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Paper Citation Record · LEDGER

AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.10054.

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

pith.paper-citation-record.v1
2410.10054 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:00:51.852982Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T09:01:24.487604Z

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 744d6b0b-ed8c-4240-ab1c-b2f5e709fced · inbound

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias cites this paper.

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:51.852982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:00:51.852982Z digest=sha256:fabad39dde0b29a40b1780ee8ecbb01fa76d281b5c220ad6dfa997769599d621

Observation 07055b55-1e84-4520-9110-fa594d18c30a · inbound

Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization cites this paper.

Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-02T19:52:20.759195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:52:20.759195Z digest=sha256:f4a41cc8fff6ca55f6ead42d7b40222ca11c2c09486b09a49ec1674a02e284e5

Observation 7cfc5459-c9d0-4868-a2fd-766ee5d4818d · inbound

Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization cites this paper.

Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-03T02:39:01.949150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:39:01.949150Z digest=sha256:8a4b928988e3feabc89f8631e87013ea6f8a56623f1bfe88109f34bd2b960de8

Observation bd5bbfdb-8dd0-4ca3-b2fa-60bf512d4182 · inbound

Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning cites this paper.

Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:01:24.489921Z

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=2026-05-07T13:22:13.763578Z digest=sha256:cf3375e1b7011d47196993d25f5fc84c91d2ab779ceb18dec051fb15f6aec1c3