{"id":"ecbe8abdd43d84940ede6c4d631a5e1aa7fce44f00f1678d57610afb57054fe7","title":"Donghyeon Cho et al. on Certificate-Guided Visual Routing for Diffusion VLMs","argument":"Donghyeon Cho, Joowon Kim, Seungho Shin and Eunho Yang argue that diffusion vision-language models (DVLMs) often spend decoding computation repeatedly processing an image, even when a prediction step may not need it. They report that visual tokens make up 89–97% of the input on the benchmarks they studied, yet removing the image changed a token being unmasked in only 22.8% of steps on average, and as few as 7.9%.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"These results are reported for ten benchmarks and LLaDA-V; the abstract does not give benchmark-level scores or establish performance in real-world use. The supplied evidence is the paper abstract, so it does not include detailed evaluation methods or results beyond the reported summary.","speaker":{"name":"Cho, Donghyeon, Kim, Joowon, Shin, Seungho, Yang, Eunho","roleAtSource":"Article author"},"source":{"title":"Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.36671","publishedAt":"2026-10-27","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors say visual tokens account for 89–97% of input on the benchmarks studied, and removing the image changes a token being unmasked in 22.8% of steps on average, with a low of 7.9%.","excerpt":"visual tokens make up 89-97% of this input on the benchmarks we study","url":"https://arxiv.org/abs/2610.36671","relationship":"Source-stated"},{"text":"The authors propose LOWN, which checks whether predictions made without the image agree with the last image-conditioned prediction; agreement allows that earlier prediction to be reused.","excerpt":"processes the image only when this prediction disagrees with the last image-conditioned prediction","url":"https://arxiv.org/abs/2610.36671","relationship":"Source-stated"},{"text":"The authors report that LOWN retains 99.6% of LLaDA-V's score on ten benchmarks while using 35.3% of its decoding FLOPs on average, without training or a tuned threshold.","excerpt":"Without training or a tuned threshold, LOWN retains 99.6% of LLaDA-V's score on ten benchmarks at 35.3% of the decoding FLOPs on average.","url":"https://arxiv.org/abs/2610.36671","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.36671","checkedAt":"2026-11-14T02:52:07.912Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How do LOWN’s score and decoding FLOPs vary across the ten benchmarks, especially where the authors found different levels of image need?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Donghyeon Cho et al. on Certificate-Guided Visual Routing for Diffusion VLMs","summary":"Donghyeon Cho, Joowon Kim, Seungho Shin and Eunho Yang argue that diffusion vision-language models (DVLMs) often spend decoding computation repeatedly processing an image, even when a prediction step may not need it. They report that visual tokens make up 89–97% of the input on the benchmarks they studied, yet removing the image changed a token being unmasked in only 22.8% of steps on average, and as few as 7.9%.","question":"How do LOWN’s score and decoding FLOPs vary across the ten benchmarks, especially where the authors found different levels of image need?","claims":[{"text":"The authors say visual tokens account for 89–97% of input on the benchmarks studied, and removing the image changes a token being unmasked in 22.8% of steps on average, with a low of 7.9%.","excerpt":"visual tokens make up 89-97% of this input on the benchmarks we study","support":"source-stated"},{"text":"The authors propose LOWN, which checks whether predictions made without the image agree with the last image-conditioned prediction; agreement allows that earlier prediction to be reused.","excerpt":"processes the image only when this prediction disagrees with the last image-conditioned prediction","support":"source-stated"},{"text":"The authors report that LOWN retains 99.6% of LLaDA-V's score on ten benchmarks while using 35.3% of its decoding FLOPs on average, without training or a tuned threshold.","excerpt":"Without training or a tuned threshold, LOWN retains 99.6% of LLaDA-V's score on ten benchmarks at 35.3% of the decoding FLOPs on average.","support":"source-stated"}],"limitations":["These results are reported for ten benchmarks and LLaDA-V; the abstract does not give benchmark-level scores or establish performance in real-world use.","The supplied evidence is the paper abstract, so it does not include detailed evaluation methods or results beyond the reported summary."],"mediaId":"eaf5537ff4da4da9730e5bb3c10efc8353c242cdee1509f0a290dbbded7808aa","correctionReason":null,"id":"ecbe8abdd43d84940ede6c4d631a5e1aa7fce44f00f1678d57610afb57054fe7","publicationId":"2830994d-8f7a-4a54-81a0-8fd2f4160d1b","version":1,"sourceUrl":"https://arxiv.org/abs/2610.36671","sourceTitle":"Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-27","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-28T08:36:41.361Z","createdAt":"2026-11-14T01:48:02.644Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"eaf5537ff4da4da9730e5bb3c10efc8353c242cdee1509f0a290dbbded7808aa","kind":"article","url":"https://arxiv.org/abs/2610.36671","sourceUrl":"https://arxiv.org/abs/2610.36671","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-30T22:49:05.988Z","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/2610.36671","checkedAt":"2026-11-14T02:52:07.912Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"eaf5537ff4da4da9730e5bb3c10efc8353c242cdee1509f0a290dbbded7808aa","confidence":0.91,"provider":"typesafe","model":"jev-1.13.0","inputHash":"f7cf2f44c4a38cb9acbed0d91e2adf15c95494a5661d1190ca77ce21fe5e2061","decidedAt":"2026-10-30T22:49:08.182Z"},"provenance":{"textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observationId":"d642f80b94ca1726c2163fdbf586bff4d0406df228f34c5416bd3b5ca4f1c369","retrievedAt":"2026-10-30T22:49:05.642Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"0b229ce1-a1fe-44ac-875e-ae2425fad4c1"},"reviewer":{"model":"gpt-6-luna","runId":"5f9a4314-f50c-4b79-824d-ec0ae3ded13f"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-14T01:48:02.644Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Cho, Donghyeon","url":null,"kind":"unknown"},{"name":"Kim, Joowon","url":null,"kind":"unknown"},{"name":"Shin, Seungho","url":null,"kind":"unknown"},{"name":"Yang, Eunho","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Cho, Donghyeon","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Cho, Donghyeon","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36671","locator":"head/meta[name=author|citation_author]"},"assertionId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-14T02:52:07.912Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","sourceUrl":"https://arxiv.org/abs/2610.36671","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z"}},{"actor":{"name":"Kim, Joowon","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Kim, Joowon","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36671","locator":"head/meta[name=author|citation_author]"},"assertionId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-14T02:52:07.912Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","sourceUrl":"https://arxiv.org/abs/2610.36671","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z"}},{"actor":{"name":"Shin, Seungho","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Shin, Seungho","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36671","locator":"head/meta[name=author|citation_author]"},"assertionId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-14T02:52:07.912Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","sourceUrl":"https://arxiv.org/abs/2610.36671","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z"}},{"actor":{"name":"Yang, Eunho","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Yang, Eunho","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36671","locator":"head/meta[name=author|citation_author]"},"assertionId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-14T02:52:07.912Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","sourceUrl":"https://arxiv.org/abs/2610.36671","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z"}}],"participationCoverage":{"total":4,"returned":4,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.36671","snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z","basis":"publisher-page-metadata","sourceLinks":45,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/ecbe8abdd43d84940ede6c4d631a5e1aa7fce44f00f1678d57610afb57054fe7/source"}},"canonicalUrl":"https://escapemeridian.com/readings/donghyeon-cho-et-al-on-certificate-guided-visual-routing-for-ecbe8abd","formats":{"html":"/readings/donghyeon-cho-et-al-on-certificate-guided-visual-routing-for-ecbe8abd","markdown":"/readings/donghyeon-cho-et-al-on-certificate-guided-visual-routing-for-ecbe8abd.md","json":"/readings/donghyeon-cho-et-al-on-certificate-guided-visual-routing-for-ecbe8abd.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":[],"conditions":[],"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"},{"id":"term-flops","name":"Floating-point operations","explanation":"A count of basic arithmetic steps; used to measure how much computation went into training a model.","referenceUrl":"https://en.wikipedia.org/wiki/FLOPS"}],"entities":[{"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":"ecbe8abdd43d84940ede6c4d631a5e1aa7fce44f00f1678d57610afb57054fe7","revision":"2830994d-8f7a-4a54-81a0-8fd2f4160d1b","revisionKind":"publication-id","title":"Donghyeon Cho et al. on Certificate-Guided Visual Routing for Diffusion VLMs","url":"https://escapemeridian.com/readings/donghyeon-cho-et-al-on-certificate-guided-visual-routing-for-ecbe8abd"},"source":{"type":"source","id":"https://arxiv.org/abs/2610.36671","revision":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","revisionKind":"sha256-source-text","title":"Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs","url":"https://arxiv.org/abs/2610.36671"},"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":"ecbe8abdd43d84940ede6c4d631a5e1aa7fce44f00f1678d57610afb57054fe7","title":"Donghyeon Cho et al. on Certificate-Guided Visual Routing for Diffusion VLMs","argument":"Donghyeon Cho, Joowon Kim, Seungho Shin and Eunho Yang argue that diffusion vision-language models (DVLMs) often spend decoding computation repeatedly processing an image, even when a prediction step may not need it. They report that visual tokens make up 89–97% of the input on the benchmarks they studied, yet removing the image changed a token being unmasked in only 22.8% of steps on average, and as few as 7.9%.","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"These results are reported for ten benchmarks and LLaDA-V; the abstract does not give benchmark-level scores or establish performance in real-world use. The supplied evidence is the paper abstract, so it does not include detailed evaluation methods or results beyond the reported summary.","speaker":{"name":"Cho, Donghyeon, Kim, Joowon, Shin, Seungho, Yang, Eunho","roleAtSource":"Article author"},"source":{"title":"Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.36671","publishedAt":"2026-10-27","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors say visual tokens account for 89–97% of input on the benchmarks studied, and removing the image changes a token being unmasked in 22.8% of steps on average, with a low of 7.9%.","excerpt":"visual tokens make up 89-97% of this input on the benchmarks we study","url":"https://arxiv.org/abs/2610.36671","relationship":"Source-stated"},{"text":"The authors propose LOWN, which checks whether predictions made without the image agree with the last image-conditioned prediction; agreement allows that earlier prediction to be reused.","excerpt":"processes the image only when this prediction disagrees with the last image-conditioned prediction","url":"https://arxiv.org/abs/2610.36671","relationship":"Source-stated"},{"text":"The authors report that LOWN retains 99.6% of LLaDA-V's score on ten benchmarks while using 35.3% of its decoding FLOPs on average, without training or a tuned threshold.","excerpt":"Without training or a tuned threshold, LOWN retains 99.6% of LLaDA-V's score on ten benchmarks at 35.3% of the decoding FLOPs on average.","url":"https://arxiv.org/abs/2610.36671","relationship":"Source-stated"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.36671","checkedAt":"2026-11-14T02:52:07.912Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How do LOWN’s score and decoding FLOPs vary across the ten benchmarks, especially where the authors found different levels of image need?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Donghyeon Cho et al. on Certificate-Guided Visual Routing for Diffusion VLMs","summary":"Donghyeon Cho, Joowon Kim, Seungho Shin and Eunho Yang argue that diffusion vision-language models (DVLMs) often spend decoding computation repeatedly processing an image, even when a prediction step may not need it. They report that visual tokens make up 89–97% of the input on the benchmarks they studied, yet removing the image changed a token being unmasked in only 22.8% of steps on average, and as few as 7.9%.","question":"How do LOWN’s score and decoding FLOPs vary across the ten benchmarks, especially where the authors found different levels of image need?","claims":[{"text":"The authors say visual tokens account for 89–97% of input on the benchmarks studied, and removing the image changes a token being unmasked in 22.8% of steps on average, with a low of 7.9%.","excerpt":"visual tokens make up 89-97% of this input on the benchmarks we study","support":"source-stated"},{"text":"The authors propose LOWN, which checks whether predictions made without the image agree with the last image-conditioned prediction; agreement allows that earlier prediction to be reused.","excerpt":"processes the image only when this prediction disagrees with the last image-conditioned prediction","support":"source-stated"},{"text":"The authors report that LOWN retains 99.6% of LLaDA-V's score on ten benchmarks while using 35.3% of its decoding FLOPs on average, without training or a tuned threshold.","excerpt":"Without training or a tuned threshold, LOWN retains 99.6% of LLaDA-V's score on ten benchmarks at 35.3% of the decoding FLOPs on average.","support":"source-stated"}],"limitations":["These results are reported for ten benchmarks and LLaDA-V; the abstract does not give benchmark-level scores or establish performance in real-world use.","The supplied evidence is the paper abstract, so it does not include detailed evaluation methods or results beyond the reported summary."],"mediaId":"eaf5537ff4da4da9730e5bb3c10efc8353c242cdee1509f0a290dbbded7808aa","correctionReason":null,"id":"ecbe8abdd43d84940ede6c4d631a5e1aa7fce44f00f1678d57610afb57054fe7","publicationId":"2830994d-8f7a-4a54-81a0-8fd2f4160d1b","version":1,"sourceUrl":"https://arxiv.org/abs/2610.36671","sourceTitle":"Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-27","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-28T08:36:41.361Z","createdAt":"2026-11-14T01:48:02.644Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"eaf5537ff4da4da9730e5bb3c10efc8353c242cdee1509f0a290dbbded7808aa","kind":"article","url":"https://arxiv.org/abs/2610.36671","sourceUrl":"https://arxiv.org/abs/2610.36671","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-30T22:49:05.988Z","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/2610.36671","checkedAt":"2026-11-14T02:52:07.912Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"eaf5537ff4da4da9730e5bb3c10efc8353c242cdee1509f0a290dbbded7808aa","confidence":0.91,"provider":"typesafe","model":"jev-1.13.0","inputHash":"f7cf2f44c4a38cb9acbed0d91e2adf15c95494a5661d1190ca77ce21fe5e2061","decidedAt":"2026-10-30T22:49:08.182Z"},"provenance":{"textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observationId":"d642f80b94ca1726c2163fdbf586bff4d0406df228f34c5416bd3b5ca4f1c369","retrievedAt":"2026-10-30T22:49:05.642Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"0b229ce1-a1fe-44ac-875e-ae2425fad4c1"},"reviewer":{"model":"gpt-6-luna","runId":"5f9a4314-f50c-4b79-824d-ec0ae3ded13f"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-14T01:48:02.644Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Cho, Donghyeon","url":null,"kind":"unknown"},{"name":"Kim, Joowon","url":null,"kind":"unknown"},{"name":"Shin, Seungho","url":null,"kind":"unknown"},{"name":"Yang, Eunho","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Cho, Donghyeon","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Cho, Donghyeon","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36671","locator":"head/meta[name=author|citation_author]"},"assertionId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-14T02:52:07.912Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","sourceUrl":"https://arxiv.org/abs/2610.36671","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z"}},{"actor":{"name":"Kim, Joowon","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Kim, Joowon","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36671","locator":"head/meta[name=author|citation_author]"},"assertionId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-14T02:52:07.912Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","sourceUrl":"https://arxiv.org/abs/2610.36671","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z"}},{"actor":{"name":"Shin, Seungho","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Shin, Seungho","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36671","locator":"head/meta[name=author|citation_author]"},"assertionId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-14T02:52:07.912Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","sourceUrl":"https://arxiv.org/abs/2610.36671","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z"}},{"actor":{"name":"Yang, Eunho","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Yang, Eunho","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36671","locator":"head/meta[name=author|citation_author]"},"assertionId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-14T02:52:07.912Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","sourceUrl":"https://arxiv.org/abs/2610.36671","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z"}}],"participationCoverage":{"total":4,"returned":4,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.36671","snapshotId":"2941778e22160a20871bad9fe8b04a9c0dbb0dec0e1e69d5919ca2bacaa32f0d","contentSha256":"bcf5fea8f4cac392224789eca6bfedc59a3bd5aafc5e895df88bc08aefa40ed5","textHash":"ed5ea3e12479509dc02172c897fb71c53dffaa93368d620458203f760f6f4886","observedAt":"2026-11-14T02:52:07.912Z","basis":"publisher-page-metadata","sourceLinks":45,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/ecbe8abdd43d84940ede6c4d631a5e1aa7fce44f00f1678d57610afb57054fe7/source"}}}],"audits":[{"publicationId":"2830994d-8f7a-4a54-81a0-8fd2f4160d1b","verdict":"uncertain","findings":[{"dimension":"attribution","status":"pass","explanation":"The draft correctly credits the four listed authors (Donghyeon Cho, Joowon Kim, Seungho Shin, Eunho Yang). It keeps findings attributed with phrases like 'argue', 'report' and 'the authors say'.","excerpt":"Authors:Donghyeon Cho, Joowon Kim, Seungho Shin, Eunho Yang"},{"dimension":"summary","status":"uncertain","explanation":"The summary states the problem and the image-need measurement but never names LOWN or its headline result, retaining 99.6% of the score at 35.3% of decoding FLOPs. Those appear only in the claims. It also does not say why this matters, such as cheaper inference. Central terms like 'denoising/decoding step', 'token being unmasked' and 'decoding FLOPs' are not explained or flagged for tooltips. A non-specialist would not learn what changed. Smallest repair: add one sentence naming LOWN, its skip-the-image-when-predictions-agree mechanism and its reported compute saving. Also suggest term-library entries for 'unmasking' and 'FLOPs'.","excerpt":"We therefore propose LOWN, a decoding method that decides at every step whether to process the image and how many tokens to unmask."},{"dimension":"date","status":"pass","explanation":"originalPublished is 2026-10-27, which matches the v1 submission date. It is not the later feed date of 2026-10-29.","excerpt":"[Submitted on 27 Oct 2026]"},{"dimension":"support","status":"pass","explanation":"All three claims match the abstract. These are the token share and step percentages, the agreement-based reuse mechanism, and 99.6% of the score at 35.3% FLOPs without training or a tuned threshold. The question follows from the source's observation that image need differs across benchmarks. The limitations appropriately note that only abstract-level, benchmark-reported results are available.","excerpt":"removing the image changes a token being unmasked at only 22.8% of steps on average and at as few as 7.9%"},{"dimension":"media","status":"pass","explanation":"The selected media is the verified direct arXiv abstract page for this paper, which is the most direct access to the source.","excerpt":null}],"createdAt":"2026-11-14T01:48:02.644Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}