{"as_of":"2026-08-13T12:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b5f7fdf05cc1987dbe67d13bad538654b739956862bec4dbe86637c7f331b419","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:19:57.378916Z","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":"2026-07-04T10:39:45.486717Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-08-10T16:19:57.378916Z","title":"Optimize weight rounding via signed gradient descent for the quantization of llms, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13331","last_updated":"2025-02-05T08:10:45Z","snapshot_observed_at":"2026-08-11T03:03:57.665652Z","submitted_at":"2025-01-23T02:20:08Z","title":"Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T16:19:57.378916Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2501.13331"},"observation_digest":"sha256:64bab400388926c995ae21043925e4f143f91b9b99ec38274c389983477de9fe","observation_id":"1c748489-ba7c-4797-a677-e32498911cd4","resolution":{"observed_at":"2026-08-10T16:19:57.378916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":"2309.05516","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-07-04T10:39:45.486717Z","title":"Opti- mize weight rounding via signed gradient descent for the quantization of llms","venue":null,"work_id":"dec635a4-1f00-40e8-9a5a-f202ac27ffb5","year":2023},"citing_paper":{"arxiv_id":"2502.15761","last_updated":"2026-05-12T18:27:21Z","snapshot_observed_at":"2026-08-12T12:16:55.785276Z","submitted_at":"2025-02-13T20:55:48Z","title":"AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-23T03:18:44.070648Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2502.15761"},"observation_digest":"sha256:46b59e2054cf35c3a6d851d8aa8727cbddd7caae9a3c9c2c4c193a81ee176e28","observation_id":"c50c2699-05fa-4df3-9c25-4c403dac3da7","resolution":{"observed_at":"2026-05-23T03:22:28.147545Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-08-07T15:36:05.672891Z","title":"Optimize weight rounding via signed gradi- ent descent for the quantization of llms,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14638","last_updated":"2025-05-20T17:26:12Z","snapshot_observed_at":"2026-08-10T14:49:15.729632Z","submitted_at":"2025-05-20T17:26:12Z","title":"Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:36:05.672891Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2505.14638"},"observation_digest":"sha256:343e9cf8426c59f5134f1c999200a382067437f9e16f98a18839846e67535af9","observation_id":"6c4e8709-e7a7-4e5d-9709-47d47966f795","resolution":{"observed_at":"2026-08-07T15:36:05.672891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-08-07T14:25:37.677654Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19115","last_updated":"2025-08-10T07:10:29Z","snapshot_observed_at":"2026-08-09T06:55:04.491282Z","submitted_at":"2025-05-25T12:14:25Z","title":"FP4 All the Way: Fully Quantized Training of LLMs","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:25:37.677654Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2505.19115"},"observation_digest":"sha256:4c32fb32135cfa8e0331fa72a1f06d633b79c1fa3c2e920420eb3552b9fca81b","observation_id":"13897263-99c9-41a4-b548-2c9e10523d40","resolution":{"observed_at":"2026-08-07T14:25:37.677654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-08-07T05:04:03.353964Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.09104","last_updated":"2025-06-10T16:26:32Z","snapshot_observed_at":"2026-08-10T16:11:43.939646Z","submitted_at":"2025-06-10T16:26:32Z","title":"Unifying Block-wise PTQ and Distillation-based QAT for Progressive Quantization toward 2-bit Instruction-Tuned LLMs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:04:03.353964Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2506.09104"},"observation_digest":"sha256:d74b7b48c653ceba5149506f9a53b264a67c7ba2dc7d3d65e68aff132c3607af","observation_id":"ea7a7258-9f9b-4821-b58d-3b2a190050b8","resolution":{"observed_at":"2026-08-07T05:04:03.353964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":"2309.05516","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-07-04T10:39:45.486717Z","title":"Opti- mize weight rounding via signed gradient descent for the quantization of llms","venue":null,"work_id":"dec635a4-1f00-40e8-9a5a-f202ac27ffb5","year":2023},"citing_paper":{"arxiv_id":"2605.01637","last_updated":"2026-05-02T22:54:29Z","snapshot_observed_at":"2026-08-13T00:57:36.513912Z","submitted_at":"2026-05-02T22:54:29Z","title":"The Banach-Butterfly Invariant: Influence-Adaptive Walsh Geometry for Ternary Polynomial Threshold Functions","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-09T14:20:09.516209Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2605.01637"},"observation_digest":"sha256:7e193c256744eb2531f03f01cab67058da758555e374de017e3cfedcfd1e90a4","observation_id":"ac8665c1-2352-42d6-bc81-66718da17911","resolution":{"observed_at":"2026-05-11T17:01:06.034768Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":"2309.05516","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-07-04T10:39:45.486717Z","title":"Opti- mize weight rounding via signed gradient descent for the quantization of llms","venue":null,"work_id":"dec635a4-1f00-40e8-9a5a-f202ac27ffb5","year":2023},"citing_paper":{"arxiv_id":"2605.14844","last_updated":"2026-05-14T13:52:31Z","snapshot_observed_at":"2026-08-06T09:00:02.069223Z","submitted_at":"2026-05-14T13:52:31Z","title":"XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-06-30T21:28:36.358474Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2605.14844"},"observation_digest":"sha256:750d5323eda228216c274dbb7c8e1e3306e99bcdc8362875e3fb74be8bc59548","observation_id":"9655b8fb-b687-4b26-9637-5895bce65ffa","resolution":{"observed_at":"2026-06-30T21:35:04.696590Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":"2309.05516","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-07-04T10:39:45.486717Z","title":"Opti- mize weight rounding via signed gradient descent for the quantization of llms","venue":null,"work_id":"dec635a4-1f00-40e8-9a5a-f202ac27ffb5","year":2023},"citing_paper":{"arxiv_id":"2605.25203","last_updated":"2026-05-24T18:05:37Z","snapshot_observed_at":"2026-08-04T10:11:25.132076Z","submitted_at":"2026-05-24T18:05:37Z","title":"Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T12:13:18.805668Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2605.25203"},"observation_digest":"sha256:00b1ac43a988aee7a5d5217b68bdb6a7ae410f00460045bfd3abecb149817768","observation_id":"51cbc768-980b-4c8e-84df-767742d27da3","resolution":{"observed_at":"2026-06-30T12:14:39.035548Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":"2309.05516","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-07-04T10:39:45.486717Z","title":"Opti- mize weight rounding via signed gradient descent for the quantization of llms","venue":null,"work_id":"dec635a4-1f00-40e8-9a5a-f202ac27ffb5","year":2023},"citing_paper":{"arxiv_id":"2605.26339","last_updated":"2026-05-25T21:28:46Z","snapshot_observed_at":"2026-08-13T11:33:44.706676Z","submitted_at":"2026-05-25T21:28:46Z","title":"QAM-W: Joint 2D Codebook Quantization for LLM Weights via Hadamard Rotation and Activation-Aware Scaling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T22:14:08.808333Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2605.26339"},"observation_digest":"sha256:b8762d522d1df6a35e6ce5cd6f36e25d4228f4bcbb3f79337e831e2dc8816b9f","observation_id":"0b51e980-e882-4b1a-a20d-f5696eb83eb8","resolution":{"observed_at":"2026-06-29T22:34:02.848788Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs","version":5},"cited_work":{"arxiv_id":"2309.05516","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2309.05516","snapshot_observed_at":"2026-07-04T10:39:45.486717Z","title":"Opti- mize weight rounding via signed gradient descent for the quantization of llms","venue":null,"work_id":"dec635a4-1f00-40e8-9a5a-f202ac27ffb5","year":2023},"citing_paper":{"arxiv_id":"2606.23419","last_updated":"2026-06-22T14:42:34Z","snapshot_observed_at":"2026-08-12T12:58:28.591609Z","submitted_at":"2026-06-22T14:42:34Z","title":"GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T08:38:32.577228Z"},"links":{"cited_paper":"/paper/2309.05516","citing_paper":"/paper/2606.23419"},"observation_digest":"sha256:b158d0b1661a29eee6c43cb96077527d84d4c1ccf7ffbe0708a0c916f11266d0","observation_id":"4fd0ac99-272f-4b02-9f98-05b82fae10df","resolution":{"observed_at":"2026-07-04T10:39:45.488116Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2309.05516/citation-record","integrity":"/paper/2309.05516/integrity","json":"/paper/2309.05516/citation-record.json","paper":"/paper/2309.05516"},"outbound":[],"paper":{"arxiv_id":"2309.05516","last_updated":"2024-10-08T02:02:35Z","latest_version":5,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T10:15:58.245974Z","submitted_at":"2023-09-11T14:58:23Z","title":"Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2309.05516."}