{"schema":"record/v1","id":"art_01M5MBXWFBT6C2YQFT4NNM9YS1","type":"article","revision":1,"etag":"\"r1.s0\"","title":"If an LLM updates the numbers (state variables) of a game or story each turn, how often did it get the update wrong in published tests?","summary":"RPGBench (arXiv 2502.00595 v1) had LLMs act as an RPG engine for 10 rounds: they planned events, narrated, and wrote updated state variables. Scripts, not an LLM judge, checked event conditions and variable updates. The best model, Gemini 2.0 Flash Exp, had 76.5% of rounds free of mechanical errors; GPT-4o 69.3%; the lowest were 11.3-20.4% (Claude 3.5 Sonnet, GPT-4o mini, Llama 3.1/3.3 70B). Variable-update error rates per variable ran from 3.4% to 30.8%. Accuracy fell as rounds increased for GPT-4o (69.3% at 10 rounds to 66.8% at 25). Combined with Wang et al. (-02), the sources support expecting frequent state errors unless a script checks them.","status":"active","body":{"text":"RPGBench (arXiv 2502.00595 v1) had LLMs act as an RPG engine for 10 rounds: they planned events, narrated, and wrote updated state variables. Scripts, not an LLM judge, checked event conditions and variable updates. The best model, Gemini 2.0 Flash Exp, had 76.5% of rounds free of mechanical errors; GPT-4o 69.3%; the lowest were 11.3-20.4% (Claude 3.5 Sonnet, GPT-4o mini, Llama 3.1/3.3 70B). Variable-update error rates per variable ran from 3.4% to 30.8%. Accuracy fell as rounds increased for GPT-4o (69.3% at 10 rounds to 66.8% at 25). Combined with Wang et al. (-02), the sources support expecting frequent state errors unless a script checks them.\n\nCurator qualifications: Verified: the text's 'perform comparably well' for Gemini 2.0 Flash Exp, GPT-4o and Gemini 1.5 Pro is not borne out by Table 3 (0.765, 0.693, 0.554).\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/2502.00595v1","note":"RPGBench text vs Table 3"},{"url":"https://arxiv.org/abs/2406.06485v1","note":"RPGBench text vs Table 3"}]},"categories":["T01","T15","T18"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2502.00595v1","claim":"RPGBench (arXiv 2502.00595 v1) had LLMs act as an RPG engine for 10 rounds: they planned events, narrated, and wrote updated state variables. Scripts, not an LLM judge, checked event conditions and variable updates. The best model, Gemini 2.0 Flash Exp, had 76.5% of rounds free of mechanical errors; GPT-4o 69.3%; the lowest were 11.3-20.4% (Claude 3.5 Sonnet, GPT-4o mini, Llama 3.1/3.3 70B). Variable-update error rates per variable ran from 3.4% to 30.8%. Accuracy fell as rounds increased for GPT-4o (69.3% at 10 rounds to 66.8% at 25). Combined with Wang et al. (-02), the sources support expecting frequent state errors unless a script checks them.","locator":"RPGBench text vs Table 3","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-650cd47156bfad51d33ea9f294709e37baa1e5cb/record.json#p-650cd47156bfad51d33ea9f294709e37baa1e5cb/record","import_batch_id":"pipe-7a7f13d3fa285fa16744ae5535878dd718a28d50","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-7a7f13d3fa285fa16744ae5535878dd718a28d50","original":{"body":{"text":"RPGBench (arXiv 2502.00595 v1) had LLMs act as an RPG engine for 10 rounds: they planned events, narrated, and wrote updated state variables. Scripts, not an LLM judge, checked event conditions and variable updates. The best model, Gemini 2.0 Flash Exp, had 76.5% of rounds free of mechanical errors; GPT-4o 69.3%; the lowest were 11.3-20.4% (Claude 3.5 Sonnet, GPT-4o mini, Llama 3.1/3.3 70B). Variable-update error rates per variable ran from 3.4% to 30.8%. Accuracy fell as rounds increased for GPT-4o (69.3% at 10 rounds to 66.8% at 25). Combined with Wang et al. (-02), the sources support expecting frequent state errors unless a script checks them.\n\nCurator qualifications: Verified: the text's 'perform comparably well' for Gemini 2.0 Flash Exp, GPT-4o and Gemini 1.5 Pro is not borne out by Table 3 (0.765, 0.693, 0.554).\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/2502.00595v1","note":"RPGBench text vs Table 3"},{"url":"https://arxiv.org/abs/2406.06485v1","note":"RPGBench text vs Table 3"}]},"tags":[],"type":"article","links":[],"title":"If an LLM updates the numbers (state variables) of a game or story each turn, how often did it get the update wrong in published tests?","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":"RPGBench (arXiv 2502.00595 v1) had LLMs act as an RPG engine for 10 rounds: they planned events, narrated, and wrote updated state variables. Scripts, not an LLM judge, checked event conditions and variable updates. The best model, Gemini 2.0 Flash Exp, had 76.5% of rounds free of mechanical errors; GPT-4o 69.3%; the lowest were 11.3-20.4% (Claude 3.5 Sonnet, GPT-4o mini, Llama 3.1/3.3 70B). Variable-update error rates per variable ran from 3.4% to 30.8%. Accuracy fell as rounds increased for GPT-4o (69.3% at 10 rounds to 66.8% at 25). Combined with Wang et al. (-02), the sources support expecting frequent state errors unless a script checks them.","evidence":{"claims":[{"url":"https://arxiv.org/abs/2502.00595v1","claim":"RPGBench (arXiv 2502.00595 v1) had LLMs act as an RPG engine for 10 rounds: they planned events, narrated, and wrote updated state variables. Scripts, not an LLM judge, checked event conditions and variable updates. The best model, Gemini 2.0 Flash Exp, had 76.5% of rounds free of mechanical errors; GPT-4o 69.3%; the lowest were 11.3-20.4% (Claude 3.5 Sonnet, GPT-4o mini, Llama 3.1/3.3 70B). Variable-update error rates per variable ran from 3.4% to 30.8%. Accuracy fell as rounds increased for GPT-4o (69.3% at 10 rounds to 66.8% at 25). Combined with Wang et al. (-02), the sources support expecting frequent state errors unless a script checks them.","locator":"RPGBench text vs Table 3","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T01","T15","T18"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":3,"topic_ids":["CAT:T01.01.b","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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