{"as_of":"2026-08-08T06:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8d323e48fad7a0dcb30b37ed1fa42652b11e9606a2fb74b8146be02a38b4ada2","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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":"2026-08-07T12:09:10.222780Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T05:56:57.204200Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.00425","last_updated":"2025-08-10T04:13:03Z","snapshot_observed_at":"2026-08-03T13:46:15.251113Z","submitted_at":"2025-02-01T13:08:02Z","title":"MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00425","snapshot_observed_at":"2026-08-07T12:09:10.222780Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.00479","last_updated":"2025-05-31T09:10:43Z","snapshot_observed_at":"2026-08-07T12:02:00.195241Z","submitted_at":"2025-05-31T09:10:43Z","title":"EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-07T12:09:10.222780Z"},"links":{"cited_paper":"/paper/2502.00425","citing_paper":"/paper/2506.00479"},"observation_digest":"sha256:f5cb2c6e0cc694d031a156610619f95b12115084b6217231154dd332acc6241c","observation_id":"d3466021-9e02-4e90-a8f3-43e92a6c9323","resolution":{"observed_at":"2026-08-07T12:09:10.222780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00425","last_updated":"2025-08-10T04:13:03Z","snapshot_observed_at":"2026-08-03T13:46:15.251113Z","submitted_at":"2025-02-01T13:08:02Z","title":"MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization","version":2},"cited_work":{"arxiv_id":"2502.00425","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.00425","snapshot_observed_at":"2026-08-07T05:56:57.204200Z","title":"MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization","venue":"cs.CV","work_id":"1ddf5ae0-58ac-4c03-8e43-787b44730e95","year":2025},"citing_paper":{"arxiv_id":"2506.06579","last_updated":"2025-06-06T23:13:08Z","snapshot_observed_at":"2026-08-07T05:51:34.237166Z","submitted_at":"2025-06-06T23:13:08Z","title":"Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T05:56:57.016882Z"},"links":{"cited_paper":"/paper/2502.00425","citing_paper":"/paper/2506.06579"},"observation_digest":"sha256:ae9c2e6991757554f74757a33e34fe4444d2ba9869a137ce4c529bad725f4519","observation_id":"0a64c309-4bed-4ea1-9647-9e54130324de","resolution":{"observed_at":"2026-08-07T05:56:57.208416Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.00425/citation-record","integrity":"/paper/2502.00425/integrity","json":"/paper/2502.00425/citation-record.json","paper":"/paper/2502.00425"},"outbound":[],"paper":{"arxiv_id":"2502.00425","last_updated":"2025-08-10T04:13:03Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-03T13:46:15.251113Z","submitted_at":"2025-02-01T13:08:02Z","title":"MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.00425."}