{"id":"e91d068db2b93f76507311a9de5fd883ce30793b470b7393df9a0f486b878ab7","title":"Truncated backpropagation through time induces a two-sided gradient turnpike property","argument":"Julian D. Schiller and Matthias A. Müller argue that truncated backpropagation through time creates two boundary effects when training recurrent models: the forward state misses past information, and the backward recursion omits future information. Their proposed analysis separates the interval used to average gradient contributions for a parameter update from the larger temporal context used to compute them. Under exponential decay of the state-transition Jacobians and mild regularity conditions, they derive a two-sided gradient turnpike property: the error between truncated- and full-context gradient contributions decays exponentially with distance from either context boundary. The authors also derive bounds for averaged update gradients, including guarantees on gradient alignment and approximate full-context stationarity. Under unbiased interval sampling and a Polyak-Lojasiewicz condition, they say these results imply a bound on the resulting optimality gap. Numerical experiments on the Wiener-Hammerstein benchmark illustrate the roles of past and future context and a trade-off between predictive performance and computational cost. The abstract reports theoretical results under stated conditions and experiments on that benchmark; it does not establish how broadly the findings apply beyond it.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The supplied evidence is the paper's abstract, not its full derivations or experimental results. The abstract states assumptions for the theoretical guarantees and reports experiments on the Wiener-Hammerstein benchmark; it does not establish how broadly the findings apply beyond that benchmark.","speaker":{"name":"Schiller, Julian D., Müller, Matthias A.","roleAtSource":"Article author"},"source":{"title":"Truncated backpropagation through time induces a two-sided gradient turnpike property","publisher":"arXiv eess.SY","format":"Article","url":"https://arxiv.org/abs/2611.07415","publishedAt":"2026-11-05","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors say truncated backpropagation through time reduces training computation by restricting gradient calculations to shorter temporal contexts, but this creates missing-past and omitted-future effects.","excerpt":"This induces two distinct boundary effects: missing past information perturbs the forward state evolution, while truncating the backward recursion omits future information.","url":"https://arxiv.org/abs/2611.07415","relationship":"Source-stated"},{"text":"Under stated decay and regularity assumptions, the authors derive a two-sided gradient turnpike property: gradient-contribution error decreases exponentially with distance from both context boundaries.","excerpt":"the error between truncated- and full-context gradient contributions decays exponentially with the distance from both context boundaries.","url":"https://arxiv.org/abs/2611.07415","relationship":"Source-stated"},{"text":"The authors report that numerical experiments on the Wiener-Hammerstein benchmark illustrate the roles of past and future context and the trade-off between predictive performance and computational cost.","excerpt":"Numerical experiments on the Wiener-Hammerstein benchmark illustrate the role of past and future context and the trade-off between predictive performance and computational cost.","url":"https://arxiv.org/abs/2611.07415","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2611.07415","checkedAt":"2026-11-08T22:44:06.586Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"On the Wiener-Hammerstein benchmark, how do the roles of past and future context shape the trade-off between predictive performance and computational cost?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Truncated backpropagation through time induces a two-sided gradient turnpike property","summary":"Julian D. Schiller and Matthias A. Müller argue that truncated backpropagation through time creates two boundary effects when training recurrent models: the forward state misses past information, and the backward recursion omits future information. Their proposed analysis separates the interval used to average gradient contributions for a parameter update from the larger temporal context used to compute them. Under exponential decay of the state-transition Jacobians and mild regularity conditions, they derive a two-sided gradient turnpike property: the error between truncated- and full-context gradient contributions decays exponentially with distance from either context boundary. The authors also derive bounds for averaged update gradients, including guarantees on gradient alignment and approximate full-context stationarity. Under unbiased interval sampling and a Polyak-Lojasiewicz condition, they say these results imply a bound on the resulting optimality gap. Numerical experiments on the Wiener-Hammerstein benchmark illustrate the roles of past and future context and a trade-off between predictive performance and computational cost. The abstract reports theoretical results under stated conditions and experiments on that benchmark; it does not establish how broadly the findings apply beyond it.","question":"On the Wiener-Hammerstein benchmark, how do the roles of past and future context shape the trade-off between predictive performance and computational cost?","claims":[{"text":"The authors say truncated backpropagation through time reduces training computation by restricting gradient calculations to shorter temporal contexts, but this creates missing-past and omitted-future effects.","excerpt":"This induces two distinct boundary effects: missing past information perturbs the forward state evolution, while truncating the backward recursion omits future information.","support":"source-stated"},{"text":"Under stated decay and regularity assumptions, the authors derive a two-sided gradient turnpike property: gradient-contribution error decreases exponentially with distance from both context boundaries.","excerpt":"the error between truncated- and full-context gradient contributions decays exponentially with the distance from both context boundaries.","support":"source-stated"},{"text":"The authors report that numerical experiments on the Wiener-Hammerstein benchmark illustrate the roles of past and future context and the trade-off between predictive performance and computational cost.","excerpt":"Numerical experiments on the Wiener-Hammerstein benchmark illustrate the role of past and future context and the trade-off between predictive performance and computational cost.","support":"source-stated"}],"limitations":["The supplied evidence is the paper's abstract, not its full derivations or experimental results.","The abstract states assumptions for the theoretical guarantees and reports experiments on the Wiener-Hammerstein benchmark; it does not establish how broadly the findings apply beyond that benchmark."],"mediaId":"2cec17e5171cfd8b0c7525d3b841e25a2d8ab511b72720ffe85d0efbca92e346","correctionReason":null,"id":"e91d068db2b93f76507311a9de5fd883ce30793b470b7393df9a0f486b878ab7","publicationId":"e422a73e-f829-4c08-8215-e3f7c4d4e0fa","version":1,"sourceUrl":"https://arxiv.org/abs/2611.07415","sourceTitle":"Truncated backpropagation through time induces a two-sided gradient turnpike property","publisher":"arXiv eess.SY","publishedAt":null,"originalPublished":{"value":"2026-11-05","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-11-06T05:00:53.752Z","createdAt":"2026-11-08T21:40:29.861Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"2cec17e5171cfd8b0c7525d3b841e25a2d8ab511b72720ffe85d0efbca92e346","kind":"article","url":"https://arxiv.org/abs/2611.07415","sourceUrl":"https://arxiv.org/abs/2611.07415","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-11-08T21:39:50.403Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2611.07415","checkedAt":"2026-11-08T22:44:06.586Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"2cec17e5171cfd8b0c7525d3b841e25a2d8ab511b72720ffe85d0efbca92e346","confidence":0.52,"provider":"typesafe","model":"jev-1.13.0","inputHash":"c6c05fd3f275737356a643af2b564762c507f21f512304d15831291010d690fb","decidedAt":"2026-11-08T21:39:52.244Z"},"provenance":{"textHash":"99c447b9b0fca3186e8835c758225744e2aef1bf2a3cdc2654be26f9189cc374","observationId":"579834f61bbd9e139c9261a1c9f98c225a7e01fe701fcaa68ed118194bfeb769","retrievedAt":"2026-11-08T21:39:50.096Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"5b42a7c8-7a7b-4c98-86c5-5ce5ace96b5a"},"reviewer":{"model":"gpt-6-luna","runId":"a24fa017-53e6-4408-8297-78ff33347171"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-08T21:40:29.861Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Schiller, Julian D.","url":null,"kind":"unknown"},{"name":"Müller, Matthias A.","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Schiller, Julian D.","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Schiller, Julian D.","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.07415","locator":"head/meta[name=author|citation_author]"},"assertionId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-08T22:44:06.586Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027","sourceUrl":"https://arxiv.org/abs/2611.07415","contentSha256":"6d227052aad9ef74d757a19b488f7ae494b14a1151f631ff53db07450f861498","textHash":"99c447b9b0fca3186e8835c758225744e2aef1bf2a3cdc2654be26f9189cc374","observedAt":"2026-11-08T22:44:06.586Z"}},{"actor":{"name":"Müller, Matthias A.","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Müller, Matthias A.","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.07415","locator":"head/meta[name=author|citation_author]"},"assertionId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-08T22:44:06.586Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027","sourceUrl":"https://arxiv.org/abs/2611.07415","contentSha256":"6d227052aad9ef74d757a19b488f7ae494b14a1151f631ff53db07450f861498","textHash":"99c447b9b0fca3186e8835c758225744e2aef1bf2a3cdc2654be26f9189cc374","observedAt":"2026-11-08T22:44:06.586Z"}}],"participationCoverage":{"total":2,"returned":2,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2611.07415","snapshotId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027","contentSha256":"6d227052aad9ef74d757a19b488f7ae494b14a1151f631ff53db07450f861498","textHash":"99c447b9b0fca3186e8835c758225744e2aef1bf2a3cdc2654be26f9189cc374","observedAt":"2026-11-08T22:44:06.586Z","basis":"publisher-page-metadata","sourceLinks":44,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/e91d068db2b93f76507311a9de5fd883ce30793b470b7393df9a0f486b878ab7/source"}},"canonicalUrl":"https://escapemeridian.com/readings/truncated-backpropagation-through-time-induces-a-two-sided-e91d068d","formats":{"html":"/readings/truncated-backpropagation-through-time-induces-a-two-sided-e91d068d","markdown":"/readings/truncated-backpropagation-through-time-induces-a-two-sided-e91d068d.md","json":"/readings/truncated-backpropagation-through-time-induces-a-two-sided-e91d068d.json"},"attribution":"Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.","maintainedGuideLinks":[],"readerContext":{"version":"reader-context-v1","attribution":"Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.","terms":[{"id":"term-benchmark","name":"Benchmark","explanation":"A defined set of tests used to compare systems. Results depend on the tasks and measures selected.","referenceUrl":"https://arxiv.org/abs/2211.09110"}],"entities":[{"id":"time","name":"TIME","kind":"organization","context":"TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.","referenceUrls":["https://time.com/"],"path":"/entities/time","json":"/entities/time.json","markdown":"/entities/time.md"},{"id":"arxiv","name":"arXiv","kind":"organization","context":"arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.","referenceUrls":["https://arxiv.org/","https://info.arxiv.org/help/api/index.html"],"path":"/entities/arxiv","json":"/entities/arxiv.json","markdown":"/entities/arxiv.md"}]},"researchQuestions":[],"withdrawn":false,"references":{"schemaVersion":1,"target":{"type":"reading","id":"e91d068db2b93f76507311a9de5fd883ce30793b470b7393df9a0f486b878ab7","revision":"e422a73e-f829-4c08-8215-e3f7c4d4e0fa","revisionKind":"publication-id","title":"Truncated backpropagation through time induces a two-sided gradient turnpike property","url":"https://escapemeridian.com/readings/truncated-backpropagation-through-time-induces-a-two-sided-e91d068d"},"source":{"type":"source","id":"https://arxiv.org/abs/2611.07415","revision":"99c447b9b0fca3186e8835c758225744e2aef1bf2a3cdc2654be26f9189cc374","revisionKind":"sha256-source-text","title":"Truncated backpropagation through time induces a two-sided gradient turnpike property","url":"https://arxiv.org/abs/2611.07415"},"status":"available","items":[],"next":null,"scope":"Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter."},"revisions":[{"id":"e91d068db2b93f76507311a9de5fd883ce30793b470b7393df9a0f486b878ab7","title":"Truncated backpropagation through time induces a two-sided gradient turnpike property","argument":"Julian D. Schiller and Matthias A. Müller argue that truncated backpropagation through time creates two boundary effects when training recurrent models: the forward state misses past information, and the backward recursion omits future information. Their proposed analysis separates the interval used to average gradient contributions for a parameter update from the larger temporal context used to compute them. Under exponential decay of the state-transition Jacobians and mild regularity conditions, they derive a two-sided gradient turnpike property: the error between truncated- and full-context gradient contributions decays exponentially with distance from either context boundary. The authors also derive bounds for averaged update gradients, including guarantees on gradient alignment and approximate full-context stationarity. Under unbiased interval sampling and a Polyak-Lojasiewicz condition, they say these results imply a bound on the resulting optimality gap. Numerical experiments on the Wiener-Hammerstein benchmark illustrate the roles of past and future context and a trade-off between predictive performance and computational cost. The abstract reports theoretical results under stated conditions and experiments on that benchmark; it does not establish how broadly the findings apply beyond it.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The supplied evidence is the paper's abstract, not its full derivations or experimental results. The abstract states assumptions for the theoretical guarantees and reports experiments on the Wiener-Hammerstein benchmark; it does not establish how broadly the findings apply beyond that benchmark.","speaker":{"name":"Schiller, Julian D., Müller, Matthias A.","roleAtSource":"Article author"},"source":{"title":"Truncated backpropagation through time induces a two-sided gradient turnpike property","publisher":"arXiv eess.SY","format":"Article","url":"https://arxiv.org/abs/2611.07415","publishedAt":"2026-11-05","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors say truncated backpropagation through time reduces training computation by restricting gradient calculations to shorter temporal contexts, but this creates missing-past and omitted-future effects.","excerpt":"This induces two distinct boundary effects: missing past information perturbs the forward state evolution, while truncating the backward recursion omits future information.","url":"https://arxiv.org/abs/2611.07415","relationship":"Source-stated"},{"text":"Under stated decay and regularity assumptions, the authors derive a two-sided gradient turnpike property: gradient-contribution error decreases exponentially with distance from both context boundaries.","excerpt":"the error between truncated- and full-context gradient contributions decays exponentially with the distance from both context boundaries.","url":"https://arxiv.org/abs/2611.07415","relationship":"Source-stated"},{"text":"The authors report that numerical experiments on the Wiener-Hammerstein benchmark illustrate the roles of past and future context and the trade-off between predictive performance and computational cost.","excerpt":"Numerical experiments on the Wiener-Hammerstein benchmark illustrate the role of past and future context and the trade-off between predictive performance and computational cost.","url":"https://arxiv.org/abs/2611.07415","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2611.07415","checkedAt":"2026-11-08T22:44:06.586Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"On the Wiener-Hammerstein benchmark, how do the roles of past and future context shape the trade-off between predictive performance and computational cost?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Truncated backpropagation through time induces a two-sided gradient turnpike property","summary":"Julian D. Schiller and Matthias A. Müller argue that truncated backpropagation through time creates two boundary effects when training recurrent models: the forward state misses past information, and the backward recursion omits future information. Their proposed analysis separates the interval used to average gradient contributions for a parameter update from the larger temporal context used to compute them. Under exponential decay of the state-transition Jacobians and mild regularity conditions, they derive a two-sided gradient turnpike property: the error between truncated- and full-context gradient contributions decays exponentially with distance from either context boundary. The authors also derive bounds for averaged update gradients, including guarantees on gradient alignment and approximate full-context stationarity. Under unbiased interval sampling and a Polyak-Lojasiewicz condition, they say these results imply a bound on the resulting optimality gap. Numerical experiments on the Wiener-Hammerstein benchmark illustrate the roles of past and future context and a trade-off between predictive performance and computational cost. The abstract reports theoretical results under stated conditions and experiments on that benchmark; it does not establish how broadly the findings apply beyond it.","question":"On the Wiener-Hammerstein benchmark, how do the roles of past and future context shape the trade-off between predictive performance and computational cost?","claims":[{"text":"The authors say truncated backpropagation through time reduces training computation by restricting gradient calculations to shorter temporal contexts, but this creates missing-past and omitted-future effects.","excerpt":"This induces two distinct boundary effects: missing past information perturbs the forward state evolution, while truncating the backward recursion omits future information.","support":"source-stated"},{"text":"Under stated decay and regularity assumptions, the authors derive a two-sided gradient turnpike property: gradient-contribution error decreases exponentially with distance from both context boundaries.","excerpt":"the error between truncated- and full-context gradient contributions decays exponentially with the distance from both context boundaries.","support":"source-stated"},{"text":"The authors report that numerical experiments on the Wiener-Hammerstein benchmark illustrate the roles of past and future context and the trade-off between predictive performance and computational cost.","excerpt":"Numerical experiments on the Wiener-Hammerstein benchmark illustrate the role of past and future context and the trade-off between predictive performance and computational cost.","support":"source-stated"}],"limitations":["The supplied evidence is the paper's abstract, not its full derivations or experimental results.","The abstract states assumptions for the theoretical guarantees and reports experiments on the Wiener-Hammerstein benchmark; it does not establish how broadly the findings apply beyond that benchmark."],"mediaId":"2cec17e5171cfd8b0c7525d3b841e25a2d8ab511b72720ffe85d0efbca92e346","correctionReason":null,"id":"e91d068db2b93f76507311a9de5fd883ce30793b470b7393df9a0f486b878ab7","publicationId":"e422a73e-f829-4c08-8215-e3f7c4d4e0fa","version":1,"sourceUrl":"https://arxiv.org/abs/2611.07415","sourceTitle":"Truncated backpropagation through time induces a two-sided gradient turnpike property","publisher":"arXiv eess.SY","publishedAt":null,"originalPublished":{"value":"2026-11-05","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-11-06T05:00:53.752Z","createdAt":"2026-11-08T21:40:29.861Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"2cec17e5171cfd8b0c7525d3b841e25a2d8ab511b72720ffe85d0efbca92e346","kind":"article","url":"https://arxiv.org/abs/2611.07415","sourceUrl":"https://arxiv.org/abs/2611.07415","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-11-08T21:39:50.403Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2611.07415","checkedAt":"2026-11-08T22:44:06.586Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"2cec17e5171cfd8b0c7525d3b841e25a2d8ab511b72720ffe85d0efbca92e346","confidence":0.52,"provider":"typesafe","model":"jev-1.13.0","inputHash":"c6c05fd3f275737356a643af2b564762c507f21f512304d15831291010d690fb","decidedAt":"2026-11-08T21:39:52.244Z"},"provenance":{"textHash":"99c447b9b0fca3186e8835c758225744e2aef1bf2a3cdc2654be26f9189cc374","observationId":"579834f61bbd9e139c9261a1c9f98c225a7e01fe701fcaa68ed118194bfeb769","retrievedAt":"2026-11-08T21:39:50.096Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"5b42a7c8-7a7b-4c98-86c5-5ce5ace96b5a"},"reviewer":{"model":"gpt-6-luna","runId":"a24fa017-53e6-4408-8297-78ff33347171"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-08T21:40:29.861Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Schiller, Julian D.","url":null,"kind":"unknown"},{"name":"Müller, Matthias A.","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Schiller, Julian D.","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Schiller, Julian D.","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.07415","locator":"head/meta[name=author|citation_author]"},"assertionId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-08T22:44:06.586Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027","sourceUrl":"https://arxiv.org/abs/2611.07415","contentSha256":"6d227052aad9ef74d757a19b488f7ae494b14a1151f631ff53db07450f861498","textHash":"99c447b9b0fca3186e8835c758225744e2aef1bf2a3cdc2654be26f9189cc374","observedAt":"2026-11-08T22:44:06.586Z"}},{"actor":{"name":"Müller, Matthias A.","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Müller, Matthias A.","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2611.07415","locator":"head/meta[name=author|citation_author]"},"assertionId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-08T22:44:06.586Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027","sourceUrl":"https://arxiv.org/abs/2611.07415","contentSha256":"6d227052aad9ef74d757a19b488f7ae494b14a1151f631ff53db07450f861498","textHash":"99c447b9b0fca3186e8835c758225744e2aef1bf2a3cdc2654be26f9189cc374","observedAt":"2026-11-08T22:44:06.586Z"}}],"participationCoverage":{"total":2,"returned":2,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2611.07415","snapshotId":"e93b52f32cf88aaed85d2ba1dc0b1a5bc5af1cc51609c34b6d5c5f6abe195027","contentSha256":"6d227052aad9ef74d757a19b488f7ae494b14a1151f631ff53db07450f861498","textHash":"99c447b9b0fca3186e8835c758225744e2aef1bf2a3cdc2654be26f9189cc374","observedAt":"2026-11-08T22:44:06.586Z","basis":"publisher-page-metadata","sourceLinks":44,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/e91d068db2b93f76507311a9de5fd883ce30793b470b7393df9a0f486b878ab7/source"}}}],"audits":[{"publicationId":"e422a73e-f829-4c08-8215-e3f7c4d4e0fa","verdict":"uncertain","findings":[{"dimension":"attribution","status":"pass","explanation":"The reading correctly attributes the paper and its claims to Julian D. Schiller and Matthias A. Müller. It frames the results as the authors' derivations and reports rather than as established facts.","excerpt":"Authors:Julian D. Schiller, Matthias A. Müller"},{"dimension":"summary","status":"uncertain","explanation":"The summary is faithful but closely restates the abstract's jargon without plain-language explanation. Central terms are undefined: truncated backpropagation through time, recurrent models, state-transition Jacobians, turnpike property, full-context stationarity, the Polyak-Lojasiewicz condition and the Wiener-Hammerstein benchmark. It also does not say why the result might matter, such as cheaper training of sequence models with error bounds. A non-specialist cannot follow it. Smallest repair: add plain-language glosses or term-library tooltips and one sentence on the training-cost significance the source supports.","excerpt":"Truncated backpropagation through time reduces the computational burden of training recurrent models by restricting gradient computations to shorter temporal contexts."},{"dimension":"date","status":"pass","explanation":"The originalPublished date of 2026-11-05 matches the v1 submission date. The feed date of Nov 6 is correctly not used.","excerpt":"[Submitted on 5 Nov 2026]"},{"dimension":"support","status":"pass","explanation":"Each claim and the summary's conditional results are supported verbatim by the abstract, and their conditions are preserved. The limitations accurately note that only the abstract was reviewed and that generality is not established. The question follows from the reported experiments without assuming an answer. No benefits or costs are invented.","excerpt":"under unbiased interval sampling and a Polyak-Lojasiewicz condition, these further imply a bound on the resulting optimality gap"},{"dimension":"media","status":"pass","explanation":"The selected media is the verified arXiv abstract page for this paper. It is the direct original source.","excerpt":null}],"createdAt":"2026-11-08T21:40:29.861Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}