{"as_of":"2026-08-09T14:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1d57805e5a8a61adf4e1b21b56318617b19de868ececd4dfaa028a2dd3b5bd25","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:19:19.371243Z","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":"2026-05-11T12:06:04.817121Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.05676","last_updated":"2025-04-29T08:43:15Z","snapshot_observed_at":"2026-07-06T18:59:16.978029Z","submitted_at":"2024-08-11T02:31:13Z","title":"Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.05676","snapshot_observed_at":"2026-08-07T15:19:19.371243Z","title":"A decoding acceleration framework for industrial deployable llm-based recommender systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15872","last_updated":"2025-05-23T10:16:01Z","snapshot_observed_at":"2026-08-08T15:07:56.550488Z","submitted_at":"2025-05-21T14:44:40Z","title":"InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T15:19:19.371243Z"},"links":{"cited_paper":"/paper/2408.05676","citing_paper":"/paper/2505.15872"},"observation_digest":"sha256:1e4114c94fcee2d16859371c7a924f86429edc9a207298f9c0c8438723ffdb9e","observation_id":"d2326f22-5198-4046-bafc-e7f8c1597f2b","resolution":{"observed_at":"2026-08-07T15:19:19.371243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.05676","last_updated":"2025-04-29T08:43:15Z","snapshot_observed_at":"2026-07-06T18:59:16.978029Z","submitted_at":"2024-08-11T02:31:13Z","title":"Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.05676","snapshot_observed_at":"2026-08-07T01:06:07.463756Z","title":"A decoding acceleration framework for industrial deployable llm-based recommender systems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11999","last_updated":"2025-07-01T04:48:20Z","snapshot_observed_at":"2026-08-07T00:56:10.721393Z","submitted_at":"2025-06-13T17:54:12Z","title":"Generative Representational Learning of Foundation Models for Recommendation","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T01:06:07.463756Z"},"links":{"cited_paper":"/paper/2408.05676","citing_paper":"/paper/2506.11999"},"observation_digest":"sha256:2cda6797d11536f690c8941b0428280ca462d782b0bd219cd266f1ca87990836","observation_id":"f39d3b09-9562-41bf-8a06-da957b0b0ef8","resolution":{"observed_at":"2026-08-07T01:06:07.463756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.05676","last_updated":"2025-04-29T08:43:15Z","snapshot_observed_at":"2026-07-06T18:59:16.978029Z","submitted_at":"2024-08-11T02:31:13Z","title":"Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.05676","snapshot_observed_at":"2026-08-06T21:18:51.342589Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.00715","last_updated":"2025-07-01T12:42:06Z","snapshot_observed_at":"2026-08-06T21:06:27.845057Z","submitted_at":"2025-07-01T12:42:06Z","title":"EARN: Efficient Inference Acceleration for LLM-based Generative Recommendation by Register Tokens","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:18:51.342589Z"},"links":{"cited_paper":"/paper/2408.05676","citing_paper":"/paper/2507.00715"},"observation_digest":"sha256:00c3240bb82a03aa1390e205028864e05311c40c87db070b9bcdafb33f144fe8","observation_id":"94cc61ca-0653-49eb-8f7f-f613f3745899","resolution":{"observed_at":"2026-08-06T21:18:51.342589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.05676","last_updated":"2025-04-29T08:43:15Z","snapshot_observed_at":"2026-07-06T18:59:16.978029Z","submitted_at":"2024-08-11T02:31:13Z","title":"Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding","version":2},"cited_work":{"arxiv_id":"2408.05676","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.05676","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"42906cb9-37b4-4651-8014-307f9c000c29","year":2024},"citing_paper":{"arxiv_id":"2604.18146","last_updated":"2026-04-21T07:16:41Z","snapshot_observed_at":"2026-07-06T23:05:04.279268Z","submitted_at":"2026-04-20T12:08:58Z","title":"Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-10T04:09:10.125285Z"},"links":{"cited_paper":"/paper/2408.05676","citing_paper":"/paper/2604.18146"},"observation_digest":"sha256:43217839afdce3dcf4decebdafd2b53acab3fd96de3c54237408d0e6cbdcf30c","observation_id":"4a955c63-25f5-4901-88bf-d8c96cec4c7b","resolution":{"observed_at":"2026-05-11T12:06:04.824341Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2408.05676/citation-record","integrity":"/paper/2408.05676/integrity","json":"/paper/2408.05676/citation-record.json","paper":"/paper/2408.05676"},"outbound":[],"paper":{"arxiv_id":"2408.05676","last_updated":"2025-04-29T08:43:15Z","latest_version":2,"primary_category":"cs.IR","snapshot_observed_at":"2026-07-06T18:59:16.978029Z","submitted_at":"2024-08-11T02:31:13Z","title":"Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2408.05676."}