Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
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 9 inbound Pith citation observations for arXiv:2403.01241.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T00:38:47.293646Z
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 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.