EinSort: Sorting is All We Need for Tensorizing LLM | Paper Citation Record

Observation 060c5dbb-8c5c-4923-a9b1-b112a2c3620c · outbound

This paper cites Phi-4 Technical Report.

EinSort: Sorting is All We Need for Tensorizing LLM Phi-4 Technical Report

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T22:57:26.696503Z

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Observation 80f64914-1c6f-483e-b062-253aec610cd6 · outbound

This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

EinSort: Sorting is All We Need for Tensorizing LLM Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.490409Z

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Observation d634edd9-47ba-44a1-8e5c-872f8fb263b3 · outbound

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

EinSort: Sorting is All We Need for Tensorizing LLM TQCompressor : improving tensor decomposition methods in neural networks via permutations

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation 212d80d5-a560-4159-8ad1-055316ea5eeb · outbound

This paper cites Phi-4-reasoning-vision-15b technical report.

EinSort: Sorting is All We Need for Tensorizing LLM Phi-4-reasoning-vision-15b technical report

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.456602Z

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Observation c3ba677f-2eae-4235-920e-5b671dc7abd6 · outbound

This paper cites Quantization error propagation: Revisiting layer-wise post-training quantization.

EinSort: Sorting is All We Need for Tensorizing LLM Quantization error propagation: Revisiting layer-wise post-training quantization

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.471531Z

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Observation 96969584-42d7-4d26-95be-c068fd9ce6fc · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

EinSort: Sorting is All We Need for Tensorizing LLM V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.496372Z

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Observation b1747d63-cc58-4b66-90fa-046b8a7ff847 · outbound

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

EinSort: Sorting is All We Need for Tensorizing LLM Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.494000Z

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Observation 884e62bf-1f11-4fdc-a963-096bf82432ee · outbound

This paper cites SparseLLM: Towards Global Pruning for Pre-trained Language Models.

EinSort: Sorting is All We Need for Tensorizing LLM SparseLLM: Towards Global Pruning for Pre-trained Language Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.462625Z

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Observation 0b44070d-3054-46a1-98a8-c6d639331ef7 · outbound

This paper cites Qwen3-VL Technical Report.

EinSort: Sorting is All We Need for Tensorizing LLM Qwen3-VL Technical Report

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.473977Z

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Observation dde72244-8249-4e61-9cd1-1b8c57a63ef6 · outbound

This paper cites LoTR: Low Tensor Rank Weight Adaptation.

EinSort: Sorting is All We Need for Tensorizing LLM LoTR: Low Tensor Rank Weight Adaptation

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.498803Z

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Observation 69e8b5d7-2fe0-4726-af3a-62f7dc21dfbb · outbound

This paper cites PaliGemma: A versatile 3B VLM for transfer.

EinSort: Sorting is All We Need for Tensorizing LLM PaliGemma: A versatile 3B VLM for transfer

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T22:57:26.703938Z

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Observation 5a15239c-9a3c-4c75-b2a2-9958025828bb · outbound

This paper cites K., Hachtel, G.

EinSort: Sorting is All We Need for Tensorizing LLM K., Hachtel, G

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation bd7f7b1f-d13f-432d-8819-eb4dc6abb964 · outbound

This paper cites Pyramid KV : Dynamic KV cache compression based on pyramidal information funneling.

EinSort: Sorting is All We Need for Tensorizing LLM Pyramid KV : Dynamic KV cache compression based on pyramidal information funneling

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation af4941be-b6f9-4ab6-80a6-1de7e319cc81 · outbound

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

EinSort: Sorting is All We Need for Tensorizing LLM Palu: Compressing KV-Cache with Low-Rank Projection

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:57:26.699258Z

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Observation 31c41499-bcae-44c0-83e6-8d4e30a5259c · outbound

This paper cites One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning.

EinSort: Sorting is All We Need for Tensorizing LLM One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:57:26.701714Z

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Observation 004165f5-ab75-4178-846c-83e859c5312c · outbound

This paper cites SuperLoRA : Parameter-efficient unified adaptation for large vision models.

EinSort: Sorting is All We Need for Tensorizing LLM SuperLoRA : Parameter-efficient unified adaptation for large vision models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation 9c511318-fa50-4861-b5a0-4dd7aa660edc · outbound

This paper cites QuanTA : Efficient high-rank fine-tuning of LLMs with quantum-informed tensor adaptation.

EinSort: Sorting is All We Need for Tensorizing LLM QuanTA : Efficient high-rank fine-tuning of LLMs with quantum-informed tensor adaptation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation 33321887-bc6b-4919-932f-9bbc9ac08d7e · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

EinSort: Sorting is All We Need for Tensorizing LLM Training Verifiers to Solve Math Word Problems

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-02T22:57:26.736167Z

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Observation d492969c-292b-4891-87ea-c15a1db94774 · outbound

This paper cites an unresolved cited work.

EinSort: Sorting is All We Need for Tensorizing LLM Unresolved cited work

Reference 19

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Observation d9fdb5d9-ce87-4a5e-8cd7-320475167b91 · outbound

This paper cites Predicting parameters in deep learning.

EinSort: Sorting is All We Need for Tensorizing LLM Predicting parameters in deep learning

Reference 20

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Observation 4975695e-94c0-4ec3-be36-8d97d9a3ecde · outbound

This paper cites L., Zaremba, W., Bruna, J., LeCun, Y., and Fergus, R.

EinSort: Sorting is All We Need for Tensorizing LLM L., Zaremba, W., Bruna, J., LeCun, Y., and Fergus, R

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation a9e7a6d8-dcb3-4a9b-b7c1-8f8f6cc8b2cd · outbound

This paper cites DaViT : Dual attention vision transformers.

EinSort: Sorting is All We Need for Tensorizing LLM DaViT : Dual attention vision transformers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation eaf3e1c9-f091-463a-ad5a-7089236e24b5 · outbound

This paper cites Learning to prune deep neural networks via layer-wise optimal brain surgeon.

EinSort: Sorting is All We Need for Tensorizing LLM Learning to prune deep neural networks via layer-wise optimal brain surgeon

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation ca01fbb4-4bab-4466-af2f-7646441fb8d9 · outbound

This paper cites W., and Keutzer, K.

EinSort: Sorting is All We Need for Tensorizing LLM W., and Keutzer, K

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation 4cfd3732-a135-46f9-a031-111376ed4518 · outbound

This paper cites KronA: Parameter Efficient Tuning with Kronecker Adapter.

EinSort: Sorting is All We Need for Tensorizing LLM KronA: Parameter Efficient Tuning with Kronecker Adapter

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.472139Z

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Observation 580138e9-7bf6-44c7-af28-ba9e6c21b7f7 · outbound

This paper cites Gauge fixing, canonical forms, and optimal truncations in tensor networks with closed loops.

EinSort: Sorting is All We Need for Tensorizing LLM Gauge fixing, canonical forms, and optimal truncations in tensor networks with closed loops

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation 54349636-6361-429b-8255-f00c034ba563 · outbound

This paper cites and Alistarh, D.

EinSort: Sorting is All We Need for Tensorizing LLM and Alistarh, D

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation 14938294-507e-4396-a6df-3dcdbe7fae72 · outbound

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

EinSort: Sorting is All We Need for Tensorizing LLM GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.487873Z

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Observation 2c371ff5-c263-4d74-b7a6-54972b79fc42 · outbound

This paper cites S., Thomas, A., Spector, B., Poli, M., Rudra, A., and R \'e , C.

EinSort: Sorting is All We Need for Tensorizing LLM S., Thomas, A., Spector, B., Poli, M., Rudra, A., and R \'e , C

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation e813fe44-f814-48dc-ba6d-6beec70aed3c · outbound

This paper cites Gemma 3 Technical Report.

EinSort: Sorting is All We Need for Tensorizing LLM Gemma 3 Technical Report

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.450287Z

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Observation d0d19f3d-09ed-490a-b073-6ef1d4496c4b · outbound

This paper cites Stochastic Optimization of Sorting Networks via Continuous Relaxations.

EinSort: Sorting is All We Need for Tensorizing LLM Stochastic Optimization of Sorting Networks via Continuous Relaxations

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-02T22:57:26.748956Z

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Observation 26b60772-c88a-4836-aef9-d6f1e9fcb58b · outbound

This paper cites Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle.

EinSort: Sorting is All We Need for Tensorizing LLM Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:57:26.746294Z

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Observation c0cfa905-53a9-4b88-b096-0b5d89e2075e · outbound

This paper cites Optimal brain surgeon: Extensions and performance comparisons.

EinSort: Sorting is All We Need for Tensorizing LLM Optimal brain surgeon: Extensions and performance comparisons

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation c69931b6-53fc-4f81-b779-6bc1317481ba · outbound

This paper cites LoRA +: Efficient low rank adaptation of large models.

EinSort: Sorting is All We Need for Tensorizing LLM LoRA +: Efficient low rank adaptation of large models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation c87ca0ea-f034-43bd-b243-db4bb0ba1723 · outbound

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

EinSort: Sorting is All We Need for Tensorizing LLM Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-07-02T22:57:26.751796Z

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Observation 31693dd3-b2df-4e3b-940e-bf867f3eed50 · outbound

This paper cites J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al.

EinSort: Sorting is All We Need for Tensorizing LLM J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation b08211e4-6093-41c7-811a-674aafe0ac07 · outbound

This paper cites B., and Stoudenmire, E.

EinSort: Sorting is All We Need for Tensorizing LLM B., and Stoudenmire, E

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-27T18:31:01.804061Z

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Observation 39e4a619-9de8-4409-8ddb-a6dfcc804cfe · outbound

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

EinSort: Sorting is All We Need for Tensorizing LLM PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation

Reference 38

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T22:57:26.744827Z

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Observation fce13e10-9a4d-4f86-b779-7059c554c9dd · outbound

This paper cites $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization.

EinSort: Sorting is All We Need for Tensorizing LLM $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.476673Z

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Observation 3ac16a52-80b3-40fe-ad10-7de7ced2a15d · outbound

This paper cites Speeding up Convolutional Neural Networks with Low Rank Expansions.

EinSort: Sorting is All We Need for Tensorizing LLM Speeding up Convolutional Neural Networks with Low Rank Expansions

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-07-02T22:57:26.749992Z

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Observation 109b7f3e-de9c-4967-b633-ae68cb0bbef0 · outbound

This paper cites M., Bommarito, M.

EinSort: Sorting is All We Need for Tensorizing LLM M., Bommarito, M

Reference 41

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Observation ac90eb9b-e415-47cc-8e6d-dc15263edfaf · outbound

This paper cites $\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts.

EinSort: Sorting is All We Need for Tensorizing LLM $\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.479351Z

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Observation ed77df24-774b-4be4-b540-5b9579921fcc · outbound

This paper cites Quantum-PEFT: Ultra parameter-efficient fine-tuning.

EinSort: Sorting is All We Need for Tensorizing LLM Quantum-PEFT: Ultra parameter-efficient fine-tuning

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.493147Z

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Observation 73a8ba03-dd22-41d4-abed-4246ccd9a78f · outbound

This paper cites P., and Brand, M.

EinSort: Sorting is All We Need for Tensorizing LLM P., and Brand, M

Reference 44

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Observation 4a96e608-a837-4c21-85f2-63208606e294 · outbound

This paper cites TTQ : Activation-aware test-time quantization to accelerate LLM inference on the fly.

EinSort: Sorting is All We Need for Tensorizing LLM TTQ : Activation-aware test-time quantization to accelerate LLM inference on the fly

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.453424Z

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Observation 7abe5dc3-dbc2-4278-b6d8-ee1696e6773e · outbound

This paper cites Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.

EinSort: Sorting is All We Need for Tensorizing LLM Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.501585Z

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Observation a3bfb80d-431b-44d3-876d-c78f3ea70b2d · outbound

This paper cites Optimal brain damage.

EinSort: Sorting is All We Need for Tensorizing LLM Optimal brain damage

Reference 47

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EinSort: Sorting is All We Need for Tensorizing LLM Unresolved cited work

Reference 48

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This paper cites Reversible simulation of irreversible computation.

EinSort: Sorting is All We Need for Tensorizing LLM Reversible simulation of irreversible computation

Reference 49

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Observation f0d7d801-4cbc-4576-b93b-ed301bfd1898 · outbound

This paper cites Optimal brain decomposition for accurate llm low-rank approximation.

EinSort: Sorting is All We Need for Tensorizing LLM Optimal brain decomposition for accurate llm low-rank approximation

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.463179Z

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Observation 67682217-0106-450c-a1c4-ebadb7b96b9b · outbound

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

EinSort: Sorting is All We Need for Tensorizing LLM MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.481893Z

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Observation 7622605c-7ca3-48f3-99a9-2e0d6f2658f2 · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

EinSort: Sorting is All We Need for Tensorizing LLM AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.496015Z

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Observation e42a57cf-b99b-4dfb-ac3c-68362ebe91cb · outbound

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

EinSort: Sorting is All We Need for Tensorizing LLM AWQ : Activation-aware weight quantization for on-device LLM compression and acceleration

Reference 53

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Observation 2c025af0-ad03-4445-bdda-522b59d218f3 · outbound

This paper cites DeepSeek-V3 Technical Report.

EinSort: Sorting is All We Need for Tensorizing LLM DeepSeek-V3 Technical Report

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.498634Z

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Observation 378a430a-ad46-4339-a715-2719c1f5c2a4 · outbound

This paper cites LIBERO : Benchmarking knowledge transfer for lifelong robot learning.

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Reference 55

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Reference 56

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Reference 57

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Reference 58

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Reference 59

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Reference 60

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Reference 61

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Reference 62

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Reference 63

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Reference 64

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Reference 65

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Reference 66

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Reference 67

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Reference 68

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Reference 69

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Reference 70

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Reference 71

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Reference 72

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Reference 73

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Reference 74

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Reference 75

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Reference 76

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Reference 77

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Reference 78

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Reference 79

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Reference 80

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Reference 81

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Reference 82

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Reference 83

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Reference 84

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Reference 85

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Reference 86

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Reference 87

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Reference 88

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Reference 89

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Reference 90

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Observation 19356b44-6f10-4d08-957d-8dbe3fb4392e · outbound

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Reference 91

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Reference 92

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Reference 93

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Reference 94

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Observation 3420d640-d087-426c-81ca-b9aca2433c85 · outbound

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Reference 95

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Reference 96

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Reference 97

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Reference 98

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Observation ca1e1377-2758-4419-8d21-f5950428660c · outbound

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Reference 99

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Reference 100

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