{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PI37GHEYSLTTSHRPSPBXFZEM5D","short_pith_number":"pith:PI37GHEY","schema_version":"1.0","canonical_sha256":"7a37f31c9892e7391e2f93c372e48ce8fab7acfa1864803594f0e5e3045ebad7","source":{"kind":"arxiv","id":"2405.05294","version":1},"attestation_state":"computed","paper":{"title":"Harmonizing Program Induction with Rate-Distortion Theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.IT","cs.LG","cs.SC","math.IT","stat.ML"],"primary_cat":"cs.HC","authors_text":"Charley M. Wu, David G. Nagy, Hanqi Zhou","submitted_at":"2024-05-08T10:03:50Z","abstract_excerpt":"Many aspects of human learning have been proposed as a process of constructing mental programs: from acquiring symbolic number representations to intuitive theories about the world. In parallel, there is a long-tradition of using information processing to model human cognition through Rate Distortion Theory (RDT). Yet, it is still poorly understood how to apply RDT when mental representations take the form of programs. In this work, we adapt RDT by proposing a three way trade-off among rate (description length), distortion (error), and computational costs (search budget). We use simulations on"},"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":"2405.05294","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-05-08T10:03:50Z","cross_cats_sorted":["cs.CL","cs.IT","cs.LG","cs.SC","math.IT","stat.ML"],"title_canon_sha256":"8242d2ecce25348d3da03fe4d1426cccd9ed063d25f69684118c77e8a9984861","abstract_canon_sha256":"1f12019e9c959ff61147d5dcfa8d078cc47f0a13d230a9f6ff862cca06bbe577"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:17:25.915112Z","signature_b64":"wCXLMi2RixiMnW/PuNycT0G6+RWlSx8bDhA5S7xr8yaCPxTA0xJml6ry14nCgjP+aXCLPvu98UWnlVaGQk7gAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a37f31c9892e7391e2f93c372e48ce8fab7acfa1864803594f0e5e3045ebad7","last_reissued_at":"2026-07-05T08:17:25.914664Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:17:25.914664Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Harmonizing Program Induction with Rate-Distortion Theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.IT","cs.LG","cs.SC","math.IT","stat.ML"],"primary_cat":"cs.HC","authors_text":"Charley M. Wu, David G. Nagy, Hanqi Zhou","submitted_at":"2024-05-08T10:03:50Z","abstract_excerpt":"Many aspects of human learning have been proposed as a process of constructing mental programs: from acquiring symbolic number representations to intuitive theories about the world. In parallel, there is a long-tradition of using information processing to model human cognition through Rate Distortion Theory (RDT). Yet, it is still poorly understood how to apply RDT when mental representations take the form of programs. In this work, we adapt RDT by proposing a three way trade-off among rate (description length), distortion (error), and computational costs (search budget). We use simulations on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.05294","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/2405.05294/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":"2405.05294","created_at":"2026-07-05T08:17:25.914724+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.05294v1","created_at":"2026-07-05T08:17:25.914724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.05294","created_at":"2026-07-05T08:17:25.914724+00:00"},{"alias_kind":"pith_short_12","alias_value":"PI37GHEYSLTT","created_at":"2026-07-05T08:17:25.914724+00:00"},{"alias_kind":"pith_short_16","alias_value":"PI37GHEYSLTTSHRP","created_at":"2026-07-05T08:17:25.914724+00:00"},{"alias_kind":"pith_short_8","alias_value":"PI37GHEY","created_at":"2026-07-05T08:17:25.914724+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20623","citing_title":"Path-dependent program induction under resource constraints explains human sequence learning","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PI37GHEYSLTTSHRPSPBXFZEM5D","json":"https://pith.science/pith/PI37GHEYSLTTSHRPSPBXFZEM5D.json","graph_json":"https://pith.science/api/pith-number/PI37GHEYSLTTSHRPSPBXFZEM5D/graph.json","events_json":"https://pith.science/api/pith-number/PI37GHEYSLTTSHRPSPBXFZEM5D/events.json","paper":"https://pith.science/paper/PI37GHEY"},"agent_actions":{"view_html":"https://pith.science/pith/PI37GHEYSLTTSHRPSPBXFZEM5D","download_json":"https://pith.science/pith/PI37GHEYSLTTSHRPSPBXFZEM5D.json","view_paper":"https://pith.science/paper/PI37GHEY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.05294&json=true","fetch_graph":"https://pith.science/api/pith-number/PI37GHEYSLTTSHRPSPBXFZEM5D/graph.json","fetch_events":"https://pith.science/api/pith-number/PI37GHEYSLTTSHRPSPBXFZEM5D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PI37GHEYSLTTSHRPSPBXFZEM5D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PI37GHEYSLTTSHRPSPBXFZEM5D/action/storage_attestation","attest_author":"https://pith.science/pith/PI37GHEYSLTTSHRPSPBXFZEM5D/action/author_attestation","sign_citation":"https://pith.science/pith/PI37GHEYSLTTSHRPSPBXFZEM5D/action/citation_signature","submit_replication":"https://pith.science/pith/PI37GHEYSLTTSHRPSPBXFZEM5D/action/replication_record"}},"created_at":"2026-07-05T08:17:25.914724+00:00","updated_at":"2026-07-05T08:17:25.914724+00:00"}