Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 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 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:30:56.152073Z

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

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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.

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.

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.

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.

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.

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 2026-08-08T06:32:00.761636+00:00.

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.

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 2026-08-08T06:32:00.761636+00:00.

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.