Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2206.01861.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T16:13:19.988135Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T22:16:16.046751Z
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 5d495910-c8b9-4bfa-b361-25c92364875a · inbound
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cites this paper.
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 171
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T13:35:36.185243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 03b4f879-9892-4bcb-abf0-9017dbf482d2 · inbound
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers cites this paper.
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 15
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T17:18:35.274980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3b7bbd66-67a3-49f0-b4ba-f25606e2f5ac · inbound
Accelerating Large Language Model Decoding with Speculative Sampling cites this paper.
Accelerating Large Language Model Decoding with Speculative Sampling ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 21
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T07:29:36.307343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f6589bb8-472a-4d55-b1fb-2a71b309c258 · inbound
QLoRA: Efficient Finetuning of Quantized LLMs cites this paper.
QLoRA: Efficient Finetuning of Quantized LLMs ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 69
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T13:29:53.790343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 419f3a01-d77c-4b52-9378-02df1fff1d21 · inbound
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration cites this paper.
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T08:29:11.224966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 29d83d7a-d3a3-4d48-b1df-85a29bb200fd · inbound
H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models cites this paper.
H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 25
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-17T18:00:50.460651Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d1c187c5-2f9a-4c8e-b164-77642734c8de · inbound
OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting cites this paper.
OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T16:13:19.988135Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a138f024-ee03-4bc0-97c7-7b1d9e2e4d83 · inbound
PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling cites this paper.
PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:42:41.045802Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 934d023b-00d6-45e5-a64e-f1d9d38e76e3 · inbound
Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study cites this paper.
Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:22:47.848505Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3f54d90-0c07-462f-b783-f990656e6967 · inbound
Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models cites this paper.
Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 11
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T17:31:08.335975Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 38c47fdd-5b79-4043-b192-f7df2b5a3817 · inbound
A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models cites this paper.
A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 25
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T14:25:29.242563Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b3d0d9e1-e4d3-4c83-882e-1e7635f2e040 · inbound
Motion-Compensated Weight Compression cites this paper.
Motion-Compensated Weight Compression ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 65
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T13:04:40.440211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2687967a-64b3-4698-8ad1-f18a8546b249 · inbound
Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation cites this paper.
Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
Reference 17
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T22:16:16.048491Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.