{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:J4PSAILTB2NI6EICPLQ3IA2RCK","short_pith_number":"pith:J4PSAILT","schema_version":"1.0","canonical_sha256":"4f1f2021730e9a8f11027ae1b4035112a5711b139ff2836f2365b38f0da498b6","source":{"kind":"arxiv","id":"2606.29717","version":1},"attestation_state":"computed","paper":{"title":"Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Boris I. Yakobson, Chenmu Zhang","submitted_at":"2026-06-29T02:52:50Z","abstract_excerpt":"Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science. A decade of work has produced standard public benchmarks and many published machine-learning models for the task (Dunn et al., 2020). The task's fixed metric and these baselines make it a natural setting for autonomous agent research (Karpathy, 2026). On the MatBench band-gap benchmark ($>$100k crystals), a general-purpose coding agent autonomously built the most accurate model trained without external pretraining, ahead of all seventeen expert-designed models reported"},"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":"2606.29717","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-29T02:52:50Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"dd27776dab60901e2b69cec23236811fb946967c30fbb2621d029719384aaa4e","abstract_canon_sha256":"5c8637e18de4d35884f9bb4bbcbda7190c415968da872c3f726c267dbe7305ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:31.921548Z","signature_b64":"TNtLQBay8HKbBYqv9oR9PW/OHBJaR58xKhMOzWhZpRVTk9wbatNMCOrg4+r7Ptd72uGS2BSfqR5nMS3mvPikCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f1f2021730e9a8f11027ae1b4035112a5711b139ff2836f2365b38f0da498b6","last_reissued_at":"2026-06-30T02:17:31.921059Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:31.921059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loop","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Boris I. Yakobson, Chenmu Zhang","submitted_at":"2026-06-29T02:52:50Z","abstract_excerpt":"Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science. A decade of work has produced standard public benchmarks and many published machine-learning models for the task (Dunn et al., 2020). The task's fixed metric and these baselines make it a natural setting for autonomous agent research (Karpathy, 2026). On the MatBench band-gap benchmark ($>$100k crystals), a general-purpose coding agent autonomously built the most accurate model trained without external pretraining, ahead of all seventeen expert-designed models reported"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29717","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/2606.29717/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":"2606.29717","created_at":"2026-06-30T02:17:31.921118+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.29717v1","created_at":"2026-06-30T02:17:31.921118+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29717","created_at":"2026-06-30T02:17:31.921118+00:00"},{"alias_kind":"pith_short_12","alias_value":"J4PSAILTB2NI","created_at":"2026-06-30T02:17:31.921118+00:00"},{"alias_kind":"pith_short_16","alias_value":"J4PSAILTB2NI6EIC","created_at":"2026-06-30T02:17:31.921118+00:00"},{"alias_kind":"pith_short_8","alias_value":"J4PSAILT","created_at":"2026-06-30T02:17:31.921118+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J4PSAILTB2NI6EICPLQ3IA2RCK","json":"https://pith.science/pith/J4PSAILTB2NI6EICPLQ3IA2RCK.json","graph_json":"https://pith.science/api/pith-number/J4PSAILTB2NI6EICPLQ3IA2RCK/graph.json","events_json":"https://pith.science/api/pith-number/J4PSAILTB2NI6EICPLQ3IA2RCK/events.json","paper":"https://pith.science/paper/J4PSAILT"},"agent_actions":{"view_html":"https://pith.science/pith/J4PSAILTB2NI6EICPLQ3IA2RCK","download_json":"https://pith.science/pith/J4PSAILTB2NI6EICPLQ3IA2RCK.json","view_paper":"https://pith.science/paper/J4PSAILT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.29717&json=true","fetch_graph":"https://pith.science/api/pith-number/J4PSAILTB2NI6EICPLQ3IA2RCK/graph.json","fetch_events":"https://pith.science/api/pith-number/J4PSAILTB2NI6EICPLQ3IA2RCK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J4PSAILTB2NI6EICPLQ3IA2RCK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J4PSAILTB2NI6EICPLQ3IA2RCK/action/storage_attestation","attest_author":"https://pith.science/pith/J4PSAILTB2NI6EICPLQ3IA2RCK/action/author_attestation","sign_citation":"https://pith.science/pith/J4PSAILTB2NI6EICPLQ3IA2RCK/action/citation_signature","submit_replication":"https://pith.science/pith/J4PSAILTB2NI6EICPLQ3IA2RCK/action/replication_record"}},"created_at":"2026-06-30T02:17:31.921118+00:00","updated_at":"2026-06-30T02:17:31.921118+00:00"}