Paper Citation Record · LEDGER

IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2403.01241.

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

pith.paper-citation-record.v1
2403.01241 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:09:46.694762Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T17:41:03.716618Z

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 d02947e6-8f34-49e9-b09c-6394f35d6ac1 · inbound

When Attention Sink Emerges in Language Models: An Empirical View cites this paper.

When Attention Sink Emerges in Language Models: An Empirical View IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-16T17:41:03.718849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 51094686-93a1-4205-aac6-a4f8de8edb1a · inbound

Deploying Foundation Model Powered Agent Services: A Survey cites this paper.

Deploying Foundation Model Powered Agent Services: A Survey IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 206

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:46.694762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7307b7b-18a3-45af-b139-cee9934838b2 · inbound

A Survey on Large Language Model Acceleration based on KV Cache Management cites this paper.

A Survey on Large Language Model Acceleration based on KV Cache Management IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-11T00:38:47.293646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 422ee90a-9303-45ac-9d62-6c3b679dc066 · inbound

AKVQ-VL: Attention-Aware KV Cache Adaptive 2-Bit Quantization for Vision-Language Models cites this paper.

AKVQ-VL: Attention-Aware KV Cache Adaptive 2-Bit Quantization for Vision-Language Models IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:02.630189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c2203b8-2520-431d-a05e-4d434bd9b0ec · inbound

RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via Outlier-Aware Adaptive Rotations cites this paper.

RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via Outlier-Aware Adaptive Rotations IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:54:22.679371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3c491b0-3ab9-45ff-a0f2-11633560713e · inbound

Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models cites this paper.

Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T20:34:01.278582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f527dd68-b32b-4c45-b5a5-b05aabf80ba0 · inbound

Rethinking Causal Mask Attention for Vision-Language Inference cites this paper.

Rethinking Causal Mask Attention for Vision-Language Inference IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:31.562561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a78bd41-a5c3-49e6-af84-7363afeb1cac · inbound

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse cites this paper.

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:47:37.276443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 20ffc094-af89-4ae2-b44a-b86a26623485 · inbound

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse cites this paper.

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T05:50:24.347749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1261d31-41ff-497a-bbe4-50dd39b211a6 · inbound

The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity cites this paper.

The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:21:08.513009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.