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Paper Citation Record · LEDGER

Pushing the Limits of Large Language Model Quantization via the Linearity Theorem

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2411.17525.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.17525 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:46:01.500880Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T10:29:45.503006Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7660a5e6-f3c7-4897-aecd-a5e13d5e036c · inbound

KV Cache Offloading for Context-Intensive Tasks cites this paper.

KV Cache Offloading for Context-Intensive Tasks Pushing the Limits of Large Language Model Quantization via the Linearity Theorem

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:06:01.042670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-10T16:49:13.353700Z digest=sha256:1763047d19952db600a25aa821f60d90989ff09c9ca61f17aabf85df3a3cbc9b

Observation 69b2aa2a-6198-48c1-b62a-6ea3ecd0af6c · inbound

KV Cache Offloading for Context-Intensive Tasks cites this paper.

KV Cache Offloading for Context-Intensive Tasks Pushing the Limits of Large Language Model Quantization via the Linearity Theorem

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-11T04:20:56.317662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-11T01:48:21.341462Z digest=sha256:4dea18a0cf4ea4535ff0335f5db57295f7e23f2e28bd12f85cb5aec4b5e128fd

Observation efc361d6-9c2b-4631-9de5-5a2be0490d6e · inbound

KV Cache Offloading for Context-Intensive Tasks cites this paper.

KV Cache Offloading for Context-Intensive Tasks Pushing the Limits of Large Language Model Quantization via the Linearity Theorem

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:37:29.365075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-13T07:35:30.155592Z digest=sha256:0773814d85654711677a27d750b64e20f8a838457284185f14aa8f0b68804196

Observation a729e4b6-e5e5-43fa-9169-c18a4b781040 · inbound

KV Cache Offloading for Context-Intensive Tasks cites this paper.

KV Cache Offloading for Context-Intensive Tasks Pushing the Limits of Large Language Model Quantization via the Linearity Theorem

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-19T16:42:39.689196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-19T16:41:30.021302Z digest=sha256:dad6ffc758ed3bc7c143d16f2c0751dec5688979b9e73fd529e80b7291c58a93

Observation f8aab825-e78f-41ce-a5fd-3a528d9b95e3 · inbound

HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models cites this paper.

HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models Pushing the Limits of Large Language Model Quantization via the Linearity Theorem

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-04T10:29:45.504441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-26T08:46:01.500880Z digest=sha256:0174990427a43cdfe838ed199c9e7c4953ead0cc361d3637538b2a57d775e1eb