Paper Citation Record · LEDGER

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

As of 19 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 11 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 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:13:24.887316Z

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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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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This paper cites Trustworthiness in Retrieval-Augmented Generation Systems: A Survey.

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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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 5857c144-905d-4581-be3f-f290685d06d5 · inbound

QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation cites this paper.

QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:24.887316Z

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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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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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