{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NLTJ5RLJE2VCQYWNVWXHGLTOFB","short_pith_number":"pith:NLTJ5RLJ","canonical_record":{"source":{"id":"2409.18996","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-19T02:51:54Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.MM"],"title_canon_sha256":"00a6e261c2dfcedd1b9fd7b1a32ecb0ea2b597f8bd72f2841cd9ed61d4ee55bd","abstract_canon_sha256":"5533af6374668ad7b65677dd1e7c6c30977fc96668b934a9215d53f7f3089c1e"},"schema_version":"1.0"},"canonical_sha256":"6ae69ec56926aa2862cdadae732e6e285db453fc4faffe26b1591acf86415d25","source":{"kind":"arxiv","id":"2409.18996","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.18996","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"arxiv_version","alias_value":"2409.18996v1","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.18996","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"pith_short_12","alias_value":"NLTJ5RLJE2VC","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"pith_short_16","alias_value":"NLTJ5RLJE2VCQYWN","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"pith_short_8","alias_value":"NLTJ5RLJ","created_at":"2026-07-05T09:13:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NLTJ5RLJE2VCQYWNVWXHGLTOFB","target":"record","payload":{"canonical_record":{"source":{"id":"2409.18996","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-19T02:51:54Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.MM"],"title_canon_sha256":"00a6e261c2dfcedd1b9fd7b1a32ecb0ea2b597f8bd72f2841cd9ed61d4ee55bd","abstract_canon_sha256":"5533af6374668ad7b65677dd1e7c6c30977fc96668b934a9215d53f7f3089c1e"},"schema_version":"1.0"},"canonical_sha256":"6ae69ec56926aa2862cdadae732e6e285db453fc4faffe26b1591acf86415d25","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:09.128322Z","signature_b64":"eYQEX7nJoOu0tYoVJXDKR3pvu4SL6FphdsKTDH2ISAj+wYB/SFesjV/sGxiZwZLJ0aWGGA/v6vCJKWQZDig6Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ae69ec56926aa2862cdadae732e6e285db453fc4faffe26b1591acf86415d25","last_reissued_at":"2026-07-05T09:13:09.127916Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:09.127916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.18996","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":"2026-07-05T09:13:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9zTF3v1/W/+nrSd+8JoY/a1M6x4gbXYBg7MCah0xxg/L4z2IpBSzntNH3xMLZ99GvFYFcF17ciajj4cP5yrgAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T13:45:41.797590Z"},"content_sha256":"0cdeaddbcbfb422bdb1dd10f62b84720d4e508b3837d0598cdaaa287de31d214","schema_version":"1.0","event_id":"sha256:0cdeaddbcbfb422bdb1dd10f62b84720d4e508b3837d0598cdaaa287de31d214"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NLTJ5RLJE2VCQYWNVWXHGLTOFB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.MM"],"primary_cat":"cs.CL","authors_text":"Bing Wang, Changsheng Xu, Dizhan Xue, Shengsheng Qian, Zuyi Zhou","submitted_at":"2024-09-19T02:51:54Z","abstract_excerpt":"Cross-modal reasoning (CMR), the intricate process of synthesizing and drawing inferences across divergent sensory modalities, is increasingly recognized as a crucial capability in the progression toward more sophisticated and anthropomorphic artificial intelligence systems. Large Language Models (LLMs) represent a class of AI algorithms specifically engineered to parse, produce, and engage with human language on an extensive scale. The recent trend of deploying LLMs to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.18996","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/2409.18996/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":"2026-07-05T09:13:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"261oL0phlewq6fLbspwPxTBLIFPcUdhz9Hex2S/XQwAqZMKpdN3teoFgC42vxvAIi3wIxJF0bUwmIkRlOa25Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T13:45:41.798583Z"},"content_sha256":"676cd0a7e31efc4a7ffb0645fd221f78f518683dbe3f7ba3a4ec6883d6d33642","schema_version":"1.0","event_id":"sha256:676cd0a7e31efc4a7ffb0645fd221f78f518683dbe3f7ba3a4ec6883d6d33642"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NLTJ5RLJE2VCQYWNVWXHGLTOFB/bundle.json","state_url":"https://pith.science/pith/NLTJ5RLJE2VCQYWNVWXHGLTOFB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NLTJ5RLJE2VCQYWNVWXHGLTOFB/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":"2026-08-05T13:45:41Z","links":{"resolver":"https://pith.science/pith/NLTJ5RLJE2VCQYWNVWXHGLTOFB","bundle":"https://pith.science/pith/NLTJ5RLJE2VCQYWNVWXHGLTOFB/bundle.json","state":"https://pith.science/pith/NLTJ5RLJE2VCQYWNVWXHGLTOFB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NLTJ5RLJE2VCQYWNVWXHGLTOFB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NLTJ5RLJE2VCQYWNVWXHGLTOFB","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":"5533af6374668ad7b65677dd1e7c6c30977fc96668b934a9215d53f7f3089c1e","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.MM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-19T02:51:54Z","title_canon_sha256":"00a6e261c2dfcedd1b9fd7b1a32ecb0ea2b597f8bd72f2841cd9ed61d4ee55bd"},"schema_version":"1.0","source":{"id":"2409.18996","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.18996","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"arxiv_version","alias_value":"2409.18996v1","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.18996","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"pith_short_12","alias_value":"NLTJ5RLJE2VC","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"pith_short_16","alias_value":"NLTJ5RLJE2VCQYWN","created_at":"2026-07-05T09:13:09Z"},{"alias_kind":"pith_short_8","alias_value":"NLTJ5RLJ","created_at":"2026-07-05T09:13:09Z"}],"graph_snapshots":[{"event_id":"sha256:676cd0a7e31efc4a7ffb0645fd221f78f518683dbe3f7ba3a4ec6883d6d33642","target":"graph","created_at":"2026-07-05T09:13:09Z","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/2409.18996/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Cross-modal reasoning (CMR), the intricate process of synthesizing and drawing inferences across divergent sensory modalities, is increasingly recognized as a crucial capability in the progression toward more sophisticated and anthropomorphic artificial intelligence systems. Large Language Models (LLMs) represent a class of AI algorithms specifically engineered to parse, produce, and engage with human language on an extensive scale. The recent trend of deploying LLMs to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced ex","authors_text":"Bing Wang, Changsheng Xu, Dizhan Xue, Shengsheng Qian, Zuyi Zhou","cross_cats":["cs.AI","cs.CV","cs.LG","cs.MM"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-19T02:51:54Z","title":"From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.18996","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":"sha256:0cdeaddbcbfb422bdb1dd10f62b84720d4e508b3837d0598cdaaa287de31d214","target":"record","created_at":"2026-07-05T09:13:09Z","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":"5533af6374668ad7b65677dd1e7c6c30977fc96668b934a9215d53f7f3089c1e","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.MM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-19T02:51:54Z","title_canon_sha256":"00a6e261c2dfcedd1b9fd7b1a32ecb0ea2b597f8bd72f2841cd9ed61d4ee55bd"},"schema_version":"1.0","source":{"id":"2409.18996","kind":"arxiv","version":1}},"canonical_sha256":"6ae69ec56926aa2862cdadae732e6e285db453fc4faffe26b1591acf86415d25","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6ae69ec56926aa2862cdadae732e6e285db453fc4faffe26b1591acf86415d25","first_computed_at":"2026-07-05T09:13:09.127916Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:13:09.127916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eYQEX7nJoOu0tYoVJXDKR3pvu4SL6FphdsKTDH2ISAj+wYB/SFesjV/sGxiZwZLJ0aWGGA/v6vCJKWQZDig6Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:13:09.128322Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.18996","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0cdeaddbcbfb422bdb1dd10f62b84720d4e508b3837d0598cdaaa287de31d214","sha256:676cd0a7e31efc4a7ffb0645fd221f78f518683dbe3f7ba3a4ec6883d6d33642"],"state_sha256":"4c070aa64e57e30bddc55d035ca10278952a7840f33ae491156dd63a7eebfeb8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BOl6jnDht8mG2R/YfmRWNDTo2yntXGGyEOWv7MwHp0l+O9JRRdgVRUXoarug9fhMp0LhGDPN9T/e82CyRvzqBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T13:45:41.807792Z","bundle_sha256":"dbc1777d1e156b9310b340a83b2f936d1cdbcb7af911e7f4cda7d58786a3f527"}}