Paper Citation Record · LEDGER

LOST: Low-rank and Sparse Pre-training for Large Language Models

As of 15 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 8 inbound Pith citation observations for arXiv:2508.02668.

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

pith.paper-citation-record.v1
2508.02668 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:44:20.123934Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:01:14.921682Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T02:06:42.157809Z

Reference resolution

46 of 46 outbound references displayed

verified exact0
verified fuzzy10
unresolved36
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 653e2635-a16e-4d75-b532-157bfb08ee6a · outbound

This paper cites From words to watts: Benchmarking the energy costs of large language model inference.

LOST: Low-rank and Sparse Pre-training for Large Language Models From words to watts: Benchmarking the energy costs of large language model inference

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.975978Z

Source-reported events for the cited work

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Observation f23d8d50-c46a-4ffa-bcfd-51bcbc315a77 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

LOST: Low-rank and Sparse Pre-training for Large Language Models Lora: Low-rank adaptation of large language models

Reference 2

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Observation c49d1c7f-7aa2-4255-b26d-8bdb5643372f · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine-tuning.

LOST: Low-rank and Sparse Pre-training for Large Language Models Adaptive budget allocation for parameter-efficient fine-tuning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:19.933961Z

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Observation 1eb9db9f-e9fc-4a14-991e-c238ebc1a325 · outbound

This paper cites Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying.

LOST: Low-rank and Sparse Pre-training for Large Language Models Tied-Lora: Enhancing parameter efficiency of LoRA with weight tying

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:19.938654Z

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Observation 3b867b7f-f663-4bb4-9db2-8fdb7b139ab2 · outbound

This paper cites S-LoRA: Serving Thousands of Concurrent LoRA Adapters.

LOST: Low-rank and Sparse Pre-training for Large Language Models S-LoRA: Serving Thousands of Concurrent LoRA Adapters

Reference 5

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Observation 479367a4-81b3-4ff0-8794-b7755d974e28 · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

LOST: Low-rank and Sparse Pre-training for Large Language Models DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:19.948554Z

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Observation 0703212b-4aa1-4d22-8783-926ab7db9cd9 · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

LOST: Low-rank and Sparse Pre-training for Large Language Models VeRA: Vector-based Random Matrix Adaptation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:19.953651Z

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Observation d00ccf2c-9f3e-439c-b135-39af7336f0f0 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

LOST: Low-rank and Sparse Pre-training for Large Language Models Qlora: Efficient finetuning of quantized llms

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:19.958401Z

Source-reported events for the cited work

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Observation 309eec51-1224-43fa-aad6-6084f28ae426 · outbound

This paper cites Initialization and regular- ization of factorized neural layers.

LOST: Low-rank and Sparse Pre-training for Large Language Models Initialization and regular- ization of factorized neural layers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.930648Z

Source-reported events for the cited work

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Observation 0a56494e-30e8-48cc-8140-8819ceaaf75c · outbound

This paper cites On the Initialisation of Wide Low-Rank Feedforward Neural Networks.

LOST: Low-rank and Sparse Pre-training for Large Language Models On the Initialisation of Wide Low-Rank Feedforward Neural Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:19.967395Z

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Observation c1f8dcf2-c60c-4465-91ff-823ea3ea38ca · outbound

This paper cites Exploring Low Rank Training of Deep Neural Networks.

LOST: Low-rank and Sparse Pre-training for Large Language Models Exploring Low Rank Training of Deep Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:19.971937Z

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Observation f4a609db-ac42-47f6-81a9-00614a8e714a · outbound

This paper cites Relora: High-rank training through low-rank updates.

LOST: Low-rank and Sparse Pre-training for Large Language Models Relora: High-rank training through low-rank updates

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.916242Z

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Observation 023937b8-2daa-4d79-872f-e23c85288eff · outbound

This paper cites From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications.

LOST: Low-rank and Sparse Pre-training for Large Language Models From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 13

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Observation dce4fa70-208c-4224-9b8b-ed52f94270a4 · outbound

This paper cites Investigating low-rank training in transformer language models: Efficiency and scaling analysis.

LOST: Low-rank and Sparse Pre-training for Large Language Models Investigating low-rank training in transformer language models: Efficiency and scaling analysis

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.901928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f9bfbb79-d420-4d1f-bacc-1548378ada1a · outbound

This paper cites Full-rank no more: Low-rank weight training for modern speech recognition models.

LOST: Low-rank and Sparse Pre-training for Large Language Models Full-rank no more: Low-rank weight training for modern speech recognition models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.888036Z

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Observation cd650398-4963-4ded-9624-0102046aa521 · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

LOST: Low-rank and Sparse Pre-training for Large Language Models GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 16

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Observation d8ae0927-724c-4885-b45f-0d66cdc61c8b · outbound

This paper cites Fira: Can we achieve full-rank training of llms under low-rank constraint? arXiv preprint arXiv:2410.01623, 2024.

LOST: Low-rank and Sparse Pre-training for Large Language Models Fira: Can we achieve full-rank training of llms under low-rank constraint? arXiv preprint arXiv:2410.01623, 2024

Reference 17

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Observation 21ef9879-1b50-426c-ae10-2f909a2f3e20 · outbound

This paper cites APOLLO: SGD-like Memory, AdamW-level Performance.

LOST: Low-rank and Sparse Pre-training for Large Language Models APOLLO: SGD-like Memory, AdamW-level Performance

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.002334Z

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Observation 07b3e26b-c6b7-4ab6-8c57-a7488d74c387 · outbound

This paper cites Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients.

LOST: Low-rank and Sparse Pre-training for Large Language Models Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.006961Z

Source-reported events for the cited work

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Observation ce310c51-70c4-4cc9-9cd5-5b12863b6af1 · outbound

This paper cites SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining.

LOST: Low-rank and Sparse Pre-training for Large Language Models SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.874153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 481246bc-ec1c-48b8-8b9e-b7bcc6a136f9 · outbound

This paper cites Losparse: Structured compression of large language models based on low-rank and sparse approximation.

LOST: Low-rank and Sparse Pre-training for Large Language Models Losparse: Structured compression of large language models based on low-rank and sparse approximation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.015537Z

Source-reported events for the cited work

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Observation 775de906-647b-46ff-a9dc-7c25d8265aa3 · outbound

This paper cites Dynamic Low-Rank Sparse Adaptation for Large Language Models.

LOST: Low-rank and Sparse Pre-training for Large Language Models Dynamic Low-Rank Sparse Adaptation for Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.019624Z

Source-reported events for the cited work

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Observation 0e40d821-8688-444c-8112-c90f61ba3cbb · outbound

This paper cites Sparse Low-rank Adaptation of Pre-trained Language Models.

LOST: Low-rank and Sparse Pre-training for Large Language Models Sparse Low-rank Adaptation of Pre-trained Language Models

Reference 23

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Observation 1babcc04-b00e-47de-b94c-0006ad552cab · outbound

This paper cites Pissa: Principal singular values and singular vectors adaptation of large language models.

LOST: Low-rank and Sparse Pre-training for Large Language Models Pissa: Principal singular values and singular vectors adaptation of large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.851405Z

Source-reported events for the cited work

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Observation 01dffb2d-65cf-4d65-b454-a57ec7ade47a · outbound

This paper cites LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters.

LOST: Low-rank and Sparse Pre-training for Large Language Models LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.032658Z

Source-reported events for the cited work

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Observation b389bf80-8f3b-4c60-80a4-906c95d75456 · outbound

This paper cites NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models.

LOST: Low-rank and Sparse Pre-training for Large Language Models NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.037368Z

Source-reported events for the cited work

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Observation a9ece71d-9139-4c73-98c9-32e93ab62ffc · outbound

This paper cites Parameter efficient fine-tuning via explained variance adaptation.

LOST: Low-rank and Sparse Pre-training for Large Language Models Parameter efficient fine-tuning via explained variance adaptation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.041836Z

Source-reported events for the cited work

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Observation 316709d5-49ea-40bc-aff3-b62a6857813d · outbound

This paper cites EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition.

LOST: Low-rank and Sparse Pre-training for Large Language Models EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.046020Z

Source-reported events for the cited work

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Observation fd054f5f-97d6-45b5-ae4d-0e9248a7e069 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

LOST: Low-rank and Sparse Pre-training for Large Language Models Qlora: Efficient finetuning of quantized llms

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.050577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e66f8373-0d84-44a7-b1eb-d518d337be62 · outbound

This paper cites QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models.

LOST: Low-rank and Sparse Pre-training for Large Language Models QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.054800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f607441-46e9-482a-bbeb-7059a00d3df5 · outbound

This paper cites LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models.

LOST: Low-rank and Sparse Pre-training for Large Language Models LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.059283Z

Source-reported events for the cited work

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Observation 88e11372-3695-43e5-82e2-bac5eee9ccfe · outbound

This paper cites Pixelated butterfly: Simple and efficient sparse training for neural network models.

LOST: Low-rank and Sparse Pre-training for Large Language Models Pixelated butterfly: Simple and efficient sparse training for neural network models

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.828501Z

Source-reported events for the cited work

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Observation 72dc95d8-c7c8-4ef1-a7a8-90a8a797a8b4 · outbound

This paper cites OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition.

LOST: Low-rank and Sparse Pre-training for Large Language Models OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.067925Z

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Observation 7a2c524c-3dd1-4f7e-a3a8-2ce1de59cfb1 · outbound

This paper cites Robust principal component analysis? Journal of the ACM (JACM), 58(3):1–37, 2011.

LOST: Low-rank and Sparse Pre-training for Large Language Models Robust principal component analysis? Journal of the ACM (JACM), 58(3):1–37, 2011

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.072472Z

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Observation 71852d66-0be9-480d-bbe8-31d0c08372ca · outbound

This paper cites HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs.

LOST: Low-rank and Sparse Pre-training for Large Language Models HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.076512Z

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Observation 09a150ee-47ee-4fed-b6c7-d75afbf2316a · outbound

This paper cites Parameter and memory efficient pretraining via low-rank riemannian optimization.

LOST: Low-rank and Sparse Pre-training for Large Language Models Parameter and memory efficient pretraining via low-rank riemannian optimization

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.805054Z

Source-reported events for the cited work

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Observation 70bc2e89-9a5e-448a-8f98-fe1482358d88 · outbound

This paper cites Cola: Compute-efficient pre-training of llms via low-rank activa- tion.

LOST: Low-rank and Sparse Pre-training for Large Language Models Cola: Compute-efficient pre-training of llms via low-rank activa- tion

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.085505Z

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Observation 524183ae-6472-4c77-a1a3-0d5cb17343ca · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

LOST: Low-rank and Sparse Pre-training for Large Language Models Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.089552Z

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Observation ddf5e120-3a58-44ac-ad60-b83fb32a9afd · outbound

This paper cites SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression.

LOST: Low-rank and Sparse Pre-training for Large Language Models SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.093532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcef69b6-096e-4f6c-a3ca-bafeb2785d8f · outbound

This paper cites Language model compression with weighted low-rank factorization.

LOST: Low-rank and Sparse Pre-training for Large Language Models Language model compression with weighted low-rank factorization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:44:20.781391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 830b6fcd-018b-4a74-9f91-e09280c73814 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

LOST: Low-rank and Sparse Pre-training for Large Language Models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.102159Z

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Unavailable: canonical work link unavailable.

Observation e4afd30e-ab33-43c9-ae62-78fa93399540 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

LOST: Low-rank and Sparse Pre-training for Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.106626Z

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Observation 2ef5faf2-9f46-46db-99db-134e5c0a9e3c · outbound

This paper cites Root mean square layer normalization.

LOST: Low-rank and Sparse Pre-training for Large Language Models Root mean square layer normalization

Reference 43

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Observation 77639cfb-d69d-46b3-84e2-9dc613567e3c · outbound

This paper cites GLU Variants Improve Transformer.

LOST: Low-rank and Sparse Pre-training for Large Language Models GLU Variants Improve Transformer

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.115079Z

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Observation 33b522a7-07d4-4747-a7c7-9b216e301268 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

LOST: Low-rank and Sparse Pre-training for Large Language Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.119536Z

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Unavailable: canonical work link unavailable.

Observation 3ebfeae4-f2ae-4ba3-9e0e-936aef17e034 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

LOST: Low-rank and Sparse Pre-training for Large Language Models GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:20.123934Z

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Pith citing papers

Observation 93b6c660-bcd1-4dda-9f8a-60a1ea3f25cc · inbound

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure cites this paper.

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure LOST: Low-rank and Sparse Pre-training for Large Language Models

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-18T14:52:41.334819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f2a6803d-cf78-40f8-977a-7e89afb9b216 · inbound

BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models cites this paper.

BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models LOST: Low-rank and Sparse Pre-training for Large Language Models

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:21:21.574771Z

Source-reported events for the cited work

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Observation 14b5ae28-76fd-4843-9180-75a2e7fd78ab · inbound

SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference cites this paper.

SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference LOST: Low-rank and Sparse Pre-training for Large Language Models

Reference 10

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Observation 431d40f3-7e33-4534-9efe-08c4e1ff636f · inbound

Spectral Compact Training: Pre-Training Large Language Models via Permanent Truncated SVD and Stiefel QR Retraction cites this paper.

Spectral Compact Training: Pre-Training Large Language Models via Permanent Truncated SVD and Stiefel QR Retraction LOST: Low-rank and Sparse Pre-training for Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:58:24.130724Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 88079b2f-06d7-49d5-9aec-0f4af1e9a2b1 · inbound

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

Low-Rank Adaptation Redux for Large Models LOST: Low-rank and Sparse Pre-training for Large Language Models

Reference 107

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:26:03.945876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6c295afb-2937-4f93-acd1-776ca5c533dc · inbound

Beyond Perplexity: A Geometric and Spectral Study of Low-Rank Pre-Training cites this paper.

Beyond Perplexity: A Geometric and Spectral Study of Low-Rank Pre-Training LOST: Low-rank and Sparse Pre-training for Large Language Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:19:23.495124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dc1cc48d-ca4d-44dd-a91f-e8baaed1dd73 · inbound

Beyond Perplexity: A Geometric and Spectral Study of Low-Rank Pre-Training cites this paper.

Beyond Perplexity: A Geometric and Spectral Study of Low-Rank Pre-Training LOST: Low-rank and Sparse Pre-training for Large Language Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:28:59.916751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 730d5a5a-15dd-481e-9583-4151a9f46e6d · inbound

SLORR: Simple and Efficient In-Training Low-Rank Regularization cites this paper.

SLORR: Simple and Efficient In-Training Low-Rank Regularization LOST: Low-rank and Sparse Pre-training for Large Language Models

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-10T02:06:42.159982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.