Paper Citation Record · LEDGER

Power Law Guided Dynamic Sifting for Efficient Attention

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.05300.

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

pith.paper-citation-record.v1
2506.05300 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:24:27.928826Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

31 of 31 outbound references displayed

verified exact1
verified fuzzy13
unresolved16
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74077666-a4ef-468f-b19a-4b9880d6df09 · outbound

This paper cites https://openai.

Power Law Guided Dynamic Sifting for Efficient Attention https://openai

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.444823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7fc8f2da-397c-4670-a0ae-7d8a392156f8 · outbound

This paper cites Neural machine translation by jointly learning to align and translate.

Power Law Guided Dynamic Sifting for Efficient Attention Neural machine translation by jointly learning to align and translate

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.427432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ebca9c3e-09b4-4cd2-b792-5828cdc7c8a8 · outbound

This paper cites Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding.

Power Law Guided Dynamic Sifting for Efficient Attention Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:24:28.119897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e55a2a4e-0c0b-4f49-9ada-b0ed3ac7d8e7 · outbound

This paper cites HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics.

Power Law Guided Dynamic Sifting for Efficient Attention HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.797796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3f05dba-9183-455d-b0de-eb6771809a6b · outbound

This paper cites Open llm leaderboard v2.

Power Law Guided Dynamic Sifting for Efficient Attention Open llm leaderboard v2

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.802839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76ec73af-94a5-4ab7-970b-fcbfbd2d9ee0 · outbound

This paper cites The llama 3 herd of models, 2024.

Power Law Guided Dynamic Sifting for Efficient Attention The llama 3 herd of models, 2024

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.807637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e97d933-5039-405c-a9c7-73d870709794 · outbound

This paper cites Memory-efficient Transformers via Top-$k$ Attention.

Power Law Guided Dynamic Sifting for Efficient Attention Memory-efficient Transformers via Top-$k$ Attention

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.813137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a95fec0-428f-4311-9d45-6ac1f1f6a947 · outbound

This paper cites Data movement is all you need: A case study on optimizing transformers.

Power Law Guided Dynamic Sifting for Efficient Attention Data movement is all you need: A case study on optimizing transformers

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.365638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6a86935d-29e9-408a-aedf-73d6f7c058f4 · outbound

This paper cites Mistral 7B.

Power Law Guided Dynamic Sifting for Efficient Attention Mistral 7B

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.823651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05c23738-ec40-4958-93ae-0c7b3f561df2 · outbound

This paper cites InfiniGen: Efficient generative inference of large language models with dynamic KV cache management.

Power Law Guided Dynamic Sifting for Efficient Attention InfiniGen: Efficient generative inference of large language models with dynamic KV cache management

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.349370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f7c2ba93-f23e-45da-b333-bb374b356ffd · outbound

This paper cites Accelerating attention through gradient-based learned runtime pruning.

Power Law Guided Dynamic Sifting for Efficient Attention Accelerating attention through gradient-based learned runtime pruning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.333646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 401a3eb3-448a-437d-a999-339ba30fe238 · outbound

This paper cites Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time.

Power Law Guided Dynamic Sifting for Efficient Attention Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.837909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a659b0b-9fca-492e-82cc-6881f8088efc · outbound

This paper cites Pointer Sentinel Mixture Models.

Power Law Guided Dynamic Sifting for Efficient Attention Pointer Sentinel Mixture Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.842761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3146998c-b91e-4c49-93f2-4eeb1ed3dab9 · outbound

This paper cites Linear Log-Normal Attention with Unbiased Concentration.

Power Law Guided Dynamic Sifting for Efficient Attention Linear Log-Normal Attention with Unbiased Concentration

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.847626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e32a359-1215-4bd3-a58b-95c2fb69a47f · outbound

This paper cites Nvidia nsight compute.

Power Law Guided Dynamic Sifting for Efficient Attention Nvidia nsight compute

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.852244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation caca6fe1-5524-45c8-9d76-4bad809bc8ea · outbound

This paper cites Automatic differentiation in pytorch.

Power Law Guided Dynamic Sifting for Efficient Attention Automatic differentiation in pytorch

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.857623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81cc7fd9-f485-41b4-b6db-05053430d3f9 · outbound

This paper cites an unresolved cited work.

Power Law Guided Dynamic Sifting for Efficient Attention Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.862367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f4326c2-3618-4f90-b65b-926656dd0bfc · outbound

This paper cites Sparq attention: Bandwidth-efficient llm inference, 2023.

Power Law Guided Dynamic Sifting for Efficient Attention Sparq attention: Bandwidth-efficient llm inference, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.285761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ffa05f8c-47a3-470e-a07b-759ad51e116b · outbound

This paper cites AxoNN: An asynchronous, message-driven parallel framework for extreme-scale deep learning.

Power Law Guided Dynamic Sifting for Efficient Attention AxoNN: An asynchronous, message-driven parallel framework for extreme-scale deep learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.268279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60301ef0-6756-403f-a45b-1d754180aec1 · outbound

This paper cites Democratizing AI: Open-source scalable LLM training on GPU-based supercomputers.

Power Law Guided Dynamic Sifting for Efficient Attention Democratizing AI: Open-source scalable LLM training on GPU-based supercomputers

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.252311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8442784a-d719-49af-bc3b-6e5889bba6b1 · outbound

This paper cites Ranjan, Zack Sating, and Abhinav Bhatele.

Power Law Guided Dynamic Sifting for Efficient Attention Ranjan, Zack Sating, and Abhinav Bhatele

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.236411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 92454305-aaad-4ad5-8de7-73867d7fdd78 · outbound

This paper cites Loki: Low-rank keys for efficient sparse attention.

Power Law Guided Dynamic Sifting for Efficient Attention Loki: Low-rank keys for efficient sparse attention

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.221449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ff6532a2-db3f-41a2-8bd5-da387a31c1e5 · outbound

This paper cites Pytorch 2.0: Our next generation release that is faster, more pythonic and dynamic as ever.

Power Law Guided Dynamic Sifting for Efficient Attention Pytorch 2.0: Our next generation release that is faster, more pythonic and dynamic as ever

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.205696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 889d5025-cb8d-472a-80cf-a9f9a8fef3fb · outbound

This paper cites Attention is all you need.

Power Law Guided Dynamic Sifting for Efficient Attention Attention is all you need

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.895730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 155a4e28-7b78-4608-b62e-b81353cfc3c6 · outbound

This paper cites LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs.

Power Law Guided Dynamic Sifting for Efficient Attention LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.900314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ad8a3fe-516e-41ed-91c0-7e4d7613fa5f · outbound

This paper cites Qwen2.5 Technical Report.

Power Law Guided Dynamic Sifting for Efficient Attention Qwen2.5 Technical Report

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.905032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb59260a-de63-45e3-b34c-c2cd94e96690 · outbound

This paper cites Zeroquant-v2: Exploring post-training quantization in llms from comprehensive study to low rank compensation.

Power Law Guided Dynamic Sifting for Efficient Attention Zeroquant-v2: Exploring post-training quantization in llms from comprehensive study to low rank compensation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.179953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3cb83ece-a43a-4a25-a213-b702cbdae523 · outbound

This paper cites Parallel top-k algorithms on gpu: A comprehensive study and new methods.

Power Law Guided Dynamic Sifting for Efficient Attention Parallel top-k algorithms on gpu: A comprehensive study and new methods

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:24:28.164444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aa89239b-eded-432e-93fc-ab7fe461df0f · outbound

This paper cites H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models.

Power Law Guided Dynamic Sifting for Efficient Attention H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.923861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aea9aeaa-8f9e-49a2-8dae-b6030fbb0e87 · outbound

This paper cites Instruction-following evaluation for large language models, 2023.

Power Law Guided Dynamic Sifting for Efficient Attention Instruction-following evaluation for large language models, 2023

Reference 30

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T10:24:28.138496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 30257cfd-aa48-4b9d-a665-fda66e1870e4 · outbound

This paper cites an unresolved cited work.

Power Law Guided Dynamic Sifting for Efficient Attention Unresolved cited work

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T10:24:27.919133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.