{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZGPTOX6AME3XIXNHPOURAZRJEY","short_pith_number":"pith:ZGPTOX6A","schema_version":"1.0","canonical_sha256":"c99f375fc06137745da77ba910662926237ada601e64859e12bf15ff81959216","source":{"kind":"arxiv","id":"2502.11881","version":2},"attestation_state":"computed","paper":{"title":"Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Hyunwoo Kim, Joshua B. Tenenbaum, Lance Ying, Melanie Sclar, Sydney Levine, Tan Zhi-Xuan, Yang Liu, Yejin Choi","submitted_at":"2025-02-17T15:08:50Z","abstract_excerpt":"Existing LLM reasoning methods have shown impressive capabilities across various tasks, such as solving math and coding problems. However, applying these methods to scenarios without ground-truth answers or rule-based verification methods - such as tracking the mental states of an agent - remains challenging. Inspired by the sequential Monte Carlo algorithm, we introduce thought-tracing, an inference-time reasoning algorithm designed to trace the mental states of specific agents by generating hypotheses and weighting them based on observations without relying on ground-truth solutions to quest"},"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":"2502.11881","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-17T15:08:50Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"96342d549d1250f5ee8bc643f17c122d1aecfdf5d788f591e7165c56768bc08b","abstract_canon_sha256":"bfd1f033fd9a30dc4c043caa5cd17dfa7312b7ebc09476bcd181c360153c8850"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:31.549464Z","signature_b64":"Dq7Ts38xdF9pz6iGFdF5hNQ2TTC9Ydpo9GhZqU7/IhN+EZUE6e5FX35ADpWjd3XwKsjYy0ioa207aDjzd21JCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c99f375fc06137745da77ba910662926237ada601e64859e12bf15ff81959216","last_reissued_at":"2026-07-05T11:50:31.548976Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:31.548976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Hyunwoo Kim, Joshua B. Tenenbaum, Lance Ying, Melanie Sclar, Sydney Levine, Tan Zhi-Xuan, Yang Liu, Yejin Choi","submitted_at":"2025-02-17T15:08:50Z","abstract_excerpt":"Existing LLM reasoning methods have shown impressive capabilities across various tasks, such as solving math and coding problems. However, applying these methods to scenarios without ground-truth answers or rule-based verification methods - such as tracking the mental states of an agent - remains challenging. Inspired by the sequential Monte Carlo algorithm, we introduce thought-tracing, an inference-time reasoning algorithm designed to trace the mental states of specific agents by generating hypotheses and weighting them based on observations without relying on ground-truth solutions to quest"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11881","kind":"arxiv","version":2},"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/2502.11881/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":"2502.11881","created_at":"2026-07-05T11:50:31.549032+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.11881v2","created_at":"2026-07-05T11:50:31.549032+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11881","created_at":"2026-07-05T11:50:31.549032+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZGPTOX6AME3X","created_at":"2026-07-05T11:50:31.549032+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZGPTOX6AME3XIXNH","created_at":"2026-07-05T11:50:31.549032+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZGPTOX6A","created_at":"2026-07-05T11:50:31.549032+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.22748","citing_title":"Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond","ref_index":180,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28425","citing_title":"Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems","ref_index":90,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31916","citing_title":"Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27721","citing_title":"UserHarness: Harnessing User Minds for Stronger Agent Theory-of-Mind","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15205","citing_title":"Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2511.06091","citing_title":"Characterizing AI Manipulation Risks in Brazilian YouTube Climate Discourse","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22748","citing_title":"Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond","ref_index":180,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZGPTOX6AME3XIXNHPOURAZRJEY","json":"https://pith.science/pith/ZGPTOX6AME3XIXNHPOURAZRJEY.json","graph_json":"https://pith.science/api/pith-number/ZGPTOX6AME3XIXNHPOURAZRJEY/graph.json","events_json":"https://pith.science/api/pith-number/ZGPTOX6AME3XIXNHPOURAZRJEY/events.json","paper":"https://pith.science/paper/ZGPTOX6A"},"agent_actions":{"view_html":"https://pith.science/pith/ZGPTOX6AME3XIXNHPOURAZRJEY","download_json":"https://pith.science/pith/ZGPTOX6AME3XIXNHPOURAZRJEY.json","view_paper":"https://pith.science/paper/ZGPTOX6A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.11881&json=true","fetch_graph":"https://pith.science/api/pith-number/ZGPTOX6AME3XIXNHPOURAZRJEY/graph.json","fetch_events":"https://pith.science/api/pith-number/ZGPTOX6AME3XIXNHPOURAZRJEY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZGPTOX6AME3XIXNHPOURAZRJEY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZGPTOX6AME3XIXNHPOURAZRJEY/action/storage_attestation","attest_author":"https://pith.science/pith/ZGPTOX6AME3XIXNHPOURAZRJEY/action/author_attestation","sign_citation":"https://pith.science/pith/ZGPTOX6AME3XIXNHPOURAZRJEY/action/citation_signature","submit_replication":"https://pith.science/pith/ZGPTOX6AME3XIXNHPOURAZRJEY/action/replication_record"}},"created_at":"2026-07-05T11:50:31.549032+00:00","updated_at":"2026-07-05T11:50:31.549032+00:00"}