{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3UQ4YQUC77QGA5W4OBURBCEPCU","short_pith_number":"pith:3UQ4YQUC","schema_version":"1.0","canonical_sha256":"dd21cc4282ffe06076dc706910888f1536ce4f3c6a9c387152ed5e128e1d66cd","source":{"kind":"arxiv","id":"2212.01681","version":1},"attestation_state":"computed","paper":{"title":"Language Models as Agent Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.CL","authors_text":"Jacob Andreas","submitted_at":"2022-12-03T20:18:16Z","abstract_excerpt":"Language models (LMs) are trained on collections of documents, written by individual human agents to achieve specific goals in an outside world. During training, LMs have access only to text of these documents, with no direct evidence of the internal states of the agents that produced them -- a fact often used to argue that LMs are incapable of modeling goal-directed aspects of human language production and comprehension. Can LMs trained on text learn anything at all about the relationship between language and use? I argue that LMs are models of intentional communication in a specific, narrow "},"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":"2212.01681","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-03T20:18:16Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"1487ddbe21820b7a262e32c4160f005aa960b2f724a6ed24d26ecfed32e1f67c","abstract_canon_sha256":"70e7464a4f66e569576fc4740a2ae1389e53d217ef1d438d550bd99ce8fc194a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:22:15.219652Z","signature_b64":"jiZrStsiWQfejCCOjIh2bOOXsnFp478RdpdzQOlKe1k9oaF6kT2jvvvRaojiIw57EyBzdwheBFit5ocUtTvyBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd21cc4282ffe06076dc706910888f1536ce4f3c6a9c387152ed5e128e1d66cd","last_reissued_at":"2026-07-05T05:22:15.219246Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:22:15.219246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Language Models as Agent Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.CL","authors_text":"Jacob Andreas","submitted_at":"2022-12-03T20:18:16Z","abstract_excerpt":"Language models (LMs) are trained on collections of documents, written by individual human agents to achieve specific goals in an outside world. During training, LMs have access only to text of these documents, with no direct evidence of the internal states of the agents that produced them -- a fact often used to argue that LMs are incapable of modeling goal-directed aspects of human language production and comprehension. Can LMs trained on text learn anything at all about the relationship between language and use? I argue that LMs are models of intentional communication in a specific, narrow "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.01681","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/2212.01681/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":"2212.01681","created_at":"2026-07-05T05:22:15.219298+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.01681v1","created_at":"2026-07-05T05:22:15.219298+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.01681","created_at":"2026-07-05T05:22:15.219298+00:00"},{"alias_kind":"pith_short_12","alias_value":"3UQ4YQUC77QG","created_at":"2026-07-05T05:22:15.219298+00:00"},{"alias_kind":"pith_short_16","alias_value":"3UQ4YQUC77QGA5W4","created_at":"2026-07-05T05:22:15.219298+00:00"},{"alias_kind":"pith_short_8","alias_value":"3UQ4YQUC","created_at":"2026-07-05T05:22:15.219298+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23668","citing_title":"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22502","citing_title":"Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13339","citing_title":"Probing Persona-Dependent Preferences in Language Models","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2309.16797","citing_title":"Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2603.19282","citing_title":"Framing Effects in Independent-Agent Large Language Models: A Cross-Family Behavioral Analysis","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2406.11717","citing_title":"Refusal in Language Models Is Mediated by a Single Direction","ref_index":111,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03160","citing_title":"Steering grids for sparse-autoencoder features: when a top-context label names an activation regime rather than a causal axis","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3UQ4YQUC77QGA5W4OBURBCEPCU","json":"https://pith.science/pith/3UQ4YQUC77QGA5W4OBURBCEPCU.json","graph_json":"https://pith.science/api/pith-number/3UQ4YQUC77QGA5W4OBURBCEPCU/graph.json","events_json":"https://pith.science/api/pith-number/3UQ4YQUC77QGA5W4OBURBCEPCU/events.json","paper":"https://pith.science/paper/3UQ4YQUC"},"agent_actions":{"view_html":"https://pith.science/pith/3UQ4YQUC77QGA5W4OBURBCEPCU","download_json":"https://pith.science/pith/3UQ4YQUC77QGA5W4OBURBCEPCU.json","view_paper":"https://pith.science/paper/3UQ4YQUC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.01681&json=true","fetch_graph":"https://pith.science/api/pith-number/3UQ4YQUC77QGA5W4OBURBCEPCU/graph.json","fetch_events":"https://pith.science/api/pith-number/3UQ4YQUC77QGA5W4OBURBCEPCU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3UQ4YQUC77QGA5W4OBURBCEPCU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3UQ4YQUC77QGA5W4OBURBCEPCU/action/storage_attestation","attest_author":"https://pith.science/pith/3UQ4YQUC77QGA5W4OBURBCEPCU/action/author_attestation","sign_citation":"https://pith.science/pith/3UQ4YQUC77QGA5W4OBURBCEPCU/action/citation_signature","submit_replication":"https://pith.science/pith/3UQ4YQUC77QGA5W4OBURBCEPCU/action/replication_record"}},"created_at":"2026-07-05T05:22:15.219298+00:00","updated_at":"2026-07-05T05:22:15.219298+00:00"}