{"schema":"record/v1","id":"art_01M5P09QSA5T51Q996GEN7R42Y","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How does WorldRoamBench test whether an interactive world model remembers a place after revisit, why does it avoid frame-pair matching, and what did it find about memory decay?","summary":"Problem: an action-conditioned model may not return to the same viewpoint, so comparing the 'return' frame with the first frame confuses memory with action-following error. Conditions: WASD and IJKL interaction of 10 to 60 seconds in Nature, Urban and Indoor scenes, first and third person; v5 abstract states 1000+ test cases and 10+ open and closed models. Method: four dimensions (per-frame action, sliding-window visual drift, physics gated by camera-motion and tracking checks, memory). Scene memory splits the rollout at the executed observation-revisit transition, reconstructs a point cloud of each segment, aligns the two, and normalises by reconstruction sampling density, since two passes over the same area are never point-wise identical. Subject memory uses tracking plus a VLM. Its authors argue trajectory metrics (RPE, ATE) are unfair across models and can hide per-frame failures. Findings (as reported): models regenerate a different scene on a symmetric return path; memory rank...","status":"active","body":{"text":"Problem: an action-conditioned model may not return to the same viewpoint, so comparing the 'return' frame with the first frame confuses memory with action-following error. Conditions: WASD and IJKL interaction of 10 to 60 seconds in Nature, Urban and Indoor scenes, first and third person; v5 abstract states 1000+ test cases and 10+ open and closed models. Method: four dimensions (per-frame action, sliding-window visual drift, physics gated by camera-motion and tracking checks, memory). Scene memory splits the rollout at the executed observation-revisit transition, reconstructs a point cloud of each segment, aligns the two, and normalises by reconstruction sampling density, since two passes over the same area are never point-wise identical. Subject memory uses tracking plus a VLM. Its authors argue trajectory metrics (RPE, ATE) are unfair across models and can hide per-frame failures. Findings (as reported): models regenerate a different scene on a symmetric return path; memory rankings are unstable across horizons (11 of 12 models change rank in Memory from short to long, against 5 in Overall), so early memory scores do not predict long-horizon memory. Limitations: VLM judges are used for subject memory and physics (the paper includes a human verification section I did not read); point-cloud reconstruction quality bounds the score. Version and date: arXiv 2606.31672 v5, 2026-09-18 (title also shown as WorldOdysseyBench in some listings). This extends the throughput-only use of the benchmark in C3-B225-W40-03 and does not rank models.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources. Correction folded 2026-10-24 from C3-B809-03 (curator-reviewed, C3R-0613/0616): Problem. C3-B756-07 adds an aside that the WorldRoamBench paper is \"also shown as WorldOdysseyBench in some listings\". Method. The lead re-fetched the cited arXiv abstract and HTML pages for 2606.31672 on 2026-10-24 at about 00:20Z: the title is \"WorldRoamBench: An Open-World Benchmark for Long-Horizon Stability of Interactive World Models\" and the string \"WorldOdyssey\" does not appear on either page. Treat the alternative name as unsupported; the rest of C3-B756-07 was graded supported by the C3-B762 audit worker, who flagged this aside.\n\nLimitations: Read only the abstract page and the HTML text (math garbled); I did not read the human-verification appendix or the per-model tables. Case counts differ between versions (v5 says 1000+; a search-result summary of an earlier version said 600+). The ranking observation is the authors' own. Corrected by C3-B809-03 (2026-10-24); where the correction disagrees, the correction governs.\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/2606.31672","note":"Abstract; Introduction; Appendix H scene memory; Memory results; Introduction"},{"url":"https://arxiv.org/html/2606.31672","note":"Abstract; Introduction; Appendix H scene memory; Memory results; Introduction"}]},"categories":["T06","T15","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/2606.31672","claim":"Problem: an action-conditioned model may not return to the same viewpoint, so comparing the 'return' frame with the first frame confuses memory with action-following error. Conditions: WASD and IJKL interaction of 10 to 60 seconds in Nature, Urban and Indoor scenes, first and third person; v5 abstract states 1000+ test cases and 10+ open and closed models. Method: four dimensions (per-frame action, sliding-window visual drift, physics gated by camera-motion and tracking checks, memory). Scene memory splits the rollout at the executed observation-revisit transition, reconstructs a point cloud of each segment, aligns the two, and normalises by reconstruction sampling density, since two passes over the same area are never point-wise identical. Subject memory uses tracking plus a VLM. Its authors argue trajectory metrics (RPE, ATE) are unfair across models and can hide per-frame failures. Findings (as reported): models regenerate a different scene on a symmetric return path; memory rankings are unstable across horizons (11 of 12 models change rank in Memory from short to long, against 5 in Overall), so early memory scores do not predict long-horizon memory. Limitations: VLM judges are used for subject memory and physics (the paper includes a human verification section I did not read); point-cloud reconstruction quality bounds the score. Version and date: arXiv 2606.31672 v5, 2026-09-18 (title also shown as WorldOdysseyBench in some listings). This extends the throughput-only use of the benchmark in C3-B225-W40-03 and does not rank models.","locator":"Abstract; Introduction; Appendix H scene memory; Memory results; Introduction","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-8b2ac451d7bccc002d01a6958bc7a7d3ba1a8dd4/record.json#p-8b2ac451d7bccc002d01a6958bc7a7d3ba1a8dd4/record","import_batch_id":"pipe-6bcee4e13880cd1ddbacf7e57bb97ec8c0db11ed","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-6bcee4e13880cd1ddbacf7e57bb97ec8c0db11ed","original":{"body":{"text":"Problem: an action-conditioned model may not return to the same viewpoint, so comparing the 'return' frame with the first frame confuses memory with action-following error. Conditions: WASD and IJKL interaction of 10 to 60 seconds in Nature, Urban and Indoor scenes, first and third person; v5 abstract states 1000+ test cases and 10+ open and closed models. Method: four dimensions (per-frame action, sliding-window visual drift, physics gated by camera-motion and tracking checks, memory). Scene memory splits the rollout at the executed observation-revisit transition, reconstructs a point cloud of each segment, aligns the two, and normalises by reconstruction sampling density, since two passes over the same area are never point-wise identical. Subject memory uses tracking plus a VLM. Its authors argue trajectory metrics (RPE, ATE) are unfair across models and can hide per-frame failures. Findings (as reported): models regenerate a different scene on a symmetric return path; memory rankings are unstable across horizons (11 of 12 models change rank in Memory from short to long, against 5 in Overall), so early memory scores do not predict long-horizon memory. Limitations: VLM judges are used for subject memory and physics (the paper includes a human verification section I did not read); point-cloud reconstruction quality bounds the score. Version and date: arXiv 2606.31672 v5, 2026-09-18 (title also shown as WorldOdysseyBench in some listings). This extends the throughput-only use of the benchmark in C3-B225-W40-03 and does not rank models.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources. Correction folded 2026-10-24 from C3-B809-03 (curator-reviewed, C3R-0613/0616): Problem. C3-B756-07 adds an aside that the WorldRoamBench paper is \"also shown as WorldOdysseyBench in some listings\". Method. The lead re-fetched the cited arXiv abstract and HTML pages for 2606.31672 on 2026-10-24 at about 00:20Z: the title is \"WorldRoamBench: An Open-World Benchmark for Long-Horizon Stability of Interactive World Models\" and the string \"WorldOdyssey\" does not appear on either page. Treat the alternative name as unsupported; the rest of C3-B756-07 was graded supported by the C3-B762 audit worker, who flagged this aside.\n\nLimitations: Read only the abstract page and the HTML text (math garbled); I did not read the human-verification appendix or the per-model tables. Case counts differ between versions (v5 says 1000+; a search-result summary of an earlier version said 600+). The ranking observation is the authors' own. Corrected by C3-B809-03 (2026-10-24); where the correction disagrees, the correction governs.\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/2606.31672","note":"Abstract; Introduction; Appendix H scene memory; Memory results; Introduction"},{"url":"https://arxiv.org/html/2606.31672","note":"Abstract; Introduction; Appendix H scene memory; Memory results; Introduction"}]},"tags":[],"type":"article","links":[],"title":"How does WorldRoamBench test whether an interactive world model remembers a place after revisit, why does it avoid frame-pair matching, and what did it find about memory decay?","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":"Problem: an action-conditioned model may not return to the same viewpoint, so comparing the 'return' frame with the first frame confuses memory with action-following error. Conditions: WASD and IJKL interaction of 10 to 60 seconds in Nature, Urban and Indoor scenes, first and third person; v5 abstract states 1000+ test cases and 10+ open and closed models. Method: four dimensions (per-frame action, sliding-window visual drift, physics gated by camera-motion and tracking checks, memory). Scene memory splits the rollout at the executed observation-revisit transition, reconstructs a point cloud of each segment, aligns the two, and normalises by reconstruction sampling density, since two passes over the same area are never point-wise identical. Subject memory uses tracking plus a VLM. Its authors argue trajectory metrics (RPE, ATE) are unfair across models and can hide per-frame failures. Findings (as reported): models regenerate a different scene on a symmetric return path; memory rank...","evidence":{"claims":[{"url":"https://arxiv.org/abs/2606.31672","claim":"Problem: an action-conditioned model may not return to the same viewpoint, so comparing the 'return' frame with the first frame confuses memory with action-following error. Conditions: WASD and IJKL interaction of 10 to 60 seconds in Nature, Urban and Indoor scenes, first and third person; v5 abstract states 1000+ test cases and 10+ open and closed models. Method: four dimensions (per-frame action, sliding-window visual drift, physics gated by camera-motion and tracking checks, memory). Scene memory splits the rollout at the executed observation-revisit transition, reconstructs a point cloud of each segment, aligns the two, and normalises by reconstruction sampling density, since two passes over the same area are never point-wise identical. Subject memory uses tracking plus a VLM. Its authors argue trajectory metrics (RPE, ATE) are unfair across models and can hide per-frame failures. Findings (as reported): models regenerate a different scene on a symmetric return path; memory rankings are unstable across horizons (11 of 12 models change rank in Memory from short to long, against 5 in Overall), so early memory scores do not predict long-horizon memory. Limitations: VLM judges are used for subject memory and physics (the paper includes a human verification section I did not read); point-cloud reconstruction quality bounds the score. Version and date: arXiv 2606.31672 v5, 2026-09-18 (title also shown as WorldOdysseyBench in some listings). This extends the throughput-only use of the benchmark in C3-B225-W40-03 and does not rank models.","locator":"Abstract; Introduction; Appendix H scene memory; Memory results; Introduction","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T06","T15","T20"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":["CAT:WM.09"],"confidence":"Read only the abstract page and the HTML text (math garbled); I did not read the human-verification appendix or the per-model tables. Case counts differ between versions (v5 says 1000+; a search-result summary of an earlier version said 600+). The ranking observation is the authors' own.","limitations":["Read only the abstract page and the HTML text (math garbled); I did not read the human-verification appendix or the per-model tables. Case counts differ between versions (v5 says 1000+; a search-result summary of an earlier version said 600+). The ranking observation is the authors' own.","Corrected by C3-B809-03 (2026-10-24); where the correction disagrees, the correction governs.","Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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Case counts differ between versions (v5 says 1000+; a search-result summary of an earlier version said 600+). The ranking observation is the authors' own.","limitations":["Read only the abstract page and the HTML text (math garbled); I did not read the human-verification appendix or the per-model tables. Case counts differ between versions (v5 says 1000+; a search-result summary of an earlier version said 600+). The ranking observation is the authors' own.","Corrected by C3-B809-03 (2026-10-24); where the correction disagrees, the correction governs.","Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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