Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2308.09723.
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 2026-08-10T19:29:57.342704Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-10T04:06:44.634261Z
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 b4fc1a86-8a96-4b2a-96b8-ec18c0d3d1e6 · inbound
A Survey on Efficient Inference for Large Language Models cites this paper.
A Survey on Efficient Inference for Large Language Models FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
Reference 199
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T02:39:33.219954Z
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 0e47903f-e0b9-494a-8a65-9aed501f3e0a · inbound
Accelerating Large Language Models through Partially Linear Feed-Forward Network cites this paper.
Accelerating Large Language Models through Partially Linear Feed-Forward Network FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T19:29:57.342704Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 68bbad84-4a39-4852-b8ee-8274757435f6 · inbound
BiSCo-LLM: Lookup-Free Binary Spherical Coding for Extreme Low-Bit Large Language Model Compression cites this paper.
BiSCo-LLM: Lookup-Free Binary Spherical Coding for Extreme Low-Bit Large Language Model Compression FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-10T04:06:44.636474Z
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.