{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Z54RYTFEABGFK6WD2F6Z7W7JC5","short_pith_number":"pith:Z54RYTFE","schema_version":"1.0","canonical_sha256":"cf791c4ca4004c557ac3d17d9fdbe917643c867d8fed2eb7b2e50905b5cd78cc","source":{"kind":"arxiv","id":"2509.13389","version":7},"attestation_state":"computed","paper":{"title":"From Next Token Prediction to (STRIPS) World Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Next-token prediction on action traces yields STRIPS world models accurate enough for planning on unseen states and goals.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Carlos N\\'u\\~nez-Molina, Hector Geffner, Vicen\\c{c} G\\'omez","submitted_at":"2025-09-16T14:03:58Z","abstract_excerpt":"We study whether next-token prediction can yield world models that truly support planning, in a controlled symbolic setting where propositional STRIPS action models are learned from action traces alone and correctness can be evaluated exactly. We introduce two architectures. The first is the STRIPS Transformer, a symbolically aligned model grounded in theoretical results linking transformers and the formal language structure of STRIPS domains. The second is a standard transformer architecture without explicit symbolic structure built in, for which we study different positional encoding schemes"},"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":"2509.13389","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-09-16T14:03:58Z","cross_cats_sorted":[],"title_canon_sha256":"04fffbe5ca69d4e35db0406b1d7bafa4a7543d8fdfba3bf3cded295d6c0f8e90","abstract_canon_sha256":"f5c3b28e3735b7c63a2649dc552be9bf0267af4481943e7f836a5a8fe1758a4b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:05:02.080457Z","signature_b64":"t+Ejxy5uieDMpYdkjh1Fu5voLPRGoHWbNsu6yQ+VnQz46QVkbuJ04k9bC0LhWoYec5VCV6axWMBe/V4NN3xOBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf791c4ca4004c557ac3d17d9fdbe917643c867d8fed2eb7b2e50905b5cd78cc","last_reissued_at":"2026-05-26T02:05:02.079507Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:05:02.079507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Next Token Prediction to (STRIPS) World Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Next-token prediction on action traces yields STRIPS world models accurate enough for planning on unseen states and goals.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Carlos N\\'u\\~nez-Molina, Hector Geffner, Vicen\\c{c} G\\'omez","submitted_at":"2025-09-16T14:03:58Z","abstract_excerpt":"We study whether next-token prediction can yield world models that truly support planning, in a controlled symbolic setting where propositional STRIPS action models are learned from action traces alone and correctness can be evaluated exactly. We introduce two architectures. The first is the STRIPS Transformer, a symbolically aligned model grounded in theoretical results linking transformers and the formal language structure of STRIPS domains. The second is a standard transformer architecture without explicit symbolic structure built in, for which we study different positional encoding schemes"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Both the STRIPS Transformer and a standard transformer with stick-breaking attention can be used to produce models that support planning with off-the-shelf STRIPS planners over exponentially many unseen initial states and goals.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The learned next-token models are sufficiently accurate and complete to serve as drop-in STRIPS action models for arbitrary unseen states and goals, with correctness evaluated exactly in the symbolic setting (abstract, evaluation section implied by results on generalization and planning performance).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Transformers trained via next-token prediction on action traces can learn STRIPS action models that support planning over exponentially many unseen initial states and goals.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Next-token prediction on action traces yields STRIPS world models accurate enough for planning on unseen states and goals.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"cda8e7471192580d7771e5564c42e9ff1cc2359f7c6a8563565fc6171b18c921"},"source":{"id":"2509.13389","kind":"arxiv","version":7},"verdict":{"id":"4bac0768-2c65-4d4a-8ffc-84b0840420c2","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T16:17:00.156670Z","strongest_claim":"Both the STRIPS Transformer and a standard transformer with stick-breaking attention can be used to produce models that support planning with off-the-shelf STRIPS planners over exponentially many unseen initial states and goals.","one_line_summary":"Transformers trained via next-token prediction on action traces can learn STRIPS action models that support planning over exponentially many unseen initial states and goals.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The learned next-token models are sufficiently accurate and complete to serve as drop-in STRIPS action models for arbitrary unseen states and goals, with correctness evaluated exactly in the symbolic setting (abstract, evaluation section implied by results on generalization and planning performance).","pith_extraction_headline":"Next-token prediction on action traces yields STRIPS world models accurate enough for planning on unseen states and goals."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.13389/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":"2509.13389","created_at":"2026-05-26T02:05:02.079645+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.13389v7","created_at":"2026-05-26T02:05:02.079645+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.13389","created_at":"2026-05-26T02:05:02.079645+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z54RYTFEABGF","created_at":"2026-05-26T02:05:02.079645+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z54RYTFEABGFK6WD","created_at":"2026-05-26T02:05:02.079645+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z54RYTFE","created_at":"2026-05-26T02:05:02.079645+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.13282","citing_title":"Differentiable Learning of Lifted Action Schemas for Classical Planning","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5","json":"https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5.json","graph_json":"https://pith.science/api/pith-number/Z54RYTFEABGFK6WD2F6Z7W7JC5/graph.json","events_json":"https://pith.science/api/pith-number/Z54RYTFEABGFK6WD2F6Z7W7JC5/events.json","paper":"https://pith.science/paper/Z54RYTFE"},"agent_actions":{"view_html":"https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5","download_json":"https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5.json","view_paper":"https://pith.science/paper/Z54RYTFE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.13389&json=true","fetch_graph":"https://pith.science/api/pith-number/Z54RYTFEABGFK6WD2F6Z7W7JC5/graph.json","fetch_events":"https://pith.science/api/pith-number/Z54RYTFEABGFK6WD2F6Z7W7JC5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5/action/storage_attestation","attest_author":"https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5/action/author_attestation","sign_citation":"https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5/action/citation_signature","submit_replication":"https://pith.science/pith/Z54RYTFEABGFK6WD2F6Z7W7JC5/action/replication_record"}},"created_at":"2026-05-26T02:05:02.079645+00:00","updated_at":"2026-05-26T02:05:02.079645+00:00"}