{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LDTPMLSD7477YEOD2O7OYA7YOY","short_pith_number":"pith:LDTPMLSD","canonical_record":{"source":{"id":"2505.14131","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-20T09:36:17Z","cross_cats_sorted":[],"title_canon_sha256":"a2c03123f5057afd794f4a12fb433064022d807d65caae15cead128d2a2ce99b","abstract_canon_sha256":"0f759763be54027a6de85b5078063e0f924d58dbd3a8ea1ce95bc5101ab7eb63"},"schema_version":"1.0"},"canonical_sha256":"58e6f62e43ff3ffc11c3d3beec03f87616a5a346ba5a712d6fd59f8dc39c1dfe","source":{"kind":"arxiv","id":"2505.14131","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.14131","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"arxiv_version","alias_value":"2505.14131v1","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.14131","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"pith_short_12","alias_value":"LDTPMLSD7477","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"pith_short_16","alias_value":"LDTPMLSD7477YEOD","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"pith_short_8","alias_value":"LDTPMLSD","created_at":"2026-07-05T11:05:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LDTPMLSD7477YEOD2O7OYA7YOY","target":"record","payload":{"canonical_record":{"source":{"id":"2505.14131","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-20T09:36:17Z","cross_cats_sorted":[],"title_canon_sha256":"a2c03123f5057afd794f4a12fb433064022d807d65caae15cead128d2a2ce99b","abstract_canon_sha256":"0f759763be54027a6de85b5078063e0f924d58dbd3a8ea1ce95bc5101ab7eb63"},"schema_version":"1.0"},"canonical_sha256":"58e6f62e43ff3ffc11c3d3beec03f87616a5a346ba5a712d6fd59f8dc39c1dfe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:05:56.644162Z","signature_b64":"YgSBcFERU483s1OiIPp/3E3NX8R2B/NA67c8M/xFOavfMbgaCWrI1s0NyppPFJGRack/JBu2KSVeh+roIWCUDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58e6f62e43ff3ffc11c3d3beec03f87616a5a346ba5a712d6fd59f8dc39c1dfe","last_reissued_at":"2026-07-05T11:05:56.643776Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:05:56.643776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.14131","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":"2026-07-05T11:05:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uvw/CuSR1049kfNL9Xr7MArJSAszBmzUBjNKnl6JZ5mrpHC/r30HdOEbyvN8eJV04L1TMhSv1wg6Fat45Wc6DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T15:31:06.090025Z"},"content_sha256":"b9e5fce2432bd0ffd1dc7e9ae098b7eaf63975c1951969b483bec92b1d63c03e","schema_version":"1.0","event_id":"sha256:b9e5fce2432bd0ffd1dc7e9ae098b7eaf63975c1951969b483bec92b1d63c03e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LDTPMLSD7477YEOD2O7OYA7YOY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Annemarie Friedrich, Heike Adel, Mohsen Mesgar, Wei Zhou","submitted_at":"2025-05-20T09:36:17Z","abstract_excerpt":"In table question answering (TQA), tables are encoded as either texts or images. Prior work suggests that passing images of tables to multi-modal large language models (MLLMs) performs comparably to or even better than using textual input with large language models (LLMs). However, the lack of controlled setups limits fine-grained distinctions between these approaches. In this paper, we conduct the first controlled study on the effectiveness of several combinations of table representations and models from two perspectives: question complexity and table size. We build a new benchmark based on e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.14131","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/2505.14131/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":"2026-07-05T11:05:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ieq6vFL1FSjMNlmaZFEXYqDKw9Y1p67G5+L5CwdCdrMKI7czfZRJCTpTeHBqI4sDPLCcMyC35CkCbDs1hVAaCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T15:31:06.090943Z"},"content_sha256":"62c1fa28c00c3fadf2a35132fcfeb14a879584dabdcae9f352322ba6f31025ad","schema_version":"1.0","event_id":"sha256:62c1fa28c00c3fadf2a35132fcfeb14a879584dabdcae9f352322ba6f31025ad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LDTPMLSD7477YEOD2O7OYA7YOY/bundle.json","state_url":"https://pith.science/pith/LDTPMLSD7477YEOD2O7OYA7YOY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LDTPMLSD7477YEOD2O7OYA7YOY/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":"2026-08-11T15:31:06Z","links":{"resolver":"https://pith.science/pith/LDTPMLSD7477YEOD2O7OYA7YOY","bundle":"https://pith.science/pith/LDTPMLSD7477YEOD2O7OYA7YOY/bundle.json","state":"https://pith.science/pith/LDTPMLSD7477YEOD2O7OYA7YOY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LDTPMLSD7477YEOD2O7OYA7YOY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LDTPMLSD7477YEOD2O7OYA7YOY","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":"0f759763be54027a6de85b5078063e0f924d58dbd3a8ea1ce95bc5101ab7eb63","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-20T09:36:17Z","title_canon_sha256":"a2c03123f5057afd794f4a12fb433064022d807d65caae15cead128d2a2ce99b"},"schema_version":"1.0","source":{"id":"2505.14131","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.14131","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"arxiv_version","alias_value":"2505.14131v1","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.14131","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"pith_short_12","alias_value":"LDTPMLSD7477","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"pith_short_16","alias_value":"LDTPMLSD7477YEOD","created_at":"2026-07-05T11:05:56Z"},{"alias_kind":"pith_short_8","alias_value":"LDTPMLSD","created_at":"2026-07-05T11:05:56Z"}],"graph_snapshots":[{"event_id":"sha256:62c1fa28c00c3fadf2a35132fcfeb14a879584dabdcae9f352322ba6f31025ad","target":"graph","created_at":"2026-07-05T11:05:56Z","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/2505.14131/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In table question answering (TQA), tables are encoded as either texts or images. Prior work suggests that passing images of tables to multi-modal large language models (MLLMs) performs comparably to or even better than using textual input with large language models (LLMs). However, the lack of controlled setups limits fine-grained distinctions between these approaches. In this paper, we conduct the first controlled study on the effectiveness of several combinations of table representations and models from two perspectives: question complexity and table size. We build a new benchmark based on e","authors_text":"Annemarie Friedrich, Heike Adel, Mohsen Mesgar, Wei Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-20T09:36:17Z","title":"Texts or Images? A Fine-grained Analysis on the Effectiveness of Input Representations and Models for Table Question Answering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.14131","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:b9e5fce2432bd0ffd1dc7e9ae098b7eaf63975c1951969b483bec92b1d63c03e","target":"record","created_at":"2026-07-05T11:05:56Z","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":"0f759763be54027a6de85b5078063e0f924d58dbd3a8ea1ce95bc5101ab7eb63","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-20T09:36:17Z","title_canon_sha256":"a2c03123f5057afd794f4a12fb433064022d807d65caae15cead128d2a2ce99b"},"schema_version":"1.0","source":{"id":"2505.14131","kind":"arxiv","version":1}},"canonical_sha256":"58e6f62e43ff3ffc11c3d3beec03f87616a5a346ba5a712d6fd59f8dc39c1dfe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"58e6f62e43ff3ffc11c3d3beec03f87616a5a346ba5a712d6fd59f8dc39c1dfe","first_computed_at":"2026-07-05T11:05:56.643776Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:05:56.643776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YgSBcFERU483s1OiIPp/3E3NX8R2B/NA67c8M/xFOavfMbgaCWrI1s0NyppPFJGRack/JBu2KSVeh+roIWCUDA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:05:56.644162Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.14131","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9e5fce2432bd0ffd1dc7e9ae098b7eaf63975c1951969b483bec92b1d63c03e","sha256:62c1fa28c00c3fadf2a35132fcfeb14a879584dabdcae9f352322ba6f31025ad"],"state_sha256":"b7dcdb002d46ed603523eccac071c0052357e107f56f385f6a5430e412487c95"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LmgdQPBNe16U7aT2ksd2PniLyD67lQ0uON9nd6myK+qqwH8SAWTa6ysOR5xr+/l79nWW+o2VW2WFCaavs9m5Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T15:31:06.096736Z","bundle_sha256":"6c72875f59f13a5c9cce87ef46f9d3d30599af8e5f6566441831feb8c0735b8d"}}