{"schema":"record/v1","id":"art_01M5NP1AQT0SN51CJ6S182080F","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Instead of trusting an LLM to simulate each step, can it write an explicit rule model (PDDL) that a validator and planner then use, and how many errors did the LLM make doing so?","summary":"Partially verified: the curator re-read none of the cited sources; S3 rests on the producer's pinned reading. Guan et al. (NeurIPS 2023, arXiv 2305.14909 v2) use GPT-4 to write a PDDL domain model (actions with preconditions and effects) that humans and a validator correct, then feed a classical planner. GPT-4 made 53 errors on a 22-action Household model, 2 on Logistics (6 actions) and 4 on Tyreworld (13 actions); most were missing preconditions or effects, not syntax. With corrected models, Fast Downward found valid plans in 95% (Household) and 100% (Logistics) versus 15% and 0% for an LLM-only planner. The paper argues that using LLMs directly as the world model adds another layer of errors; it cites prior work for this rather than testing it.","status":"active","body":{"text":"Partially verified: the curator re-read none of the cited sources; S3 rests on the producer's pinned reading.\n\nGuan et al. (NeurIPS 2023, arXiv 2305.14909 v2) use GPT-4 to write a PDDL domain model (actions with preconditions and effects) that humans and a validator correct, then feed a classical planner. GPT-4 made 53 errors on a 22-action Household model, 2 on Logistics (6 actions) and 4 on Tyreworld (13 actions); most were missing preconditions or effects, not syntax. With corrected models, Fast Downward found valid plans in 95% (Household) and 100% (Logistics) versus 15% and 0% for an LLM-only planner. The paper argues that using LLMs directly as the world model adds another layer of errors; it cites prior work for this rather than testing it.\n\nCurator qualifications: Partially verified: the curator re-read none of the cited sources; S3 rests on the producer's pinned reading. Curator GET 2026-10-23 of the sources not re-read (mechanical source evidence, not semantic verification): S3: HTTP 200, bytes differ from the producer pin.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2305.14909v2","note":"candidate evidence excerpts"}]},"categories":["T09","T15","T18"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"metadata_checked","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2305.14909v2","claim":"Guan et al. (NeurIPS 2023, arXiv 2305.14909 v2) use GPT-4 to write a PDDL domain model (actions with preconditions and effects) that humans and a validator correct, then feed a classical planner. GPT-4 made 53 errors on a 22-action Household model, 2 on Logistics (6 actions) and 4 on Tyreworld (13 actions); most were missing preconditions or effects, not syntax. With corrected models, Fast Downward found valid plans in 95% (Household) and 100% (Logistics) versus 15% and 0% for an LLM-only planner. The paper argues that using LLMs directly as the world model adds another layer of errors; it cites prior work for this rather than testing it.","locator":"candidate evidence excerpts","classification":"sourced_fact"}],"execution_record_ids":[]},"rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-e21ade8941e0b888b0f350148a0cb47338fd1979/record.json#p-e21ade8941e0b888b0f350148a0cb47338fd1979/record","import_batch_id":"pipe-97c60d945153452318644acb694077d4c9f7dc19","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-97c60d945153452318644acb694077d4c9f7dc19","original":{"body":{"text":"Partially verified: the curator re-read none of the cited sources; S3 rests on the producer's pinned reading.\n\nGuan et al. (NeurIPS 2023, arXiv 2305.14909 v2) use GPT-4 to write a PDDL domain model (actions with preconditions and effects) that humans and a validator correct, then feed a classical planner. GPT-4 made 53 errors on a 22-action Household model, 2 on Logistics (6 actions) and 4 on Tyreworld (13 actions); most were missing preconditions or effects, not syntax. With corrected models, Fast Downward found valid plans in 95% (Household) and 100% (Logistics) versus 15% and 0% for an LLM-only planner. The paper argues that using LLMs directly as the world model adds another layer of errors; it cites prior work for this rather than testing it.\n\nCurator qualifications: Partially verified: the curator re-read none of the cited sources; S3 rests on the producer's pinned reading. Curator GET 2026-10-23 of the sources not re-read (mechanical source evidence, not semantic verification): S3: HTTP 200, bytes differ from the producer pin.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2305.14909v2","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Instead of trusting an LLM to simulate each step, can it write an explicit rule model (PDDL) that a validator and planner then use, and how many errors did the LLM make doing so?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Partially verified: the curator re-read none of the cited sources; S3 rests on the producer's pinned reading. Guan et al. (NeurIPS 2023, arXiv 2305.14909 v2) use GPT-4 to write a PDDL domain model (actions with preconditions and effects) that humans and a validator correct, then feed a classical planner. GPT-4 made 53 errors on a 22-action Household model, 2 on Logistics (6 actions) and 4 on Tyreworld (13 actions); most were missing preconditions or effects, not syntax. With corrected models, Fast Downward found valid plans in 95% (Household) and 100% (Logistics) versus 15% and 0% for an LLM-only planner. The paper argues that using LLMs directly as the world model adds another layer of errors; it cites prior work for this rather than testing it.","evidence":{"claims":[{"url":"https://arxiv.org/abs/2305.14909v2","claim":"Guan et al. (NeurIPS 2023, arXiv 2305.14909 v2) use GPT-4 to write a PDDL domain model (actions with preconditions and effects) that humans and a validator correct, then feed a classical planner. GPT-4 made 53 errors on a 22-action Household model, 2 on Logistics (6 actions) and 4 on Tyreworld (13 actions); most were missing preconditions or effects, not syntax. With corrected models, Fast Downward found valid plans in 95% (Household) and 100% (Logistics) versus 15% and 0% for an LLM-only planner. The paper argues that using LLMs directly as the world model adds another layer of errors; it cites prior work for this rather than testing it.","locator":"candidate evidence excerpts","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"metadata_checked","execution_record_ids":[]},"language":"en","categories":["T09","T15","T18"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":["CAT:T01.03"],"confidence":"Not stated; see limitations.","limitations":["Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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