Paper Citation Record · LEDGER

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning

As of 18 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 2 inbound Pith citation observations for arXiv:2506.09105.

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

pith.paper-citation-record.v1
2506.09105 v3

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:05:00.505157Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T21:10:14.720338Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T21:12:47.202680Z

Reference resolution

13 of 13 outbound references displayed

verified exact2
verified fuzzy2
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d71639ba-bbae-46ca-9a54-a60e0ad5a28a · outbound

This paper cites RandLoRA: Full-rank parameter-efficient fine-tuning of large models.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning RandLoRA: Full-rank parameter-efficient fine-tuning of large models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:00.462706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f64cab14-29b6-450a-b900-1cdaf7c81680 · outbound

This paper cites Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:00.477361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 249123c0-b099-47c6-b818-a7445e9ff65b · outbound

This paper cites FETTA: Flexible and Efficient Hardware Accelerator for Tensorized Neural Network Training.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning FETTA: Flexible and Efficient Hardware Accelerator for Tensorized Neural Network Training

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-07T05:05:00.780922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8afc4e61-db51-4ecc-be45-a4c3bdbaa20d · outbound

This paper cites LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:00.491232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2e2d026-ca3e-4344-9028-664d3188f341 · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:00.494848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e8319ff-4c78-4f53-bf73-645f3a1cefdc · outbound

This paper cites Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:00.498469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 485d37b8-55a2-4478-ad74-4ca4730fa50d · outbound

This paper cites Learning to prompt for vision-language models.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning Learning to prompt for vision-language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:00.892549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 34d6a7fa-bf32-4f97-bca2-226582f7d520 · outbound

This paper cites an unresolved cited work.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:05:00.882834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 86d25c6b-c0dd-4ea8-a94e-7cc38154b0aa · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:05:00.902914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 91bc811d-e8f3-4e0e-9db9-658cddb6ac22 · outbound

This paper cites Randomized block krylov methods for stronger and faster ap- proximate singular value decomposition.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning Randomized block krylov methods for stronger and faster ap- proximate singular value decomposition

Reference 2022

Resolution: verified exact; raw_fallback, observed 2026-08-07T05:05:00.713106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f56998b0-21d8-463c-a10d-68a04974c3c3 · outbound

This paper cites Mistral 7B.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning Mistral 7B

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:00.473723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec9c802c-05a8-45b3-8695-ab715478dd91 · outbound

This paper cites Ultimate tensorization: compressing convolutional and fc layers alike.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning Ultimate tensorization: compressing convolutional and fc layers alike

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:00.470240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48af388e-9b30-42d2-b445-1f24e23d330f · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:00.487591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation bc88f14c-554e-4902-9d4d-d0ac9a9379b3 · inbound

Low-Rank Adaptation Redux for Large Models cites this paper.

Low-Rank Adaptation Redux for Large Models MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning

Reference 120

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T02:17:05.654691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3f1f59e0-8d1a-49c8-b96a-761bb9163fbd · inbound

GLT-PEFT: Gated Lie-Tucker Parameter-Efficient Fine-Tuning for Alzheimer's Disease Diagnosis with Hippocampal Segmentation Pretraining cites this paper.

GLT-PEFT: Gated Lie-Tucker Parameter-Efficient Fine-Tuning for Alzheimer's Disease Diagnosis with Hippocampal Segmentation Pretraining MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:17:05.654691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.