Paper Citation Record · LEDGER

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation

As of 16 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2509.04820.

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

pith.paper-citation-record.v1
2509.04820 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:30:36.919297Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy4
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4fbcfb42-53e3-4172-aac3-e292366d7b4f · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.734792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48412180-b15a-4344-b498-059962a9d84f · outbound

This paper cites DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.849031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08b143a1-89dd-4d9a-97b7-8654b245872d · outbound

This paper cites Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.859240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae7d1827-d86b-4377-a424-0fa299bbd59e · outbound

This paper cites Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.863963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44a60c0b-eec8-4180-9637-3a8565b3f958 · outbound

This paper cites InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5303–5315.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5303–5315

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:30:37.199919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a1376a10-658a-46ae-8e79-ee179a816022 · outbound

This paper cites Measuring and Narrowing the Compositionality Gap in Language Models.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Measuring and Narrowing the Compositionality Gap in Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.873506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ed24a9a-ff49-435e-9c2b-3bfff8d1c988 · outbound

This paper cites Agentic Retrieval-Augmented Generation for Time Series Analysis.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Agentic Retrieval-Augmented Generation for Time Series Analysis

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.877861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d6d30de-f5d6-4f14-9029-d6beeaf80406 · outbound

This paper cites InNeurIPS 2023 Foundation Models for Decision Making Workshop.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation InNeurIPS 2023 Foundation Models for Decision Making Workshop

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:30:37.185647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9a0b2383-2388-442b-aeb1-0e74c11f16c1 · outbound

This paper cites Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.887177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b32bdfc-2478-4ebd-9107-fe6b64929f11 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation ReAct: Synergizing Reasoning and Acting in Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.891905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d54f37d-abb5-4afc-9d40-396cf4ad6d62 · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.896374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecd50149-ece8-421e-be5e-383028e1932c · outbound

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

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Retrieval-Augmented Generation for AI-Generated Content: A Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.901061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 465ac0f5-8001-4c2b-957c-032f4132df50 · outbound

This paper cites A Survey of Large Language Models.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation A Survey of Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.905704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab969a23-dc6e-4d89-aa2d-f1aadbbe190e · outbound

This paper cites DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.910064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 246ce947-778e-4509-8435-57e11d8d7785 · outbound

This paper cites Are Large Language Models Good Statisticians?.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Are Large Language Models Good Statisticians?

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.914455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 678ebebf-41db-40a8-9bd4-d76f740db04e · outbound

This paper cites Resulting Error:Without the correct chunk, the model provided an incorrect answer based on incomplete information.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Resulting Error:Without the correct chunk, the model provided an incorrect answer based on incomplete information

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:30:37.171099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9bcce76d-4f05-46c1-9ae3-d8a0547200a8 · outbound

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

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.729853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae97a162-eb09-446d-906a-adc1b759571a · outbound

This paper cites InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 371–385.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 371–385

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:30:37.214634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59a65385-e55d-4242-8edf-f9df31401340 · outbound

This paper cites ReZero: Enhancing LLM search ability by trying one-more-time.

Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation ReZero: Enhancing LLM search ability by trying one-more-time

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-15T16:30:36.724744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.