Paper Citation Record · LEDGER

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices

As of 23 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.13514.

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

pith.paper-citation-record.v1
2506.13514 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:05:49.018439Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 8e2ae83c-0a0f-4302-a385-1582db9a0108 · outbound

This paper cites write newline.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices write newline

Reference 1

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Observation 4b2dbd79-6b84-4bdb-aedb-e2954bb115d5 · outbound

This paper cites S mol LM.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices S mol LM

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:05:49.688434Z

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Observation 096d5103-b231-4ff5-9ca9-c6f4788ff257 · outbound

This paper cites TQCompressor: improving tensor decomposition methods in neural networks via permutations.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices TQCompressor: improving tensor decomposition methods in neural networks via permutations

Reference 3

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This paper cites Online embedding compression for text classification using low rank matrix factorization.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Online embedding compression for text classification using low rank matrix factorization

Reference 4

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Observation 687e1e87-bcd0-481e-8bb4-9e116184c792 · outbound

This paper cites Direction is what you need: Improving Word Embedding Compression in Large Language Models.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Direction is what you need: Improving Word Embedding Compression in Large Language Models

Reference 5

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Observation 061bd846-ea40-4588-91d3-bc893f58cada · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Piqa: Reasoning about physical commonsense in natural language

Reference 6

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Observation 21da2f3f-709e-4746-8034-092bb7587eef · outbound

This paper cites Language Models are Few-Shot Learners.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Language Models are Few-Shot Learners

Reference 7

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Observation 3a4ab5cb-6f3a-4991-9327-182f9481632e · outbound

This paper cites Efficient GPT model pre-training using tensor train matrix representation.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Efficient GPT model pre-training using tensor train matrix representation

Reference 8

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Observation fe1dc146-262c-4fed-9aba-89f156fbb013 · outbound

This paper cites Efficient GPT Model Pre-training using Tensor Train Matrix Representation.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Efficient GPT Model Pre-training using Tensor Train Matrix Representation

Reference 9

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Observation 04118ae3-d401-4778-ae65-e2d23e24cdd9 · outbound

This paper cites One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling

Reference 10

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Observation b1cd3950-c020-4ba3-99d6-2128fb2f754b · outbound

This paper cites Groupreduce: Block-wise low-rank approximation for neural language model shrinking.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Groupreduce: Block-wise low-rank approximation for neural language model shrinking

Reference 11

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Observation 1c46903c-6817-42a4-9d69-3fd0d53cc23d · outbound

This paper cites Drone: Data-aware low-rank compression for large nlp models.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Drone: Data-aware low-rank compression for large nlp models

Reference 12

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Observation 89d7b371-73ce-4e59-a2e5-199efec1872f · outbound

This paper cites R., and Sun, Y.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices R., and Sun, Y

Reference 13

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Observation bca0c8f3-0792-4bff-95ea-203fadd4e365 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 14

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Observation b63d794f-de14-4a1a-8d75-f69484d54330 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 15

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Observation e41ec1fa-21e6-44f3-a37a-4c1c49c26bfe · outbound

This paper cites S., Desai, A., Poli, M., Grogan, J., Liu, A., Rao, A., Rudra, A., and Re, C.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices S., Desai, A., Poli, M., Grogan, J., Liu, A., Rao, A., Rudra, A., and Re, C

Reference 16

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Observation c0412028-06e2-44ca-afdf-febb77c88730 · outbound

This paper cites Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster

Reference 17

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Observation 84aa4f1e-f85d-4650-a997-4688d484e5a0 · outbound

This paper cites Kronecker decomposition for GPT compression.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Kronecker decomposition for GPT compression

Reference 18

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Tensorized embedding layers

Reference 19

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

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Language model compression with weighted low-rank factorization

Reference 20

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Observation f0e3b06d-2744-44fa-8edf-29b05baf09ab · outbound

This paper cites MELTing point: Mobile Evaluation of Language Transformers.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices MELTing point: Mobile Evaluation of Language Transformers

Reference 21

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Observation 1bf48ed5-8582-4e11-a5af-41f9318467b8 · outbound

This paper cites MoDeGPT: Modular Decomposition for Large Language Model Compression.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices MoDeGPT: Modular Decomposition for Large Language Model Compression

Reference 22

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Mcunet: Tiny deep learning on iot devices

Reference 23

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This paper cites A., and Rezagholizadeh, M.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices A., and Rezagholizadeh, M

Reference 24

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This paper cites Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search

Reference 25

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices M obile LLM : Optimizing sub-billion parameter language models for on-device use cases

Reference 26

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Observation b697e34a-3625-4ea3-8b48-5adb76c24978 · outbound

This paper cites Small Language Models: Survey, Measurements, and Insights.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Small Language Models: Survey, Measurements, and Insights

Reference 27

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This paper cites Addition is All You Need for Energy-efficient Language Models.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Addition is All You Need for Energy-efficient Language Models

Reference 28

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices L., Daly, R

Reference 29

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression

Reference 30

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices OpenELM: An Efficient Language Model Family with Open Training and Inference Framework

Reference 31

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Pointer sentinel mixture models

Reference 32

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Unresolved cited work

Reference 33

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This paper cites Compute Better Spent: Replacing Dense Layers with Structured Matrices.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Compute Better Spent: Replacing Dense Layers with Structured Matrices

Reference 34

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Language models are unsupervised multitask learners

Reference 35

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices L., Bhagavatula, C., and Choi, Y

Reference 36

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 37

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Social iqa: Commonsense reasoning about social interactions

Reference 38

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This paper cites K ronecker BERT : Significant compression of pre-trained language models through kronecker decomposition and knowledge distillation.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices K ronecker BERT : Significant compression of pre-trained language models through kronecker decomposition and knowledge distillation

Reference 39

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This paper cites Lighttoken: A task and model-agnostic lightweight token embedding framework for pre-trained language models.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Lighttoken: A task and model-agnostic lightweight token embedding framework for pre-trained language models

Reference 40

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This paper cites A method to estimate the energy consumption of deep neural networks.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices A method to estimate the energy consumption of deep neural networks

Reference 41

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TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

Reference 42

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This paper cites Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:49.018439Z

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Pith citing papers

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