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Paper Citation Record · LEDGER

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration

As of 5 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2604.00004.

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

pith.paper-citation-record.v1
2604.00004 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T18:36:58.738624Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

21 of 21 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8374c949-af56-49d9-a21c-f1b3ec089061 · outbound

This paper cites Mathqa: Towards interpretable math word problem solving with operation-based for- malisms.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Mathqa: Towards interpretable math word problem solving with operation-based for- malisms

Reference 1

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source=pdf_text observed=2026-08-02T18:36:56.772379Z digest=sha256:69b513ff168f0c041becaef14104f362b572c9f776690d3f2fc253d65f1d26cb

Observation 8f13807a-c46a-4973-a619-2c920ec3f85a · outbound

This paper cites MiniLLM: On-Policy Distillation of Large Language Models.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration MiniLLM: On-Policy Distillation of Large Language Models

Reference 8

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source=pdf_text observed=2026-08-02T18:36:57.505155Z digest=sha256:73a8588efb9c8e2702a249e9934bc0ab36ea1db855bac34508a293c3d10ea808

Observation 13006a82-957c-4953-acda-85add7212fb6 · outbound

This paper cites Seekr: Selective attention-guided knowledge retention for continual learning of large language models.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Seekr: Selective attention-guided knowledge retention for continual learning of large language models

Reference 9

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source=pdf_text observed=2026-08-02T18:36:57.596676Z digest=sha256:4865d42122759c00ffe97130c180f909da6db67e4ec74668a82a272ee7a48faf

Observation c09d01a6-b0cb-4257-9ecc-0de36fa206d9 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Measuring Massive Multitask Language Understanding

Reference 10

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source=pdf_text observed=2026-08-02T18:36:57.714533Z digest=sha256:3490638d8fccc031f1c6b6dbd2743bbd025d2b76d5b9ef8a5b96be6899affb78

Observation 29209fcf-baf5-4af9-8f91-a6aa6013c5d3 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Distilling the Knowledge in a Neural Network

Reference 11

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source=pdf_text observed=2026-08-02T18:36:57.830555Z digest=sha256:8b32bea13388eeb819fa61c1a6fa5a8ac2dbb973e09412b6c09fd80d211a1a97

Observation 24e3ec75-1195-4eac-84a1-ad6c5708bdbb · outbound

This paper cites Continual Pre-training of Language Models.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Continual Pre-training of Language Models

Reference 14

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source=pdf_text observed=2026-08-02T18:36:58.134724Z digest=sha256:2903ee65de9cc9c3ea146211f88dca8ee0a81ef640c5564f4a55d7c77aef6114

Observation eecc9e0f-96d6-4efd-9d5d-d6253df7e48a · outbound

This paper cites Ring Attention with Blockwise Transformers for Near-Infinite Context.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 15

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source=pdf_text observed=2026-08-02T18:36:58.226812Z digest=sha256:4e4c2952b40d851b420dd97cba8bab296535ea4dc1ccb87d5b989fff5fd0ef0c

Observation d31e00f6-235b-456f-bd67-964d303fbd17 · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 16

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source=pdf_text observed=2026-08-02T18:36:58.331001Z digest=sha256:26ba5ec21d446001a00588047d092b546a0ec1b6dada561931b662544c6edaec

Observation 10851ea7-cfdb-4909-9690-c82d3371ae35 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 18

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source=pdf_text observed=2026-08-02T18:36:58.507852Z digest=sha256:428d6e7b260f01bc513adf03e7a353a61df02097af7bcf289a0d6ad235629443

Observation 4d87b88b-e1df-4ad7-90e5-677193b745f5 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration SocialIQA: Commonsense Reasoning about Social Interactions

Reference 19

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source=pdf_text observed=2026-08-02T18:36:58.597130Z digest=sha256:5c929bcaecf0257c596d26665c68d8f86d09fb0ffec81bfac627ece49050046d

Observation 4e94fba5-c75e-4bde-82a2-b1656a3cc761 · outbound

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

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 20

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source=pdf_text observed=2026-08-02T18:36:58.660947Z digest=sha256:686319a5750f3118b30bc9d5d333a821fc8e81ccfc31cb6f642d9345cbbf9df2

Observation e7f012fa-99a7-4e72-bd3d-45afcfc72190 · outbound

This paper cites Minilmv2: Multi-head self-attention relation distillation for compressing pretrained transformers.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Minilmv2: Multi-head self-attention relation distillation for compressing pretrained transformers

Reference 21

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source=pdf_text observed=2026-08-02T18:36:58.738624Z digest=sha256:47bc8690a5d4cc29aa1a545816d3e0f5883d7aac8ad05580a924863816ddc597

Observation ee7d8a08-9594-425a-81be-3c30b3bafeae · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 2015

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source=pdf_text observed=2026-08-02T18:36:57.917048Z digest=sha256:b2952885f9d4e9793d9fdcb36362216867e8aff25ec91d838255ca67fc0e4131

Observation 5c4575a3-1d43-4a34-9150-dda75b142b3c · outbound

This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration YaRN: Efficient Context Window Extension of Large Language Models

Reference 2016

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source=pdf_text observed=2026-08-02T18:36:58.433919Z digest=sha256:6c1e204291d0069028443d3238f5baf5b5a1fd341e22853ff71f5b994cbf3c26

Observation db9b9e8c-d98e-4d83-8470-64976c521391 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 2018

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source=pdf_text observed=2026-08-02T18:36:57.205639Z digest=sha256:00cf0f5ba56af617cb812d86e0be9a15f04651135cf25a9c416ac4bb5f15693d

Observation eca1bcc6-7d82-4288-bc07-4a7fc2defb23 · outbound

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

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 2019

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source=pdf_text observed=2026-08-02T18:36:57.114048Z digest=sha256:f5dd1969e0b424ab6caf620edb2715cf9d1785dd1b9153988793b7460eead498

Observation 17bd620d-ea2c-460e-95be-c60fe0029410 · outbound

This paper cites Extending Context Window of Large Language Models via Positional Interpolation.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Extending Context Window of Large Language Models via Positional Interpolation

Reference 2020

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source=pdf_text observed=2026-08-02T18:36:56.893746Z digest=sha256:af4b0a3387bc6cdfe593c22162db1f2c23745b207776f80de02d62520c113535

Observation 989aaedd-66e9-46f0-86f7-bbca0612b0fe · outbound

This paper cites Tinybert: Distilling bert for natural language understanding.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration Tinybert: Distilling bert for natural language understanding

Reference 2022

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source=pdf_text observed=2026-08-02T18:36:58.019283Z digest=sha256:4c63f1d4d6daeab86813c65ff5b34a5b0395d50384924a7da85165d9dfdada73

Observation 5ad0a4e4-b2a1-408c-b795-eb13a3df7cb3 · outbound

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

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 2023

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source=pdf_text observed=2026-08-02T18:36:57.007642Z digest=sha256:dddeb95504617959d19e30da4d5b36a202d3c542b25d6cc287642c331ea2a025

Observation 29e05937-0a56-4a95-a2ec-8d0fcde0e92b · outbound

This paper cites LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation

Reference 2024

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source=pdf_text observed=2026-08-02T18:36:57.326532Z digest=sha256:6702ebbd810ee579832738c15bebb51c015091ac80830803861d5c96eac82473

Observation 2524449e-6b1a-47cb-bcf9-c2e81416e808 · outbound

This paper cites The Llama 3 Herd of Models.

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration The Llama 3 Herd of Models

Reference 2025

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source=pdf_text observed=2026-08-02T18:36:57.394526Z digest=sha256:1fa916585c25d3192f598bc5e5b16df06073e81f6c4b554e5a738661837a1513

Pith citing papers

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