Paper Citation Record · LEDGER

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning

As of 13 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2411.13814.

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

pith.paper-citation-record.v1
2411.13814 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:55:35.796602Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy5
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a5b14f80-b30d-4143-bae1-e3ab0e6c5b36 · outbound

This paper cites 2.Open the project and create a new folder named "ios" in the project folder.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning 2.Open the project and create a new folder named "ios" in the project folder

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:55:35.934135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2ba73653-dfae-43c7-a9b7-21114ea62de4 · outbound

This paper cites Create a new folder in the project folder to store your app's assets.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning Create a new folder in the project folder to store your app's assets

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:55:35.896807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e9691f5a-6279-462d-8ab4-9eb255865e2e · outbound

This paper cites app.json.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning app.json

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:55:35.924762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 30b7a4b5-cbec-4459-abf6-a1ecab284ac5 · outbound

This paper cites name": "MyApp.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning name": "MyApp

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:55:35.915804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f0f0499d-2098-4b51-bbb9-e41af34b6696 · outbound

This paper cites an unresolved cited work.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:55:35.906414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7bed9e40-7290-4068-bcd1-99647df03141 · outbound

This paper cites an unresolved cited work.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:55:35.886907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 55042a90-6291-4379-958a-e15a207ba969 · outbound

This paper cites an unresolved cited work.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:55:35.876996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a05e59aa-33fc-4233-b52a-efc3e155817d · outbound

This paper cites an unresolved cited work.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:55:35.868220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 78e2467c-6a7b-4e03-934d-2b7536864bda · outbound

This paper cites an unresolved cited work.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:55:35.859105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d566bf53-c823-442d-91fd-72863d2227a1 · outbound

This paper cites an unresolved cited work.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:55:35.848756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1b9a371d-6a9e-483d-95a1-f7672f1417db · outbound

This paper cites Figure 3: Sample generation for LLaMA-7B and Vicuna-7B models with a 20% pruning rate.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning Figure 3: Sample generation for LLaMA-7B and Vicuna-7B models with a 20% pruning rate

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:55:35.838807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation de793964-36c6-4955-a4d1-a7c08505a15c · outbound

This paper cites What Matters In The Structured Pruning of Generative Language Models?.

AutoMixQ: Self-Adjusting Quantization for High Performance Memory-Efficient Fine-Tuning What Matters In The Structured Pruning of Generative Language Models?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T15:55:35.756424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.