{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2TY67GXUU7FBROBAEBMAYRGHBO","short_pith_number":"pith:2TY67GXU","canonical_record":{"source":{"id":"2506.04088","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T15:46:30Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"b400a5b723e39d365a90227ba66da19cb6c8f57db9cfc94032c010d09c5bff97","abstract_canon_sha256":"fdc540e44e033da4c2a94eab571517bd954063b2a065dca846538a03044dfe3c"},"schema_version":"1.0"},"canonical_sha256":"d4f1ef9af4a7ca18b82020580c44c70bab22e801ff9d7264ba2e2efb604239c4","source":{"kind":"arxiv","id":"2506.04088","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.04088","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"arxiv_version","alias_value":"2506.04088v1","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04088","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"pith_short_12","alias_value":"2TY67GXUU7FB","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"pith_short_16","alias_value":"2TY67GXUU7FBROBA","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"pith_short_8","alias_value":"2TY67GXU","created_at":"2026-07-05T11:15:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2TY67GXUU7FBROBAEBMAYRGHBO","target":"record","payload":{"canonical_record":{"source":{"id":"2506.04088","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T15:46:30Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"b400a5b723e39d365a90227ba66da19cb6c8f57db9cfc94032c010d09c5bff97","abstract_canon_sha256":"fdc540e44e033da4c2a94eab571517bd954063b2a065dca846538a03044dfe3c"},"schema_version":"1.0"},"canonical_sha256":"d4f1ef9af4a7ca18b82020580c44c70bab22e801ff9d7264ba2e2efb604239c4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:56.754677Z","signature_b64":"FBQNL0ginN7qvJbVseYLQ3Z3TV91KTf8vvSIoQtW/1LF8iyElhkjIMCtGLlV8GBZOC43Ix94Trp6GAuN4euoAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4f1ef9af4a7ca18b82020580c44c70bab22e801ff9d7264ba2e2efb604239c4","last_reissued_at":"2026-07-05T11:15:56.753973Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:56.753973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.04088","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":"2026-07-05T11:15:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cJtArpfCgNLuGbnK2sijmbQAQgSPa0Ihq1+jzhPCrRKBXuhN30iplZgF7q0Mqc2ciIH3YQxORIx/KQXsxhoBBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:53:22.406467Z"},"content_sha256":"9729c7baab10625bab99bebe9846e8d04280e50518eae4746ae65f17552a76be","schema_version":"1.0","event_id":"sha256:9729c7baab10625bab99bebe9846e8d04280e50518eae4746ae65f17552a76be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2TY67GXUU7FBROBAEBMAYRGHBO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multimodal Tabular Reasoning with Privileged Structured Information","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.LG","authors_text":"De-Chuan Zhan, Hai-Long Sun, Han-Jia Ye, Jun-Peng Jiang, Kaifu Zhang, Qing-Guo Chen, Shiyin Lu, Weihua Luo, Yu Xia","submitted_at":"2025-06-04T15:46:30Z","abstract_excerpt":"Tabular reasoning involves multi-step information extraction and logical inference over tabular data. While recent advances have leveraged large language models (LLMs) for reasoning over structured tables, such high-quality textual representations are often unavailable in real-world settings, where tables typically appear as images. In this paper, we tackle the task of tabular reasoning from table images, leveraging privileged structured information available during training to enhance multimodal large language models (MLLMs). The key challenges lie in the complexity of accurately aligning str"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04088","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/2506.04088/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":"2026-07-05T11:15:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hg4YLwuCBWR0A6b4i1UESZxYp6z6mXr9j6rsNu2bUqsbHPVr2F2lDmRIkzvhXsdSSDG62edeVHu2OImI2pMzBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:53:22.406982Z"},"content_sha256":"bd15a4a45121cee8ff265e4b9a7590e400eaf165b2e477ca1853d1ba9fd48117","schema_version":"1.0","event_id":"sha256:bd15a4a45121cee8ff265e4b9a7590e400eaf165b2e477ca1853d1ba9fd48117"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2TY67GXUU7FBROBAEBMAYRGHBO/bundle.json","state_url":"https://pith.science/pith/2TY67GXUU7FBROBAEBMAYRGHBO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2TY67GXUU7FBROBAEBMAYRGHBO/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":"2026-08-07T19:53:22Z","links":{"resolver":"https://pith.science/pith/2TY67GXUU7FBROBAEBMAYRGHBO","bundle":"https://pith.science/pith/2TY67GXUU7FBROBAEBMAYRGHBO/bundle.json","state":"https://pith.science/pith/2TY67GXUU7FBROBAEBMAYRGHBO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2TY67GXUU7FBROBAEBMAYRGHBO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2TY67GXUU7FBROBAEBMAYRGHBO","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":"fdc540e44e033da4c2a94eab571517bd954063b2a065dca846538a03044dfe3c","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T15:46:30Z","title_canon_sha256":"b400a5b723e39d365a90227ba66da19cb6c8f57db9cfc94032c010d09c5bff97"},"schema_version":"1.0","source":{"id":"2506.04088","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.04088","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"arxiv_version","alias_value":"2506.04088v1","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04088","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"pith_short_12","alias_value":"2TY67GXUU7FB","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"pith_short_16","alias_value":"2TY67GXUU7FBROBA","created_at":"2026-07-05T11:15:56Z"},{"alias_kind":"pith_short_8","alias_value":"2TY67GXU","created_at":"2026-07-05T11:15:56Z"}],"graph_snapshots":[{"event_id":"sha256:bd15a4a45121cee8ff265e4b9a7590e400eaf165b2e477ca1853d1ba9fd48117","target":"graph","created_at":"2026-07-05T11:15:56Z","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/2506.04088/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Tabular reasoning involves multi-step information extraction and logical inference over tabular data. While recent advances have leveraged large language models (LLMs) for reasoning over structured tables, such high-quality textual representations are often unavailable in real-world settings, where tables typically appear as images. In this paper, we tackle the task of tabular reasoning from table images, leveraging privileged structured information available during training to enhance multimodal large language models (MLLMs). The key challenges lie in the complexity of accurately aligning str","authors_text":"De-Chuan Zhan, Hai-Long Sun, Han-Jia Ye, Jun-Peng Jiang, Kaifu Zhang, Qing-Guo Chen, Shiyin Lu, Weihua Luo, Yu Xia","cross_cats":["cs.AI","cs.CL","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T15:46:30Z","title":"Multimodal Tabular Reasoning with Privileged Structured Information"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04088","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":"sha256:9729c7baab10625bab99bebe9846e8d04280e50518eae4746ae65f17552a76be","target":"record","created_at":"2026-07-05T11:15:56Z","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":"fdc540e44e033da4c2a94eab571517bd954063b2a065dca846538a03044dfe3c","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T15:46:30Z","title_canon_sha256":"b400a5b723e39d365a90227ba66da19cb6c8f57db9cfc94032c010d09c5bff97"},"schema_version":"1.0","source":{"id":"2506.04088","kind":"arxiv","version":1}},"canonical_sha256":"d4f1ef9af4a7ca18b82020580c44c70bab22e801ff9d7264ba2e2efb604239c4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d4f1ef9af4a7ca18b82020580c44c70bab22e801ff9d7264ba2e2efb604239c4","first_computed_at":"2026-07-05T11:15:56.753973Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:15:56.753973Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FBQNL0ginN7qvJbVseYLQ3Z3TV91KTf8vvSIoQtW/1LF8iyElhkjIMCtGLlV8GBZOC43Ix94Trp6GAuN4euoAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:15:56.754677Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.04088","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9729c7baab10625bab99bebe9846e8d04280e50518eae4746ae65f17552a76be","sha256:bd15a4a45121cee8ff265e4b9a7590e400eaf165b2e477ca1853d1ba9fd48117"],"state_sha256":"048f40cbac72e416243272012e65451b694bdf7706cff0cdb891d5ae7704228b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8wZjWCMi3LmKvh7c8Um6RD/zRyXjooihGBaPR23akPAgv/S6Tbuni+gsHBKyiQSpSab6hmGFQjymKhEYCPyJDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T19:53:22.412287Z","bundle_sha256":"77d742a9c8d1efe19d0c23b42931b3d4f47b95cdff73c610cd19cce86c884038"}}