{"as_of":"2026-08-16T19:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ad9d8efda88c5f07b5b6fb314d4d026858281b235add732e71bf6aa9a3531842","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:17:34.858836Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T07:43:30.763192Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T12:26:56.815339Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"cited_work":{"arxiv_id":"2505.07858","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.07858","snapshot_observed_at":"2026-07-02T12:26:56.815339Z","title":"arXiv preprint arXiv:2505.07858 , year=","venue":null,"work_id":"73a38b4b-9921-417f-9368-ed0eba51dc10","year":2025},"citing_paper":{"arxiv_id":"2605.29707","last_updated":"2026-05-28T10:07:44Z","snapshot_observed_at":"2026-08-02T05:37:29.736109Z","submitted_at":"2026-05-28T10:07:44Z","title":"Domino: Decoupling Causal Modeling from Autoregressive Drafting in Speculative Decoding","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-06-29T07:43:30.763192Z"},"links":{"cited_paper":"/paper/2505.07858","citing_paper":"/paper/2605.29707"},"observation_digest":"sha256:23c9d79f4a04587969e58a3d2ce63db2e5076e8b86a7e22c516054db9319ad92","observation_id":"13276186-f9e5-44b5-b73c-09663e07f529","resolution":{"observed_at":"2026-06-29T07:53:14.179186Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"cited_work":{"arxiv_id":"2505.07858","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.07858","snapshot_observed_at":"2026-07-02T12:26:56.815339Z","title":"arXiv preprint arXiv:2505.07858 , year=","venue":null,"work_id":"73a38b4b-9921-417f-9368-ed0eba51dc10","year":2025},"citing_paper":{"arxiv_id":"2606.05883","last_updated":"2026-06-17T13:39:13Z","snapshot_observed_at":"2026-08-16T11:36:06.331491Z","submitted_at":"2026-06-04T08:53:58Z","title":"Geometry-Aware Dataset Condensation for Diffusion Model Training","version":2},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-06-28T02:07:54.718436Z"},"links":{"cited_paper":"/paper/2505.07858","citing_paper":"/paper/2606.05883"},"observation_digest":"sha256:f7c162e6a849214f5f9f619bc09cd04824683b9bb79ee4bc4cfad98e3ce57b79","observation_id":"8e9f2e97-b40f-42ed-86b1-bc30d7b9b20a","resolution":{"observed_at":"2026-07-02T12:26:56.816800Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.07858/citation-record","integrity":"/paper/2505.07858/integrity","json":"/paper/2505.07858/citation-record.json","paper":"/paper/2505.07858"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-08-13T17:41:53.092611Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-15T23:17:34.692295Z","title":"Scaling laws for neural language models","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.692295Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:72bc7c0235b1afd178c24fc7f33a9e28cca86c64953159b30aebea0709ce4ac7","observation_id":"a67170a9-3092-4871-9b02-9002644661bf","resolution":{"observed_at":"2026-08-15T23:17:34.692295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15556","last_updated":"2022-03-29T13:38:03Z","snapshot_observed_at":"2026-08-09T19:52:33.533277Z","submitted_at":"2022-03-29T13:38:03Z","title":"Training Compute-Optimal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15556","snapshot_observed_at":"2026-08-15T23:17:34.698002Z","title":"Training compute-optimal large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.698002Z"},"links":{"cited_paper":"/paper/2203.15556","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:b7873ec734a9b68b72ba78d209585892e37f3862f04f06facbdcf165a306bb4e","observation_id":"08a3fb10-7212-4093-98b1-23f90fffc150","resolution":{"observed_at":"2026-08-15T23:17:34.698002Z","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":"2026-08-15T23:17:34.703410Z","title":"DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.703410Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:7c7139b17d3bcd3dfbca929463bdc691010b9fc812a5d5da88cfa6f996aef307","observation_id":"0414dbde-3ea7-4e33-be25-afc34a79fc76","resolution":{"observed_at":"2026-08-15T23:17:34.703410Z","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":"2026-07-06T17:17:56.276857Z","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":"2026-08-15T23:17:34.707831Z","title":"Medusa: Simple LLM inference acceleration framework with multiple decoding heads","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.707831Z"},"links":{"cited_paper":"/paper/2401.10774","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:e9d40319ebdd98e710ee78d99307530e840c1ff41e6cdb99fb6c9bbb3cab413e","observation_id":"cd5aad38-7b3a-461e-b73e-6bffb6354da4","resolution":{"observed_at":"2026-08-15T23:17:34.707831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.15077","last_updated":"2025-03-04T13:58:39Z","snapshot_observed_at":"2026-08-03T09:40:31.365295Z","submitted_at":"2024-01-26T18:59:01Z","title":"EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15077","snapshot_observed_at":"2026-08-15T23:17:34.712667Z","title":"EAGLE: Speculative sampling requires rethinking feature uncertainty","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.712667Z"},"links":{"cited_paper":"/paper/2401.15077","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:0996b32091e160d32f07dba2c7f2fe0c86863ae21e066b686e24b0a23bdee404","observation_id":"3e38735e-b35f-42e0-9165-49add1098637","resolution":{"observed_at":"2026-08-15T23:17:34.712667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16858","last_updated":"2024-06-30T15:03:25Z","snapshot_observed_at":"2026-08-16T13:40:01.657235Z","submitted_at":"2024-06-24T17:59:11Z","title":"EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16858","snapshot_observed_at":"2026-08-15T23:17:34.717314Z","title":"EAGLE-2: Faster inference of language models with dynamic draft trees","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.717314Z"},"links":{"cited_paper":"/paper/2406.16858","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:185272bd79ece85a52daa39fee4a8876d00048d039f7f262d1c637377548d625","observation_id":"0697af12-cf29-409d-9c86-10738bb96f97","resolution":{"observed_at":"2026-08-15T23:17:34.717314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.01840","last_updated":"2025-04-23T07:08:17Z","snapshot_observed_at":"2026-08-13T11:30:48.832931Z","submitted_at":"2025-03-03T18:59:04Z","title":"EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.01840","snapshot_observed_at":"2026-08-15T23:17:34.722062Z","title":"EAGLE-3: Scaling up inference acceleration of large language models via training-time test","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.722062Z"},"links":{"cited_paper":"/paper/2503.01840","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:d0cec7e01fa0018ed364ca511358a119c2642a05807a54d8bf9bea8f5bc64f5e","observation_id":"67644dfa-ed9d-4859-8220-5a1700418004","resolution":{"observed_at":"2026-08-15T23:17:34.722062Z","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":"2026-08-15T23:17:34.728131Z","title":"Training language models to follow instructions with human feedback","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.728131Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:ce2757c81b739c12086c8dc12ff1fd02db514a152e2fc8232c7196504ecfedf6","observation_id":"91750ef8-97a3-4786-b597-d1a21f227b13","resolution":{"observed_at":"2026-08-15T23:17:34.728131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-15T23:17:34.732629Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.732629Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:bb213c1e52871d5522c7b033b29e9210ce03e7b6e8a5ca17ee2c2c8b7acc53f0","observation_id":"8058b0dd-85ef-402a-8912-bb93b4974eb4","resolution":{"observed_at":"2026-08-15T23:17:34.732629Z","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":"2026-08-15T23:17:34.737671Z","title":"Blockwise parallel decoding for deep autoregressive models","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.737671Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:9cd3524d18610b03dbf1657c6e92a2ce07fbba360f82cfc3204c5dd24dbc1143","observation_id":"9b9b99f0-8ea2-42b3-906b-a003d50206be","resolution":{"observed_at":"2026-08-15T23:17:34.737671Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-15T23:17:34.741787Z","title":"Llama 2: Open foundation and fine-tuned chat models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.741787Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:98249c3d6d7532d900d70000446d7d6a0d8d31e2c40119ae2ec680c3a5332886","observation_id":"84848e63-b412-450d-b0ec-b9d466c18d10","resolution":{"observed_at":"2026-08-15T23:17:34.741787Z","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":"2026-08-15T23:17:35.374533Z","title":"Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality.See https://vicuna","venue":null,"work_id":"21e3c488-f6ee-4cf4-99af-1a925c2c3bb8","year":2023},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.745824Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:80e931347b4530d3815217775569ec8ab210d3d476f39d3078e69470747c15fb","observation_id":"e3c0ffc9-2712-4bd6-a95c-ed3888dbb4be","resolution":{"observed_at":"2026-08-15T23:17:35.379528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-15T23:17:34.749630Z","title":"Qwen2.5 technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.749630Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:abb6c26ac8a391a3b3f0cf43b16ae4f4651b1fac59aeeb4e2506c03edd9b6938","observation_id":"8ca9a76e-cd65-4833-b831-b6d21a3ddaa8","resolution":{"observed_at":"2026-08-15T23:17:34.749630Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-15T23:17:34.753513Z","title":"The Llama 3 herd of models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.753513Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:5178ba70f905ab5b7d4364dbf7b2762a27c0c700f78192176c014b46dd6fe759","observation_id":"cab6ca2c-6854-46e5-bab9-a66f86be8b15","resolution":{"observed_at":"2026-08-15T23:17:34.753513Z","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":"2026-08-15T23:17:35.360187Z","title":"Judging LLM-as-a-judge with MT-bench and chatbot arena","venue":null,"work_id":"ffc57b50-4487-49c0-a83c-df84a94b67a8","year":2023},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.757717Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:2a88a33ed6f53b410ffa750bfa941040fd56d52e8289c5f889fc1ca13f7c06d3","observation_id":"bdb7d2f0-7d43-499b-bbef-0c962bc49c2a","resolution":{"observed_at":"2026-08-15T23:17:35.365358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T23:17:34.762563Z","title":"Evaluating large language models trained on code","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.762563Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:435e4df941f07be09ec3e21afda21b46b5302ffcf679febb1873e9fcd90879da","observation_id":"2533a5d2-186f-4888-b034-3d84c368f853","resolution":{"observed_at":"2026-08-15T23:17:34.762563Z","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":"2026-08-15T23:17:34.767055Z","title":"Training verifiers to solve math word problems","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.767055Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:54cf7c9b0e915ab018840cc27b5b978baf2e4d5db4e6ab6a8b2d81834c91f7a6","observation_id":"3a0c90d6-a40b-45c9-8f80-4708c4b0837d","resolution":{"observed_at":"2026-08-15T23:17:34.767055Z","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":"2026-08-15T23:17:34.771616Z","title":"Alpaca: A strong, replicable instruction- following model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.771616Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:a99230b23771163f1b83bfea388f75aed4daaba0c6e568d3117dbd15d36e121b","observation_id":"5221ba61-5eba-457e-a7d6-9f0da7eeab90","resolution":{"observed_at":"2026-08-15T23:17:34.771616Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1602.06023","last_updated":"2016-08-26T16:13:13Z","snapshot_observed_at":"2026-08-14T22:09:41.573524Z","submitted_at":"2016-02-19T02:04:18Z","title":"Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.06023","snapshot_observed_at":"2026-08-15T23:17:34.776007Z","title":"Abstractive text sum- marization using sequence-to-sequence RNNs and beyond","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.776007Z"},"links":{"cited_paper":"/paper/1602.06023","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:b593253c22ffbfbfcba1bf726fa432b6b408ae3f859703adb1f98b0ed3a0750a","observation_id":"4ba6577e-1e5a-4ae8-9d1a-3c3e1261052b","resolution":{"observed_at":"2026-08-15T23:17:34.776007Z","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":"2026-08-15T23:17:34.780100Z","title":"Natural questions: a benchmark for question answering research","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.780100Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:bfaaa6a93a3971315b72ab71951eb477f266a80e8061cd9994581d1884b0cc21","observation_id":"f165359b-4346-4764-ac16-32fac7ff8bb4","resolution":{"observed_at":"2026-08-15T23:17:34.780100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.01318","last_updated":"2023-02-02T18:44:11Z","snapshot_observed_at":"2026-08-13T23:55:57.074762Z","submitted_at":"2023-02-02T18:44:11Z","title":"Accelerating Large Language Model Decoding with Speculative Sampling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.01318","snapshot_observed_at":"2026-08-15T23:17:34.784721Z","title":"Accelerating large language model decoding with speculative sampling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.784721Z"},"links":{"cited_paper":"/paper/2302.01318","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:c3cd241752317b76abec1ac0cf3ed4c92035f0efdb3018b8b87c67a00bd242f6","observation_id":"198686b6-5b97-41ce-929e-5391c9d2956b","resolution":{"observed_at":"2026-08-15T23:17:34.784721Z","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":"2026-08-15T23:17:34.788732Z","title":"Fast inference from transformers via speculative decoding","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.788732Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:e35a2448889406cd3f1c033533840ea9959f5f950c54ac394bf79a60d67251ae","observation_id":"39fc45a9-4233-48a9-9c43-843035a55741","resolution":{"observed_at":"2026-08-15T23:17:34.788732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07177","last_updated":"2024-06-10T01:36:31Z","snapshot_observed_at":"2026-08-16T14:53:13.373780Z","submitted_at":"2023-10-11T04:03:42Z","title":"Online Speculative Decoding","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07177","snapshot_observed_at":"2026-08-15T23:17:34.793543Z","title":"Online speculative decoding","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.793543Z"},"links":{"cited_paper":"/paper/2310.07177","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:d82166122097bc80db1560557f431797f73c560b040fe2e396445b5482573a5b","observation_id":"4e8c69f8-3cbd-4c85-b248-1496aebbca62","resolution":{"observed_at":"2026-08-15T23:17:34.793543Z","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":"2026-08-15T23:17:35.321937Z","title":"Lookahead: An inference acceleration framework for large language model with lossless generation accuracy","venue":null,"work_id":"6a4f5f84-eb80-48bb-aa1a-c583161d88c3","year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.797678Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:5d1286b51418880b88065963cdd2715743caa1dbf2aba609143eedb197e104de","observation_id":"4d8d4680-1461-4681-b914-d5373c44cb94","resolution":{"observed_at":"2026-08-15T23:17:35.326769Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T23:17:35.308110Z","title":"Ouroboros: Speculative decoding with large model enhanced drafting","venue":null,"work_id":"64a82e6a-4384-4062-a977-89e6901cb128","year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.801266Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:6484c16cfd550d1f3ed542847bd89d2576e1f0881cc41f638aeced86b981e0a5","observation_id":"495df7bc-fceb-492f-87d9-19961c7906b3","resolution":{"observed_at":"2026-08-15T23:17:35.312743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02057","last_updated":"2024-02-03T06:37:50Z","snapshot_observed_at":"2026-08-16T14:21:54.969828Z","submitted_at":"2024-02-03T06:37:50Z","title":"Break the Sequential Dependency of LLM Inference Using Lookahead Decoding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02057","snapshot_observed_at":"2026-08-15T23:17:34.805214Z","title":"Break the sequential dependency of LLM inference using lookahead decoding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.805214Z"},"links":{"cited_paper":"/paper/2402.02057","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:49ad9423792a6070d6a40565c757f420b2b12a88d36fdcab7657e0f7cd1257bd","observation_id":"585b9b35-7c6c-4cd5-a061-e62d9f26b8eb","resolution":{"observed_at":"2026-08-15T23:17:34.805214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08461","last_updated":"2024-03-31T03:06:51Z","snapshot_observed_at":"2026-08-16T14:52:38.095793Z","submitted_at":"2023-10-12T16:21:04Z","title":"DistillSpec: Improving Speculative Decoding via Knowledge Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08461","snapshot_observed_at":"2026-08-15T23:17:34.809724Z","title":"DistillSpec: Improving speculative decoding via knowledge distillation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.809724Z"},"links":{"cited_paper":"/paper/2310.08461","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:a32846467952585ac38bd89105c7fa8fd606bf7bc8dfc572213466a087e9bea5","observation_id":"69275d1a-5eef-44ff-873e-3934041833df","resolution":{"observed_at":"2026-08-15T23:17:34.809724Z","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":"2026-08-15T23:17:35.292868Z","title":"CLLMs: Consistency large language models","venue":null,"work_id":"5ce0f5d4-a37e-480d-bbea-e0501babe253","year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.814730Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:a08c52e504ade63d3268b79ac5b4f7c92b465ae40a38d1c7bc23c5728d4b55a1","observation_id":"919b5ee7-ad91-42b6-8971-575f9918e862","resolution":{"observed_at":"2026-08-15T23:17:35.297745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12374","last_updated":"2025-07-05T03:31:01Z","snapshot_observed_at":"2026-08-16T14:17:13.546713Z","submitted_at":"2024-02-19T18:58:32Z","title":"Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12374","snapshot_observed_at":"2026-08-15T23:17:34.819505Z","title":"Sequoia: Scalable, robust, and hardware-aware speculative decoding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.819505Z"},"links":{"cited_paper":"/paper/2402.12374","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:2c52f8b668486ac7b80e568440ef497be7c989265fe88e54d77516e21fc47f41","observation_id":"599f3793-47c3-4f1f-a4e7-3001c3020c51","resolution":{"observed_at":"2026-08-15T23:17:34.819505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.05894","last_updated":"2026-06-03T10:08:54Z","snapshot_observed_at":"2026-08-16T13:01:44.955655Z","submitted_at":"2024-11-08T14:23:02Z","title":"SSSD: Simply-Scalable Speculative Decoding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05894","snapshot_observed_at":"2026-08-15T23:17:34.823908Z","title":"SSSD: Simply-scalable speculative decoding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.823908Z"},"links":{"cited_paper":"/paper/2411.05894","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:51e6132c2981d481bbffb2f10a5d7a9c500e459855df28a252d173a7f81c5f06","observation_id":"4e46eef2-056f-4480-952b-b9c56bfeb498","resolution":{"observed_at":"2026-08-15T23:17:34.823908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19737","last_updated":"2024-04-30T17:33:57Z","snapshot_observed_at":"2026-08-16T14:11:52.376290Z","submitted_at":"2024-04-30T17:33:57Z","title":"Better & Faster Large Language Models via Multi-token Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19737","snapshot_observed_at":"2026-08-15T23:17:34.828410Z","title":"Better & faster large language models via multi-token prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.828410Z"},"links":{"cited_paper":"/paper/2404.19737","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:4d928328dcc8b972524d8b342142019e4b2a01944c508904638ba49084cc3663","observation_id":"a4c654dc-573c-45f7-8f64-bed91df0d204","resolution":{"observed_at":"2026-08-15T23:17:34.828410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.00263","last_updated":"2024-05-01T00:46:22Z","snapshot_observed_at":"2026-08-16T13:56:25.333777Z","submitted_at":"2024-05-01T00:46:22Z","title":"Clover: Regressive Lightweight Speculative Decoding with Sequential Knowledge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.00263","snapshot_observed_at":"2026-08-15T23:17:34.832568Z","title":"Clover: Regressive lightweight speculative decoding with sequential knowledge","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.832568Z"},"links":{"cited_paper":"/paper/2405.00263","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:e93fdb84433080aa8e8582c9ab9a7ecd06fc5ec908be6988227061bf91309005","observation_id":"6c33076b-ef44-4a9b-a953-ea5cdbf7edb2","resolution":{"observed_at":"2026-08-15T23:17:34.832568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00264","last_updated":"2024-08-01T03:43:32Z","snapshot_observed_at":"2026-08-16T13:29:21.629524Z","submitted_at":"2024-08-01T03:43:32Z","title":"Clover-2: Accurate Inference for Regressive Lightweight Speculative Decoding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00264","snapshot_observed_at":"2026-08-15T23:17:34.837115Z","title":"Clover-2: Accurate inference for regressive lightweight speculative decoding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.837115Z"},"links":{"cited_paper":"/paper/2408.00264","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:a45ae60fea6ae4ef300f883722584d630d4c6f0410e71916a14c467de3ce4225","observation_id":"a0a4cfb5-8c27-4c91-8edc-5e46e8e01416","resolution":{"observed_at":"2026-08-15T23:17:34.837115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15766","last_updated":"2025-02-26T11:47:58Z","snapshot_observed_at":"2026-08-16T13:23:12.653915Z","submitted_at":"2024-08-28T12:59:12Z","title":"Learning Harmonized Representations for Speculative Sampling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15766","snapshot_observed_at":"2026-08-15T23:17:34.842401Z","title":"Learning harmonized represen- tations for speculative sampling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.842401Z"},"links":{"cited_paper":"/paper/2408.15766","citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:865b316b54691eb5d83dc1e43e51864ff2d18e25c115eb400ea2427c1a18c804","observation_id":"c4aac5eb-856d-4673-ad0d-f4cedbaacc14","resolution":{"observed_at":"2026-08-15T23:17:34.842401Z","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":"2026-08-15T23:17:35.278718Z","title":"Sinkhorn distance minimization for knowledge distillation","venue":null,"work_id":"36c3950f-06e7-4625-a575-171429905bf3","year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.847130Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:1e361f4d0e01753939fd25ee0d0dab8f5598488469012b65565525568b9c5779","observation_id":"e9d577f8-60cd-402e-8e88-4999a2143842","resolution":{"observed_at":"2026-08-15T23:17:35.283112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T23:17:35.264125Z","title":"Multi-level optimal transport for universal cross-tokenizer knowledge distillation on language models","venue":null,"work_id":"63e9cb64-6845-4927-8797-fe5b3e7ee311","year":2025},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.851122Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:987c000f93b8d502d59c0e4e85b980eb1c83d04868954b9c4965387f124c559a","observation_id":"6f9b6511-d788-4cd0-9606-8bd5b455d814","resolution":{"observed_at":"2026-08-15T23:17:35.269428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-15T23:17:34.854943Z","title":"Sinkd: Sinkhorn distance minimization for knowledge distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.854943Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:862eb90c5af03f56378fd7310a8059f863781e807a14a35a302742729a707e3e","observation_id":"9329acb0-aed3-4e51-9f02-71eda2311630","resolution":{"observed_at":"2026-08-15T23:17:34.854943Z","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":"2026-08-15T23:17:35.241295Z","title":"Kangaroo: Lossless self-speculative decoding for accelerating llms via double early exiting","venue":null,"work_id":"ac043617-8e8d-4df4-8dab-967cd6b6c0b7","year":2024},"citing_paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T23:17:34.858836Z"},"links":{"citing_paper":"/paper/2505.07858"},"observation_digest":"sha256:8c05c78e59d11d58a31b065f38d1e25115026e01498644ad032e1f82f2b4349f","observation_id":"8d0c76ff-e751-4ad3-b56b-0bd0e9ca6b94","resolution":{"observed_at":"2026-08-15T23:17:35.247562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.07858","last_updated":"2025-05-08T11:10:15Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T12:41:53.166698Z","submitted_at":"2025-05-08T11:10:15Z","title":"Scaling Laws for Speculative Decoding"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":0,"verified_fuzzy":8},"total_outbound_references":38},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2505.07858."}