Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2306.02272.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T00:50:44.559064Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T22:39:09.880895Z
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 981e6f17-9beb-4865-8615-a8c17d8332c5 · inbound
A Comprehensive Overview of Large Language Models cites this paper.
A Comprehensive Overview of Large Language Models OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Reference 259
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T20:28:39.166699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c2da2ac5-335f-44e3-b560-1d3f7bcc9a04 · inbound
A Survey on Efficient Inference for Large Language Models cites this paper.
A Survey on Efficient Inference for Large Language Models OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Reference 195
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T02:39:33.207831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c04afc0c-c6e3-4371-87c7-d4323d6bb726 · inbound
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models cites this paper.
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T23:03:44.613095Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8fb75c14-1379-48bc-bb2b-e1624725b7a9 · inbound
Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs cites this paper.
Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:16:45.817948Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bbbe0ef8-ddc7-4ec3-aac2-6476f3513623 · inbound
Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs cites this paper.
Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:52:44.959022Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 664e504c-0fab-4ea9-9f97-826376f2a68c · inbound
Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding cites this paper.
Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T11:31:01.370992Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 56612787-0de7-4db8-9e85-830dc314a282 · inbound
Theory-optimal Quantization Based on Flatness cites this paper.
Theory-optimal Quantization Based on Flatness OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T22:39:09.885888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0991b924-24ed-47fa-8a34-34e6cbf43aa0 · inbound
Break Through the Compression Bottleneck: From Theory to Practice cites this paper.
Break Through the Compression Bottleneck: From Theory to Practice OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T14:29:28.307829Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e38827b4-81b1-46d4-992f-b2762537cbb5 · inbound
Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs cites this paper.
Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T00:50:44.559064Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.