Paper Citation Record · LEDGER

Characterising Bias in Compressed Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2010.03058.

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

pith.paper-citation-record.v1
2010.03058 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:26:06.814900Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T15:59:57.077506Z

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 520af9da-d73a-476e-bf7c-8fb17bef443a · inbound

Software Fairness: An Analysis and Survey cites this paper.

Software Fairness: An Analysis and Survey Characterising Bias in Compressed Models

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-24T12:06:10.964545Z

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 2844ef8c-8284-4985-95ee-46cf78bdce46 · inbound

Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications cites this paper.

Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications Characterising Bias in Compressed Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-24T01:08:41.923341Z

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 969618a4-b9b3-4db4-8a12-ee0c980cd963 · inbound

Laplace Sample Information: Data Informativeness Through a Bayesian Lens cites this paper.

Laplace Sample Information: Data Informativeness Through a Bayesian Lens Characterising Bias in Compressed Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:06.814900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c009c363-0808-4771-9512-ac87d69d8ef0 · inbound

Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information cites this paper.

Quality over Quantity: An Effective Large-Scale Data Reduction Strategy Based on Pointwise V-Information Characterising Bias in Compressed Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T23:51:46.110585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6991b5d6-c370-4949-940b-5bc2ca994345 · inbound

The Uneven Impact of Post-Training Quantization in Machine Translation cites this paper.

The Uneven Impact of Post-Training Quantization in Machine Translation Characterising Bias in Compressed Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T14:49:26.506296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f036a984-a1d8-458f-b7c2-1f4ca8bde5bf · inbound

Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models cites this paper.

Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models Characterising Bias in Compressed Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:16:15.111916Z

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 414e7758-8f17-426b-a3d6-7425518e5386 · inbound

Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI cites this paper.

Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI Characterising Bias in Compressed Models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.629816Z

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 86517bc9-8ba0-4979-879f-0e75fa39d9cb · inbound

Quantized Reasoning Models Think They Need to Think Longer, but They Do Not cites this paper.

Quantized Reasoning Models Think They Need to Think Longer, but They Do Not Characterising Bias in Compressed Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:16:00.126890Z

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 e729d730-bb14-4556-8ddc-7df6794342e4 · inbound

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study cites this paper.

On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study Characterising Bias in Compressed Models

Reference 98

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:08:03.533761Z

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 fa0434f0-ea87-4e80-8f2a-eb3b5bb30a9d · inbound

Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration cites this paper.

Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration Characterising Bias in Compressed Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:59:57.079261Z

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 950779f9-8151-4876-b207-82c9900e7db4 · inbound

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs cites this paper.

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs Characterising Bias in Compressed Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:54.495639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18911a9a-7ac6-40c0-b5f7-f66baf16d75b · inbound

The Asymmetric Effects of Knowledge Distillation on Bias in Small Language Models cites this paper.

The Asymmetric Effects of Knowledge Distillation on Bias in Small Language Models Characterising Bias in Compressed Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T00:56:34.018513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.