Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 5 inbound Pith citation observations for arXiv:2506.10408.

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

pith.paper-citation-record.v1
2506.10408 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

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measured 47 of 47 standing notices

One-hop event checks from named stored sources.

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measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:50:47.142987Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T08:25:33.102243Z

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation d1bccbce-ea6f-4987-8bad-17cd24fbe6d4 · outbound

This paper cites Self-rag: Learning to retrieve, generate, and critique through self- reflection,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Self-rag: Learning to retrieve, generate, and critique through self- reflection,

Reference 1

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Observation 965de259-5abd-4d7a-8df5-653ab16b6e11 · outbound

This paper cites Function call- ing and other api updates, June.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Function call- ing and other api updates, June

Reference 5

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Observation 4434c6c7-f304-4468-b2e3-190b817aa898 · outbound

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

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 6

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Observation 731a7a50-8c6e-432b-bf77-b4c1dd6a20f5 · outbound

This paper cites Modular rag: Transforming rag systems into lego-like reconfigurable frameworks,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Modular rag: Transforming rag systems into lego-like reconfigurable frameworks,

Reference 7

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Observation 33d92bdb-47a6-4422-8c3f-0075a16cbae7 · outbound

This paper cites Mcts-rag: En- hancing retrieval-augmented generation with monte carlo tree search,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Mcts-rag: En- hancing retrieval-augmented generation with monte carlo tree search,

Reference 8

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Observation c8bf8266-c3bf-4944-b32e-00c6a271c1f9 · outbound

This paper cites A survey on hallucination in large language models: Principles, tax- onomy, challenges, and open questions.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges A survey on hallucination in large language models: Principles, tax- onomy, challenges, and open questions

Reference 9

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Observation cc640923-1db2-4118-af75-7a7bd828d1c0 · outbound

This paper cites Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:12.395031Z

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Observation 32bbb949-cb99-4606-9406-2ea78d1fd460 · outbound

This paper cites Deepretrieval: Hacking real search engines and retriev- ers with large language models via reinforcement learning,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Deepretrieval: Hacking real search engines and retriev- ers with large language models via reinforcement learning,

Reference 11

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Observation 702403c4-b669-4c95-9806-47b58b5f7ea7 · outbound

This paper cites Search-r1: Training llms to reason and leverage search engines with reinforcement learning,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Search-r1: Training llms to reason and leverage search engines with reinforcement learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:12.044117Z

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Observation 9b488c47-05f8-4184-b473-afb94c42c7b6 · outbound

This paper cites Tptu- v2: Boosting task planning and tool usage of large language model-based agents in real-world industry systems.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Tptu- v2: Boosting task planning and tool usage of large language model-based agents in real-world industry systems

Reference 13

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Observation 0b184179-ada3-4000-95dc-96d89c4e79fb · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:11.706947Z

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Observation 5e22f85d-2f1f-4d78-82c7-350e7522f634 · outbound

This paper cites From System 1 to System 2: A Survey of Reasoning Large Language Models.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges From System 1 to System 2: A Survey of Reasoning Large Language Models

Reference 16

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Observation 89dffebc-c6b6-41c9-9285-b5e962eb9880 · outbound

This paper cites Query rewrit- ing in retrieval-augmented large language models.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Query rewrit- ing in retrieval-augmented large language models

Reference 17

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Observation ff225eb9-6fda-40fb-8efc-6da852dc8b6a · outbound

This paper cites Multi-modal Retrieval Augmented Multi-modal Generation: Datasets, Evaluation Metrics and Strong Baselines.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Multi-modal Retrieval Augmented Multi-modal Generation: Datasets, Evaluation Metrics and Strong Baselines

Reference 18

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This paper cites Openai o1 system card,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Openai o1 system card,

Reference 19

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Observation adc7ec38-2afc-4f54-aece-a7f061507358 · outbound

This paper cites Deep research system card, Febru- ary.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Deep research system card, Febru- ary

Reference 20

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Observation c2c00811-b776-4b89-aedc-d3899a56eb49 · outbound

This paper cites Measuring and narrowing the compositionality gap in language models,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Measuring and narrowing the compositionality gap in language models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:11.290536Z

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Observation 0d66e4a9-790e-4b44-b96c-19534c876fe8 · outbound

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

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Agentic Retrieval-Augmented Generation for Time Series Analysis

Reference 22

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Observation 6b143e2d-5b25-4878-9d77-4328e665d740 · outbound

This paper cites The troubling emergence of hallucination in large lan- guage models-an extensive definition, quantification, and prescriptive remediations.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges The troubling emergence of hallucination in large lan- guage models-an extensive definition, quantification, and prescriptive remediations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:11.142070Z

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Observation 783285ad-0144-499c-abf2-e2801ae0ad85 · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges The probabilistic relevance framework: Bm25 and beyond

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:10.990475Z

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Observation 7dfbb8d4-207e-4a28-97fa-4ee74c964c49 · outbound

This paper cites Rap- tor: Recursive abstractive processing for tree-organized retrieval,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Rap- tor: Recursive abstractive processing for tree-organized retrieval,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:10.679538Z

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Observation 74ad154f-322a-4c4e-a4f5-b252abd6fc67 · outbound

This paper cites Proximal policy optimization algorithms,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Proximal policy optimization algorithms,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:10.564137Z

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Observation 0d7ad2a1-62eb-421a-88a0-c95742fc3f51 · outbound

This paper cites Exploring language models: A comprehensive survey and analysis.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Exploring language models: A comprehensive survey and analysis

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:10.299726Z

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Observation 9a96dfb4-427b-4e30-a7a2-bfebb277ccc4 · outbound

This paper cites R1- searcher: Incentivizing the search capability in llms via reinforcement learning,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges R1- searcher: Incentivizing the search capability in llms via reinforcement learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:10.168247Z

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Observation 5a003428-e9fe-4dde-9334-7c1ee9f72528 · outbound

This paper cites Retrieval-Augmented Generation with Conflicting Evidence.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Retrieval-Augmented Generation with Conflicting Evidence

Reference 31

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Observation 1a9666ea-62ce-4c43-9515-48e46c161cf9 · outbound

This paper cites Chain- of-thought prompting elicits reasoning in large language models,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Chain- of-thought prompting elicits reasoning in large language models,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:10.013791Z

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Observation 88d1c67e-3838-4eb2-b38a-d779a6111b70 · outbound

This paper cites Corrective retrieval augmented generation,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Corrective retrieval augmented generation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:09.832197Z

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This paper cites LLM2: Let Large Language Models Harness System 2 Reasoning.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges LLM2: Let Large Language Models Harness System 2 Reasoning

Reference 34

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This paper cites React: Synergizing reasoning and acting in language models,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges React: Synergizing reasoning and acting in language models,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:09.696351Z

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Observation f8f6b3b5-16ed-4479-89af-dfb1be378405 · outbound

This paper cites VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents

Reference 36

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This paper cites MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation

Reference 37

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Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 38

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Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges A Survey of Large Language Models

Reference 39

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

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Retrieval-Augmented Generation for AI-Generated Content: A Survey

Reference 40

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This paper cites Deep- researcher: Scaling deep research via reinforcement learn- ing in real-world environments,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Deep- researcher: Scaling deep research via reinforcement learn- ing in real-world environments,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:09.547572Z

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This paper cites Are Large Language Models Good Statisticians?.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Are Large Language Models Good Statisticians?

Reference 42

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Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Tptu: Task planning and tool usage of large language model- based ai agents

Reference 2009

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Observation 568d48fe-d811-4273-8754-bcdc84e15bf5 · outbound

This paper cites Deepseekmath: Pushing the limits of mathematical rea- soning in open language models,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Deepseekmath: Pushing the limits of mathematical rea- soning in open language models,

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:10.429595Z

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Observation 2a9f8471-bff2-4b7b-a1e3-cf9be5a7eeb9 · outbound

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

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases

Reference 2020

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This paper cites Bench- marking large language models in retrieval-augmented gen- eration.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Bench- marking large language models in retrieval-augmented gen- eration

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:13.888091Z

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Observation e6bc1334-12c7-4c11-9834-73559fcf97c2 · outbound

This paper cites Rezero: En- hancing llm search ability by trying one-more-time,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Rezero: En- hancing llm search ability by trying one-more-time,

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:30:13.720771Z

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Observation dcc3b8f5-1b2d-446f-aaef-792ad7d6556e · outbound

This paper cites Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,.

Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,

Reference 2025

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Pith citing papers

Observation 94ca7c1f-0236-42ef-a232-656251d590ff · inbound

Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation cites this paper.

Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges

Reference 48

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Memory in the Age of AI Agents cites this paper.

Memory in the Age of AI Agents Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges

Reference 56

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Observation b1d0bf17-1dca-416a-88e6-e7307b70faef · inbound

Towards Trustworthy Report Generation: A Deep Research Agent with Progressive Confidence Estimation and Calibration cites this paper.

Towards Trustworthy Report Generation: A Deep Research Agent with Progressive Confidence Estimation and Calibration Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c1b95d17-f709-4531-91ff-5e914621071c · inbound

A Survey of Reasoning-Intensive Retrieval: Progress and Challenges cites this paper.

A Survey of Reasoning-Intensive Retrieval: Progress and Challenges Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:56:05.343140Z

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Observation 53803730-837a-4636-bbb1-41cbf0783499 · inbound

TransResAI: A Compound AI System for Coastal Transportation Resilience cites this paper.

TransResAI: A Compound AI System for Coastal Transportation Resilience Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T08:25:33.104571Z

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