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Paper Citation Record · LEDGER

Generate rather than Retrieve: Large Language Models are Strong Context Generators

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2209.10063.

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

pith.paper-citation-record.v1
2209.10063 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:59:00.849494Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T05:13:57.239839Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
  • unresolved0
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9cc21ba1-72e4-4849-8a7d-679dc257b467 · inbound

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection cites this paper.

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 74

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T14:15:11.274846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-12T14:15:10.907921Z digest=sha256:9739a9bc66c9532f245d6d19f2136bf0477a99da7e86dffd9b7d00148389b5e0

Observation c9ad5a87-067e-4690-acfe-8d07c9fca10e · inbound

Retrieval-Augmented Generation for Large Language Models: A Survey cites this paper.

Retrieval-Augmented Generation for Large Language Models: A Survey Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-24T05:13:57.242828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-24T05:10:25.171044Z digest=sha256:1e40f211279bae742f84c825cefbd4bdd547d5f65dc53533511fde42691b95aa

Observation a5e272bc-d12c-44fe-86be-02ca228c7966 · inbound

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval cites this paper.

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-05-15T13:07:16.353149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-15T13:07:16.151160Z digest=sha256:2ac10d0b97574bd78e2f2ab1a8d290e5dde21015add0a7b6b545236625fd07a2

Observation 172d6671-44b9-4bb0-a841-06d1e3af608a · inbound

Retrieval-Augmented Generation for AI-Generated Content: A Survey cites this paper.

Retrieval-Augmented Generation for AI-Generated Content: A Survey Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 163

Resolution
verified exact
arxiv_id, observed 2026-05-15T13:32:17.429797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-15T13:32:17.177021Z digest=sha256:a067f8eca9657ac6d8d8c189f694771aa560d8ecf3d50d6316413253e717c0aa

Observation 90a79ec7-9d47-4214-a59f-856d2e777e6a · inbound

SiReRAG: Indexing Similar and Related Information for Multihop Reasoning cites this paper.

SiReRAG: Indexing Similar and Related Information for Multihop Reasoning Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-11T19:59:00.849494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T19:59:00.849494Z digest=sha256:10a5b82efda0d4475dbe4bb60e144b836c9cd8ed9efcf2a7c05a52b0f0b43df3

Observation 4ac0c563-7a90-4fa6-9658-e0a15824020e · inbound

Context Filtering with Reward Modeling in Question Answering cites this paper.

Context Filtering with Reward Modeling in Question Answering Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-11T14:44:14.176433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:44:14.176433Z digest=sha256:0e52bb0de45c7586daf7aaa83716e7c3429f7db572de42f383630224b861c017

Observation 3516d4b7-cf86-471d-a203-94cfa38c45d8 · inbound

EvoWiki: Evaluating LLMs on Evolving Knowledge cites this paper.

EvoWiki: Evaluating LLMs on Evolving Knowledge Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T13:04:20.254547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:04:20.254547Z digest=sha256:c4d4ba513e656e1d93153c954ffc1e9a1fdefed5d863d10171fc24b1bc6a23f7

Observation 6a8ab6c4-6003-4ef2-a69e-49505e36ac37 · inbound

A Survey on Large Language Models with some Insights on their Capabilities and Limitations cites this paper.

A Survey on Large Language Models with some Insights on their Capabilities and Limitations Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 238

Resolution
unresolved
no resolver link, observed 2026-08-10T22:17:56.330121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:17:56.330121Z digest=sha256:3ee9d28ffb12172c60c4d427890f625225ebf26f4a9733bdfa7f04db702fcdb1

Observation f7c91e87-fe15-43d3-a5fe-a8501c5118b5 · inbound

FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs cites this paper.

FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-10T19:35:35.442151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:35:35.442151Z digest=sha256:f03bf42cdf1d2fe7cc30f6a672e257e5a22d049c75044a2fe195e8f80dbfa695

Observation 7b94ef69-e4dc-4c39-abd9-6501a80aad68 · inbound

Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge cites this paper.

Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-10T15:58:24.903641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:58:24.903641Z digest=sha256:55955daaed79f7db5c8a82df2187545dd98c6b1a5bfc321bf27b954d17b4a9eb

Observation 2d7ded58-96ce-4f0c-b1bb-40d438c96301 · inbound

Fake News Detection After LLM Laundering: Measurement and Explanation cites this paper.

Fake News Detection After LLM Laundering: Measurement and Explanation Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-10T04:36:03.762922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T04:36:03.762922Z digest=sha256:ad2027ca66909acf4adffb93aae48fd487b4c0bb707c3be36b1b0ccec1495fd0

Observation 43719403-031d-45a7-b483-16f867465c6e · inbound

DeepRAG: Thinking to Retrieve Step by Step for Large Language Models cites this paper.

DeepRAG: Thinking to Retrieve Step by Step for Large Language Models Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-09T16:31:17.754529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T16:31:17.754529Z digest=sha256:d3dc80fbf20375132731d1acfefce60c0aeffea0ec6204c728e222110f3306dd

Observation 75369b63-5a28-4e5d-8b6a-a435089e70cf · inbound

ZeroSearch: Incentivize the Search Capability of LLMs without Searching cites this paper.

ZeroSearch: Incentivize the Search Capability of LLMs without Searching Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-17T17:44:13.432787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-17T17:44:13.310155Z digest=sha256:445ef046a699056920511fe96febf2546733429e2067d03fe80fe6eb9f7c2068

Observation e4a03570-0332-4bb7-b95e-65484bc90b04 · inbound

ZeroSearch: Incentivize the Search Capability of LLMs without Searching cites this paper.

ZeroSearch: Incentivize the Search Capability of LLMs without Searching Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-22T16:06:46.013992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-22T16:05:04.715678Z digest=sha256:c24ea4b8103605f03dbe20ce3a6765840bfed9c125ddfb0a06a08279849c88e5

Observation 58b4c4e0-f525-4b39-a15a-1f80447ad69c · inbound

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis cites this paper.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T14:30:56.152073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:30:56.152073Z digest=sha256:5050903db58a4b349df612247dc5b51674d304a1126128d3c219bce47bbc0f58

Observation 45b274f0-df37-4269-8f61-47d2e6d1856e · inbound

Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models cites this paper.

Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 83

Resolution
unresolved
no resolver link, observed 2026-08-06T21:38:16.791784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:38:16.791784Z digest=sha256:9a84b9b822bf6bcdd095c8cbfbb96f57cd6a4596e046752d39272f8f122cdec8

Observation 2c0adfdb-3b7c-43c4-95bc-99c1396f43c1 · inbound

Generative Recommendation with Semantic IDs: A Practitioner's Handbook cites this paper.

Generative Recommendation with Semantic IDs: A Practitioner's Handbook Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-06T12:01:13.393174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:01:13.393174Z digest=sha256:41cbc492a8d9cb35d49ff0ed21eaa250a549a0862acbcae370b6d6cfd0f27a80

Observation 6116a972-a279-43a7-a276-feb53d4d646d · inbound

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting cites this paper.

QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 120

Resolution
unresolved
no resolver link, observed 2026-08-05T17:39:28.589597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:39:28.589597Z digest=sha256:e55b36f8c9c4cc02d3e2319737a2a97fd505e71f51b6f66818324234be231b42

Observation 47abec7d-6c94-45f0-9274-fff2a78206eb · inbound

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models cites this paper.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-05T17:15:15.391228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:15:15.391228Z digest=sha256:49f4adb660fb18ba83dff334b762378ece12f6074edfa881d6722c9966b93906

Observation d3cd0605-b312-42df-aeda-62bcae5577aa · inbound

MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning cites this paper.

MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:00:34.565330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-18T00:57:25.902674Z digest=sha256:997472c3d4a7235f51a69bf6f1b3cfaae233e0bf26e1225c433267be2f0387bd

Observation f270c882-97cb-4d7c-82d0-a34cfcdf9a3d · inbound

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation cites this paper.

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 37

Resolution
unresolved
no resolver link, observed 2026-07-13T19:45:45.674967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:45:45.674967Z digest=sha256:80115df50bea008d2e9a67d64c469b6f27ca6184bf5a21e8b2fc34590d88eee2

Observation 7198c262-47a3-4e63-8e62-668c929dd86a · inbound

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts cites this paper.

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 95

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:15:59.063723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T17:42:31.465077Z digest=sha256:6dd99be71a67972384991c28e4b41181f3b015ba5fe233acd42bb72c4a9ba9fe

Observation 4ef20845-7303-4fc7-b64f-855a71505425 · inbound

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation cites this paper.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-02T14:42:46.446916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:42:46.446916Z digest=sha256:c243dfdb0ff9440393dea3083fdf98133ee2a57a8b21fbfaff8396cf9b469570