Paper Citation Record · LEDGER

Why Attend to Everything? Focus is the Key

As of 27 July 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2604.03260.

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

pith.paper-citation-record.v1
2604.03260 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-15T11:55:36.701888Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-27T06:30:09.085275+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T06:47:09.927626Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact12
verified fuzzy27
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e91c470d-cd75-450c-9a4c-b60e82615fcd · outbound

This paper cites Longformer: The Long-Document Transformer.

Why Attend to Everything? Focus is the Key Longformer: The Long-Document Transformer

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.658623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 864d52e2-f369-4589-981d-1be02db5e156 · outbound

This paper cites LoRA learns less and forgets less.

Why Attend to Everything? Focus is the Key LoRA learns less and forgets less

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.612800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation e7552d8b-ef72-4e54-82a2-05fc83093321 · outbound

This paper cites Class-based n-gram models of natural language.

Why Attend to Everything? Focus is the Key Class-based n-gram models of natural language

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.607900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 93b1ccd6-459b-48de-ae0e-e57eff36444a · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

Why Attend to Everything? Focus is the Key Unsupervised learning of visual features by contrasting cluster assignments

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.610062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 1cbd6a8a-0f0f-46c6-8eb0-3cbb757212af · outbound

This paper cites Rethinking attention with performers.

Why Attend to Everything? Focus is the Key Rethinking attention with performers

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.615168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 56b31ee3-5b77-415d-86c3-f2f088f94f86 · outbound

This paper cites Flash A ttention-2: Faster attention with better parallelism and work partitioning.

Why Attend to Everything? Focus is the Key Flash A ttention-2: Faster attention with better parallelism and work partitioning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.605436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation ce32d245-644e-41ab-b039-d1afca75cb92 · outbound

This paper cites Transformers are SSM s: Generalized models and efficient algorithms through structured state space duality.

Why Attend to Everything? Focus is the Key Transformers are SSM s: Generalized models and efficient algorithms through structured state space duality

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.618462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation c05d0bed-7944-432e-b2df-aa2aff204a33 · outbound

This paper cites Flash A ttention: Fast and memory-efficient exact attention with IO -awareness.

Why Attend to Everything? Focus is the Key Flash A ttention: Fast and memory-efficient exact attention with IO -awareness

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.623934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 49ef51bd-ab98-46eb-be3f-c3e644ca080b · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Why Attend to Everything? Focus is the Key DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.650833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation d627aa84-04d0-4afa-b80c-4d60cfcc3d58 · outbound

This paper cites DeepSeek-V3 Technical Report.

Why Attend to Everything? Focus is the Key DeepSeek-V3 Technical Report

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.646671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 6821b429-88e3-492d-9aca-d5a3fa322435 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Why Attend to Everything? Focus is the Key Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.602753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 4733245d-7a10-41b9-8d0a-06ab850d682d · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Why Attend to Everything? Focus is the Key Gemma 2: Improving Open Language Models at a Practical Size

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.655178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 03f82724-093c-45f9-b9f7-4f4860adcce2 · outbound

This paper cites OLMo : Accelerating the science of language models.

Why Attend to Everything? Focus is the Key OLMo : Accelerating the science of language models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.621135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 4af22708-df37-449f-bb82-b4a1fc746b27 · outbound

This paper cites LoRA : Low-rank adaptation of large language models.

Why Attend to Everything? Focus is the Key LoRA : Low-rank adaptation of large language models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.626739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 2effe9e0-6951-45f9-a96a-b6935e547a92 · outbound

This paper cites Mistral 7B.

Why Attend to Everything? Focus is the Key Mistral 7B

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.639584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 82026207-7ccc-41c4-879e-4c82c25b8452 · outbound

This paper cites Mixtral of Experts.

Why Attend to Everything? Focus is the Key Mixtral of Experts

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.634007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation a9e14496-5891-422a-bfc7-009231ea30fc · outbound

This paper cites MI nference 1.0: Accelerating pre-filling for long-context LLM s via dynamic sparse attention.

Why Attend to Everything? Focus is the Key MI nference 1.0: Accelerating pre-filling for long-context LLM s via dynamic sparse attention

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.629171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation b0006f0a-ea51-4f59-887e-58b32f2eebe3 · outbound

This paper cites Transformers are RNN s: Fast autoregressive transformers with linear attention.

Why Attend to Everything? Focus is the Key Transformers are RNN s: Fast autoregressive transformers with linear attention

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.632005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation d1ba3ca5-4eee-4a28-bb62-156bccf56d8e · outbound

This paper cites Scaling laws for fine-grained mixture of experts.

Why Attend to Everything? Focus is the Key Scaling laws for fine-grained mixture of experts

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.637752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 2173b96b-cc88-48cf-8724-d9175cf24db5 · outbound

This paper cites Jamba: A Hybrid Transformer-Mamba Language Model.

Why Attend to Everything? Focus is the Key Jamba: A Hybrid Transformer-Mamba Language Model

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.615159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation ccad2847-7bd9-4e42-a6b4-86b4e7bdeacf · outbound

This paper cites Ring attention with blockwise transformers for near-infinite context.

Why Attend to Everything? Focus is the Key Ring attention with blockwise transformers for near-infinite context

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.663910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation b2e47437-62ae-41cb-ad0e-6743a6161930 · outbound

This paper cites DoRA : Weight-decomposed low-rank adaptation.

Why Attend to Everything? Focus is the Key DoRA : Weight-decomposed low-rank adaptation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.668755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation fc25650b-5a43-480f-ac51-48b47c8a6b00 · outbound

This paper cites The Llama 3 Herd of Models.

Why Attend to Everything? Focus is the Key The Llama 3 Herd of Models

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.655754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation e37bf27e-2c58-4303-a673-69373a4e434d · outbound

This paper cites MoBA: Mixture of Block Attention for Long-Context LLMs.

Why Attend to Everything? Focus is the Key MoBA: Mixture of Block Attention for Long-Context LLMs

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:15:46.352230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 9a281422-67bd-4ad2-83ba-a393fa353e3e · outbound

This paper cites Catastrophic interference in connectionist networks: The sequential learning problem.

Why Attend to Everything? Focus is the Key Catastrophic interference in connectionist networks: The sequential learning problem

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.661710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 903bea6a-8f5e-485f-ba4e-fa9d03dccb99 · outbound

This paper cites From sparse to soft mixtures of experts.

Why Attend to Everything? Focus is the Key From sparse to soft mixtures of experts

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.670955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation a1ed8d98-c414-4b2d-87ca-3fa64d042427 · outbound

This paper cites Efficient content-based sparse attention with routing transformers.

Why Attend to Everything? Focus is the Key Efficient content-based sparse attention with routing transformers

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.659487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 86e95bb5-2225-4ed7-b588-bcb8aa594644 · outbound

This paper cites Flash A ttention-3: Fast and accurate attention with asynchrony and low-precision.

Why Attend to Everything? Focus is the Key Flash A ttention-3: Fast and accurate attention with asynchrony and low-precision

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.666393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation defba2c4-99ff-4780-a538-e2c7af0d8e46 · outbound

This paper cites Attention is all you need.

Why Attend to Everything? Focus is the Key Attention is all you need

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.657152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation f4522b8c-daca-4239-b4e1-c9c88d8ebb15 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

Why Attend to Everything? Focus is the Key Linformer: Self-Attention with Linear Complexity

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.628944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 6304b8a1-90ee-40af-819a-bcc7ee2437c3 · outbound

This paper cites Qwen2.5 Technical Report.

Why Attend to Everything? Focus is the Key Qwen2.5 Technical Report

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-15T11:59:59.643618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation d784cb47-b476-49bc-860d-24eef8faaced · outbound

This paper cites Differential transformer.

Why Attend to Everything? Focus is the Key Differential transformer

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.650354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 9ce6a100-8a4e-4b68-a5f2-0dc80884c166 · outbound

This paper cites Native sparse attention: Hardware-aligned and natively trainable sparse attention.

Why Attend to Everything? Focus is the Key Native sparse attention: Hardware-aligned and natively trainable sparse attention

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.655014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 5501dc51-f63a-4cd4-977c-a322890b7bcb · outbound

This paper cites Big bird: Transformers for longer sequences.

Why Attend to Everything? Focus is the Key Big bird: Transformers for longer sequences

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.648072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 857566e4-8dc6-474e-a1ce-e320625d3c88 · outbound

This paper cites The hedgehog & the porcupine: Expressive linear attentions with softmax mimicry.

Why Attend to Everything? Focus is the Key The hedgehog & the porcupine: Expressive linear attentions with softmax mimicry

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.641438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 409328f9-aa96-4efa-934c-19b1f774868e · outbound

This paper cites H _2 O : Heavy-hitter oracle for efficient generative inference of large language models.

Why Attend to Everything? Focus is the Key H _2 O : Heavy-hitter oracle for efficient generative inference of large language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.634535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 3e6c4aed-007c-465a-9860-6dc2b669f48b · outbound

This paper cites Loki: Low-rank Keys for Efficient Sparse Attention.

Why Attend to Everything? Focus is the Key Loki: Low-rank Keys for Efficient Sparse Attention

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:59:59.625594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation 14febbe8-e66e-4f20-9b66-4c5d2bdcc55f · outbound

This paper cites Spar Q Attention: Bandwidth-efficient LLM inference.

Why Attend to Everything? Focus is the Key Spar Q Attention: Bandwidth-efficient LLM inference

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.645668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Observation ff220942-5d13-4a58-8cbc-032649df65b2 · outbound

This paper cites MagicPIG : LSH sampling for efficient LLM generation.

Why Attend to Everything? Focus is the Key MagicPIG : LSH sampling for efficient LLM generation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T12:00:00.652653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

Pith citing papers

Observation 5796be41-34b0-479c-8f53-fbe54635a1f0 · inbound

Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE cites this paper.

Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE Why Attend to Everything? Focus is the Key

Reference 54

Resolution: malformed identifier; no resolver link, observed 2026-07-13T06:47:09.927626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.