{"as_of":"2026-08-11T10:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d1a417c4c83c6b519a0790990f57374a80fd122c3d1d959fa0a72d7e3fc1dd98","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T00:38:47.293646Z","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":"2026-05-16T17:41:03.716618Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","version":2},"cited_work":{"arxiv_id":"2403.01241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.01241","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Intactkv: Improving large language model quantization by keeping pivot tokens intact","venue":null,"work_id":"32a3f320-ff1b-4930-b865-a50e2e771413","year":2024},"citing_paper":{"arxiv_id":"2410.10781","last_updated":"2025-03-02T14:37:53Z","snapshot_observed_at":"2026-08-02T20:46:05.666977Z","submitted_at":"2024-10-14T17:50:28Z","title":"When Attention Sink Emerges in Language Models: An Empirical View","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-16T17:41:03.674759Z"},"links":{"cited_paper":"/paper/2403.01241","citing_paper":"/paper/2410.10781"},"observation_digest":"sha256:c2e2145ad47f4a94ad41c370b959115681e7b3e67cf3fdb294538572d680499f","observation_id":"d02947e6-8f34-49e9-b09c-6394f35d6ac1","resolution":{"observed_at":"2026-05-16T17:41:03.718849Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01241","snapshot_observed_at":"2026-08-11T00:38:47.293646Z","title":"Intactkv: Improving large language model quantization by keeping pivot tokens intact,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19442","last_updated":"2025-07-30T05:24:46Z","snapshot_observed_at":"2026-08-11T00:33:34.388194Z","submitted_at":"2024-12-27T04:17:57Z","title":"A Survey on Large Language Model Acceleration based on KV Cache Management","version":3},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T00:38:47.293646Z"},"links":{"cited_paper":"/paper/2403.01241","citing_paper":"/paper/2412.19442"},"observation_digest":"sha256:c79222bf48ba8d04168b663e4e7e80d2f66fc4a0f9bc8d7e6d1a0167d95e5ffc","observation_id":"a7307b7b-18a3-45af-b139-cee9934838b2","resolution":{"observed_at":"2026-08-11T00:38:47.293646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01241","snapshot_observed_at":"2026-08-10T14:46:02.630189Z","title":"Intactkv: Improving large language model quantization by keeping pivot tokens intact,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.15021","last_updated":"2025-01-25T02:01:56Z","snapshot_observed_at":"2026-08-11T06:31:30.411216Z","submitted_at":"2025-01-25T02:01:56Z","title":"AKVQ-VL: Attention-Aware KV Cache Adaptive 2-Bit Quantization for Vision-Language Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T14:46:02.630189Z"},"links":{"cited_paper":"/paper/2403.01241","citing_paper":"/paper/2501.15021"},"observation_digest":"sha256:219abeb122db702966dcc212ba477f1b30a3e5ccc8d00549b042cd5b871ddd8a","observation_id":"422ee90a-9303-45ac-9d62-6c3b679dc066","resolution":{"observed_at":"2026-08-10T14:46:02.630189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01241","snapshot_observed_at":"2026-08-10T14:54:22.679371Z","title":"Intactkv: Improving large language model quantization by keeping pivot tokens intact","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.16383","last_updated":"2025-02-02T03:04:54Z","snapshot_observed_at":"2026-08-10T23:44:45.932694Z","submitted_at":"2025-01-25T01:45:29Z","title":"RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via Outlier-Aware Adaptive Rotations","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T14:54:22.679371Z"},"links":{"cited_paper":"/paper/2403.01241","citing_paper":"/paper/2501.16383"},"observation_digest":"sha256:b5a50bb4d72ab00f373aa34f9b67332ce89bbd1fd3b71783cb2e22fa975c5e51","observation_id":"9c2203b8-2520-431d-a05e-4d434bd9b0ec","resolution":{"observed_at":"2026-08-10T14:54:22.679371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01241","snapshot_observed_at":"2026-08-09T20:34:01.278582Z","title":"Intactkv: Improving large language model quantization by keeping pivot tokens intact","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.19392","last_updated":"2025-02-28T18:04:52Z","snapshot_observed_at":"2026-08-09T20:48:28.727060Z","submitted_at":"2025-01-31T18:47:42Z","title":"Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models","version":4},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-09T20:34:01.278582Z"},"links":{"cited_paper":"/paper/2403.01241","citing_paper":"/paper/2501.19392"},"observation_digest":"sha256:97ece78a506ad94f880c27f31c350087b793674d79f67ab1b5ed6a93666e9b66","observation_id":"a3c491b0-3ab9-45ff-a0f2-11633560713e","resolution":{"observed_at":"2026-08-09T20:34:01.278582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01241","snapshot_observed_at":"2026-08-07T14:33:31.562561Z","title":"Intactkv: Improving large language model quantization by keeping pivot tokens intact.arXiv preprint arXiv:2403.01241, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.18605","last_updated":"2025-05-24T08:59:28Z","snapshot_observed_at":"2026-08-08T00:45:48.179953Z","submitted_at":"2025-05-24T08:59:28Z","title":"Rethinking Causal Mask Attention for Vision-Language Inference","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:33:31.562561Z"},"links":{"cited_paper":"/paper/2403.01241","citing_paper":"/paper/2505.18605"},"observation_digest":"sha256:c78c5df60994309efb061273fc6c0b387579f6c4e679cc5bf4b29efa71b6467a","observation_id":"f527dd68-b32b-4c45-b5a5-b05aabf80ba0","resolution":{"observed_at":"2026-08-07T14:33:31.562561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","version":2},"cited_work":{"arxiv_id":"2403.01241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.01241","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Intactkv: Improving large language model quantization by keeping pivot tokens intact","venue":null,"work_id":"32a3f320-ff1b-4930-b865-a50e2e771413","year":2024},"citing_paper":{"arxiv_id":"2602.01203","last_updated":"2026-05-27T09:56:04Z","snapshot_observed_at":"2026-08-04T22:42:52.512248Z","submitted_at":"2026-02-01T12:45:39Z","title":"Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-16T08:47:29.236561Z"},"links":{"cited_paper":"/paper/2403.01241","citing_paper":"/paper/2602.01203"},"observation_digest":"sha256:8a988dc0d601af6295171183c3d3efcea89db8da83b1ae4f73c32624628909cb","observation_id":"8a78bd41-a5c3-49e6-af84-7363afeb1cac","resolution":{"observed_at":"2026-05-16T08:47:37.276443Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01241","snapshot_observed_at":"2026-08-03T05:50:24.347749Z","title":"Intactkv: Improving large language model quantization by keeping pivot tokens intact.ArXiv, abs/2403.01241,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.01203","last_updated":"2026-05-27T09:56:04Z","snapshot_observed_at":"2026-08-04T22:42:52.512248Z","submitted_at":"2026-02-01T12:45:39Z","title":"Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T05:50:24.347749Z"},"links":{"cited_paper":"/paper/2403.01241","citing_paper":"/paper/2602.01203"},"observation_digest":"sha256:8b8be25e8c014e5ba2897a0e3c9f9e405bb9163b38bed8b3cf9e9943f64d0854","observation_id":"20ffc094-af89-4ae2-b44a-b86a26623485","resolution":{"observed_at":"2026-08-03T05:50:24.347749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","version":2},"cited_work":{"arxiv_id":"2403.01241","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.01241","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Intactkv: Improving large language model quantization by keeping pivot tokens intact","venue":null,"work_id":"32a3f320-ff1b-4930-b865-a50e2e771413","year":2024},"citing_paper":{"arxiv_id":"2605.06611","last_updated":"2026-05-07T17:28:55Z","snapshot_observed_at":"2026-08-11T08:59:44.000364Z","submitted_at":"2026-05-07T17:28:55Z","title":"The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-08T12:11:04.146711Z"},"links":{"cited_paper":"/paper/2403.01241","citing_paper":"/paper/2605.06611"},"observation_digest":"sha256:6584e8fb9b7deb52b7b7ca92a95b7ef2b8cd56b18630650a9c180b50411667ef","observation_id":"f1261d31-41ff-497a-bbe4-50dd39b211a6","resolution":{"observed_at":"2026-05-11T19:21:08.513009Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2403.01241/citation-record","integrity":"/paper/2403.01241/integrity","json":"/paper/2403.01241/citation-record.json","paper":"/paper/2403.01241"},"outbound":[],"paper":{"arxiv_id":"2403.01241","last_updated":"2024-05-25T10:33:03Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-10T18:47:24.578837Z","submitted_at":"2024-03-02T16:05:26Z","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2403.01241."}