Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2309.15531.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T21:45:16.620506Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T12:59:52.385501Z
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 677cb0ea-fa72-48af-9a49-3fbd6ba5b39d · inbound
SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization cites this paper.
SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T21:45:16.620506Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5e1c29f3-f4cf-4a2a-b51f-03a2336030d4 · inbound
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis cites this paper.
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:43:38.230990Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5f99710-f5e1-4302-a878-360e2e730e73 · inbound
Fair-GPTQ: Bias-Aware Quantization for Large Language Models cites this paper.
Fair-GPTQ: Bias-Aware Quantization for Large Language Models Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T16:19:24.990979Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 017062aa-2408-41d3-8164-04bcf6038803 · inbound
SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference cites this paper.
SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
Reference 79
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T12:59:52.386779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 562a8a92-332f-4a00-a39c-84a64b50b957 · inbound
Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts cites this paper.
Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language Models
Reference 240
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T16:42:30.004034Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.