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Paper Citation Record · LEDGER

CompLLM: Compression for Long Context Q&A

As of 7 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 2026-08-06T06:34:29.942622+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=sha256:1918ba9bad4e783bc0af5312dd87d85381fb7382e896974576eedffb96a461a1

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:45.537232Z digest=sha256:002c89d3c5e797001ae3a2558237ca70501c7f64141f25c22bbd83766696147c

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

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:45.669579Z digest=sha256:c323cd168f482a72d538a873ea0545ddf7ae1f54b3987098883196a2c805d7d7

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

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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 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-08-04T15:40:45.756918Z digest=sha256:18d5502db9aa1b8649abe85a805d51efd91501e83a580e2216d2b6c18c948e33

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:a3f537b9fd2a5df13081723343a633ba7e5e7391a513a75db11f55b4d8446652

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=sha256:26d89f7494367dc7556ec0bbc667ccaf46ab80087f6e5159fa8cabbffcc42325

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:e4f581eda3e44f747d447f01ec113cf56e57c3d4be9fe117cdef8d40c954d756

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=sha256:7c57fcdf97266886adb0b99754a80f5c798e9ebdcfad6a17d2e3484ec8fa7e63

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:a426b1e9d415f9573ca497a37532a22fddfffa432bd88dee727c5444b9f607f4

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=sha256:3f9711cb4b748b6c9e5f0b6aeb04760993f853d9f54d0ce4594d6489b7659a09

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=sha256:8143f479c7b42cc47dd9a7b4b0c9d8b917141ea884405dc7cb91594731399a3c

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=sha256:0ad5fe20e7a2668b0b0f3ac228ef2e92ff52a56d8af87027ed651b53dfdba190

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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source=arxiv_source observed=2026-08-04T15:40:47.131995Z digest=sha256:d2cbc922ffe0b4223b5e9ba5aa7ad8f5d4e502dfd1add115c2de2dd37079f5e7

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=sha256:7a71edeb733d6066b5668b47a96745b8292d167fe20b8158a8aa7c4370942684

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=sha256:5d15d016690dc139c704132ec76752bcfb62e334bdd8002b94531767a157a72c

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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source=arxiv_source observed=2026-08-04T15:40:47.523035Z digest=sha256:e0603e348f449c2a12e9a7f45f79de9184ea57a0b18470784553cd2d77689a69

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=sha256:142f3d5679e5a54e5e6e3b116274f34d0420779211b300c564f3829441152131

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=sha256:5182ff75985b8f8171f9ff6ca31b3f297da7a1aa1a8428f94f667d1c765f39b4

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=sha256:147cf7e15e7aa2864ab45d1aa2a3e5132338646b2ee683eb5a4ea89de8cff2a9

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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source=arxiv_source observed=2026-08-04T15:40:47.992056Z digest=sha256:9b05eca4d4ece3c3a80fc29a86cd5009ec5c79560bb551882f80715b6963b549

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=sha256:56f53dc9ddaca2dbc53d395ddb75a24a9806df7a00e6bf87bf1d6fb81b3287f4

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=sha256:0cbbeed9b128147ae966ae5069943c4e13af3a792c233e62632e0ac7dc862676

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=sha256:1c22f581b5dedae55c852850c162e821ff8d2a8807dd1baa5ee6f3ed88d8b540

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=sha256:04de1db851719ccd480596e13aeb6a3a71aacced3997feb374c7fa63148261cc

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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source=arxiv_source observed=2026-08-04T15:40:48.564715Z digest=sha256:18b8abb97e46204aaaef701e764e20cc15e3d2000375c3a551c5330905e4cf0e

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:ac29ae6c36512f0c15ffa6202108599521f27d5a97ad13c39e35fdfc62351073

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:a73775683135d6138fe8d027c103e11415ff4fce6f19586e9e9349f9693fe75e

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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source=arxiv_source observed=2026-08-04T15:40:48.925608Z digest=sha256:de83f8b29c728681da62ec24e4b5576ddb9097c8f9e5843d913c18eec953fed3

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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source=arxiv_source observed=2026-08-04T15:40:49.043623Z digest=sha256:8fd6c5a8c7d055a3854d69bc5bcbc5485e93b3d2386069c3d17a1a2b5a8773f7

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T15:40:49.205776Z digest=sha256:00346c6a5dd0a30c188f7e3d077b34fa7d013f4e4eacec1ae6cc7ad757548910

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=sha256:0f2104676ac2aded476cc494900f3f6c86bbf043e30dac008c12d4b80e3d1294

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=sha256:3c60603760bca9e805f419b63cac8115e7392a4158fcbf16436748ce848be245

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=sha256:19dc77c4d46decd5ed2a835bfcca078d74ff293bb65fab935d620bac4d4db835

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=sha256:19be6b1c010ea571d5d7d1c5b21e1484a2592a35e4b1ca4067cc732fbc308cfe

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:f04f85699bff0fc2dcef2a8b0ec844878eb172c57422d43db1dc8f4e96ea14ae

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=sha256:403e5390ddeb8ed2c350b7dd1bc75d20cbf77b1185adf84e1518cb4ffef9a683

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=sha256:218d4f9031359d6da9daf4db2e91045bfb3c772715254d1749ae093fc14f20ad

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:ce0bb05aea00c0243dbf95a2bf008792a7680e160864c83497acdb0c60c0be0e

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=sha256:3873688bbb7bf7d623be0abaad5797c4b9d03d2d82c1ef21a93f3ec71f60130a

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=sha256:42c369f2d506f51915dd94ac23b208002fd38d3fb753d30d726972f373f316f5

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=sha256:58594aa75fe9f8477e682eca6945a9bed56b5664b69119da1a2a16a917b8c9e9

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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source=arxiv_source observed=2026-08-04T15:40:50.701563Z digest=sha256:60983d9cecd812407981afe631c61f45a307e3f565084f8e39718363a2a5633a

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:b6f3671725fa33ca526ee35760d1204bf3c382b3f7d8b418ed50a138a115872b

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:bb7a255a470eb408071294e1b7b764072b229411e057c85487cc8959e6e25461

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:e586d4463b861d47f4dded8faa4cd6c1f57d6a171f2920db5d6912eee8809ff5

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-reported events for the cited work

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source=arxiv_source observed=2026-08-04T15:40:51.270082Z digest=sha256:3c335308d67ef5cf4b0f142840f83eb1543f616552200d85032ea379b2c17161

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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no resolver link, observed 2026-08-04T15:40:51.406932Z

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source=arxiv_source observed=2026-08-04T15:40:51.406932Z digest=sha256:6972483d52fd89ef062a5a03f9c4e3095dbc3fe46dd1dfd3864958b682517fda

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=sha256:14c95b7ba9e447d27b9c3c737ffc36c2ee2f4a0d3c3fa33c60ce59474a7c580d

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=sha256:1ff839b24da143e36932d9132f4dd611ca9147349799aab0913fda3a9c37d5dd

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=sha256:1b6615394e9e554886b98127bc982fc40669d423afb03f01e54a258091098195

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:b4272fbb122a0c6be466e2f4b310a1f85bd7a7aaf5e2e0b64edd2a763d6146ae

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=sha256:09930fdbb2dfab623664cda6427cef29757206ae01c0f71470c122485faff39d

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-reported events for the cited work

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source=arxiv_source observed=2026-08-04T15:40:52.373463Z digest=sha256:accdb9d859da586e2d42672e24b04f03763b23d8af327c55ca0b8a5b5da3103b

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:e37fab7e020ac8a03916f42b2927f5fa987500b5ef8f4663ffe422cefc9e882c

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=sha256:59a6a410e003967dd13bfa5c106cfc0366be3d71b179d705d81b0977cdf1952b

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:ece9d2dbe21a4373d70109f94a08b07b41a17acf2035c37d8a84e819e8f007cc

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=sha256:946903f7496d6a9857e7ef5074cd90c46f4155ff8c2c615cb7d714a51a4e6be4

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-reported events for the cited work

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source=arxiv_source observed=2026-08-04T15:40:53.216584Z digest=sha256:d40f86c707fc4d10facec343e52dadedfcedf3ef0470b3bb72be013e7a6074b0

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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no resolver link, observed 2026-08-04T15:40:53.356291Z

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source=arxiv_source observed=2026-08-04T15:40:53.356291Z digest=sha256:a8efc70e38795b157e8201ccf5b8ff8da77c3cafd0b9dfdeb2c5757fd215f5a2

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=sha256:73a1a4f62a0acaa773f8a976d81174d83cb2f4d0ed6249870b324cae7fc0741a

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 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-08-04T15:40:53.633149Z digest=sha256:c479c85a0ffe84d14f47b672aa647537a2eac98461fa6ae504b2c0ab841e362b

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:ac6a1493e5b04e62c4464b3bc1cf5f634407e70de846cc17efb6bc731b17b89b

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=sha256:27a0d79282ebae342e68504cdbb5eb31a1b7db3466effde85dd139011be400a7

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=sha256:401b949937559d1eafcb11cec55f4ad65b4c2ccaae5ca67aab1613620e608528

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:bcef1d118f2759d3568b9fd1a181bdcb9487f493b9a4d3d58a8bc38ee7e4ffba

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 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-16T02:28:37.490958Z digest=sha256:799a1503fe82ea80f9cf97a72737a7b2da6f36e909f07f9550bb9cffd5c12146

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 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-27T06:39:34.199607Z digest=sha256:b9e01e08bf12b51ce3c1b0068787f21af7993e5f1bc9e0b51382bb4c539b9897

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=sha256:027c7b80dd0276015af126c949e64c91a5672449785c1e88c79382fc6be0aeef

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=sha256:5bbe80d25acc95f2992c2d7539d0c36344469b8aff00c3abaa259dd2430193aa