Paper Citation Record · LEDGER

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2501.15674.

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

pith.paper-citation-record.v1
2501.15674 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:07:07.451001Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T11:31:25.851340Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:46:26.885322Z

Reference resolution

37 of 37 outbound references displayed

verified exact0
verified fuzzy21
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f26a2ab-c321-4217-95cf-d7bbc1c530f1 · outbound

This paper cites Language mod- els are few-shot learners,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Language mod- els are few-shot learners,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:08.108504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 86e01de0-52c9-4a5b-a4bd-9a9b3a8d798a · outbound

This paper cites GPT-4 Technical Report.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs GPT-4 Technical Report

Reference 2

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Source-reported events for the cited work

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Observation f3b77fc1-ab82-49e7-9235-246452156337 · outbound

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

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs LLaMA: Open and Efficient Foundation Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:07.273125Z

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Observation 10d14ccd-6a7a-445c-b886-a8e0c95dc248 · outbound

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

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 4

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Observation 7863bf16-b564-42da-9219-a8d3b5d64c64 · outbound

This paper cites The Llama 3 Herd of Models.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs The Llama 3 Herd of Models

Reference 5

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Observation 40579ccb-2f30-4755-8d62-d2d237e59e08 · outbound

This paper cites Learning Deep Transformer Models for Machine Translation,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Learning Deep Transformer Models for Machine Translation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:08.089330Z

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Observation d23f7f88-006b-4299-823f-c750177c7ec3 · outbound

This paper cites Language Modeling with Deep Transformers,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Language Modeling with Deep Transformers,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:08.071857Z

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Observation d0e57dfd-dd37-4ce8-aa2b-0c572cecdd85 · outbound

This paper cites Text Summarization with Pretrained Encoders,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Text Summarization with Pretrained Encoders,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:08.052881Z

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Observation 9231fbed-cae4-432e-b93b-86a80de752d4 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Distilling the Knowledge in a Neural Network

Reference 9

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Observation 84d83b32-0695-41e0-b9b5-1b32340de807 · outbound

This paper cites On the effect of dropping layers of pre-trained transformer models,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs On the effect of dropping layers of pre-trained transformer models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:08.035620Z

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Observation 756c026f-59bd-4788-ab57-1636106d641f · outbound

This paper cites Reducing Transformer Depth on Demand with Structured Dropout,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Reducing Transformer Depth on Demand with Structured Dropout,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:08.015303Z

Source-reported events for the cited work

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Observation 9642d94c-66e3-43bf-8b4b-518111f8e3d8 · outbound

This paper cites The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction,

Reference 12

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Source-reported events for the cited work

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Observation 4c459796-10f9-4258-8ace-c42cc817e4bd · outbound

This paper cites Com- pressing Large Language Models using Low Rank and Low Precision Decomposition,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Com- pressing Large Language Models using Low Rank and Low Precision Decomposition,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.980328Z

Source-reported events for the cited work

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Observation 8d405240-c8c7-408c-90e5-52602eb0eef6 · outbound

This paper cites Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation

Reference 14

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Source-reported events for the cited work

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Observation dcf166f9-4fe9-4dda-b9fc-7c39aab37575 · outbound

This paper cites TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition

Reference 15

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Source-reported events for the cited work

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Observation 77baf5c0-dce4-47ef-8883-8a860bb8dc5a · outbound

This paper cites TRAWL: Tensor Reduced and Approximated Weights for Large Language Models.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs TRAWL: Tensor Reduced and Approximated Weights for Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:07.336593Z

Source-reported events for the cited work

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Observation c9bc9e3b-076e-4ebb-9cc8-e360cfd15dc1 · outbound

This paper cites What does BERT look at? an analysis of BERT’s attention,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs What does BERT look at? an analysis of BERT’s attention,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.958434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 15b74fe5-059b-4438-ba5a-bd2759cc446a · outbound

This paper cites A Multiscale Visualization of Attention in the Transformer Model,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs A Multiscale Visualization of Attention in the Transformer Model,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.937955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 13c5ec7a-c579-4218-80d6-3bc8f9f4d95d · outbound

This paper cites Analyzing the Structure of Attention in a Transformer Language Model,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Analyzing the Structure of Attention in a Transformer Language Model,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.920506Z

Source-reported events for the cited work

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Observation ba73dd08-8299-424b-9cbb-77e0866087d4 · outbound

This paper cites A practical introduction to tensor networks: Matrix product states and projected entangled pair states,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs A practical introduction to tensor networks: Matrix product states and projected entangled pair states,

Reference 20

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Observation 3b5cdbcd-dc26-4215-8769-354f10a3e13e · outbound

This paper cites Tensor decompositions for signal processing applica- tions: From two-way to multiway component analysis,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Tensor decompositions for signal processing applica- tions: From two-way to multiway component analysis,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.891797Z

Source-reported events for the cited work

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Observation f18c04d2-7c5f-493c-9fce-e37d6f28d415 · outbound

This paper cites Tensor decompositions and applications,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Tensor decompositions and applications,

Reference 22

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Observation 1a10ffc4-c6eb-4a1a-9b8e-a9627aafa54e · outbound

This paper cites Some mathematical notes on three-mode factor analysis,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Some mathematical notes on three-mode factor analysis,

Reference 23

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Observation 9083586a-8513-4a2c-a994-1b17293d4196 · outbound

This paper cites A multilinear singular value decomposition,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs A multilinear singular value decomposition,

Reference 24

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Observation b3e7d759-dd1e-4902-a784-1da32a97b4d5 · outbound

This paper cites Multilinear analysis of image ensembles: Tensorfaces,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Multilinear analysis of image ensembles: Tensorfaces,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.836271Z

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Observation aec892dc-ee6e-462f-8513-de6983d514c6 · outbound

This paper cites Towards LLM-guided Efficient and Interpretable Multi-linear Tensor Network Rank Selection.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Towards LLM-guided Efficient and Interpretable Multi-linear Tensor Network Rank Selection

Reference 26

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Observation 119d1f5c-92a4-431b-aa1f-99f346184eec · outbound

This paper cites SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.819147Z

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Observation 8628c19c-f739-450f-b528-d9b0c8439800 · outbound

This paper cites Attention is All you Need,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Attention is All you Need,

Reference 28

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Observation 42fab33e-fe8e-43ee-ad9d-a04bae24bc05 · outbound

This paper cites Layer normalization,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Layer normalization,

Reference 29

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This paper cites TensorLy: Tensor Learning in Python,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs TensorLy: Tensor Learning in Python,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.779432Z

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Observation efa60943-eec0-4246-bea4-eb2584de0a6b · outbound

This paper cites On the best rank-1 and rank-( r1, r2, ..., rn) approximation of higher-order tensors,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs On the best rank-1 and rank-( r1, r2, ..., rn) approximation of higher-order tensors,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.762649Z

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Observation e239638c-02d1-4d08-8240-3d8eae6afe80 · outbound

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

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T14:07:07.423330Z

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Observation 8b8eae6a-e187-4a45-8158-e78dfd762381 · outbound

This paper cites GPT-J-6B: A 6 Billion Param- eter Autoregressive Language Model.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs GPT-J-6B: A 6 Billion Param- eter Autoregressive Language Model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.744086Z

Source-reported events for the cited work

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Observation e75c930d-6c3e-4154-bde2-75eeba23ccef · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi- hop Question Answering,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs HotpotQA: A Dataset for Diverse, Explainable Multi- hop Question Answering,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.726790Z

Source-reported events for the cited work

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Observation 2555d44a-11f4-4e0a-8a3e-ebe51e7464d2 · outbound

This paper cites FEVER: a Large-scale Dataset for Fact Extraction and VERification,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs FEVER: a Large-scale Dataset for Fact Extraction and VERification,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.708294Z

Source-reported events for the cited work

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Observation d0295f6c-284c-4fc1-bce0-fb1886ddbbaa · outbound

This paper cites Bias in bios: A case study of semantic representation bias in a high-stakes setting,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs Bias in bios: A case study of semantic representation bias in a high-stakes setting,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.691237Z

Source-reported events for the cited work

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Observation cdbb0aac-7c01-4b54-80da-2d5959792193 · outbound

This paper cites A large an- notated corpus for learning natural language inference,.

TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs A large an- notated corpus for learning natural language inference,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:07:07.674060Z

Source-reported events for the cited work

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Pith citing papers

Observation ddd712a5-dc44-4b1e-87d5-089ab75db9d2 · inbound

TeRA: Vector-based Random Tensor Network for High-Rank Adaptation of Large Language Models cites this paper.

TeRA: Vector-based Random Tensor Network for High-Rank Adaptation of Large Language Models TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:22:49.187866Z

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Observation b25fc94b-297d-4963-8ae3-9ce3a1001ff3 · inbound

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression cites this paper.

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:46:26.887416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.