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Paper Citation Record · LEDGER

CompLLM: Compression for Long Context Q&A

As of 20 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 4 inbound Pith citation observations for arXiv:2509.19228.

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

pith.paper-citation-record.v1
2509.19228 v2

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:40:54.406568Z

measured 69 of 69 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:49:37.437977Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T15:08:33.439599Z

Reference resolution

65 of 65 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved63
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 86fa3b02-d056-4596-b8d9-fc2d50fb0e72 · outbound

This paper cites Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression.

CompLLM: Compression for Long Context Q&A Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression

Reference 1

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no resolver link, observed 2026-08-04T15:40:45.417699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:45.417699Z digest=sha256:5b1428f2fb232c5d5654461e2d5758cc3628d5820395b4b7a460243982a9a557

Observation 23db585e-2964-4ef5-9103-89f2ce133897 · outbound

This paper cites QAMPARI : A benchmark for open-domain questions with many answers.

CompLLM: Compression for Long Context Q&A QAMPARI : A benchmark for open-domain questions with many answers

Reference 2

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no resolver link, observed 2026-08-04T15:40:45.537232Z

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source=arxiv_source observed=2026-08-04T15:40:45.537232Z digest=sha256:f02c807c0630278c545b003af18b3fbb9463c9585b839316e57aa6d7e3d4aab3

Observation 46aa21dd-554d-4997-b8a5-4cb042e79abc · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku, 2024.

CompLLM: Compression for Long Context Q&A The claude 3 model family: Opus, sonnet, haiku, 2024

Reference 3

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no resolver link, observed 2026-08-04T15:40:45.669579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:45.669579Z digest=sha256:e2b44eaddb61dba72ffaf3a237ea0b78fb09a2223d02575f2be42c6169cc7163

Observation 85384f53-506f-4d65-a0cf-ef2d1997eac3 · outbound

This paper cites Retaining key information under high compression ratios: Query-guided compressor for LLM s.

CompLLM: Compression for Long Context Q&A Retaining key information under high compression ratios: Query-guided compressor for LLM s

Reference 4

Resolution
verified exact
doi, observed 2026-08-04T15:44:07.218372Z

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.

source=arxiv_source observed=2026-08-04T15:40:45.756918Z digest=sha256:3931995f729f6ee35869cd681ea9d43ee5009744f8a7459c5d1939c1334a9298

Observation 65b16101-76b9-45fe-8a5f-65c172691938 · outbound

This paper cites KV-Distill: Nearly Lossless Learnable Context Compression for LLMs.

CompLLM: Compression for Long Context Q&A KV-Distill: Nearly Lossless Learnable Context Compression for LLMs

Reference 5

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source=arxiv_source observed=2026-08-04T15:40:45.868606Z digest=sha256:2debd594f3b2f693e4df342bc7d60446787d5f154eeed77bdfec1592cee3c03d

Observation bf9bf645-5702-45b2-87f6-99c7840cbf5d · outbound

This paper cites DAST : Context-aware compression in LLM s via dynamic allocation of soft tokens.

CompLLM: Compression for Long Context Q&A DAST : Context-aware compression in LLM s via dynamic allocation of soft tokens

Reference 6

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no resolver link, observed 2026-08-04T15:40:46.037612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:46.037612Z digest=sha256:632831f4771f4476b1e14ecdd8eef85e1bda875a229a2ac8ba8c2959415affcd

Observation 00c27a15-fc1c-4b4b-afd5-d7c29481096f · outbound

This paper cites x RAG : Extreme context compression for retrieval-augmented generation with one token.

CompLLM: Compression for Long Context Q&A x RAG : Extreme context compression for retrieval-augmented generation with one token

Reference 7

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no resolver link, observed 2026-08-04T15:40:46.159538Z

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source=arxiv_source observed=2026-08-04T15:40:46.159538Z digest=sha256:5ee522cc96f6e43619346ec0139b27581d8d0a1351b56d3aa047412af0ecc278

Observation 1be73f09-381b-4b5a-9f93-40416e3d9442 · outbound

This paper cites Selection-p: Self-supervised task-agnostic prompt compression for faithfulness and transferability.

CompLLM: Compression for Long Context Q&A Selection-p: Self-supervised task-agnostic prompt compression for faithfulness and transferability

Reference 8

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source=arxiv_source observed=2026-08-04T15:40:46.338636Z digest=sha256:5e8801675d2998e8970d9cd544daca9848077166fcd6cee2299c4449ce546b06

Observation 0a6fcb3f-ce7c-4db2-acf7-04a84cf3fe94 · outbound

This paper cites Finch: Prompt-guided key-value cache compression for large language models.

CompLLM: Compression for Long Context Q&A Finch: Prompt-guided key-value cache compression for large language models

Reference 9

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source=arxiv_source observed=2026-08-04T15:40:46.518998Z digest=sha256:73d4153ce62ae86b17bbbe00867ec8bdd8982f41be1998c330670048466de56b

Observation 5580318d-f5bd-4813-a133-4bd18d8a53f3 · outbound

This paper cites BERT: pre-training of deep bidirectional transformers for language understanding.

CompLLM: Compression for Long Context Q&A BERT: pre-training of deep bidirectional transformers for language understanding

Reference 10

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source=arxiv_source observed=2026-08-04T15:40:46.684847Z digest=sha256:ed42429ebf5126225d46cfb9d149e3c4bff4a973138549f9e243369a28f4c91f

Observation d0262d80-0f86-49d0-8388-69a56ff59abd · outbound

This paper cites Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference.

CompLLM: Compression for Long Context Q&A Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference

Reference 11

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source=arxiv_source observed=2026-08-04T15:40:46.821236Z digest=sha256:5c418790dd68832e6850fcfc366b8fd4e5eb9d941d9564e8732297a4a5533a34

Observation c3b3cc5a-cebb-403c-8f27-c76bc3a14a73 · outbound

This paper cites Beam search strategies for neural machine translation.

CompLLM: Compression for Long Context Q&A Beam search strategies for neural machine translation

Reference 12

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source=arxiv_source observed=2026-08-04T15:40:46.981365Z digest=sha256:43b0381c0e3680b79710c174c4718242aa4252ac4e8d19ba9b743af06755ea06

Observation f949de7c-336f-4860-8884-fd2e7690b0e6 · outbound

This paper cites In-context autoencoder for context compression in a large language model.

CompLLM: Compression for Long Context Q&A In-context autoencoder for context compression in a large language model

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:47.131995Z digest=sha256:d4f0287e49419518d5f2fc7a7953f099bddcafab2965d11cf1c9093b9202de75

Observation e4dd3873-6a3a-4bc1-a47a-7a7a78c4f7ea · outbound

This paper cites In-context autoencoder for context compression in a large language model.

CompLLM: Compression for Long Context Q&A In-context autoencoder for context compression in a large language model

Reference 14

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source=arxiv_source observed=2026-08-04T15:40:47.297237Z digest=sha256:4e17cf3009c5fcaee1baafad542d2a31f6bccbbe3b9c0d1f85e7e997681ea3b4

Observation 5da37440-6052-44d5-9b6c-6292a4a0fc61 · outbound

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

CompLLM: Compression for Long Context Q&A Lo RA : Low-rank adaptation of large language models

Reference 15

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source=arxiv_source observed=2026-08-04T15:40:47.408934Z digest=sha256:63a17f8b88859d0103d76e33a275724187bdd34ad4c8a85695642a15ee8d245b

Observation 3d1c149e-3591-4555-98f4-5a2088a285cd · outbound

This paper cites Recurrent Context Compression: Efficiently Expanding the Context Window of LLM.

CompLLM: Compression for Long Context Q&A Recurrent Context Compression: Efficiently Expanding the Context Window of LLM

Reference 16

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:47.523035Z digest=sha256:169a4104495e8cc37aee9cffd9a646e3f113fbc3426e5f778f19882c46ce12f8

Observation 41494c56-a32d-47a0-89d3-90be6c9efaed · outbound

This paper cites an unresolved cited work.

CompLLM: Compression for Long Context Q&A Unresolved cited work

Reference 17

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source=arxiv_source observed=2026-08-04T15:40:47.639451Z digest=sha256:a250b0469ecdc563485d3776e37f2a41211ab897626077358f20e159de36b571

Observation fac9a4b9-ae07-47b3-9af8-be95aa26385d · outbound

This paper cites Perceiver: General perception with iterative attention, 2021.

CompLLM: Compression for Long Context Q&A Perceiver: General perception with iterative attention, 2021

Reference 18

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source=arxiv_source observed=2026-08-04T15:40:47.739557Z digest=sha256:9c54107ca21ffa26ff5d5e7dc195edebbdf2a09754d8afe78f2a76897a56d6df

Observation 315a4a2f-7bda-463b-b1d4-83f61b459161 · outbound

This paper cites LLML ingua: Compressing prompts for accelerated inference of large language models.

CompLLM: Compression for Long Context Q&A LLML ingua: Compressing prompts for accelerated inference of large language models

Reference 19

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source=arxiv_source observed=2026-08-04T15:40:47.844856Z digest=sha256:b796dca78b32d1fa5435fbf082ac24e458db428529ad9084b1d3572e985eb4c0

Observation 01239274-3c96-46d7-960d-5f2e38d057e1 · outbound

This paper cites L ong LLML ingua: Accelerating and enhancing LLM s in long context scenarios via prompt compression.

CompLLM: Compression for Long Context Q&A L ong LLML ingua: Accelerating and enhancing LLM s in long context scenarios via prompt compression

Reference 20

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no resolver link, observed 2026-08-04T15:40:47.992056Z

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source=arxiv_source observed=2026-08-04T15:40:47.992056Z digest=sha256:c66cb5b4c36f5d619e587895eb0dcc99a839d17513cc1cd51ca0bc575778e988

Observation 812bd792-35a7-4c81-aa1c-bb5c32327326 · outbound

This paper cites Compressed context memory for online language model interaction.

CompLLM: Compression for Long Context Q&A Compressed context memory for online language model interaction

Reference 21

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no resolver link, observed 2026-08-04T15:40:48.087694Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:48.087694Z digest=sha256:6b06acfa730de7ff75b023865d7bc06520d5dfd8eed4b5199b986b6f148542ff

Observation b6822640-630a-41f6-95c4-8b4217fc008d · outbound

This paper cites Unsupervised multilingual sentence boundary detection.

CompLLM: Compression for Long Context Q&A Unsupervised multilingual sentence boundary detection

Reference 22

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source=arxiv_source observed=2026-08-04T15:40:48.175788Z digest=sha256:eb053c35a59d1d1bf5b10fb845b280f89f668714540a55dea5ac7a31c035defc

Observation 322e1d58-9704-40de-a1b3-acc706731f33 · outbound

This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

CompLLM: Compression for Long Context Q&A Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 23

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source=arxiv_source observed=2026-08-04T15:40:48.304413Z digest=sha256:3bc814b5af41c9e78081531c2b7617d4ce49b77d61cffe2df3d5a7ad8c239302

Observation f0391569-4381-4325-aa5c-104ce63720c3 · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

CompLLM: Compression for Long Context Q&A Gonzalez, Hao Zhang, and Ion Stoica

Reference 24

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no resolver link, observed 2026-08-04T15:40:48.420631Z

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source=arxiv_source observed=2026-08-04T15:40:48.420631Z digest=sha256:df51fc69984446c3b58c853f385c3d767fd4c26e233b9d740deda97e34cf66ed

Observation 66387029-0019-43ac-b98c-5bc6af69801c · outbound

This paper cites RACE : Large-scale R e A ding comprehension dataset from examinations.

CompLLM: Compression for Long Context Q&A RACE : Large-scale R e A ding comprehension dataset from examinations

Reference 25

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:48.564715Z digest=sha256:6779f3fd1ca92f2b98cd1a00e57127b65658afe157c7936942589870e7e82c2e

Observation 80b75a24-002e-491b-bdbe-267732a9741e · outbound

This paper cites Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?.

CompLLM: Compression for Long Context Q&A Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?

Reference 26

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source=arxiv_source observed=2026-08-04T15:40:48.710505Z digest=sha256:a05ca63b519d2c285bff3aea79a84fddf5b494b288cd25afd6a4ce221d0a0716

Observation 3ff053ce-198a-4295-839a-1acc1cbec0ac · outbound

This paper cites u ttler, Mike Lewis, Wen tau Yih, Tim Rockt \.

CompLLM: Compression for Long Context Q&A u ttler, Mike Lewis, Wen tau Yih, Tim Rockt \

Reference 27

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source=arxiv_source observed=2026-08-04T15:40:48.815009Z digest=sha256:7e680416ece83f32c1df2798e629931d6a1202d6870643fb16f39201dbc2897d

Observation 45033572-4739-4465-8cb9-1bb6ff07fe7c · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

CompLLM: Compression for Long Context Q&A u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 28

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no resolver link, observed 2026-08-04T15:40:48.925608Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:48.925608Z digest=sha256:c5382548733b05435dccc319f4df634444531d2b65ffb40c48686f0efec9fda5

Observation f401d821-35c1-4202-a263-da744f70f729 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation, 2021 a.

CompLLM: Compression for Long Context Q&A Prefix-tuning: Optimizing continuous prompts for generation, 2021 a

Reference 29

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:49.043623Z digest=sha256:dff970ec628962c1adabf84031e8c41107c4681e0bb1aab5213d385652009533

Observation 08e3c52a-5f91-4802-9abd-64ca84cb75cc · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

CompLLM: Compression for Long Context Q&A Prefix-tuning: Optimizing continuous prompts for generation

Reference 30

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no resolver link, observed 2026-08-04T15:40:49.205776Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:49.205776Z digest=sha256:247c4f6a78751c9a4f3db8bce2969f69905f46416d6c4160cc6ef8f9e8eb447c

Observation aa56ae0c-bd97-4228-a08f-b03b03d212ea · outbound

This paper cites 500x C ompressor: Generalized prompt compression for large language models.

CompLLM: Compression for Long Context Q&A 500x C ompressor: Generalized prompt compression for large language models

Reference 31

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source=arxiv_source observed=2026-08-04T15:40:49.289171Z digest=sha256:598811d3f7cec7aa0a853903b4ded9a996dac2b3488db7f1d8dd3b35cafd0de3

Observation c97183d3-5184-411a-b84e-e1121e694157 · outbound

This paper cites an unresolved cited work.

CompLLM: Compression for Long Context Q&A Unresolved cited work

Reference 32

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source=arxiv_source observed=2026-08-04T15:40:49.361725Z digest=sha256:ced35b31c72ad40c6a5491d60dbc3b2742187ef75d48e13589e73164944b850c

Observation d2e6c386-e1a5-4a59-9fb1-f03888d881f1 · outbound

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

CompLLM: Compression for Long Context Q&A Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

Reference 33

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source=arxiv_source observed=2026-08-04T15:40:49.504037Z digest=sha256:a153ed309e0be2a1e4604fa9be799d3187dfb449075a77de8535ad6b1483b2a8

Observation 124755c6-f307-4b0d-9241-bd2127d026a0 · outbound

This paper cites Cachegen: Kv cache compression and streaming for fast large language model serving.

CompLLM: Compression for Long Context Q&A Cachegen: Kv cache compression and streaming for fast large language model serving

Reference 34

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source=arxiv_source observed=2026-08-04T15:40:49.696479Z digest=sha256:852d2717c2304a0a7b1d9acb18442739ecb1b7fcf5f437614c66d3c97a2e2299

Observation 40167036-acc0-420e-a849-3cdaf14c448d · outbound

This paper cites Quest: A retrieval dataset of entity-seeking queries with implicit set operations.

CompLLM: Compression for Long Context Q&A Quest: A retrieval dataset of entity-seeking queries with implicit set operations

Reference 35

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source=arxiv_source observed=2026-08-04T15:40:49.841264Z digest=sha256:c7f27ec8aa76898cb9e6ed44caaec0120b23ae83817ad0dac650a1b79664c2cd

Observation 3023f9e9-ea2f-45da-877d-95fed2e1479b · outbound

This paper cites Learning to compress prompts with gist tokens.

CompLLM: Compression for Long Context Q&A Learning to compress prompts with gist tokens

Reference 36

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source=arxiv_source observed=2026-08-04T15:40:49.918650Z digest=sha256:f460272daca962124d01ce88f3795a15d825338169da5ddcb28348f8c9cebaf2

Observation f863954d-50dc-41ca-9017-bda34812473b · outbound

This paper cites Learning to compress prompts with gist tokens.

CompLLM: Compression for Long Context Q&A Learning to compress prompts with gist tokens

Reference 37

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source=arxiv_source observed=2026-08-04T15:40:50.024145Z digest=sha256:66ef850780cfb6783b0d689f112df65eeb700ae4317a07488d7382344363f385

Observation b42e16ae-d07c-4e40-9a79-ddd1a37dcb11 · outbound

This paper cites GPT-4o System Card.

CompLLM: Compression for Long Context Q&A GPT-4o System Card

Reference 38

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source=arxiv_source observed=2026-08-04T15:40:50.120116Z digest=sha256:ecde34a0d8087ea6f9e1dcb9042e73227cc7358d07402d9963d71617c4b0c017

Observation c09383a5-5b26-4009-a518-3006910b402f · outbound

This paper cites Vicky Zhao, Lili Qiu, and Dongmei Zhang.

CompLLM: Compression for Long Context Q&A Vicky Zhao, Lili Qiu, and Dongmei Zhang

Reference 39

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source=arxiv_source observed=2026-08-04T15:40:50.230286Z digest=sha256:9f6b078ab6e76879fb5f590cae65c8a00a57b3330ab0661475ae38657b9b0ec3

Observation 40adcfdc-a1a3-4a58-aaef-f0597c1e4413 · outbound

This paper cites PyTorch : An imperative style, high-performance deep learning library.

CompLLM: Compression for Long Context Q&A PyTorch : An imperative style, high-performance deep learning library

Reference 40

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source=arxiv_source observed=2026-08-04T15:40:50.386154Z digest=sha256:5c44849ba1c4c7ed25e175f6ff3b72d161532c94613c2063804022ed16158701

Observation 3547cc4e-95cf-4538-a6c9-3fea23e97c15 · outbound

This paper cites Long Context In-Context Compression by Getting to the Gist of Gisting.

CompLLM: Compression for Long Context Q&A Long Context In-Context Compression by Getting to the Gist of Gisting

Reference 42

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source=arxiv_source observed=2026-08-04T15:40:50.589587Z digest=sha256:03712cd78f6a5de1c382643adbbb49b48057c8636a92add8fabaede33a27f772

Observation 59d16349-3ae5-41bb-815e-8b4558efc813 · outbound

This paper cites SQ u AD : 100,000+ questions for machine comprehension of text.

CompLLM: Compression for Long Context Q&A SQ u AD : 100,000+ questions for machine comprehension of text

Reference 43

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no resolver link, observed 2026-08-04T15:40:50.701563Z

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source=arxiv_source observed=2026-08-04T15:40:50.701563Z digest=sha256:7d9c3c66d111662315639b88a7a7833bf0e83ad8d3af9b17e24070ecca3b993a

Observation 5ca4cb46-e61e-4cbe-9e58-213bdcb50c86 · outbound

This paper cites Getting closer to AI complete question answering: A set of prerequisite real tasks.

CompLLM: Compression for Long Context Q&A Getting closer to AI complete question answering: A set of prerequisite real tasks

Reference 44

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source=arxiv_source observed=2026-08-04T15:40:50.837512Z digest=sha256:822ad1cde0476c9ea4334d0b2d0b1a8c68c58e2649abaf5082d33a24d930cef1

Observation 516caa67-c2b4-4de8-af8a-f88d4c0e7d66 · outbound

This paper cites The NarrativeQA reading comprehension challenge.

CompLLM: Compression for Long Context Q&A The NarrativeQA reading comprehension challenge

Reference 45

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source=arxiv_source observed=2026-08-04T15:40:50.950299Z digest=sha256:98c7e1eecb3630ce4bfa4bf5c2f7e62f2410a222b392d5086dfa17fbcc05fd06

Observation edf3776b-f497-4fd1-b346-ce804f2862d0 · outbound

This paper cites CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation.

CompLLM: Compression for Long Context Q&A CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation

Reference 46

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no resolver link, observed 2026-08-04T15:40:51.137518Z

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source=arxiv_source observed=2026-08-04T15:40:51.137518Z digest=sha256:19db720d6adf4a4406cc4309074e238df81f5487c0901ca6d6ba0af27e30a3f4

Observation 95b144ef-4ce9-4c3a-b1f1-f2c2e3eee3f2 · outbound

This paper cites Perception compressor: A training-free prompt compression framework in long context scenarios.

CompLLM: Compression for Long Context Q&A Perception compressor: A training-free prompt compression framework in long context scenarios

Reference 47

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source=arxiv_source observed=2026-08-04T15:40:51.270082Z digest=sha256:698e2c663d71c0caefb0ac885c2efd2450be41642979d45e64283e371567f1b6

Observation 3ef456c0-5e46-4a16-a900-d8714c2ab1eb · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context , 2024.

CompLLM: Compression for Long Context Q&A Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context , 2024

Reference 48

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source=arxiv_source observed=2026-08-04T15:40:51.406932Z digest=sha256:cd71155b108ab5fd390eb1180d7d694917fac8569aa5d354bb8191aadcc96a71

Observation 773b1d11-6250-4ead-9ed7-b8ffd3243e5e · outbound

This paper cites Gemma 3 Technical Report.

CompLLM: Compression for Long Context Q&A Gemma 3 Technical Report

Reference 49

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source=arxiv_source observed=2026-08-04T15:40:51.508482Z digest=sha256:30429753801e8449cd48f3ae6baa1fd44033d498eb110b6b5bde5a467f454c43

Observation 846f9589-ad40-49fe-9168-38e0ac7502cd · outbound

This paper cites BEIR : A heterogeneous benchmark for zero-shot evaluation of information retrieval models.

CompLLM: Compression for Long Context Q&A BEIR : A heterogeneous benchmark for zero-shot evaluation of information retrieval models

Reference 50

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source=arxiv_source observed=2026-08-04T15:40:51.713483Z digest=sha256:3faac69c89103343dc2205ce58eb9df49ee04ebccd26ea01b3e1c49a44a85dc6

Observation d2e6dd4c-2f78-4ed6-b36a-e0095811b123 · outbound

This paper cites M u S i Q ue: Multihop questions via single-hop question composition.

CompLLM: Compression for Long Context Q&A M u S i Q ue: Multihop questions via single-hop question composition

Reference 51

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source=arxiv_source observed=2026-08-04T15:40:51.912413Z digest=sha256:f9741d92d03f698f1dace93d029884d2e9155b85e754a1a7be783721df9f5c21

Observation 1b9e41b8-cb9a-4a5c-a3b8-82ab7dd1c457 · outbound

This paper cites Attention is all you need.

CompLLM: Compression for Long Context Q&A Attention is all you need

Reference 52

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source=arxiv_source observed=2026-08-04T15:40:52.067534Z digest=sha256:1f31aa24bf0f62ce8d93a923e3d0d6f260108fc851fca21c4ef7e95ecf743df0

Observation 7407b3f5-96d9-4a46-965f-e23deb61f06a · outbound

This paper cites In-context former: Lightning-fast compressing context for large language model.

CompLLM: Compression for Long Context Q&A In-context former: Lightning-fast compressing context for large language model

Reference 53

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source=arxiv_source observed=2026-08-04T15:40:52.218684Z digest=sha256:a7d1f6f4f91c2c5ea69c6769f1dbda04aef46fa0fb6874ac9bd11187240d6cd7

Observation 9d452867-1b77-4351-8543-c8d69393572b · outbound

This paper cites In-context former: Lightning-fast compressing context for large language model.

CompLLM: Compression for Long Context Q&A In-context former: Lightning-fast compressing context for large language model

Reference 54

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source=arxiv_source observed=2026-08-04T15:40:52.373463Z digest=sha256:2053c16a3eb7f49ca75b1de43a80f0748edc5061a6053241f23e9451b60c9b2e

Observation 3d78ae07-a880-471f-accc-ab2463995c4f · outbound

This paper cites Chi, Quoc V.

CompLLM: Compression for Long Context Q&A Chi, Quoc V

Reference 55

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source=arxiv_source observed=2026-08-04T15:40:52.568891Z digest=sha256:36e59c30d9ba7a5324851ff7dcbc0a2f768cbf44c03fa0023cb6766ac522f99d

Observation 9cd01866-2b8b-4afd-858e-010fa12ea0e6 · outbound

This paper cites RECOMP : Improving retrieval-augmented LM s with context compression and selective augmentation.

CompLLM: Compression for Long Context Q&A RECOMP : Improving retrieval-augmented LM s with context compression and selective augmentation

Reference 56

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source=arxiv_source observed=2026-08-04T15:40:52.735619Z digest=sha256:b34b54a544adb861aec83f1396113fbfba6b810f2d1f04ac91dd4ca11ef0eb28

Observation a079dfd9-6708-4bc2-bd15-2f61c9d5cf33 · outbound

This paper cites Qwen3 Technical Report.

CompLLM: Compression for Long Context Q&A Qwen3 Technical Report

Reference 57

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source=arxiv_source observed=2026-08-04T15:40:52.914567Z digest=sha256:efc0e167bcaeb8216cafafddb0f731b6b2bb395bdc2bcf80121c55a4a946c2b3

Observation 1e5fac86-5f1e-4b00-af97-99cadedd844f · outbound

This paper cites PRCA : Fitting black-box large language models for retrieval question answering via pluggable reward-driven contextual adapter.

CompLLM: Compression for Long Context Q&A PRCA : Fitting black-box large language models for retrieval question answering via pluggable reward-driven contextual adapter

Reference 58

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source=arxiv_source observed=2026-08-04T15:40:53.022722Z digest=sha256:b610f086f67c7a3b21e3480a8d05506dafc8314b91a9c9b22d244c2faf4571f0

Observation 6a0c1f6a-4497-4d9a-872d-c3c79e4def1e · outbound

This paper cites Cohen, Ruslan Salakhutdinov, and Christopher D.

CompLLM: Compression for Long Context Q&A Cohen, Ruslan Salakhutdinov, and Christopher D

Reference 59

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source=arxiv_source observed=2026-08-04T15:40:53.216584Z digest=sha256:d162030ed614766b87e86a483db16190ebc752a08bec1d5666844ad6455eb118

Observation 83a80df8-a618-453c-bff6-95db0da0b19e · outbound

This paper cites WebVoyager: Building an End-to-End Web Agent that Masters Complex Tasks , 2024.

CompLLM: Compression for Long Context Q&A WebVoyager: Building an End-to-End Web Agent that Masters Complex Tasks , 2024

Reference 60

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source=arxiv_source observed=2026-08-04T15:40:53.356291Z digest=sha256:4abcf99ba2b9d03055a43fc4b66e9fd7334e315ef325ff798ade87e928074fc8

Observation dc1fa1a7-a205-49b8-9f82-87bcc1fcada9 · outbound

This paper cites DAC : A dynamic attention-aware approach for task-agnostic prompt compression.

CompLLM: Compression for Long Context Q&A DAC : A dynamic attention-aware approach for task-agnostic prompt compression

Reference 61

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source=arxiv_source observed=2026-08-04T15:40:53.482852Z digest=sha256:94d0284e8a57c639a28e5ae9852f0b220237d65fbdcd6289051f1111843597af

Observation 8a582282-776d-43b2-8bba-2ab5afce347a · outbound

This paper cites Leveraging attention to effectively compress prompts for long-context llms.

CompLLM: Compression for Long Context Q&A Leveraging attention to effectively compress prompts for long-context llms

Reference 62

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verified exact
doi, observed 2026-08-04T15:44:06.675319Z

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.

source=arxiv_source observed=2026-08-04T15:40:53.633149Z digest=sha256:bb7fd307d672a5bf1fc7ed51fe921f4affd5a36f9689381f3f80af75034f162e

Observation 50886068-8b40-4e2e-901a-3a27557cecb0 · outbound

This paper cites write newline.

CompLLM: Compression for Long Context Q&A write newline

Reference 63

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source=arxiv_source observed=2026-08-04T15:40:53.842764Z digest=sha256:0afbace63185bc24a2365a8177a820a02d6e8d499997dd101c211f3ce185158e

Observation 486ed240-9164-4568-8945-f8606d3b2276 · outbound

This paper cites @esa (Ref.

CompLLM: Compression for Long Context Q&A @esa (Ref

Reference 64

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source=arxiv_source observed=2026-08-04T15:40:54.008384Z digest=sha256:c12385c79cf9861b3f9b3760ddbe286f354d02eb0894d3d373f3dd39fa407d23

Observation 291f245b-90b1-4ce7-a205-8933f44f48db · outbound

This paper cites an unresolved cited work.

CompLLM: Compression for Long Context Q&A Unresolved cited work

Reference 65

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source=arxiv_source observed=2026-08-04T15:40:54.234140Z digest=sha256:ce314c822c82c93ed7fd08290c51bad9b5f016c111ccfd28dcaeb79e284a7ff9

Observation 953c4804-90b7-4be3-af3a-cbf3aebbe635 · outbound

This paper cites an unresolved cited work.

CompLLM: Compression for Long Context Q&A Unresolved cited work

Reference 66

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source=arxiv_source observed=2026-08-04T15:40:54.406568Z digest=sha256:7151cdc379f4435899aa6db6c17236d5d47f54236d0a501664746c4d7c2d6038

Pith citing papers

Observation 7226d5f0-aa0f-47dd-9dec-b14ebd719c7e · inbound

When Less is More: The LLM Scaling Paradox in Context Compression cites this paper.

When Less is More: The LLM Scaling Paradox in Context Compression CompLLM: Compression for Long Context Q&A

Reference 2

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verified exact
arxiv_id, observed 2026-07-07T01:15:58.319082Z

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.

source=pdf_text observed=2026-05-16T02:28:37.490958Z digest=sha256:eb1e992ca487720fa4222cdc7b471fc29bf8616a5363e165b31fa944dfc6e1c9

Observation 2713b3d6-79ed-44b5-b32e-052d98affad4 · inbound

ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning cites this paper.

ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning CompLLM: Compression for Long Context Q&A

Reference 3

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verified exact
arxiv_id, observed 2026-07-07T01:15:58.319082Z

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.

source=pdf_text observed=2026-06-27T06:39:34.199607Z digest=sha256:3716c2669be6abb17488563e1ce4ac56d773c56f3fa578d2c3ed8645363fa87b

Observation b794f59a-88f8-4214-ba20-b4ed65967b84 · inbound

ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning cites this paper.

ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning CompLLM: Compression for Long Context Q&A

Reference 3

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no resolver link, observed 2026-08-03T02:12:16.518796Z

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source=pdf_text observed=2026-08-03T02:12:16.518796Z digest=sha256:be7d24e18c097d599a60bb656bc1cd40c7347dafc57d144c5f35b91736874922

Observation d520dd58-a13d-4610-93a9-846431cba336 · inbound

SeDeM: Selective Decompression of Hidden-State Memories for Long-Context Question Answering cites this paper.

SeDeM: Selective Decompression of Hidden-State Memories for Long-Context Question Answering CompLLM: Compression for Long Context Q&A

Reference 2025

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source=pdf_text observed=2026-08-04T00:49:37.437977Z digest=sha256:912fe8f948a638c9660e8a3853ae24e88b425e02403c0da819edb35b42bc7b98