"f64edf3fadf6fa65f20c7c5e837f4cef28968fc8fea6488383df01e279ae79a8","schema_version":"1.0","event_id":"sha256:f64edf3fadf6fa65f20c7c5e837f4cef28968fc8fea6488383df01e279ae79a8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KZB36Y4WDZHEGCX74XWNC3NVDH/bundle.json","state_url":"https://pith.science/pith/KZB36Y4WDZHEGCX74XWNC3NVDH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KZB36Y4WDZHEGCX74XWNC3NVDH/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":"2026-08-11T17:09:21Z","links":{"resolver":"https://pith.science/pith/KZB36Y4WDZHEGCX74XWNC3NVDH","bundle":"https://pith.science/pith/KZB36Y4WDZHEGCX74XWNC3NVDH/bundle.json","state":"https://pith.science/pith/KZB36Y4WDZHEGCX74XWNC3NVDH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KZB36Y4WDZHEGCX74XWNC3NVDH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KZB36Y4WDZHEGCX74XWNC3NVDH","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":"d4841ec6ba3c5a7b3137b0a0b9e963399c15f5df6aca29e99e9ec81a0b8b6088","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-05T08:10:22Z","title_canon_sha256":"82c09e3f0ef148bdfe30a082254dd320a81d7c070949f0ad2841abe675f507fa"},"schema_version":"1.0","source":{"id":"2505.02441","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.02441","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"arxiv_version","alias_value":"2505.02441v1","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.02441","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"pith_short_12","alias_value":"KZB36Y4WDZHE","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"pith_short_16","alias_value":"KZB36Y4WDZHEGCX7","created_at":"2026-07-05T10:58:37Z"},{"alias_kind":"pith_short_8","alias_value":"KZB36Y4W","created_at":"2026-07-05T10:58:37Z"}],"graph_snapshots":[{"event_id":"sha256:f64edf3fadf6fa65f20c7c5e837f4cef28968fc8fea6488383df01e279ae79a8","target":"graph","created_at":"2026-07-05T10:58:37Z","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/2505.02441/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate identification of agricultural pests is essential for crop protection but remains challenging due to the large intra-class variance and fine-grained differences among pest species. While deep learning has advanced pest detection, most existing approaches rely solely on low-level visual features and lack effective multi-modal integration, leading to limited accuracy and poor interpretability. Moreover, the scarcity of high-quality multi-modal agricultural datasets further restricts progress in this field. To address these issues, we construct two novel multi-modal benchmarks-CTIP102 an","authors_text":"Jiaqi Zhang, Kejian Yu, Zhuodong Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-05T08:10:22Z","title":"MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.02441","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:c6a7934f1f792476ddbc333fd294853da6e320e207e9d80c97435dfd1757e19e","target":"record","created_at":"2026-07-05T10:58:37Z","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":"d4841ec6ba3c5a7b3137b0a0b9e963399c15f5df6aca29e99e9ec81a0b8b6088","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-05T08:10:22Z","title_canon_sha256":"82c09e3f0ef148bdfe30a082254dd320a81d7c070949f0ad2841abe675f507fa"},"schema_version":"1.0","source":{"id":"2505.02441","kind":"arxiv","version":1}},"canonical_sha256":"5643bf63961e4e430affe5ecd16db519e62924ba630dfa48bbf0940300baae85","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5643bf63961e4e430affe5ecd16db519e62924ba630dfa48bbf0940300baae85","first_computed_at":"2026-07-05T10:58:37.115308Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:58:37.115308Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r4Mc55tYNM3r9ILWel8j+71quFU3GO3MY2zcLGFtEeG8AVOfEc25OK09foYtOLm7WgCTxBLhdLrlTSweKoGPAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:58:37.115882Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.02441","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c6a7934f1f792476ddbc333fd294853da6e320e207e9d80c97435dfd1757e19e","sha256:f64edf3fadf6fa65f20c7c5e837f4cef28968fc8fea6488383df01e279ae79a8"],"state_sha256":"af636ef7c7335fbbba5ec4bdd32bd8f774464f1667d620683d05152eaae61a4b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KxzVawQ0AaHZfl/hfVBuZ+ESoK9vbX0FTL/K+aatE7j0FtSl5dNR8nppYEFhzrGiHw8d1s3054SY4Yd3gO5RAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T17:09:21.619168Z","bundle_sha256":"3d6cad4d57eb6f92a2866f090486feb6e2b1682f79da853a639061242577450b"}}