Paper Citation Record · LEDGER

Data Engineering for Scaling Language Models to 128K Context

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 30 inbound Pith citation observations for arXiv:2402.10171.

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

pith.paper-citation-record.v1
2402.10171 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 30 of 30 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 30 of 30 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:07:59.981503Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T20:07:33.537106Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation baf6d7cf-8aaf-4064-8e1b-4d447055843d · inbound

Yi: Open Foundation Models by 01.AI cites this paper.

Yi: Open Foundation Models by 01.AI Data Engineering for Scaling Language Models to 128K Context

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:47:27.840811Z

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 b72ba8dd-ffa1-46c3-8ede-13d4fa5b5679 · inbound

Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretraining cites this paper.

Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science with Table-Specific Pretraining Data Engineering for Scaling Language Models to 128K Context

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-24T02:45:56.152590Z

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 e1181f8f-1386-4869-85a9-5b1aae4af19d · 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? Data Engineering for Scaling Language Models to 128K Context

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:55:20.448654Z

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 d8481eb6-6ebf-4745-8035-4da3a1df440a · 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 Data Engineering for Scaling Language Models to 128K Context

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:17:00.229562Z

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 228085f8-6fa4-4317-81d2-77805279be3e · inbound

MLVU: Benchmarking Multi-task Long Video Understanding cites this paper.

MLVU: Benchmarking Multi-task Long Video Understanding Data Engineering for Scaling Language Models to 128K Context

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:55:26.552862Z

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 7664ed1c-a9c6-4c5d-892f-bf13a0359881 · inbound

Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference cites this paper.

Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference Data Engineering for Scaling Language Models to 128K Context

Reference 135

Resolution: verified exact; arxiv_id, observed 2026-05-20T17:46:47.037769Z

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 7c1730a4-5ea6-49fe-9e82-f1906eeea3b6 · inbound

TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate cites this paper.

TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate Data Engineering for Scaling Language Models to 128K Context

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:09:22.309704Z

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 d0d416bc-4c4c-4280-851d-1d139362df12 · inbound

LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions cites this paper.

LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions Data Engineering for Scaling Language Models to 128K Context

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:59.981503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74e21fdf-8cc7-4f77-9d34-24ad798e1e8a · inbound

Curse of High Dimensionality Issue in Transformer for Long-context Modeling cites this paper.

Curse of High Dimensionality Issue in Transformer for Long-context Modeling Data Engineering for Scaling Language Models to 128K Context

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:11.956205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4da642ac-0601-46e6-993a-1e492297e921 · inbound

Automated Essay Scoring Incorporating Annotations from Automated Feedback Systems cites this paper.

Automated Essay Scoring Incorporating Annotations from Automated Feedback Systems Data Engineering for Scaling Language Models to 128K Context

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:04:14.441793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebf2ab01-97cd-4065-b5c1-a70bf9fa2800 · inbound

SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models cites this paper.

SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models Data Engineering for Scaling Language Models to 128K Context

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:06.057643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 638a5d41-4de1-4dad-b484-b1bd8adac545 · inbound

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks cites this paper.

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks Data Engineering for Scaling Language Models to 128K Context

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:16.874877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62d3bfaa-5308-43fd-9496-0a6d31eda402 · inbound

LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models cites this paper.

LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models Data Engineering for Scaling Language Models to 128K Context

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T17:33:19.999164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6089dcf4-55b7-4407-a7b9-99669a33a215 · inbound

Towards Compute-Optimal Many-Shot In-Context Learning cites this paper.

Towards Compute-Optimal Many-Shot In-Context Learning Data Engineering for Scaling Language Models to 128K Context

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T15:20:38.626324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c10f6fcb-9b05-4cee-bbf8-4f4bc853c38b · inbound

HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference cites this paper.

HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference Data Engineering for Scaling Language Models to 128K Context

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T13:17:54.895716Z

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 eae3db87-d56d-40e4-a451-d00d9b48d877 · 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 Data Engineering for Scaling Language Models to 128K Context

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:00:10.310750Z

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 be511320-5e39-417e-a4b0-7f4fb0f36a44 · inbound

Tokalator: A Context Engineering Toolkit for Artificial Intelligence Coding Assistants cites this paper.

Tokalator: A Context Engineering Toolkit for Artificial Intelligence Coding Assistants Data Engineering for Scaling Language Models to 128K Context

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:36:03.095872Z

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 586d3202-9047-477c-b34e-4d37ef6f3161 · inbound

Shuffle the Context: RoPE-Perturbed Self-Distillation for Long-Context Adaptation cites this paper.

Shuffle the Context: RoPE-Perturbed Self-Distillation for Long-Context Adaptation Data Engineering for Scaling Language Models to 128K Context

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T13:45:28.128889Z

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 bb100be8-316f-402e-bc65-6b7f57f491e7 · inbound

Self-Describing Structured Data with Dual-Layer Guidance: A Lightweight Alternative to RAG for Precision Retrieval in Large-Scale LLM Knowledge Navigation cites this paper.

Self-Describing Structured Data with Dual-Layer Guidance: A Lightweight Alternative to RAG for Precision Retrieval in Large-Scale LLM Knowledge Navigation Data Engineering for Scaling Language Models to 128K Context

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:43:12.141839Z

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 136f9d8d-4ea0-4272-a21e-4865812045e3 · inbound

Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models cites this paper.

Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models Data Engineering for Scaling Language Models to 128K Context

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:01:15.271490Z

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 d86c350b-3bcc-4dc3-a58f-d1261ada812e · 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 Data Engineering for Scaling Language Models to 128K Context

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:51:29.149836Z

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 56bbecfb-1a13-470d-a521-412f18dfe504 · inbound

Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context cites this paper.

Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context Data Engineering for Scaling Language Models to 128K Context

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:17:50.134001Z

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 b9745d4b-f2bd-479d-a873-55cf963f0097 · inbound

RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts, Provably cites this paper.

RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts, Provably Data Engineering for Scaling Language Models to 128K Context

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-19T15:37:37.316022Z

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 43f4273a-6f40-4463-89ff-61c983e0ccb0 · inbound

WorkBench Revisited: Workplace Agents Two Years On cites this paper.

WorkBench Revisited: Workplace Agents Two Years On Data Engineering for Scaling Language Models to 128K Context

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:37:25.394049Z

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 05367672-8f9c-451e-adb3-b39e7e5022cb · inbound

HPP: Hierarchical Programmatic Probing for Long Video Understanding by Decoupling Perception and Reasoning cites this paper.

HPP: Hierarchical Programmatic Probing for Long Video Understanding by Decoupling Perception and Reasoning Data Engineering for Scaling Language Models to 128K Context

Reference 211

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:39:37.622643Z

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 6acecacd-d726-497b-b7b1-5f52e49b19e9 · inbound

Test-Time Training with Next-Token Prediction cites this paper.

Test-Time Training with Next-Token Prediction Data Engineering for Scaling Language Models to 128K Context

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:09:37.401933Z

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 b9ba3de5-15ad-49cf-b591-ea94623b2897 · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Data Engineering for Scaling Language Models to 128K Context

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-07-04T11:09:46.443744Z

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 9e310bf0-13cb-4690-8005-c75c3db0102e · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Data Engineering for Scaling Language Models to 128K Context

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-02T10:27:16.336372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 482814d7-bdb6-475c-962a-6d183153fa9e · inbound

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

Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE Data Engineering for Scaling Language Models to 128K Context

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:07:33.538561Z

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 8dd90981-1a13-4c1e-9f5e-b09ab1b7cc2d · inbound

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

Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE Data Engineering for Scaling Language Models to 128K Context

Reference 27

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.