{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UCU4JFFC26WB3SVFRB7HIGHMQP","short_pith_number":"pith:UCU4JFFC","schema_version":"1.0","canonical_sha256":"a0a9c494a2d7ac1dcaa5887e7418ec83f67a8e5600a620c0a7e54d1290870e82","source":{"kind":"arxiv","id":"2411.00394","version":1},"attestation_state":"computed","paper":{"title":"Right this way: Can VLMs Guide Us to See More to Answer Questions?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Diji Yang, Kalyana Suma Sree Tholeti, Lei Ding, Leilani H. Gilpin, Li Liu, Sijia Zhong, Yi Zhang","submitted_at":"2024-11-01T06:43:54Z","abstract_excerpt":"In question-answering scenarios, humans can assess whether the available information is sufficient and seek additional information if necessary, rather than providing a forced answer. In contrast, Vision Language Models (VLMs) typically generate direct, one-shot responses without evaluating the sufficiency of the information. To investigate this gap, we identify a critical and challenging task in the Visual Question Answering (VQA) scenario: can VLMs indicate how to adjust an image when the visual information is insufficient to answer a question? This capability is especially valuable for assi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.00394","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-01T06:43:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"846efe6d0fc4cc0c19d9098d04b6984d63c02b17a055133285009e6a68fc8287","abstract_canon_sha256":"6e38ffe127b6ed8360b81baec5edbbeea5ccc5f74310f7f6b97ac9646152bd3c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:29:41.848193Z","signature_b64":"CtsPtlTnOkbyNilHW8pTX1UuMG3jHAAzerR5642nGFpHABZRjyGIzuDqdKMZ4f+NXzDOgVq1tpUjG4lbKRJ8BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0a9c494a2d7ac1dcaa5887e7418ec83f67a8e5600a620c0a7e54d1290870e82","last_reissued_at":"2026-07-05T09:29:41.847677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:29:41.847677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Right this way: Can VLMs Guide Us to See More to Answer Questions?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Diji Yang, Kalyana Suma Sree Tholeti, Lei Ding, Leilani H. Gilpin, Li Liu, Sijia Zhong, Yi Zhang","submitted_at":"2024-11-01T06:43:54Z","abstract_excerpt":"In question-answering scenarios, humans can assess whether the available information is sufficient and seek additional information if necessary, rather than providing a forced answer. In contrast, Vision Language Models (VLMs) typically generate direct, one-shot responses without evaluating the sufficiency of the information. To investigate this gap, we identify a critical and challenging task in the Visual Question Answering (VQA) scenario: can VLMs indicate how to adjust an image when the visual information is insufficient to answer a question? This capability is especially valuable for assi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.00394","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/2411.00394/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.00394","created_at":"2026-07-05T09:29:41.847728+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.00394v1","created_at":"2026-07-05T09:29:41.847728+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.00394","created_at":"2026-07-05T09:29:41.847728+00:00"},{"alias_kind":"pith_short_12","alias_value":"UCU4JFFC26WB","created_at":"2026-07-05T09:29:41.847728+00:00"},{"alias_kind":"pith_short_16","alias_value":"UCU4JFFC26WB3SVF","created_at":"2026-07-05T09:29:41.847728+00:00"},{"alias_kind":"pith_short_8","alias_value":"UCU4JFFC","created_at":"2026-07-05T09:29:41.847728+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.00717","citing_title":"Vid2Coach: Transforming How-To Videos into Task Assistants","ref_index":74,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UCU4JFFC26WB3SVFRB7HIGHMQP","json":"https://pith.science/pith/UCU4JFFC26WB3SVFRB7HIGHMQP.json","graph_json":"https://pith.science/api/pith-number/UCU4JFFC26WB3SVFRB7HIGHMQP/graph.json","events_json":"https://pith.science/api/pith-number/UCU4JFFC26WB3SVFRB7HIGHMQP/events.json","paper":"https://pith.science/paper/UCU4JFFC"},"agent_actions":{"view_html":"https://pith.science/pith/UCU4JFFC26WB3SVFRB7HIGHMQP","download_json":"https://pith.science/pith/UCU4JFFC26WB3SVFRB7HIGHMQP.json","view_paper":"https://pith.science/paper/UCU4JFFC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.00394&json=true","fetch_graph":"https://pith.science/api/pith-number/UCU4JFFC26WB3SVFRB7HIGHMQP/graph.json","fetch_events":"https://pith.science/api/pith-number/UCU4JFFC26WB3SVFRB7HIGHMQP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UCU4JFFC26WB3SVFRB7HIGHMQP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UCU4JFFC26WB3SVFRB7HIGHMQP/action/storage_attestation","attest_author":"https://pith.science/pith/UCU4JFFC26WB3SVFRB7HIGHMQP/action/author_attestation","sign_citation":"https://pith.science/pith/UCU4JFFC26WB3SVFRB7HIGHMQP/action/citation_signature","submit_replication":"https://pith.science/pith/UCU4JFFC26WB3SVFRB7HIGHMQP/action/replication_record"}},"created_at":"2026-07-05T09:29:41.847728+00:00","updated_at":"2026-07-05T09:29:41.847728+00:00"}