{"as_of":"2026-08-18T13:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1ba82f66f46958be34a1310f273094cac59b646ad4aa94b21802fa8b50991383","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:09:46.698293Z","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":"2026-05-20T22:23:47.940163Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.04845","last_updated":"2024-06-07T11:19:30Z","snapshot_observed_at":"2026-08-18T00:20:19.299099Z","submitted_at":"2024-06-07T11:19:30Z","title":"FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04845","snapshot_observed_at":"2026-08-09T21:55:16.313725Z","title":"Fedllm-bench: Realistic benchmarks for federated learning of large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18980","last_updated":"2025-01-31T09:23:06Z","snapshot_observed_at":"2026-08-17T20:32:54.756575Z","submitted_at":"2025-01-31T09:23:06Z","title":"Symmetric Pruning of Large Language Models","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-09T21:55:16.313725Z"},"links":{"cited_paper":"/paper/2406.04845","citing_paper":"/paper/2501.18980"},"observation_digest":"sha256:1512ae20979ea8bdf3efab2705da559d00b9ae266adab73c4676d3cd8b8fc213","observation_id":"c8ac9e16-3bbd-4429-a5df-9be4c177fee0","resolution":{"observed_at":"2026-08-09T21:55:16.313725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04845","last_updated":"2024-06-07T11:19:30Z","snapshot_observed_at":"2026-08-18T00:20:19.299099Z","submitted_at":"2024-06-07T11:19:30Z","title":"FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04845","snapshot_observed_at":"2026-08-16T11:09:46.698293Z","title":"Fedllm-bench: Realistic benchmarks for federated learning of large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.16438","last_updated":"2025-08-19T17:56:16Z","snapshot_observed_at":"2026-08-16T14:49:12.099350Z","submitted_at":"2025-04-23T05:57:20Z","title":"POPri: Private Federated Learning using Preference-Optimized Synthetic Data","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-16T11:09:46.698293Z"},"links":{"cited_paper":"/paper/2406.04845","citing_paper":"/paper/2504.16438"},"observation_digest":"sha256:c475d7e79de4d21075f8ab1629a90b8fe6c60c6cd12a55cd6e12700d1b7f2dc2","observation_id":"ed2c2a7e-a1db-4b16-b242-10f27cc7b105","resolution":{"observed_at":"2026-08-16T11:09:46.698293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04845","last_updated":"2024-06-07T11:19:30Z","snapshot_observed_at":"2026-08-18T00:20:19.299099Z","submitted_at":"2024-06-07T11:19:30Z","title":"FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04845","snapshot_observed_at":"2026-08-04T21:06:26.591728Z","title":"Fedllm-bench: Realistic benchmarks for federated learning of large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.08233","last_updated":"2025-09-10T02:19:56Z","snapshot_observed_at":"2026-08-14T20:31:36.327522Z","submitted_at":"2025-09-10T02:19:56Z","title":"Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization","version":1},"reference_index":228,"source":"arxiv_source","source_observed_at":"2026-08-04T21:06:26.591728Z"},"links":{"cited_paper":"/paper/2406.04845","citing_paper":"/paper/2509.08233"},"observation_digest":"sha256:8997860465f10bbe0b227991a6c8569857cfc0b65096e5c230fd600f051ee2fc","observation_id":"460ad1ab-f408-4bf7-a391-1ae2b390bd14","resolution":{"observed_at":"2026-08-04T21:06:26.591728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04845","last_updated":"2024-06-07T11:19:30Z","snapshot_observed_at":"2026-08-18T00:20:19.299099Z","submitted_at":"2024-06-07T11:19:30Z","title":"FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models","version":1},"cited_work":{"arxiv_id":"2406.04845","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.04845","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"8a3b11d8-9971-4e4a-a2f6-b4d65f54cd3a","year":2024},"citing_paper":{"arxiv_id":"2605.09855","last_updated":"2026-05-18T00:37:30Z","snapshot_observed_at":"2026-08-14T22:44:32.446622Z","submitted_at":"2026-05-11T01:17:58Z","title":"Concordia: Self-Improving Synthetic Tables for Federated LLMs","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-12T04:26:50.410397Z"},"links":{"cited_paper":"/paper/2406.04845","citing_paper":"/paper/2605.09855"},"observation_digest":"sha256:67affdacb0756be91a4efa174f9135e42b4d7909c66ea4f284697ac96d455fd1","observation_id":"98359982-fe4f-4242-b1e9-bac6d66b1cad","resolution":{"observed_at":"2026-05-12T06:16:27.595346Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2406.04845","last_updated":"2024-06-07T11:19:30Z","snapshot_observed_at":"2026-08-18T00:20:19.299099Z","submitted_at":"2024-06-07T11:19:30Z","title":"FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models","version":1},"cited_work":{"arxiv_id":"2406.04845","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.04845","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"8a3b11d8-9971-4e4a-a2f6-b4d65f54cd3a","year":2024},"citing_paper":{"arxiv_id":"2605.09855","last_updated":"2026-05-18T00:37:30Z","snapshot_observed_at":"2026-08-14T22:44:32.446622Z","submitted_at":"2026-05-11T01:17:58Z","title":"Concordia: Self-Improving Synthetic Tables for Federated LLMs","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-20T22:21:03.637418Z"},"links":{"cited_paper":"/paper/2406.04845","citing_paper":"/paper/2605.09855"},"observation_digest":"sha256:47a3566a4ca5c999e527bbeb1fda22d732c61c4cbef43f81aa2ea5ea9ffc12f9","observation_id":"4ab129e2-7a9d-4f01-8f63-6de9898d8ddc","resolution":{"observed_at":"2026-05-20T22:23:47.942647Z","resolver_source":"arxiv_id","status":"verified_exact"},"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"}}],"links":{"evidence":"/evidence","html":"/paper/2406.04845/citation-record","integrity":"/paper/2406.04845/integrity","json":"/paper/2406.04845/citation-record.json","paper":"/paper/2406.04845"},"outbound":[],"paper":{"arxiv_id":"2406.04845","last_updated":"2024-06-07T11:19:30Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-18T00:20:19.299099Z","submitted_at":"2024-06-07T11:19:30Z","title":"FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models"},"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":"2026-08-18T06:34:40.430872+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 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.04845."}