Paper Citation Record · LEDGER

LatentLLM: Attention-Aware Joint Tensor Compression

As of 9 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2505.18413.

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

pith.paper-citation-record.v1
2505.18413 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:36:28.735305Z

measured 51 of 51 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 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

51 of 51 outbound references displayed

verified exact1
verified fuzzy25
unresolved25
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c2c06224-b566-46f9-b865-6b7bff397c7b · outbound

This paper cites GPT-4 Technical Report.

LatentLLM: Attention-Aware Joint Tensor Compression GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

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Observation d1deea97-6f15-4f39-bd23-c5e60d46039f · outbound

This paper cites Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models.

LatentLLM: Attention-Aware Joint Tensor Compression Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:25.329311Z

Source-reported events for the cited work

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Observation 1db0197c-8592-47b0-aec6-67b04e4343ce · outbound

This paper cites SparseLLM: Towards global pruning of pre-trained language models.

LatentLLM: Attention-Aware Joint Tensor Compression SparseLLM: Towards global pruning of pre-trained language models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:32.810350Z

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Observation c685ec87-3a1c-4d35-81be-3caa902f11fe · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

LatentLLM: Attention-Aware Joint Tensor Compression Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:25.480363Z

Source-reported events for the cited work

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Observation de377a09-f03b-457f-b1bd-0eb0dffa5fe3 · outbound

This paper cites Palu: Compressing KV-Cache with Low-Rank Projection.

LatentLLM: Attention-Aware Joint Tensor Compression Palu: Compressing KV-Cache with Low-Rank Projection

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:25.552150Z

Source-reported events for the cited work

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Observation 240a159d-da0c-4316-a833-afe69cfeaab9 · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

LatentLLM: Attention-Aware Joint Tensor Compression Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:25.615439Z

Source-reported events for the cited work

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Observation 11f4e7c6-8291-44ca-814d-f623ae7e283f · outbound

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LatentLLM: Attention-Aware Joint Tensor Compression Unresolved cited work

Reference 7

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 17241804-7cb0-4bec-a9e8-0a2140aa1046 · outbound

This paper cites Exploiting linear structure within convolutional networks for efficient evaluation.

LatentLLM: Attention-Aware Joint Tensor Compression Exploiting linear structure within convolutional networks for efficient evaluation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:32.559494Z

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Observation bac52161-ec82-4edd-92f7-0ab3091b3fd8 · outbound

This paper cites The case for 4-bit pre- cision: k-bit inference scaling laws.

LatentLLM: Attention-Aware Joint Tensor Compression The case for 4-bit pre- cision: k-bit inference scaling laws

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:32.368879Z

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Observation 0ea253db-e85d-4860-8d87-befe9b60dc66 · outbound

This paper cites SparseGPT: Massive lan- guage models can be accurately pruned in one-shot.

LatentLLM: Attention-Aware Joint Tensor Compression SparseGPT: Massive lan- guage models can be accurately pruned in one-shot

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:32.224670Z

Source-reported events for the cited work

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Observation 0b19b3af-67d6-45e6-a5cb-488ffefaae87 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

LatentLLM: Attention-Aware Joint Tensor Compression GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:25.988734Z

Source-reported events for the cited work

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Observation 7d4e12b9-8b99-4d64-ad86-811e24049369 · outbound

This paper cites Optimal brain surgeon and general network pruning.

LatentLLM: Attention-Aware Joint Tensor Compression Optimal brain surgeon and general network pruning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:32.103562Z

Source-reported events for the cited work

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Observation 8504ecd0-91f8-41a5-be47-075d2c859709 · outbound

This paper cites Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.

LatentLLM: Attention-Aware Joint Tensor Compression Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Reference 13

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Source-reported events for the cited work

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Observation 14c94767-f9f5-4430-826a-5038496469c3 · outbound

This paper cites PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation.

LatentLLM: Attention-Aware Joint Tensor Compression PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:26.200703Z

Source-reported events for the cited work

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Observation 0d8671d7-b1bf-4560-af95-bd7dceed05d2 · outbound

This paper cites Mixtral of Experts.

LatentLLM: Attention-Aware Joint Tensor Compression Mixtral of Experts

Reference 15

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Source-reported events for the cited work

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Observation 77f00abf-534c-48e9-ad7d-16d00534f531 · outbound

This paper cites GPT-4 passes the bar exam.

LatentLLM: Attention-Aware Joint Tensor Compression GPT-4 passes the bar exam

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:31.978651Z

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Observation 8145a8c0-1983-4bed-8c60-c98ed18a1a49 · outbound

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

LatentLLM: Attention-Aware Joint Tensor Compression BERT: Pre-training of deep bidirectional trans- formers for language understanding

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:31.835035Z

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Observation 7bd49f21-f817-4a6b-bc8a-ebab02acf37c · outbound

This paper cites Optimal brain damage.

LatentLLM: Attention-Aware Joint Tensor Compression Optimal brain damage

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:31.720301Z

Source-reported events for the cited work

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Observation 1071d354-ec1c-47bb-94bb-f113775a96b0 · outbound

This paper cites A well-conditioned esti- mator for large-dimensional covariance matrices.

LatentLLM: Attention-Aware Joint Tensor Compression A well-conditioned esti- mator for large-dimensional covariance matrices

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:31.559064Z

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Observation 583760f6-b70a-42c5-9c3a-8afa8482b7b6 · outbound

This paper cites LoSparse: Structured com- pression of large language models based on low-rank and sparse approximation.

LatentLLM: Attention-Aware Joint Tensor Compression LoSparse: Structured com- pression of large language models based on low-rank and sparse approximation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:31.384050Z

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Observation a6740093-4468-4bfb-98e9-ea5c20b7031a · outbound

This paper cites Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix.

LatentLLM: Attention-Aware Joint Tensor Compression Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:36:29.038387Z

Source-reported events for the cited work

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Observation 4ebe3966-64f4-492c-a488-b3e3cf0c3119 · outbound

This paper cites MoE-LLaVA: Mixture of Experts for Large Vision-Language Models.

LatentLLM: Attention-Aware Joint Tensor Compression MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:26.860447Z

Source-reported events for the cited work

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Observation a7ad3544-b528-4917-b96a-df0dcc96819b · outbound

This paper cites AWQ: Activation-aware weight quantization for on-device LLM compression and accelera- tion.

LatentLLM: Attention-Aware Joint Tensor Compression AWQ: Activation-aware weight quantization for on-device LLM compression and accelera- tion

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:31.213947Z

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Observation 8284d0aa-2043-4fd3-a339-1ebdb6dd188f · outbound

This paper cites DeepSeek-V3 Technical Report.

LatentLLM: Attention-Aware Joint Tensor Compression DeepSeek-V3 Technical Report

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:27.024844Z

Source-reported events for the cited work

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Observation e634fac1-8581-4ad3-b278-992e3eb03c67 · outbound

This paper cites Visual instruction tuning, 2023.

LatentLLM: Attention-Aware Joint Tensor Compression Visual instruction tuning, 2023

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:31.089171Z

Source-reported events for the cited work

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Observation d47d09b3-8be8-4d51-87c8-b0a01a197364 · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

LatentLLM: Attention-Aware Joint Tensor Compression Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:30.921460Z

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.

Observation 932a920d-14f5-4e6d-9c14-3e22c81754f0 · outbound

This paper cites The penn treebank: Annotating pred- icate argument structure.

LatentLLM: Attention-Aware Joint Tensor Compression The penn treebank: Annotating pred- icate argument structure

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:30.811173Z

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Observation cf4e8bf6-d8af-4ab3-bd6f-b2f5d8778f46 · outbound

This paper cites Pointer Sentinel Mixture Models.

LatentLLM: Attention-Aware Joint Tensor Compression Pointer Sentinel Mixture Models

Reference 28

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Source-reported events for the cited work

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Observation 47530378-0076-485c-a6a3-5bafe57adec1 · outbound

This paper cites PyTorch: An imperative style, high-performance deep learning li- brary.

LatentLLM: Attention-Aware Joint Tensor Compression PyTorch: An imperative style, high-performance deep learning li- brary

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:30.655344Z

Source-reported events for the cited work

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Observation e5a97581-de30-45d0-8164-2ca0c1fb69ae · outbound

This paper cites Improving language understanding by gener- ative pre-training.

LatentLLM: Attention-Aware Joint Tensor Compression Improving language understanding by gener- ative pre-training

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:30.541603Z

Source-reported events for the cited work

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Observation ded2fb4a-2627-4b35-b587-87a07647adc0 · outbound

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

LatentLLM: Attention-Aware Joint Tensor Compression Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:30.403281Z

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Observation 308176fb-f749-456e-b4df-b5e993404494 · outbound

This paper cites Compressing large language models using low rank and low precision decomposition.

LatentLLM: Attention-Aware Joint Tensor Compression Compressing large language models using low rank and low precision decomposition

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:30.218568Z

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Observation 81cb0c3b-46dc-41a3-88f7-f9d8c5d4fcb3 · outbound

This paper cites Low-rank matrix factorization for deep neural network training with high- dimensional output targets.

LatentLLM: Attention-Aware Joint Tensor Compression Low-rank matrix factorization for deep neural network training with high- dimensional output targets

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:30.045542Z

Source-reported events for the cited work

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Observation 508cc0d9-098f-4f60-b8ab-01ccc9a87c36 · outbound

This paper cites Eigen Attention: Attention in Low-Rank Space for KV Cache Compression.

LatentLLM: Attention-Aware Joint Tensor Compression Eigen Attention: Attention in Low-Rank Space for KV Cache Compression

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:27.748100Z

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Observation 20dda85b-cc72-4e9e-9352-e82849ede1a5 · outbound

This paper cites Low-rank lottery tick- ets: finding efficient low-rank neural networks via matrix differential equations.

LatentLLM: Attention-Aware Joint Tensor Compression Low-rank lottery tick- ets: finding efficient low-rank neural networks via matrix differential equations

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:29.922562Z

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Observation 2ec3fa6e-c35f-458c-951c-7b43c5e80af2 · outbound

This paper cites Green AI.

LatentLLM: Attention-Aware Joint Tensor Compression Green AI

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:29.800985Z

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.

Observation 5e7a8c2e-c841-4268-b95a-cabab14488f8 · outbound

This paper cites RoFormer: Enhanced transformer with rotary position embedding.

LatentLLM: Attention-Aware Joint Tensor Compression RoFormer: Enhanced transformer with rotary position embedding

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:27.942579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18f6aca1-b490-41b1-aa38-f50ef6ff4c85 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

LatentLLM: Attention-Aware Joint Tensor Compression A Simple and Effective Pruning Approach for Large Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:27.999333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c43f400-fd57-4aa1-8161-ce727b13c736 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

LatentLLM: Attention-Aware Joint Tensor Compression Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:28.069833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76d86c79-9ba3-471e-ae7f-c9e467fe44df · outbound

This paper cites Q-VLM: Post-training Quantization for Large Vision-Language Models.

LatentLLM: Attention-Aware Joint Tensor Compression Q-VLM: Post-training Quantization for Large Vision-Language Models

Reference 40

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Source-reported events for the cited work

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Observation 603ecd6f-dbb6-4f96-bcc4-d59ffd567631 · outbound

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

LatentLLM: Attention-Aware Joint Tensor Compression SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:28.168443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c9bb04e-07e6-4706-9bff-98ca4f8da6a1 · outbound

This paper cites Emergent Abilities of Large Language Models.

LatentLLM: Attention-Aware Joint Tensor Compression Emergent Abilities of Large Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:28.207771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34cbf179-46fa-4b85-84a3-6ce0b858f452 · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

LatentLLM: Attention-Aware Joint Tensor Compression HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:28.266940Z

Source-reported events for the cited work

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Observation bb1f7871-a812-4f81-ac68-d52ca000203e · outbound

This paper cites A survey on model com- pression and acceleration for pretrained language models.

LatentLLM: Attention-Aware Joint Tensor Compression A survey on model com- pression and acceleration for pretrained language models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:29.690027Z

Source-reported events for the cited work

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Observation 657eba76-ddd8-4c71-9aa4-d7ed7b2be546 · outbound

This paper cites CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning.

LatentLLM: Attention-Aware Joint Tensor Compression CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:28.399010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9657a837-80cd-4eb0-91fc-86a062381182 · outbound

This paper cites ZeroQuant: Ef- ficient and affordable post-training quantization for large- scale transformers.

LatentLLM: Attention-Aware Joint Tensor Compression ZeroQuant: Ef- ficient and affordable post-training quantization for large- scale transformers

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:29.560048Z

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.

Observation 58b88554-8138-461b-adcc-18dfbad1c2d7 · outbound

This paper cites ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models.

LatentLLM: Attention-Aware Joint Tensor Compression ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:28.526259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b93a57aa-0a94-4728-8bc8-8f5214d49e12 · outbound

This paper cites LLM Inference Unveiled: Survey and Roofline Model Insights.

LatentLLM: Attention-Aware Joint Tensor Compression LLM Inference Unveiled: Survey and Roofline Model Insights

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:28.556122Z

Source-reported events for the cited work

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Observation 26e09b5b-3bc5-445f-a80e-64b3cbf75517 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

LatentLLM: Attention-Aware Joint Tensor Compression OPT: Open Pre-trained Transformer Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T14:36:28.641006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242077bb-9032-41fe-a6f4-4057b8020261 · outbound

This paper cites C 1 2 O µ⊤C −1 2 (1 − µ⊤C +µ) 1 2 #.

LatentLLM: Attention-Aware Joint Tensor Compression C 1 2 O µ⊤C −1 2 (1 − µ⊤C +µ) 1 2 #

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:29.428122Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 235a3222-a37a-4581-91b0-6192fddafa9c · outbound

This paper cites (193) Plugging into the loss gives: L = X i ∥Wo,iWv,i(X − µ1⊤) − ˆWo,i ˆWv,i(X − µ1⊤)∥2 (194) = X i ∥ Wo,iWv,i| {z } Gi∈Rd×d C 1 2 0 − Bo Ao,iBv,i| {z } Hi∈Rro ×rv AvC 1 2 0 ∥2.

LatentLLM: Attention-Aware Joint Tensor Compression (193) Plugging into the loss gives: L = X i ∥Wo,iWv,i(X − µ1⊤) − ˆWo,i ˆWv,i(X − µ1⊤)∥2 (194) = X i ∥ Wo,iWv,i| {z } Gi∈Rd×d C 1 2 0 − Bo Ao,iBv,i| {z } Hi∈Rro ×rv AvC 1 2 0 ∥2

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:36:29.249964Z

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Pith citing papers

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