{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4JFFLN5NSUV6WYOAEVGWCZSS4G","short_pith_number":"pith:4JFFLN5N","schema_version":"1.0","canonical_sha256":"e24a55b7ad952beb61c0254d616652e1a9f2867f241a7b44edec55620b5b1ddf","source":{"kind":"arxiv","id":"2607.26367","version":1},"attestation_state":"computed","paper":{"title":"Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Wanbing Zhao, Wanyu Zhao","submitted_at":"2026-07-29T01:00:59Z","abstract_excerpt":"An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model. We study this skill as an AI-agent task: can LLM-based agents discover statistical mechanical mappings from a raw partition function to a tractable representation? To probe this question, we introduce StatMechBench-v0, a benchmark of six Ising-type problems covering transfer-matrix methods, gauge-removable disorder, and planar/Pfaffian structure. We evaluate a simple propose-verify-revise agent across multiple LLMs and problem phrasings. The results show that numerical feedback o"},"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":"2607.26367","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-07-29T01:00:59Z","cross_cats_sorted":[],"title_canon_sha256":"836364ca8828737775afb1c043a9e2442243823f874490cffd295314126db270","abstract_canon_sha256":"e0bdf1f8875624a1e811ba8db9d0813262d9f8badc7a166263e29b0426bd4e3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e24a55b7ad952beb61c0254d616652e1a9f2867f241a7b44edec55620b5b1ddf","last_reissued_at":"2026-07-30T01:18:13.050099Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:18:13.050099Z"},"graph_snapshot":{"paper":{"title":"Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Wanbing Zhao, Wanyu Zhao","submitted_at":"2026-07-29T01:00:59Z","abstract_excerpt":"An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model. We study this skill as an AI-agent task: can LLM-based agents discover statistical mechanical mappings from a raw partition function to a tractable representation? To probe this question, we introduce StatMechBench-v0, a benchmark of six Ising-type problems covering transfer-matrix methods, gauge-removable disorder, and planar/Pfaffian structure. We evaluate a simple propose-verify-revise agent across multiple LLMs and problem phrasings. The results show that numerical feedback o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26367","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/2607.26367/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":"2607.26367","created_at":"2026-07-30T01:18:13.055167+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.26367v1","created_at":"2026-07-30T01:18:13.055167+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26367","created_at":"2026-07-30T01:18:13.055167+00:00"},{"alias_kind":"pith_short_12","alias_value":"4JFFLN5NSUV6","created_at":"2026-07-30T01:18:13.055167+00:00"},{"alias_kind":"pith_short_16","alias_value":"4JFFLN5NSUV6WYOA","created_at":"2026-07-30T01:18:13.055167+00:00"},{"alias_kind":"pith_short_8","alias_value":"4JFFLN5N","created_at":"2026-07-30T01:18:13.055167+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/4JFFLN5NSUV6WYOAEVGWCZSS4G","json":"https://pith.science/pith/4JFFLN5NSUV6WYOAEVGWCZSS4G.json","graph_json":"https://pith.science/api/pith-number/4JFFLN5NSUV6WYOAEVGWCZSS4G/graph.json","events_json":"https://pith.science/api/pith-number/4JFFLN5NSUV6WYOAEVGWCZSS4G/events.json","paper":"https://pith.science/paper/4JFFLN5N"},"agent_actions":{"view_html":"https://pith.science/pith/4JFFLN5NSUV6WYOAEVGWCZSS4G","download_json":"https://pith.science/pith/4JFFLN5NSUV6WYOAEVGWCZSS4G.json","view_paper":"https://pith.science/paper/4JFFLN5N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.26367&json=true","fetch_graph":"https://pith.science/api/pith-number/4JFFLN5NSUV6WYOAEVGWCZSS4G/graph.json","fetch_events":"https://pith.science/api/pith-number/4JFFLN5NSUV6WYOAEVGWCZSS4G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4JFFLN5NSUV6WYOAEVGWCZSS4G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4JFFLN5NSUV6WYOAEVGWCZSS4G/action/storage_attestation","attest_author":"https://pith.science/pith/4JFFLN5NSUV6WYOAEVGWCZSS4G/action/author_attestation","sign_citation":"https://pith.science/pith/4JFFLN5NSUV6WYOAEVGWCZSS4G/action/citation_signature","submit_replication":"https://pith.science/pith/4JFFLN5NSUV6WYOAEVGWCZSS4G/action/replication_record"}},"created_at":"2026-07-30T01:18:13.055167+00:00","updated_at":"2026-07-30T01:18:13.055167+00:00"}