{"schema":"record/v1","id":"art_01M5MC78VW5MJMWNZ15HXVZMKG","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What exactly did arXiv 2610.06637 (v1) define and measure for long-horizon textual world modelling: environments, horizons, models, metrics, headline numbers, ablations, limits, release?","summary":"arXiv 2610.06637 (Wang, Gao, Yao, Dong, Walia, Das; UIC and Intuit AI Research; v1 submitted 2026-10-05 16:25:44 UTC; CC BY 4.0) fine-tunes Qwen3-4B-Instruct-2507 world models that keep a JSON \"textual state\" and predict a whole k-action sequence in one call as sparse set/remove edits that code replays (latent-SR). It evaluates horizons 1-10 (ScienceWorld, CEO-Bench) and 1-20 (Jericho) with a fixed-target protocol against raw-history recursive/direct baselines and GPT-5.2. latent-SR has the best averages on every metric (ScienceWorld Fact-F1 92.44 vs 88.94 for obs-direct; CEO-Bench SE-score 87.35 vs 81.85; Jericho BLEU 24.5 and Score MAE 1.728), with gains growing at long horizons, but obs-recursive is better on Jericho at h1 and h5. The abstract's \"nearly 30%\" Jericho error drop holds against obs-direct at h10 (28.9%) but not against obs-recursive (12.2%), which is the lower-error raw-history baseline there. No code, data or weights release is stated.","status":"active","body":{"text":"arXiv 2610.06637 (Wang, Gao, Yao, Dong, Walia, Das; UIC and Intuit AI Research; v1 submitted 2026-10-05 16:25:44 UTC; CC BY 4.0) fine-tunes Qwen3-4B-Instruct-2507 world models that keep a JSON \"textual state\" and predict a whole k-action sequence in one call as sparse set/remove edits that code replays (latent-SR). It evaluates horizons 1-10 (ScienceWorld, CEO-Bench) and 1-20 (Jericho) with a fixed-target protocol against raw-history recursive/direct baselines and GPT-5.2. latent-SR has the best averages on every metric (ScienceWorld Fact-F1 92.44 vs 88.94 for obs-direct; CEO-Bench SE-score 87.35 vs 81.85; Jericho BLEU 24.5 and Score MAE 1.728), with gains growing at long horizons, but obs-recursive is better on Jericho at h1 and h5. The abstract's \"nearly 30%\" Jericho error drop holds against obs-direct at h10 (28.9%) but not against obs-recursive (12.2%), which is the lower-error raw-history baseline there. No code, data or weights release is stated.\n\nCurator qualifications: Curator recompute: Jericho h10 error drop is 28.9% vs obs-direct but 12.2% vs obs-recursive, the stronger raw-history baseline at h10; the abstract's 'nearly 30%' therefore is not against the strongest baseline, unlike its ScienceWorld/CEO figures. At h1 obs-recursive has far lower error than latent-SR. Scope correction: report the Jericho gain as 'about 12% vs the strongest raw-history baseline (obs-recursive), about 29% vs obs-direct, at horizon 10'; latent-SR is worse at short horizons on Jericho.\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/2610.06637v1","note":"arXiv 2610.06637v1 HTML abstract and Table 3 (Jericho fixed-target, MAE lower is better)"}]},"categories":["T15","T18","T20"],"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/2610.06637v1","claim":"arXiv 2610.06637 (Wang, Gao, Yao, Dong, Walia, Das; UIC and Intuit AI Research; v1 submitted 2026-10-05 16:25:44 UTC; CC BY 4.0) fine-tunes Qwen3-4B-Instruct-2507 world models that keep a JSON \"textual state\" and predict a whole k-action sequence in one call as sparse set/remove edits that code replays (latent-SR). It evaluates horizons 1-10 (ScienceWorld, CEO-Bench) and 1-20 (Jericho) with a fixed-target protocol against raw-history recursive/direct baselines and GPT-5.2. latent-SR has the best averages on every metric (ScienceWorld Fact-F1 92.44 vs 88.94 for obs-direct; CEO-Bench SE-score 87.35 vs 81.85; Jericho BLEU 24.5 and Score MAE 1.728), with gains growing at long horizons, but obs-recursive is better on Jericho at h1 and h5. The abstract's \"nearly 30%\" Jericho error drop holds against obs-direct at h10 (28.9%) but not against obs-recursive (12.2%), which is the lower-error raw-history baseline there. No code, data or weights release is stated.","locator":"arXiv 2610.06637v1 HTML abstract and Table 3 (Jericho fixed-target, MAE lower is better)","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-28e03925008e291e102e09752f1e6ccd038060b7/record.json#p-28e03925008e291e102e09752f1e6ccd038060b7/record","import_batch_id":"pipe-790a11f75f7f7f074ac3a293e08f9a4eb1ec41b5","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-790a11f75f7f7f074ac3a293e08f9a4eb1ec41b5","original":{"body":{"text":"arXiv 2610.06637 (Wang, Gao, Yao, Dong, Walia, Das; UIC and Intuit AI Research; v1 submitted 2026-10-05 16:25:44 UTC; CC BY 4.0) fine-tunes Qwen3-4B-Instruct-2507 world models that keep a JSON \"textual state\" and predict a whole k-action sequence in one call as sparse set/remove edits that code replays (latent-SR). It evaluates horizons 1-10 (ScienceWorld, CEO-Bench) and 1-20 (Jericho) with a fixed-target protocol against raw-history recursive/direct baselines and GPT-5.2. latent-SR has the best averages on every metric (ScienceWorld Fact-F1 92.44 vs 88.94 for obs-direct; CEO-Bench SE-score 87.35 vs 81.85; Jericho BLEU 24.5 and Score MAE 1.728), with gains growing at long horizons, but obs-recursive is better on Jericho at h1 and h5. The abstract's \"nearly 30%\" Jericho error drop holds against obs-direct at h10 (28.9%) but not against obs-recursive (12.2%), which is the lower-error raw-history baseline there. No code, data or weights release is stated.\n\nCurator qualifications: Curator recompute: Jericho h10 error drop is 28.9% vs obs-direct but 12.2% vs obs-recursive, the stronger raw-history baseline at h10; the abstract's 'nearly 30%' therefore is not against the strongest baseline, unlike its ScienceWorld/CEO figures. At h1 obs-recursive has far lower error than latent-SR. Scope correction: report the Jericho gain as 'about 12% vs the strongest raw-history baseline (obs-recursive), about 29% vs obs-direct, at horizon 10'; latent-SR is worse at short horizons on Jericho.\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/2610.06637v1","note":"arXiv 2610.06637v1 HTML abstract and Table 3 (Jericho fixed-target, MAE lower is better)"}]},"tags":[],"type":"article","links":[],"title":"What exactly did arXiv 2610.06637 (v1) define and measure for long-horizon textual world modelling: environments, horizons, models, metrics, headline numbers, ablations, limits, release?","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":"arXiv 2610.06637 (Wang, Gao, Yao, Dong, Walia, Das; UIC and Intuit AI Research; v1 submitted 2026-10-05 16:25:44 UTC; CC BY 4.0) fine-tunes Qwen3-4B-Instruct-2507 world models that keep a JSON \"textual state\" and predict a whole k-action sequence in one call as sparse set/remove edits that code replays (latent-SR). It evaluates horizons 1-10 (ScienceWorld, CEO-Bench) and 1-20 (Jericho) with a fixed-target protocol against raw-history recursive/direct baselines and GPT-5.2. latent-SR has the best averages on every metric (ScienceWorld Fact-F1 92.44 vs 88.94 for obs-direct; CEO-Bench SE-score 87.35 vs 81.85; Jericho BLEU 24.5 and Score MAE 1.728), with gains growing at long horizons, but obs-recursive is better on Jericho at h1 and h5. The abstract's \"nearly 30%\" Jericho error drop holds against obs-direct at h10 (28.9%) but not against obs-recursive (12.2%), which is the lower-error raw-history baseline there. No code, data or weights release is stated.","evidence":{"claims":[{"url":"https://arxiv.org/abs/2610.06637v1","claim":"arXiv 2610.06637 (Wang, Gao, Yao, Dong, Walia, Das; UIC and Intuit AI Research; v1 submitted 2026-10-05 16:25:44 UTC; CC BY 4.0) fine-tunes Qwen3-4B-Instruct-2507 world models that keep a JSON \"textual state\" and predict a whole k-action sequence in one call as sparse set/remove edits that code replays (latent-SR). It evaluates horizons 1-10 (ScienceWorld, CEO-Bench) and 1-20 (Jericho) with a fixed-target protocol against raw-history recursive/direct baselines and GPT-5.2. latent-SR has the best averages on every metric (ScienceWorld Fact-F1 92.44 vs 88.94 for obs-direct; CEO-Bench SE-score 87.35 vs 81.85; Jericho BLEU 24.5 and Score MAE 1.728), with gains growing at long horizons, but obs-recursive is better on Jericho at h1 and h5. The abstract's \"nearly 30%\" Jericho error drop holds against obs-direct at h10 (28.9%) but not against obs-recursive (12.2%), which is the lower-error raw-history baseline there. No code, data or weights release is stated.","locator":"arXiv 2610.06637v1 HTML abstract and Table 3 (Jericho fixed-target, MAE lower is better)","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T15","T18","T20"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":["CAT:T01.03","CAT:WM.08"],"confidence":"Not stated; see limitations.","limitations":["Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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