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Paper Citation Record · LEDGER

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models

As of 8 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2506.00773.

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

pith.paper-citation-record.v1
2506.00773 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:03:44.519800Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

50 of 50 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved47
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 12c9a85e-18ab-469b-8045-f9456745db0a · outbound

This paper cites The Llama 3 Herd of Models.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models The Llama 3 Herd of Models

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:40.093460Z digest=sha256:08152f6e84e6731481f83038cceceaf6c5111aaccc7d18a10a5e44e563b593ec

Observation 02a18c03-ea77-457a-8ae5-8f90b1fd9fed · outbound

This paper cites Training-Free Long-Context Scaling of Large Language Models.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Training-Free Long-Context Scaling of Large Language Models

Reference 2

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source=arxiv_source observed=2026-08-07T12:03:40.190364Z digest=sha256:81cd3b2723df759d324f7da7f17f65f35cca0d05760e3291e123555047dbcb75

Observation c14d79ab-161c-42bf-afb9-b5aec0942410 · outbound

This paper cites Qwen Technical Report.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Qwen Technical Report

Reference 3

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source=arxiv_source observed=2026-08-07T12:03:40.303289Z digest=sha256:6de5fe9f4d545cb748adac768a715db6a3b079d91d3e1f0df360e6c326870082

Observation b275cf8e-3e98-4ddc-a9d5-0333ec3044de · outbound

This paper cites CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling

Reference 4

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local_arxiv, observed 2026-08-07T12:03:45.845512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:03:40.425788Z digest=sha256:27771ed946e6790e2f2e71750cece5891b838ac4c97b44e2ac17264b286f4fa1

Observation ac8016a0-97ab-4198-bec3-8c0da94bbb03 · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-07T12:03:40.477506Z digest=sha256:b41f3470ad039a8741e821f2da12a5671b57a825ba6a38b59f3b34209f71ba14

Observation 781cc14f-4c19-4c7d-b22e-1cfadb25b435 · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 6

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malformed identifier
no resolver link, observed 2026-08-07T12:03:40.544349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:40.544349Z digest=sha256:3b2523a76d8f491adf2adbc5ba414946657cee866fbc962dfe54f2519628a4cc

Observation 7d198958-4fb4-4cd9-82e4-3504aaf2732f · outbound

This paper cites Longformer: The Long-Document Transformer.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Longformer: The Long-Document Transformer

Reference 7

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source=arxiv_source observed=2026-08-07T12:03:40.619658Z digest=sha256:fee0deea4bb4b528f417fb5262a4876f98a84600663ac5521b704299cc49349a

Observation 36fa0ae3-839d-494a-9e2b-3c43555eab20 · outbound

This paper cites Unlimiformer: Long-Range Transformers with Unlimited Length Input.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unlimiformer: Long-Range Transformers with Unlimited Length Input

Reference 8

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source=arxiv_source observed=2026-08-07T12:03:40.693600Z digest=sha256:b4bbe3a7b1ed688703aa408ec689ab730bd29942801e0fbb90ba92d5e9920042

Observation bac1dbd7-be40-46d5-ae74-0b231b0b1487 · outbound

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

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Extending Context Window of Large Language Models via Positional Interpolation

Reference 9

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source=arxiv_source observed=2026-08-07T12:03:40.776103Z digest=sha256:6658c487bf639022748553d05a9598fff5d1cb48ca995e54f4a8093a25eb4436

Observation 0d9941c5-ec33-45d8-bec3-860ed5957826 · outbound

This paper cites LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Reference 10

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source=arxiv_source observed=2026-08-07T12:03:40.872696Z digest=sha256:5b8d6e743c579a0f0c5347368b9d4222356331f1a35f37f40b433ae2be4d7381

Observation 9615bdfe-9beb-412e-a2e1-d68775fbe483 · outbound

This paper cites Smith, and Matt Gardner.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Smith, and Matt Gardner

Reference 11

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source=arxiv_source observed=2026-08-07T12:03:40.959250Z digest=sha256:5bbc6ea7c3411a00927c63926cf3f6913c94e188e559a9ca415955565eb67d48

Observation 5d53ce6e-9661-4bfb-9adb-42e0c5140132 · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 12

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source=arxiv_source observed=2026-08-07T12:03:41.044008Z digest=sha256:a3ee6203fa5d8bf57edafa2d32534d874775373f7b357e510cf22414a1777d1f

Observation 45d42160-98d9-425e-8c53-3d88cfea9aba · outbound

This paper cites HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:41.146893Z digest=sha256:e36ea133ab3cb4e6401408687b4a5d54e8c1871cabbe3ed934e46eece81f95cb

Observation 01dd1903-0daf-432a-84f2-cf6a17cc1606 · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 14

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:41.260219Z digest=sha256:f31e2eca3bfb749fadfd68d84f94f1e6fd024d566dc1ba8618b9dee031d77c28

Observation 394d5c4d-977e-4182-95ef-d48acf03054f · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 15

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no resolver link, observed 2026-08-07T12:03:41.374429Z

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source=arxiv_source observed=2026-08-07T12:03:41.374429Z digest=sha256:d80a35232fefaccdac0cd6ff10ac91794c5f7a5f231628b0e64ff54c10b60e5a

Observation 505f9b33-adf5-4547-8ce8-426059b6a936 · outbound

This paper cites Mistral 7B.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Mistral 7B

Reference 16

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source=arxiv_source observed=2026-08-07T12:03:41.461651Z digest=sha256:8e7d5318d428b431fcceadbac552b181d6103d75245310aa56edd30087ae6b35

Observation 74dbdca3-8635-404e-86e3-82b913ce537b · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 17

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raw_fallback, observed 2026-08-07T12:03:46.206673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:03:41.593449Z digest=sha256:0afc4cfecd09fed44ea6b47566f757054520b47b2a361a746c9645bb17d99571

Observation 7ab08dbc-e1b7-4959-8723-5e5bc25ba842 · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:41.713006Z digest=sha256:89695c0c1a8d421ad321ce7d38cdb6db55f6289bea68b4798d5458c33c2334b9

Observation d8fba2f6-3aa6-4199-9bcf-84072ca3e2e8 · outbound

This paper cites LooGLE: Can Long-Context Language Models Understand Long Contexts?.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models LooGLE: Can Long-Context Language Models Understand Long Contexts?

Reference 19

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source=arxiv_source observed=2026-08-07T12:03:41.800526Z digest=sha256:b7fc792e856e4072792bdacbb0700b3cad51337b53f8d121400a0b96179e4169

Observation f07e4c77-b7bb-42bc-8709-25c1af426d98 · outbound

This paper cites Mixture of In-Context Experts Enhance LLMs' Long Context Awareness.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Mixture of In-Context Experts Enhance LLMs' Long Context Awareness

Reference 20

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source=arxiv_source observed=2026-08-07T12:03:41.896069Z digest=sha256:35bfeebb16914fe6185b61ff156c167752b5859faffa2df012585fd11c6f794e

Observation ad254740-002c-47e2-8a31-58b46d8287c0 · outbound

This paper cites Lost in the Middle: How Language Models Use Long Contexts.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Lost in the Middle: How Language Models Use Long Contexts

Reference 21

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source=arxiv_source observed=2026-08-07T12:03:42.014059Z digest=sha256:bb146211d579444950ea663c1950b93251a354498574f4a8363e2e8aaa28004c

Observation d64e8f79-6918-46ba-904a-bdcc90c9e711 · outbound

This paper cites Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

Reference 22

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source=arxiv_source observed=2026-08-07T12:03:42.110187Z digest=sha256:7dbc1be2382baf5144ab39aefb8d9aa34bd63b25fb9e149fe282e9e3348b2ca3

Observation d91dbba7-94e8-45e4-a572-7d034147c3b8 · outbound

This paper cites LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages

Reference 23

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no resolver link, observed 2026-08-07T12:03:42.203785Z

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source=arxiv_source observed=2026-08-07T12:03:42.203785Z digest=sha256:2ee513ef113a34976aa9689549a85883dc4c871e72d898de364813cf9f4cd48d

Observation d860466c-31e7-4b19-8ffc-91d98f44ca5d · outbound

This paper cites Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Reference 24

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source=arxiv_source observed=2026-08-07T12:03:42.333662Z digest=sha256:3c38c552889f1e7f323c8d3f1f398dc5ebf3fd18b3b5e46e260e92e0611a78e0

Observation e0da00ea-8990-45a2-9d58-703eb309cec5 · outbound

This paper cites GPT-4 Technical Report.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models GPT-4 Technical Report

Reference 25

Resolution
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source=arxiv_source observed=2026-08-07T12:03:42.448292Z digest=sha256:1d5c1caae81d7c9a4a8701e59afb244895fffeba8e8b9f43ed1f91f2fed059c8

Observation acd58307-ba1d-475d-989c-cde40fe46f23 · outbound

This paper cites Transformers are Multi-State RNNs.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Transformers are Multi-State RNNs

Reference 26

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source=arxiv_source observed=2026-08-07T12:03:42.533672Z digest=sha256:91eec2c28b1053349685f3239ec2d8951a055c35bb5e3041d36984ded119174b

Observation af5d7a5b-b38f-4a74-bdde-d933be8f2227 · outbound

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

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models YaRN: Efficient Context Window Extension of Large Language Models

Reference 27

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no resolver link, observed 2026-08-07T12:03:42.602545Z

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source=arxiv_source observed=2026-08-07T12:03:42.602545Z digest=sha256:421d4a2f7a47b6bf04d517960cf8f85e5a829c454367a9b2d24aa89183cec65b

Observation ad1d4e93-ce40-4d56-9551-ab6c777b90e0 · outbound

This paper cites Know What You Don't Know: Unanswerable Questions for SQuAD.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Know What You Don't Know: Unanswerable Questions for SQuAD

Reference 28

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source=arxiv_source observed=2026-08-07T12:03:42.683563Z digest=sha256:c548ca9897b646bd80b4cf45b1c4ec8a56f8030b5fe75f8a4d99d6e796ea2777

Observation a3b48dcf-2dea-4a96-b713-9946a9b6e2fd · outbound

This paper cites CoQA: A Conversational Question Answering Challenge.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models CoQA: A Conversational Question Answering Challenge

Reference 29

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no resolver link, observed 2026-08-07T12:03:42.763358Z

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source=arxiv_source observed=2026-08-07T12:03:42.763358Z digest=sha256:3b7244554df6e402da7a6978e785cebe378310f1837eaf6f479e69dfb5779399

Observation b631bb18-f4cb-4591-8aeb-e1104e87ddf5 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 30

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no resolver link, observed 2026-08-07T12:03:42.869619Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:42.869619Z digest=sha256:eabdf5e54eb62cda7fa0c2ffc6c65941f76a812acb2eda1389e094ba27bfacc3

Observation 68ecd262-64b7-4648-ad95-af566e18b89e · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 31

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verified exact
doi, observed 2026-08-07T12:03:44.785686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T12:03:42.951056Z digest=sha256:2b5b68b7a39ea503601d80c614916aeb0b5eef52e8cc1a73cdfd4c7554799d35

Observation eb52ef99-3dce-4c01-b882-a553dfacd342 · outbound

This paper cites Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs

Reference 32

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source=arxiv_source observed=2026-08-07T12:03:43.032861Z digest=sha256:14d0fc50c55470cfc783f17b112baf0e8fa8ffa6caf3fb61fee1bd0cf449cd87

Observation ba81dd5e-34a4-4013-a036-29eb78f38337 · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 33

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source=arxiv_source observed=2026-08-07T12:03:43.123191Z digest=sha256:bfde14eefc5d8248a9f53e2f20659d6e7fcb81896b7ab4ec8b659db78cb83b34

Observation d87ec26e-52f1-4691-a5fb-221ca48b3a4a · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 34

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source=arxiv_source observed=2026-08-07T12:03:43.191001Z digest=sha256:b093e46f5ee05645cea16c0ced79bb1a07e7bfe75489ffd74a0a8a6dc383091b

Observation a315ba3a-8133-4473-a4c6-0d44279f326d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 35

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source=arxiv_source observed=2026-08-07T12:03:43.280931Z digest=sha256:a9636d5b98dfbd692aca8f24a827cf7c472e8cca850a15d9d39820a9a3c7d56a

Observation 9c311621-3475-4611-9d60-834f08a68aae · outbound

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

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 36

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source=arxiv_source observed=2026-08-07T12:03:43.380073Z digest=sha256:7ca9bac6d1b0bb854761c4c940fe8c591e90efab052e87ef26c7c4cf2fcaf879

Observation 1a3f86fc-f30b-40c6-a998-35f408d867ca · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 37

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source=arxiv_source observed=2026-08-07T12:03:43.454235Z digest=sha256:9ee64ea19de2a3cf83c56d7eaedf7799487740ab1e47c93c80d63889e6bb9b82

Observation 901620cb-afb8-4703-a1ab-902341f9eb23 · outbound

This paper cites MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:43.526793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:43.526793Z digest=sha256:c7e05f7142c70b049bed7633c67549befb192af94fae5d55bb7f8b5543b684b4

Observation 906da04e-9014-4a49-88c8-1c8b38ae7e8a · outbound

This paper cites Beyond the Limits: A Survey of Techniques to Extend the Context Length in Large Language Models.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Beyond the Limits: A Survey of Techniques to Extend the Context Length in Large Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:43.604108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:43.604108Z digest=sha256:2c53b1167458d18d23e9b832ca4ca5d06db798275dc798bebeed8dad1887c713

Observation 31f8521d-a5be-4c68-a60d-6dffdfe9b143 · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:43.685649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:43.685649Z digest=sha256:1c1c2fdde04c97401fd36817c81c767627ab420441c52cadcb5c4545f169a2e6

Observation 7347e518-9724-42bb-8e9d-a4b07c2fe652 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Efficient Streaming Language Models with Attention Sinks

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:43.762036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:43.762036Z digest=sha256:7fb77544f0fcb5a03382eab4e747f2766400a193bdc6d54a57715b16cb8bbc35

Observation d238ecb2-aae5-4a22-bd50-946d0b93d14a · outbound

This paper cites Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:43.858520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:43.858520Z digest=sha256:ccc541b7410d037313336606a37267cd810ebdacab6d369a4b9816aad5a59069

Observation 9ef6671b-e7cc-4236-bddd-27a7b383bd8e · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:43.944679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:43.944679Z digest=sha256:eda97b621fa439c8bfa84a0a2eb2bf84cadaa48766a2ab65233f0ff85b26ef63

Observation c1659a60-8da7-4d04-9e93-768854a710ef · outbound

This paper cites ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:44.019447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:44.019447Z digest=sha256:2236f83d7d4c409c5f5911c08bbffd1ff6550b6e89ef77869cf7f9aa75e49fa1

Observation 5a391e87-dd8c-425e-9d04-64f02ef6544f · outbound

This paper cites an unresolved cited work.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Unresolved cited work

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:44.111117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:44.111117Z digest=sha256:a899657a135acb1ea05a6a7410f6ebde9d2347c2900d93324806b1d6e1d375e0

Observation 75fd645c-e007-4b07-8a7d-a50a6b0da8d7 · outbound

This paper cites Hashimoto.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Hashimoto

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:44.189639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:44.189639Z digest=sha256:44d4531428488fb5eb546a2698ade2d45595108a4a2ba002b63d9b5af68e6c85

Observation 98833a16-6a51-40ea-a0b6-29e12946d327 · outbound

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

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:44.277381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:44.277381Z digest=sha256:976aafa18c88789e6c978db0609d6b43e7d5b66cc7f4cc15b18e1d4b43cf1015

Observation a4e8b44a-0512-4071-86a0-0c167fb7fd0b · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:44.341997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:44.341997Z digest=sha256:9aed43642c9b6a9997189d01467de1046914e0a9a3c774c23990849260750b10

Observation f7735807-e57b-43c6-a989-f0f1be7d2bde · outbound

This paper cites online" 'onlinestring :=.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models online" 'onlinestring :=

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:44.430643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:44.430643Z digest=sha256:11c010fa04812e2b0fa07c7cc100c944d7bca59d68139691a47710c7bb467409

Observation bd50222d-d2cf-458a-bada-3db0e57ab4f5 · outbound

This paper cites write newline.

Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models write newline

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:44.519800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:03:44.519800Z digest=sha256:7a056dca916dbfe1d31e5ca1d2e5a4cd6d494652741856c47ae7903b481ed32e

Pith citing papers

No inbound Pith citation observations are available.