Paper Citation Record · LEDGER

LongNet: Scaling Transformers to 1,000,000,000 Tokens

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 41 inbound Pith citation observations for arXiv:2307.02486.

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

pith.paper-citation-record.v1
2307.02486 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 41 of 41 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:23:40.464868Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

35
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2bc4fb11-e460-4e97-b424-997ebd15a324 · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 188

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:28:39.595886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 893042cb-2f4c-4cb9-b6da-5060bc06b822 · inbound

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding cites this paper.

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-12T20:22:10.655008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 439813f7-7dac-45fa-b2da-58d1b293abb9 · inbound

Mamba: Linear-Time Sequence Modeling with Selective State Spaces cites this paper.

Mamba: Linear-Time Sequence Modeling with Selective State Spaces LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:53:06.693605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 498838b4-9f57-4190-b0bd-c8cddfe07ccf · inbound

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model cites this paper.

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:37:00.185361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4272cbb2-d1e6-4182-9ba8-2c942d28a784 · inbound

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

RULER: What's the Real Context Size of Your Long-Context Language Models? LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:55:20.565845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5f63efcc-4fa5-4b5e-9350-5afc7e81c6a9 · inbound

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention cites this paper.

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T18:17:00.225304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 503200b2-bae9-4401-a55f-8ff10a26a1de · inbound

On Efficient Variants of Segment Anything Model: A Survey cites this paper.

On Efficient Variants of Segment Anything Model: A Survey LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 189

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:43:23.468594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aedfadff-b42b-4e90-9a3d-fa558079e64d · inbound

LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models cites this paper.

LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T12:23:40.464868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 514cd672-df6f-4d31-93dc-794f790a38cc · inbound

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

MoBA: Mixture of Block Attention for Long-Context LLMs LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:15:46.217139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 969928fe-81f9-49eb-b8bc-0c3df11bde47 · inbound

MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models cites this paper.

MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:08:07.209849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a4a2321-d8b3-4ac7-848e-669d909d4ada · inbound

Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering cites this paper.

Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T14:12:23.265868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fd9b1556-fe8c-4c7b-8ee5-1cf65ebd0786 · inbound

Do Multiple Instance Learning Models Transfer? cites this paper.

Do Multiple Instance Learning Models Transfer? LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:39.109017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01f2c97b-f5b0-4c38-aaac-ce18708df54b · inbound

eLLM: Elastic Memory Management Framework for Efficient LLM Serving cites this paper.

eLLM: Elastic Memory Management Framework for Efficient LLM Serving LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:42:13.860674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 29ee0081-013f-4c42-9c2b-5598e1c035e6 · inbound

SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression cites this paper.

SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:26:55.444773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21e853b6-1b59-4817-a64f-a06b7d6d9c4c · inbound

Docopilot: Improving Multimodal Models for Document-Level Understanding cites this paper.

Docopilot: Improving Multimodal Models for Document-Level Understanding LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T15:57:00.050068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc1e33a6-66c6-4201-be3f-bbe78c148eb5 · inbound

Accelerating Prefilling via Decoding-time Contribution Sparsity cites this paper.

Accelerating Prefilling via Decoding-time Contribution Sparsity LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-19T03:06:59.963072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 62ce59f6-f3bb-4ffe-8573-e2249103063a · inbound

SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification cites this paper.

SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T10:36:59.826359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11962a2c-885d-4971-8409-7a37875131b4 · inbound

Positional Encoding via Token-Aware Phase Attention cites this paper.

Positional Encoding via Token-Aware Phase Attention LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T16:21:36.698535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c40f6a54-1ad0-431c-b955-beb850f50ded · inbound

Kimi Linear: An Expressive, Efficient Attention Architecture cites this paper.

Kimi Linear: An Expressive, Efficient Attention Architecture LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-13T23:49:11.019394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc2dba98-2e0b-44c1-ae9b-f40e1ce9bc8b · inbound

Exact Flow Linear Attention: Exact Solution from Continuous-Time Dynamics cites this paper.

Exact Flow Linear Attention: Exact Solution from Continuous-Time Dynamics LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:08:39.723808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e00b95e4-cc3d-461e-88d3-d30dd376ddf5 · inbound

BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations cites this paper.

BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:26:42.097242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a9fe698e-6540-4fbb-aabc-5f78567ae3f8 · inbound

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference cites this paper.

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:00:17.880244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 53f0d9ae-af95-48f1-8b48-2e544644f1c0 · inbound

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference cites this paper.

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-21T12:50:09.496438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation aa603f81-c326-43d3-a171-8025dcaac809 · inbound

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference cites this paper.

RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T22:05:42.990630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3945629-6ca0-44ff-8f44-2bdf5d59f2b9 · inbound

Stacked from One: Multi-Scale Self-Injection for Context Window Extension cites this paper.

Stacked from One: Multi-Scale Self-Injection for Context Window Extension LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:00:10.317900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b214591-f01d-4692-a69a-396c175fc8a8 · inbound

HiCI: Hierarchical Construction-Integration for Long-Context Attention cites this paper.

HiCI: Hierarchical Construction-Integration for Long-Context Attention LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T17:49:26.189385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd242692-f0fd-4259-9bd9-d8fa5708c4ae · inbound

MOOZY: A Patient-First Foundation Model for Computational Pathology cites this paper.

MOOZY: A Patient-First Foundation Model for Computational Pathology LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T17:15:51.142086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ff236fb-b17a-435e-b5bb-80d9512648e1 · inbound

Sessa: Selective State Space Attention cites this paper.

Sessa: Selective State Space Attention LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-10T04:50:23.007276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c39d36f0-df7f-4c30-bb5b-fe5630859138 · inbound

Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling cites this paper.

Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:19:47.000602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ef438577-df1a-4a28-b339-8701864aed92 · inbound

Where Does Long-Context Supervision Actually Go? Effective-Context Exposure Balancing cites this paper.

Where Does Long-Context Supervision Actually Go? Effective-Context Exposure Balancing LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:51:29.185396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6ec20bb0-3525-4820-9fa8-be6109046846 · inbound

Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation cites this paper.

Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T00:12:50.365339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b14958e4-e93c-4155-b0da-aee73acbee6c · inbound

Locality Does Not Imply Reachability: Boundary Repair in Block-Sparse Causal Attention cites this paper.

Locality Does Not Imply Reachability: Boundary Repair in Block-Sparse Causal Attention LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:16:16.389951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9e0aa39d-5841-4358-9f00-eee15d1b7795 · inbound

Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents cites this paper.

Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:36:59.482545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 82b59a12-7f0c-4ef0-b214-49e2933d103d · inbound

Transformer-Based Language Models Across Domain Verticals: Architectures, Applications and Critical Assessment cites this paper.

Transformer-Based Language Models Across Domain Verticals: Architectures, Applications and Critical Assessment LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:59:57.645025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation afde34f0-80a1-437d-8851-a659153134c5 · inbound

Depth-Staggered Fibonacci Spacing for Sparse Attention: Static Schedules Beat Learned Dilation and Extrapolate Where Dense Attention Fails cites this paper.

Depth-Staggered Fibonacci Spacing for Sparse Attention: Static Schedules Beat Learned Dilation and Extrapolate Where Dense Attention Fails LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:45:48.017063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b5dc1aca-f97f-4b6b-b56e-f1f328b8022f · inbound

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers cites this paper.

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:14:17.915989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 573f8ee7-c0a4-4b9b-97df-9027a41a8792 · inbound

From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving cites this paper.

From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T09:50:23.266920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d0c9fef-a38b-4d75-a49b-428c6384792b · inbound

VideoSEMA: a scalable and efficient Mamba-like attention for video understanding cites this paper.

VideoSEMA: a scalable and efficient Mamba-like attention for video understanding LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T01:25:51.548781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb0f6fc6-4060-45de-91d0-d89473ac5e10 · inbound

GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis cites this paper.

GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T15:40:54.950485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9be11add-c3cf-46e4-82df-950bbe421d36 · inbound

AGNFormer I: Reconstruction of AGN spectra using a probabilistic transformer model cites this paper.

AGNFormer I: Reconstruction of AGN spectra using a probabilistic transformer model LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 250

Resolution: unresolved; no resolver link, observed 2026-08-01T12:16:54.898499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efd9c8a1-20f5-47a5-8dde-5eea234660fe · inbound

RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory cites this paper.

RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T06:59:50.579006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.