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Paper Citation Record · LEDGER

Evaluating Quantized Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2402.18158.

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

pith.paper-citation-record.v1
2402.18158 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 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 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:56:55.140233Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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  • malformed identifier0
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External citation measurements

9
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a641a9ca-a70c-44e2-9388-49afc993aa36 · inbound

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

A Survey on Efficient Inference for Large Language Models Evaluating Quantized Large Language Models

Reference 214

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metadata mismatch
arxiv_id, observed 2026-05-15T02:39:33.265457Z

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:d828f2d1dde9d73151032c25ab118541279191345fe85782e01730ef70577eef

Observation 862a78e2-e4d7-445d-a3fd-fd1aec2ef33e · inbound

Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models cites this paper.

Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models Evaluating Quantized Large Language Models

Reference 31

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verified exact
arxiv_id, observed 2026-05-23T22:18:31.866486Z

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=2026-05-23T22:15:52.638622Z digest=sha256:2a4c18ffeb9499b513ccdef9792a833873eb214debf7e7ac3ca3778484e00f3f

Observation b6586653-72ae-4257-81b4-6bf90a70891a · inbound

Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models cites this paper.

Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models Evaluating Quantized Large Language Models

Reference 24

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metadata mismatch
arxiv_id, observed 2026-05-23T17:33:15.804739Z

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=2026-05-23T17:30:54.204300Z digest=sha256:4d1facba4a803db720788146abefd6093bcd685854d7b8ac75fa3b5cecf8f977

Observation f2a28fcf-820d-414d-baf0-d4d999d9731b · inbound

Inference-time sparse attention with asymmetric indexing cites this paper.

Inference-time sparse attention with asymmetric indexing Evaluating Quantized Large Language Models

Reference 2022

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unresolved
no resolver link, observed 2026-08-08T05:56:55.140233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:56:55.140233Z digest=sha256:8581f61a69f23ead993f36e7db42b78f1bfe3c576f6b2f83c0bf2d08169e68c4

Observation 49c2c6c4-0842-4523-82b2-4b6c13b7baf4 · inbound

QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model cites this paper.

QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model Evaluating Quantized Large Language Models

Reference 9

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metadata mismatch
arxiv_id, observed 2026-05-22T19:45:03.999245Z

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=2026-05-22T19:44:16.630377Z digest=sha256:603b155717cab7b45aaf0670ea47a6d803beff6bca0e0f6b7c206b683d56a04b

Observation 791978d9-f2a4-4084-8f0a-e4bacc79b3eb · inbound

Rethinking the Outlier Distribution in Large Language Models: An In-depth Study cites this paper.

Rethinking the Outlier Distribution in Large Language Models: An In-depth Study Evaluating Quantized Large Language Models

Reference 19

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unresolved
no resolver link, observed 2026-08-07T13:30:39.231092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:30:39.231092Z digest=sha256:a78093abf398772f82efe0a1442300ed1bbc5d91a0e0dcd0e9b6997899d397d7

Observation 28dadb7d-edbc-4464-bdb0-e51cb846929c · inbound

Pruning General Large Language Models into Customized Expert Models cites this paper.

Pruning General Large Language Models into Customized Expert Models Evaluating Quantized Large Language Models

Reference 26

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unresolved
no resolver link, observed 2026-08-07T11:27:14.258704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:27:14.258704Z digest=sha256:984fdcd575c09b5980ed4d9d92642756d2670f59b49c046a42cd5eea0006e398

Observation 8c4cb3a6-46e1-4183-bac2-86cdaabd1031 · inbound

Unifying Block-wise PTQ and Distillation-based QAT for Progressive Quantization toward 2-bit Instruction-Tuned LLMs cites this paper.

Unifying Block-wise PTQ and Distillation-based QAT for Progressive Quantization toward 2-bit Instruction-Tuned LLMs Evaluating Quantized Large Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T05:04:02.889275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:04:02.889275Z digest=sha256:7d9c4d665dc1459250fe649c03797234d2af6bd5996ee110770662b96e9045b8

Observation 1cede3b5-eac3-48a3-bf2d-db4b57c70020 · inbound

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models cites this paper.

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models Evaluating Quantized Large Language Models

Reference 12

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unresolved
no resolver link, observed 2026-08-06T18:33:17.787323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:33:17.787323Z digest=sha256:9087b1462225bcd59ea391eb7d3047fe6613f4f0e536283b37c1b76637deb16f

Observation 5321d1b0-82b7-4fe2-b846-3797573e3090 · inbound

Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation cites this paper.

Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation Evaluating Quantized Large Language Models

Reference 26

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unresolved
no resolver link, observed 2026-08-05T10:38:33.171501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:38:33.171501Z digest=sha256:1b12781f04fb0292411091dc2dc9f4e985775f17dc12465914818ad52e7844b1

Observation 994bf317-485b-406e-9b17-b7dc8e9239e8 · inbound

Are Large Language Models Economically Viable for Industry Deployment? cites this paper.

Are Large Language Models Economically Viable for Industry Deployment? Evaluating Quantized Large Language Models

Reference 53

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T13:01:18.858666Z

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=2026-05-10T02:28:12.686424Z digest=sha256:1c30bdf67689d818402236370f4962d96ffb8137ddc9fe2c41ed1464d17163e2

Observation be665ed4-b785-4c91-aa89-6a310314efce · inbound

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization cites this paper.

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization Evaluating Quantized Large Language Models

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T02:38:17.211730Z

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=2026-05-10T02:32:50.182859Z digest=sha256:928c851407fe934d91ab06703f4a14a0c9e81629b2816303fca9fd0e1edb2f17

Observation 9c61f32c-77f3-4168-b6dd-c6aabcdfd732 · inbound

Perplexity Can Miss SAE Feature Damage Under Quantization cites this paper.

Perplexity Can Miss SAE Feature Damage Under Quantization Evaluating Quantized Large Language Models

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T01:36:25.768691Z

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=2026-06-28T11:41:18.460538Z digest=sha256:9e0738e32245e75b7462bb867704972a433f60741bf563be5b765219ac04d39e

Observation f3e9302a-6a15-4969-a71b-1c4e382d91d0 · inbound

Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts cites this paper.

Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts Evaluating Quantized Large Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-07-14T01:09:27.619062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T01:09:27.619062Z digest=sha256:87d9f892a78c73fd8d81e25f8f63d71c0fa0cf0f0847a70d7930aef61a1b581c

Observation aa195c14-2a97-447f-b0b5-67b817eb6da2 · inbound

Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts cites this paper.

Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts Evaluating Quantized Large Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-02T07:32:09.425235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T07:32:09.425235Z digest=sha256:37c750b01b6b4cfad0c746ce6c8ab5c546b1a66704e8932edb100ea471e71ca1

Observation d07172d7-c5a5-4169-ae9c-fa21a4da35a0 · inbound

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

Reliability Scaling Laws for Quantized Large Language Models Evaluating Quantized Large Language Models

Reference 147

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:c5ccb11857544d39701708fb78bc1f0b51d4332c173bdc73f93e436c2e063aa5

Observation 77a50fc8-4e32-4fd5-abfa-13ed2c24cc7d · inbound

HindsightBench: A Black-Box Behavioral Audit Protocol for Parametric Hindsight in Time-Indexed LLM Decision Tasks cites this paper.

HindsightBench: A Black-Box Behavioral Audit Protocol for Parametric Hindsight in Time-Indexed LLM Decision Tasks Evaluating Quantized Large Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-01T14:11:08.947017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:11:08.947017Z digest=sha256:5170679a69e247a58999e24f6b403d38a46053defda762beffa7808f86347b19

Observation 95b8362e-7bda-4fa3-97a6-70d21818ffdd · inbound

Where Facts Go Missing: A Layerwise Taxonomy and Per-Layer Attribution of Information Omission in Air-Gapped LLMAgent Pipelines cites this paper.

Where Facts Go Missing: A Layerwise Taxonomy and Per-Layer Attribution of Information Omission in Air-Gapped LLMAgent Pipelines Evaluating Quantized Large Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-01T04:48:26.573189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T04:48:26.573189Z digest=sha256:d35d4b4e474801b162caa8044c0152230acfad6bb78d00e3dd972cabd1024e9d

Observation ec3223d8-5603-4358-8451-54a6824152ac · inbound

Studying quantization trade-offs for efficient inference deployment in machine translation cites this paper.

Studying quantization trade-offs for efficient inference deployment in machine translation Evaluating Quantized Large Language Models

Reference 26

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unresolved
no resolver link, observed 2026-08-03T07:51:21.284174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T07:51:21.284174Z digest=sha256:f4e462fcb0a58d3a77764f7e893fc07585d7e92e8055e9dd69fb2d33bfafc564

Observation 21f944eb-a174-4f2c-8927-6569e53c812e · inbound

Studying quantization trade-offs for efficient inference deployment in machine translation cites this paper.

Studying quantization trade-offs for efficient inference deployment in machine translation Evaluating Quantized Large Language Models

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-05T04:25:21.969659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T04:25:21.969659Z digest=sha256:481ef4192b25a869aee761d5b65dbcea5ad2b212fa58f6756fd74aba9cede352