{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XBAYRK2LKSGFMASIMX2YY6QSV5","short_pith_number":"pith:XBAYRK2L","schema_version":"1.0","canonical_sha256":"b84188ab4b548c56024865f58c7a12af7796f9cecf6a0d5577288375166067db","source":{"kind":"arxiv","id":"2506.03735","version":1},"attestation_state":"computed","paper":{"title":"Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Anna Rutkiewicz, April Yi Wang, Junling Wang, Mrinmaya Sachan","submitted_at":"2025-06-04T09:08:11Z","abstract_excerpt":"Visuals are valuable tools for teaching math word problems (MWPs), helping young learners interpret textual descriptions into mathematical expressions before solving them. However, creating such visuals is labor-intensive and there is a lack of automated methods to support this process. In this paper, we present Math2Visual, an automatic framework for generating pedagogically meaningful visuals from MWP text descriptions. Math2Visual leverages a pre-defined visual language and a design space grounded in interviews with math teachers, to illustrate the core mathematical relationships in MWPs. U"},"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":"2506.03735","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-04T09:08:11Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"66e460a0d8cb84c23408b9c654f8a6d4dd6ede7d1c6bd529aa4e48a7bdbc8152","abstract_canon_sha256":"a59e3b4d3f1de6613224225ab284ca63294524857991c82c57723bff82eb0cc2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:50.949173Z","signature_b64":"0fp0Iw1XKrqcHMK/KpPeC5ZEkPrysYGfRqK0TTopf9EM//QckmKF+iWGve0i8AhB/hyUuWILUiGf8Om4USQ4Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b84188ab4b548c56024865f58c7a12af7796f9cecf6a0d5577288375166067db","last_reissued_at":"2026-07-05T11:15:50.948677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:50.948677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Anna Rutkiewicz, April Yi Wang, Junling Wang, Mrinmaya Sachan","submitted_at":"2025-06-04T09:08:11Z","abstract_excerpt":"Visuals are valuable tools for teaching math word problems (MWPs), helping young learners interpret textual descriptions into mathematical expressions before solving them. However, creating such visuals is labor-intensive and there is a lack of automated methods to support this process. In this paper, we present Math2Visual, an automatic framework for generating pedagogically meaningful visuals from MWP text descriptions. Math2Visual leverages a pre-defined visual language and a design space grounded in interviews with math teachers, to illustrate the core mathematical relationships in MWPs. U"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.03735","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/2506.03735/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":"2506.03735","created_at":"2026-07-05T11:15:50.948753+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.03735v1","created_at":"2026-07-05T11:15:50.948753+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.03735","created_at":"2026-07-05T11:15:50.948753+00:00"},{"alias_kind":"pith_short_12","alias_value":"XBAYRK2LKSGF","created_at":"2026-07-05T11:15:50.948753+00:00"},{"alias_kind":"pith_short_16","alias_value":"XBAYRK2LKSGFMASI","created_at":"2026-07-05T11:15:50.948753+00:00"},{"alias_kind":"pith_short_8","alias_value":"XBAYRK2L","created_at":"2026-07-05T11:15:50.948753+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2603.01070","citing_title":"How RL Unlocks the Aha Moment in Geometric Interleaved Reasoning","ref_index":69,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XBAYRK2LKSGFMASIMX2YY6QSV5","json":"https://pith.science/pith/XBAYRK2LKSGFMASIMX2YY6QSV5.json","graph_json":"https://pith.science/api/pith-number/XBAYRK2LKSGFMASIMX2YY6QSV5/graph.json","events_json":"https://pith.science/api/pith-number/XBAYRK2LKSGFMASIMX2YY6QSV5/events.json","paper":"https://pith.science/paper/XBAYRK2L"},"agent_actions":{"view_html":"https://pith.science/pith/XBAYRK2LKSGFMASIMX2YY6QSV5","download_json":"https://pith.science/pith/XBAYRK2LKSGFMASIMX2YY6QSV5.json","view_paper":"https://pith.science/paper/XBAYRK2L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.03735&json=true","fetch_graph":"https://pith.science/api/pith-number/XBAYRK2LKSGFMASIMX2YY6QSV5/graph.json","fetch_events":"https://pith.science/api/pith-number/XBAYRK2LKSGFMASIMX2YY6QSV5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XBAYRK2LKSGFMASIMX2YY6QSV5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XBAYRK2LKSGFMASIMX2YY6QSV5/action/storage_attestation","attest_author":"https://pith.science/pith/XBAYRK2LKSGFMASIMX2YY6QSV5/action/author_attestation","sign_citation":"https://pith.science/pith/XBAYRK2LKSGFMASIMX2YY6QSV5/action/citation_signature","submit_replication":"https://pith.science/pith/XBAYRK2LKSGFMASIMX2YY6QSV5/action/replication_record"}},"created_at":"2026-07-05T11:15:50.948753+00:00","updated_at":"2026-07-05T11:15:50.948753+00:00"}