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Paper Citation Record · LEDGER

Compressing LLMs: The Truth is Rarely Pure and Never Simple

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.01382.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2310.01382 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:37:39.615850Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T02:39:33.333826Z

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 afabfb92-2e9f-46d1-8e15-a93e896d6897 · inbound

A Survey on Efficient Inference for Large Language Models cites this paper.

A Survey on Efficient Inference for Large Language Models Compressing LLMs: The Truth is Rarely Pure and Never Simple

Reference 235

Resolution
verified exact
arxiv_id, observed 2026-05-15T02:39:33.335929Z

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.

source=pdf_text observed=2026-05-15T02:39:33.007894Z digest=sha256:b5161d100108869ae9f9c9141a91301ee988fb24f8862b5402c800cff8396cac

Observation 6a15da23-7dd0-4138-8b6c-b2475b3ffc78 · inbound

Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense cites this paper.

Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense Compressing LLMs: The Truth is Rarely Pure and Never Simple

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-09T17:37:39.615850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T17:37:39.615850Z digest=sha256:4ea6a9f53e882a9bd3f64ccb82a71df8e5026c02b99b08d93365167d04040d62

Observation 7635ee2f-f159-473c-9459-a4a3901a698e · inbound

Fair-GPTQ: Bias-Aware Quantization for Large Language Models cites this paper.

Fair-GPTQ: Bias-Aware Quantization for Large Language Models Compressing LLMs: The Truth is Rarely Pure and Never Simple

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-04T16:19:25.244046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T16:19:25.244046Z digest=sha256:08f69d07b6825061a6bbd10d53fa13b2209e6e809c86f121453012cceb7f82d0

Observation 1467d78d-fc7b-4f0f-a6fd-d449913b343f · inbound

Representation-Guided Parameter-Efficient LLM Unlearning cites this paper.

Representation-Guided Parameter-Efficient LLM Unlearning Compressing LLMs: The Truth is Rarely Pure and Never Simple

Reference 133

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T06:06:19.144781Z

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.

source=arxiv_source observed=2026-05-10T06:01:46.885030Z digest=sha256:4f9f1974871e9a8335aa34b45f1dde6c0fdbda69df596caecc57574dc124b6d3

Observation e35a557c-3005-4627-8394-4ae1d31ca258 · inbound

Reliability Scaling Laws for Quantized Large Language Models cites this paper.

Reliability Scaling Laws for Quantized Large Language Models Compressing LLMs: The Truth is Rarely Pure and Never Simple

Reference 73

Resolution
unresolved
no resolver link, observed 2026-07-14T08:45:52.855783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T08:45:52.855783Z digest=sha256:ea4b915fc1e8320295ab889e4383d7ef37407fd67ecc52d6f6fe09dbc2c936f3

Observation 239c5939-5447-4848-9b24-f0ccbe8fa40a · 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 Compressing LLMs: The Truth is Rarely Pure and Never Simple

Reference 241

Resolution
unresolved
no resolver link, observed 2026-08-01T16:42:30.129066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T16:42:30.129066Z digest=sha256:112487bb769fed42534e8e462d1da038046b7088664bb1dbaa2f4c566eeabc02