Paper Citation Record · LEDGER

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

As of 9 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 10 inbound Pith citation observations for arXiv:2506.20963.

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

pith.paper-citation-record.v1
2506.20963 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:44:04.946360Z

measured 67 of 67 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T07:06:05.559182Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:28:55.951900Z

Reference resolution

57 of 57 outbound references displayed

verified exact1
verified fuzzy13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c0224478-3920-4f09-86c8-c4aaf1388ffa · outbound

This paper cites GPT-4 Technical Report.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora GPT-4 Technical Report

Reference 1

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Observation 69c73ba9-5abe-40b8-b56b-3b45a1f6b082 · outbound

This paper cites Qwen2.5 Technical Report.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Qwen2.5 Technical Report

Reference 2

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Observation 9f4a143b-b385-4478-9f9b-0906f8b179b6 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora LLaMA: Open and Efficient Foundation Language Models

Reference 3

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Observation 624f3545-e491-4725-bae5-bb42b485789a · outbound

This paper cites A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods

Reference 4

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Observation 10fd2167-574a-4693-9d25-85d3c2451f45 · outbound

This paper cites Llm-based code generation method for golang compiler testing.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Llm-based code generation method for golang compiler testing

Reference 5

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Observation 33d12c52-6780-4edc-ba04-77da5e147745 · outbound

This paper cites LLM-SR: Scientific Equation Discovery via Programming with Large Language Models.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

Reference 6

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Observation 52a04137-34b6-49e8-9179-0ff68e0e35ff · outbound

This paper cites A Survey of Graph Meets Large Language Model: Progress and Future Directions.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora A Survey of Graph Meets Large Language Model: Progress and Future Directions

Reference 7

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Observation 48ab8693-af50-4e92-9c7d-3947e5ddfa38 · outbound

This paper cites Beyond one-model-fits-all: A survey of domain specialization for large language models.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Beyond one-model-fits-all: A survey of domain specialization for large language models

Reference 8

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Observation 706a406d-970d-43a5-bb4c-0e02af7bab3f · outbound

This paper cites Openagi: When llm meets domain experts.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Openagi: When llm meets domain experts

Reference 9

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Observation 598ae65b-1105-4b92-b7cf-676e5de2e8c0 · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 10

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Observation 71f0ecf2-3ae1-4e8d-b65b-27acc177a4c6 · outbound

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

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

Reference 11

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Observation b941c1e2-2f5d-4dd5-a024-b6b17658da2c · outbound

This paper cites LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples

Reference 12

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Observation 29f86ffd-cb2c-43b0-badf-3f835637a52e · outbound

This paper cites When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Reference 13

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This paper cites A Closer Look at the Limitations of Instruction Tuning.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora A Closer Look at the Limitations of Instruction Tuning

Reference 14

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This paper cites Getting it right: the limits of fine-tuning large language models.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Getting it right: the limits of fine-tuning large language models

Reference 15

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Observation db4b8f53-7063-4a2e-98f3-b0362549feba · outbound

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

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 16

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Observation 07e15a44-8a4a-4258-b1b7-2aa3a3540519 · outbound

This paper cites A survey on rag meeting llms: Towards retrieval-augmented large language models.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora A survey on rag meeting llms: Towards retrieval-augmented large language models

Reference 17

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This paper cites Retrieval-Augmented Generation for Natural Language Processing: A Survey.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Retrieval-Augmented Generation for Natural Language Processing: A Survey

Reference 18

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Observation ea971c73-1678-47c7-9045-11e218c9c493 · outbound

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

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Retrieval-Augmented Generation for AI-Generated Content: A Survey

Reference 20

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Trustworthiness in Retrieval-Augmented Generation Systems: A Survey

Reference 21

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Observation 891f6ee1-e69b-45dc-8125-ca2b63f05d03 · outbound

This paper cites Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 22

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

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases

Reference 23

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems

Reference 24

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora How Much Can RAG Help the Reasoning of LLM?

Reference 25

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Retrieval-Augmented Generation with Graphs (GraphRAG)

Reference 26

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora A survey of graph retrieval-augmented generation for customized large language models

Reference 27

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora GRAG: Graph Retrieval-Augmented Generation

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Graph Retrieval-Augmented Generation: A Survey

Reference 30

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora In-depth Analysis of Graph-based RAG in a Unified Framework

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora From Local to Global: A Graph RAG Approach to Query-Focused Summarization

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Approximate nearest neighbors: Towards removing the curse of dimensionality

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Locality-sensitive hashing scheme based on p-stable distributions

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Razenshteyn, and Ludwig Schmidt

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Lightrag: Simple and fast retrieval-augmented generation, 2024

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Gslb: The graph structure learning benchmark

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora A Survey on Locality Sensitive Hashing Algorithms and their Applications

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora QuALITY: Question Answering with Long Input Texts, Yes!

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Musique: Multihop questions via single-hop question com- position

Reference 47

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Large language models are zero-shot reasoners

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval

Reference 49

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Retrieval-augmented generation for knowledge- intensive nlp tasks

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Hipporag: Neurobiologically inspired long-term memory for large language models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:44:06.374367Z

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Toolformer: Language models can teach themselves to use tools

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:44:06.207772Z

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

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EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T22:44:04.596340Z

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Observation e9b72f9b-a344-4ac7-905e-34ca5660ef2d · outbound

This paper cites A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora A General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:44:05.142608Z

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

Observation 36d79908-6ee9-405c-a7eb-99d934a96936 · outbound

This paper cites M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:44:06.059990Z

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

Observation 3bbe0b2b-07f0-4181-8883-66b9e444ae18 · outbound

This paper cites MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T22:44:04.826487Z

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Observation e42123be-4497-4b8f-9c67-76dd21d7f4c3 · outbound

This paper cites Language models are few-shot learners.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora Language models are few-shot learners

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T22:44:04.891202Z

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Observation 871d0ca4-5930-4f5f-8378-7e659668fe87 · outbound

This paper cites The Chronicles of RAG: The Retriever, the Chunk and the Generator.

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora The Chronicles of RAG: The Retriever, the Chunk and the Generator

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T22:44:04.946360Z

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Pith citing papers

Observation 0810a5c1-d984-4722-9e43-9d07423e0488 · inbound

EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagation cites this paper.

EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagation EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 102

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:02:25.166902Z

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 576f9127-bca3-4c6f-9e3e-fb5d04323ef3 · inbound

LLM-Oriented Information Retrieval: A Denoising-First Perspective cites this paper.

LLM-Oriented Information Retrieval: A Denoising-First Perspective EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 220

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:01:19.789665Z

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

Observation e8913526-603b-4e27-b7c6-96ec5fe04c2e · inbound

LLM-Oriented Information Retrieval: A Denoising-First Perspective cites this paper.

LLM-Oriented Information Retrieval: A Denoising-First Perspective EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 230

Resolution: verified exact; arxiv_id, observed 2026-05-21T00:19:16.438599Z

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

Observation 892f4d7f-2625-48ad-829a-ced658f762a0 · inbound

Position: How can Graphs Help Large Language Models? cites this paper.

Position: How can Graphs Help Large Language Models? EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:10:42.903826Z

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

Observation b4c900dc-5fbf-4985-9e11-84989e080ba7 · inbound

A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications cites this paper.

A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 144

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:20:57.538087Z

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

Observation 5fec0a0d-0925-42da-a551-71e7b0d3d483 · inbound

A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications cites this paper.

A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 146

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:19:14.823326Z

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

Observation fc88736d-4b32-4af4-92cd-937a35e6e776 · inbound

A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications cites this paper.

A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 138

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:25:07.393247Z

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

Observation 853cb3a7-c004-4f80-914d-218a2743e6bc · inbound

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation cites this paper.

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-06-28T20:42:37.984526Z

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 14aa0775-3f5b-4082-a936-3452a6ba8092 · inbound

A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation cites this paper.

A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:28:55.953568Z

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

Observation be408f80-03ee-4265-855e-57ff85ce9385 · inbound

Beyond Retrieval: Analytic Memory for Multimodal Agents cites this paper.

Beyond Retrieval: Analytic Memory for Multimodal Agents EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T07:06:05.559182Z

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