{"as_of":"2026-08-18T22:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a87716a486cd44510aa19f50ce4734500c5b8d491d56bac84d45e46ee4b22d7f","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T04:28:45.737861Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.02989/citation-record","integrity":"/paper/2608.02989/integrity","json":"/paper/2608.02989/citation-record.json","paper":"/paper/2608.02989"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.00342","last_updated":"2026-05-01T01:52:01Z","snapshot_observed_at":"2026-08-03T04:07:32.005688Z","submitted_at":"2026-05-01T01:52:01Z","title":"Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding","version":1},"cited_work":{"arxiv_id":"2605.00342","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.00342","snapshot_observed_at":"2026-08-08T04:28:46.409497Z","title":"Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding","venue":"cs.CL","work_id":"581bf5c3-4bae-4baa-adb7-076b292d5560","year":2026},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.230646Z"},"links":{"cited_paper":"/paper/2605.00342","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:ee6c59ea88f14b0d8042a651675c390679acb6d244c70898f0c9150ca837b40a","observation_id":"40891e98-c9b6-40e4-bbbd-599796115124","resolution":{"observed_at":"2026-08-08T04:28:46.463927Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.249203Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.249203Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:b479d7bfa14a85b3a2fa2456ef337fd551f1997e328f890d850b695e5919270c","observation_id":"f982ab5e-4fbe-4dbe-b715-5723364ae50c","resolution":{"observed_at":"2026-08-08T04:28:45.249203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:47.419513Z","title":"Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=","venue":null,"work_id":"c076d8e7-6a98-47d9-9952-b3aaa4373804","year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.304313Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:1ac384fc584b622e748be4bf58f153155800787377756075980a4700285a986a","observation_id":"eaa22819-ae9f-4525-b4ef-8b42c05e1c1e","resolution":{"observed_at":"2026-08-08T04:28:47.422643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.326826Z","title":"International Conference on Machine Learning , pages=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.326826Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:73bd079f73c4611ed53aa12672401dffcdee6bf459b7416fea8cd49c57840cf3","observation_id":"ba906e9e-ccf1-4d2b-83ef-ab80e51e217b","resolution":{"observed_at":"2026-08-08T04:28:45.326826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10774","last_updated":"2024-06-14T23:32:32Z","snapshot_observed_at":"2026-08-17T10:13:54.763177Z","submitted_at":"2024-01-19T15:48:40Z","title":"Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10774","snapshot_observed_at":"2026-08-08T04:28:45.376233Z","title":"arXiv preprint arXiv:2401.10774 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.376233Z"},"links":{"cited_paper":"/paper/2401.10774","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:00eebabaace8ac2bc306b0ed3060039659b4c46cfddabc5b8378a228423a8804","observation_id":"c2967bb7-6d58-4591-9511-e227af83b518","resolution":{"observed_at":"2026-08-08T04:28:45.376233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:47.404788Z","title":"Proceedings of the 41st International Conference on Machine Learning , pages=","venue":null,"work_id":"61fb19c8-06a8-49f1-baad-6ba2371f61bb","year":2024},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.431784Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:9212612ca50f16172d291141a786b71d4c855c7d20896138485421407ddf1654","observation_id":"4d026f86-d32e-478c-80c7-9b31df619ec4","resolution":{"observed_at":"2026-08-08T04:28:47.407865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.480710Z","title":"Proceedings of the 2024 conference on empirical methods in natural language processing , pages=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.480710Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:4f8fdc410044f5249afbecde23e8a08e7698879b41c5833109b03235ab393148","observation_id":"197e7dc6-7e38-4a04-85f7-86e3cece165e","resolution":{"observed_at":"2026-08-08T04:28:45.480710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.503248Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.503248Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:b7d962b5922140d7b60854c430ef4eedf581b0beb975cfb03e7914241d1cdafd","observation_id":"cd67aca6-68ae-4a82-ae65-3519d8285285","resolution":{"observed_at":"2026-08-08T04:28:45.503248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.506728Z","title":"arXiv preprint arXiv:2602.00879 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.506728Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:2aa66d4b0b8bc57dd1ba618c19f47e0cd91cd99e486db8354e20d79e5401d7a1","observation_id":"c66d6c16-b8bc-4bb5-904c-45c69943f671","resolution":{"observed_at":"2026-08-08T04:28:45.506728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.510282Z","title":"arXiv preprint arXiv:2602.07265 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.510282Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:235c9db6dc56d501df053d13c02b8324a09b75e982a93c08ac93763d064ad476","observation_id":"72af2458-dedb-4f58-a9eb-2b890fe3f011","resolution":{"observed_at":"2026-08-08T04:28:45.510282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.514248Z","title":"arXiv preprint arXiv:2602.16052 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.514248Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:dd5f0641cab784a041a76498cc796c8c5adf5ca6189da6f11e6347fd6d4ee820","observation_id":"09e1ad2c-f11c-400f-b4d9-d3694b06e949","resolution":{"observed_at":"2026-08-08T04:28:45.514248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:47.382178Z","title":"Advances in Neural Information Processing Systems , volume=","venue":null,"work_id":"308d94c5-3f8e-4ef5-abf7-5669032f894c","year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.518794Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:2d135b435c919bb802018d9a148d48fe289b92dffe9024e42422cd5dbd856e49","observation_id":"261d7fbf-fe80-4230-96e7-29c56164caae","resolution":{"observed_at":"2026-08-08T04:28:47.386696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.20675","last_updated":"2025-06-17T20:06:08Z","snapshot_observed_at":"2026-08-17T10:07:57.659273Z","submitted_at":"2025-06-17T20:06:08Z","title":"Utility-Driven Speculative Decoding for Mixture-of-Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.20675","snapshot_observed_at":"2026-08-08T04:28:45.522404Z","title":"arXiv preprint arXiv:2506.20675 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.522404Z"},"links":{"cited_paper":"/paper/2506.20675","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:2da6fe437241c2fc0bc2be76a8d08c32c926212cd51484d4f3837781ac3110f9","observation_id":"d2e98d84-367e-4c92-bfcd-7f427db58668","resolution":{"observed_at":"2026-08-08T04:28:45.522404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.526228Z","title":"arXiv preprint arXiv:2510.10302 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.526228Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:4f93370d23e853bcbbc2684fe7748afbd76d1fefc1092bc678cd8b50fa8a6703","observation_id":"05f779c5-1e18-45ee-a7c4-86f9e68ca0bd","resolution":{"observed_at":"2026-08-08T04:28:45.526228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.529649Z","title":"arXiv preprint arXiv:2511.14102 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.529649Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:94b769daa5c7314574c0bfc773c44c555fc57e05f4020ae36b2b0da975a24185","observation_id":"feb84e96-8b1e-459c-a140-ca894955dc31","resolution":{"observed_at":"2026-08-08T04:28:45.529649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2607.12696","last_updated":"2026-07-14T12:22:55Z","snapshot_observed_at":"2026-08-18T08:49:36.544673Z","submitted_at":"2026-07-14T12:22:55Z","title":"Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts","version":1},"cited_work":{"arxiv_id":"2607.12696","doi":null,"metadata_source":"pith","pith_arxiv_id":"2607.12696","snapshot_observed_at":"2026-08-08T04:28:45.916523Z","title":"Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts","venue":"cs.CL","work_id":"8ec482f2-e556-4c4a-85ea-c84b6d01abb1","year":2026},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.532879Z"},"links":{"cited_paper":"/paper/2607.12696","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:440b9b761f109a9ce73bcb41c977bd4fa94ea68386e370ad89abde52e9da7553","observation_id":"297cf30c-4967-4e5e-afe4-89dd6058c12d","resolution":{"observed_at":"2026-08-08T04:28:45.963003Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.10925","last_updated":"2025-08-08T19:24:38Z","snapshot_observed_at":"2026-08-14T02:46:12.121034Z","submitted_at":"2025-08-08T19:24:38Z","title":"gpt-oss-120b & gpt-oss-20b Model Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.10925","snapshot_observed_at":"2026-08-08T04:28:45.536387Z","title":"arXiv preprint arXiv:2508.10925 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.536387Z"},"links":{"cited_paper":"/paper/2508.10925","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:d3f496455f2c7894461ef1ee46fab1da7116886d7f953b945c4c099b2c88046a","observation_id":"b8c0daca-02c6-47b7-8a09-66a6e7a13747","resolution":{"observed_at":"2026-08-08T04:28:45.536387Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-08-08T04:28:45.539970Z","title":"arXiv preprint arXiv:2505.09388 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.539970Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:c7117d7292dd24eb130d36a7b7d596b864231c18a3e7f42d90acfa3804bdc9d5","observation_id":"0a7ac98e-ddeb-42a4-bccd-366a1d57bd7a","resolution":{"observed_at":"2026-08-08T04:28:45.539970Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-08T04:28:45.543636Z","title":"arXiv preprint arXiv:2501.12948 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.543636Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:9c1ae2faef838b4aac097c9f074d5a963e93be0a10742eba24861e4fe8d5e233","observation_id":"e9ed16b7-e723-4dce-8711-2f377fd87a48","resolution":{"observed_at":"2026-08-08T04:28:45.543636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.00277","last_updated":"2022-06-02T03:44:04Z","snapshot_observed_at":"2026-08-16T16:56:06.467083Z","submitted_at":"2022-06-01T07:09:01Z","title":"Task-Specific Expert Pruning for Sparse Mixture-of-Experts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.00277","snapshot_observed_at":"2026-08-08T04:28:45.546886Z","title":"arXiv preprint arXiv:2206.00277 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.546886Z"},"links":{"cited_paper":"/paper/2206.00277","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:55b179c2f0485d737114518b4c031ff2667dd00a0e90640b2a8d6a40390a6c92","observation_id":"43d2ea65-847b-47c0-a16d-36c36afef197","resolution":{"observed_at":"2026-08-08T04:28:45.546886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:47.371676Z","title":"Findings of the Association for Computational Linguistics: ACL 2025 , pages=","venue":null,"work_id":"e22fe610-ea0c-47c7-b20d-766f5dc9553e","year":2025},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.550905Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:a7b5c94419ab435ae95411f6f51a0bb39be1160d36191a771a9903d8e4876ea7","observation_id":"1e68fcb3-c105-4fc6-942c-ddbbdd580b73","resolution":{"observed_at":"2026-08-08T04:28:47.375132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04088","last_updated":"2024-01-08T18:47:34Z","snapshot_observed_at":"2026-08-13T19:43:49.936776Z","submitted_at":"2024-01-08T18:47:34Z","title":"Mixtral of Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04088","snapshot_observed_at":"2026-08-08T04:28:45.555055Z","title":"arXiv preprint arXiv:2401.04088 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.555055Z"},"links":{"cited_paper":"/paper/2401.04088","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:611d3866b88f54a534d82a592fd310c20d1e087297f8df421d5820a2c29f3396","observation_id":"868d3776-de7f-4b0e-8bf8-20bb08ed6e10","resolution":{"observed_at":"2026-08-08T04:28:45.555055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:47.361016Z","title":", author=","venue":null,"work_id":"3ea26b10-cd69-425e-9cc9-896283f8d3c3","year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.558578Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:3d4cb44cc6ff7174228b21cb77faac90dee0cc8c4bf1df0f11ed0bac7d490c7b","observation_id":"7b3357a0-f2c3-47e3-8aeb-1e1099804456","resolution":{"observed_at":"2026-08-08T04:28:47.364766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-08T04:28:45.567902Z","title":"arXiv preprint arXiv:2107.03374 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.567902Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:af51d856b89a94f2ed55ac48137fa29e27b4d54205574fe8614553d4dd4170cf","observation_id":"2bc1aa3c-32b7-4f8f-b306-6683cefe1102","resolution":{"observed_at":"2026-08-08T04:28:45.567902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07732","last_updated":"2021-08-16T03:57:30Z","snapshot_observed_at":"2026-08-15T17:40:38.050939Z","submitted_at":"2021-08-16T03:57:30Z","title":"Program Synthesis with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07732","snapshot_observed_at":"2026-08-08T04:28:45.622086Z","title":"arXiv preprint arXiv:2108.07732 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.622086Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:3929b5e584447abf25eaba520602815d59e012f617c18971f025aebfb7fdf802","observation_id":"2d3118eb-73ee-476f-bd0f-39ec16a5406a","resolution":{"observed_at":"2026-08-08T04:28:45.622086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-14T02:43:01.480086Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-08T04:28:45.667947Z","title":"arXiv preprint arXiv:2110.14168 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.667947Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:a72efae74972cc16d01d67ce9339b6407bafb3c78e87e86285d4d93cc5d82bba","observation_id":"27e1af3d-e322-4312-98a5-d5b5aa25af59","resolution":{"observed_at":"2026-08-08T04:28:45.667947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.714220Z","title":"International Conference on Learning Representations , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.714220Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:145b39ba60842f3f0489913bc67d7366b0cef780332b146e0591510dc37b8504","observation_id":"ad90437c-bbd7-467b-8cc1-5ed87c28d249","resolution":{"observed_at":"2026-08-08T04:28:45.714220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.06669","last_updated":"2024-09-10T17:36:15Z","snapshot_observed_at":"2026-08-16T13:19:40.855704Z","submitted_at":"2024-09-10T17:36:15Z","title":"DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.06669","snapshot_observed_at":"2026-08-08T04:28:45.717663Z","title":"arXiv preprint arXiv:2409.06669 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.717663Z"},"links":{"cited_paper":"/paper/2409.06669","citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:6959ec104639dada348d564256babad764c6bbfb0c49b63cbbb7d4ee46cec688","observation_id":"4b475ee3-b301-4d0b-a27e-7bccad20a828","resolution":{"observed_at":"2026-08-08T04:28:45.717663Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.721255Z","title":"arXiv preprint arXiv:2511.02237 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.721255Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:22eb737a3e7d950c765b88449f2c2c06b25d4b2252f93b24ca44014d6a3338d0","observation_id":"17e6196b-3d7a-40cc-9c4d-958ad1f4c081","resolution":{"observed_at":"2026-08-08T04:28:45.721255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:47.169520Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=","venue":null,"work_id":"e7cfc9fe-046d-4af3-8e8a-5ba32735a785","year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.724688Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:cda7a1fdf7c7f5ded8fae733a00e5581c6b0ced066372d16fa2f723efd5ab90a","observation_id":"a12c9ff6-734e-46d3-9dff-3e6967b724d1","resolution":{"observed_at":"2026-08-08T04:28:47.256213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:46.906592Z","title":"Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=","venue":null,"work_id":"5f231938-ad68-40ae-b5a6-b6308f053b49","year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.727988Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:c8fc6e84bce6209b8343aa90c7a3ec2b6a505671efaa70ee0d41dcd5ba828418","observation_id":"752e7dcd-4143-48e0-b7cc-4de765aeee9c","resolution":{"observed_at":"2026-08-08T04:28:47.035331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:46.594101Z","title":"International Conference on Learning Representations , volume=","venue":null,"work_id":"37e533c1-d648-49fb-9623-d2d2e7a7ca5a","year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.731140Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:5e8d58387d19704d64dbfc540096424277625b517fe17f48ce285d3a0dabb4b8","observation_id":"b59d7a5c-a467-42d3-aaf7-1572bdba0ea2","resolution":{"observed_at":"2026-08-08T04:28:46.751206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:46.540514Z","title":"Proceedings of the 15th European Signal Processing Conference (EUSIPCO) , pages=","venue":null,"work_id":"86dc3788-a36e-4d6c-9314-2bca2540456e","year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.734440Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:b3738a3a070c8e166b9fec3a0818d11a79cc4e8c3204b04410c8635f3441e6eb","observation_id":"20c36199-48e3-47df-baa1-3606736b90bb","resolution":{"observed_at":"2026-08-08T04:28:46.551897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T04:28:45.737861Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-08T04:28:45.737861Z"},"links":{"citing_paper":"/paper/2608.02989"},"observation_digest":"sha256:dc6cc20e4ded1800ccca5f4d4970ec38a606eb3e6b4a9ff7dd823252ce00e2af","observation_id":"abb4f435-3d02-49a2-826e-000c21b0be87","resolution":{"observed_at":"2026-08-08T04:28:45.737861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.02989","last_updated":"2026-08-04T01:01:12Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T20:30:21.068545Z","submitted_at":"2026-08-04T01:01:12Z","title":"AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":23,"verified_exact":0,"verified_fuzzy":9},"total_outbound_references":34},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2608.02989."}