RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings | Paper Citation Record

Observation acf7fa1e-c8e4-400f-b88f-8279c6205a37 · outbound

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

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:41.071078Z

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Observation a21e70d9-933f-424e-b515-c7382f275b80 · outbound

This paper cites Leveraging passage retrieval with generative models for open domain question answering.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Leveraging passage retrieval with generative models for open domain question answering

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:55.839994Z

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Observation 87ac33ba-124e-4e02-8871-15d8bcde5cc6 · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 3

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Observation d7359a9d-2658-4df6-b872-2f582cbabe7c · outbound

This paper cites Learning to Plan and Generate Text with Citations.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Learning to Plan and Generate Text with Citations

Reference 4

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Observation 6772fa55-f515-439a-81d6-439e60130188 · outbound

This paper cites Kanell, Peter Xu, Omar Khattab, and Monica S.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Kanell, Peter Xu, Omar Khattab, and Monica S

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:55.594703Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 549bfc47-4e87-41f6-bca6-b9895b5ebba1 · outbound

This paper cites REPLUG: Retrieval-Augmented Black-Box Language Models.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings REPLUG: Retrieval-Augmented Black-Box Language Models

Reference 6

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Observation dc284e69-4de1-44f4-ab57-4db3c1945448 · outbound

This paper cites Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:24:49.230186Z

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Observation 80fc8168-3c8c-4cbe-b67b-7cefedf556eb · outbound

This paper cites Mindmap: Knowledge graph prompting sparks graph of thoughts in large language models.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Mindmap: Knowledge graph prompting sparks graph of thoughts in large language models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:55.350509Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c565aa9e-adca-487c-b8b2-4f9cf7aafae8 · outbound

This paper cites Mirage: Scaling test-time inference with parallel graph-retrieval-augmented reasoning chains.arXiv preprint arXiv:2508.18260, 2025.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Mirage: Scaling test-time inference with parallel graph-retrieval-augmented reasoning chains.arXiv preprint arXiv:2508.18260, 2025

Reference 9

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Observation 4db9dcbb-5a6f-4744-9a01-fd4a17f71f1c · outbound

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

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 10

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Observation 7f0b42dc-2453-4a39-bebd-5f2a5cb8d4a6 · outbound

This paper cites Factscore: Fine-grained atomic evaluation of factual precision in long form text generation.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Factscore: Fine-grained atomic evaluation of factual precision in long form text generation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:55.070115Z

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Observation 408b7ade-6784-44a5-8906-a4a0a312a886 · outbound

This paper cites Alignscore: Evaluating factual consistency with a unified alignment function.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Alignscore: Evaluating factual consistency with a unified alignment function

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:54.802054Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 73761409-4cb5-45d5-839d-718e3909c6f6 · outbound

This paper cites SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:42.457819Z

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Observation 80c48667-fede-430e-a11c-acf8e55e85ad · outbound

This paper cites Wayback machine.https://archive.org/web/, 2024.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Wayback machine.https://archive.org/web/, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:54.582040Z

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Observation 1a5c1f30-4d34-4948-ad52-b017fb493b88 · outbound

This paper cites Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:42.723650Z

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Observation 0beca5af-fdc1-4329-bddc-4cc46d56a768 · outbound

This paper cites Mistral 7B.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Mistral 7B

Reference 16

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Observation c506a304-2915-482d-848e-ccb683c615f9 · outbound

This paper cites The Llama 3 Herd of Models.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings The Llama 3 Herd of Models

Reference 17

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Observation fe208088-2df4-400f-b40f-0be6d397a059 · outbound

This paper cites Qwen2.5 Technical Report.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Qwen2.5 Technical Report

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:43.059118Z

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Observation 90940bd7-5fe3-4c9a-87dd-7d672c64ad76 · outbound

This paper cites Xing, Ion Stoica, and Joseph E.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Xing, Ion Stoica, and Joseph E

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:54.335040Z

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Observation 60930da3-cdeb-426e-a8fa-90a3a1b5c86f · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond.Foundations and Trends in Information Retrieval, 2009.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings The probabilistic relevance framework: Bm25 and beyond.Foundations and Trends in Information Retrieval, 2009

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:54.120639Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0e26f3b-9095-46e8-b2bd-ba346a03eb60 · outbound

This paper cites Leveraging Large Language Models in Code Question Answering: Baselines and Issues.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Leveraging Large Language Models in Code Question Answering: Baselines and Issues

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:24:48.434703Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9399b5f3-dfcc-4967-9068-8defbb6faa01 · outbound

This paper cites Reliable, Adaptable, and Attributable Language Models with Retrieval.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Reliable, Adaptable, and Attributable Language Models with Retrieval

Reference 22

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Observation abb0babd-da61-4da0-a22d-fa2bf1530f66 · outbound

This paper cites Longformer: The Long-Document Transformer.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Longformer: The Long-Document Transformer

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:43.679174Z

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Observation 57e32bde-4fb9-4ff8-ab20-ceaa24f58f89 · outbound

This paper cites Unlimiformer: Long-Range Transformers with Unlimited Length Input.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Unlimiformer: Long-Range Transformers with Unlimited Length Input

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:43.820633Z

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Observation c8b3f963-bed9-4ab4-a1af-70a4063b1106 · outbound

This paper cites GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T00:24:47.989266Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e4472114-c5f6-40f8-85f8-50fcbccd9f12 · outbound

This paper cites Attributed question answering: Evaluation and modeling for attributed large language models.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Attributed question answering: Evaluation and modeling for attributed large language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:53.943596Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fd111d2c-c7a5-4fda-86c5-39e1b432f36c · outbound

This paper cites Booookscore: A systematic exploration of book-length summarization in the era of llms.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Booookscore: A systematic exploration of book-length summarization in the era of llms

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:53.723852Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1fb17bb4-5e54-46bd-941a-15773d26da87 · outbound

This paper cites Extending Context Window of Large Language Models via Positional Interpolation.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Extending Context Window of Large Language Models via Positional Interpolation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:44.314513Z

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Observation 8893493d-b293-419b-821c-1f099d6c755e · outbound

This paper cites LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:44.407443Z

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Observation c65c9d88-c735-4362-b2ec-a74d67b734ff · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Generating Long Sequences with Sparse Transformers

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:44.535188Z

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Observation d31371a8-fa54-4a91-8392-a90db4305495 · outbound

This paper cites A discourse-aware attention model for abstractive summarization of long documents.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings A discourse-aware attention model for abstractive summarization of long documents

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:53.486865Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a35bd917-19b4-490f-8dd8-bc422acfaa47 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:44.823401Z

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Observation 99514397-1228-45a7-8087-25b4f90930c4 · outbound

This paper cites Enabling large language models to generate text with citations.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Enabling large language models to generate text with citations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:53.058484Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation efc4ac4c-a0a3-4063-b6d5-d3236dc5b105 · outbound

This paper cites Memsum: Extractive summarization of long documents using multi-step episodic markov decision processes.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Memsum: Extractive summarization of long documents using multi-step episodic markov decision processes

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:52.805583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 528771f1-14c5-4250-b603-bb7c300897a2 · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:45.263115Z

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Observation 1cdc5a89-6a35-4efe-88cc-206a60fedbd0 · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.ICML, 2020.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Transformers are rnns: Fast autoregressive transformers with linear attention.ICML, 2020

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:52.575285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a1792c7-677b-4fa6-b620-9b0d88cbe784 · outbound

This paper cites Booksum: A collection of datasets for long-form narrative summarization.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Booksum: A collection of datasets for long-form narrative summarization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:52.257913Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 47cae494-6a96-47a4-a6c8-c94a54562870 · outbound

This paper cites Long-context LLMs Struggle with Long In-context Learning.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Long-context LLMs Struggle with Long In-context Learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:45.625119Z

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Observation 9a890a9c-3bb1-42cf-8464-dcdfd628f637 · outbound

This paper cites Rouge: A package for automatic evaluation of summaries.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Rouge: A package for automatic evaluation of summaries

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:52.034753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 296625d5-9ba1-4f6a-8758-19201c8d5357 · outbound

This paper cites Text summarization with pretrained encoders.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Text summarization with pretrained encoders

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:51.677378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f886263c-cc2d-4882-b347-6f0bef096da9 · outbound

This paper cites Zeroscrolls: A zero-shot benchmark for long text understanding.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Zeroscrolls: A zero-shot benchmark for long text understanding

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:51.403497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 840a3813-1770-4139-8864-479e43a778b2 · outbound

This paper cites Extractive is not faithful: An investigation of broad unfaithfulness problems in extractive summarization.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Extractive is not faithful: An investigation of broad unfaithfulness problems in extractive summarization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:51.215435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94bf3325-3f9b-4b56-802d-c1811a63bba4 · outbound

This paper cites Benchmarking foundation models with language- model-as-an-examiner.Advances in Neural Information Processing Systems, 2023.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Benchmarking foundation models with language- model-as-an-examiner.Advances in Neural Information Processing Systems, 2023

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:50.954752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 718a4ffc-6eed-4416-ada0-1ad21d066cc0 · outbound

This paper cites LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T00:24:46.403756Z

Source-reported events for the cited work

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Observation 40ae6733-2466-43e8-a293-8cc984889b77 · outbound

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RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-06T00:24:50.751564Z

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Observation c05c0935-64c8-4bb7-9b0b-5235162bd012 · outbound

This paper cites an unresolved cited work.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-06T00:24:50.470660Z

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Observation 1168bac8-e447-4966-86bd-638849fc85fb · outbound

This paper cites section_title.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings section_title

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:50.104823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation afec0b05-988d-45e4-9aac-44719b6675a9 · outbound

This paper cites It explains how traditional education ne- glects these topics and how they can be learned through experiential learning and CASHFLOW games.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings It explains how traditional education ne- glects these topics and how they can be learned through experiential learning and CASHFLOW games

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:24:49.877329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 12a93edd-70e5-43dc-8cf6-43089413df79 · outbound

This paper cites It explains how giving first fosters reciprocity and how creating income involves making money work for you, not working for money.

RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings It explains how giving first fosters reciprocity and how creating income involves making money work for you, not working for money

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T00:24:49.579082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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