Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2408.08554.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T15:08:46.300864Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T23:26:21.845195Z
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 0d773aca-fed5-4a1e-8759-5a1153c86cf0 · inbound
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study cites this paper.
Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T15:08:46.300864Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6d83da0-3181-4787-ab72-925ceedb025f · inbound
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation cites this paper.
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T12:46:04.651352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 074c5cdd-70a5-4a25-8f77-e2c44cc31e48 · inbound
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery cites this paper.
Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Reference 38
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T23:26:21.848625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.