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Paper Citation Record · LEDGER

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

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 34 inbound Pith citation observations for arXiv:2409.14924.

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

pith.paper-citation-record.v1
2409.14924 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 34 of 34 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T01:03:21.632740Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T07:33:13.582327Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3e22683c-34c5-4c5e-8f83-19b750fc404d · inbound

Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval cites this paper.

Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 29

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source=pdf_text observed=2026-08-11T10:29:26.683314Z digest=sha256:5571ec928289286e9b2572aa28176333cd51ef0ad7f7776bc785b632f6af0017

Observation 6693c21f-3d3a-41e2-a69e-eb024695a232 · inbound

Addressing the sustainable AI trilemma: a case study on LLM agents and RAG cites this paper.

Addressing the sustainable AI trilemma: a case study on LLM agents and RAG 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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source=pdf_text observed=2026-08-10T20:34:34.702629Z digest=sha256:37b7a28e4fe858eff0649a153c0e67279daf078833b27691a8d2aa52e83d4f37

Observation b8b8dad1-8dc5-4f52-9692-a72fae36e138 · inbound

LLM-based event log analysis techniques: A survey cites this paper.

LLM-based event log analysis techniques: A survey Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 99

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no resolver link, observed 2026-08-09T18:11:46.699831Z

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source=pdf_text observed=2026-08-09T18:11:46.699831Z digest=sha256:be3e29a3333ee5d409290100e2a7bdef3dd8654b101362fa35f98a324b7b30cc

Observation dc3ce3bd-4227-4a52-b99b-7bf3bb2d0383 · inbound

O1 Embedder: Let Retrievers Think Before Action cites this paper.

O1 Embedder: Let Retrievers Think Before Action Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 70

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source=pdf_text observed=2026-08-08T12:25:12.440105Z digest=sha256:ebd034337323fdfca9cba68ef4390bf814c9914358e2cc1df54fa499a6b8a5a9

Observation 00abbb2c-a9ee-4764-bf57-987a4e2ee918 · inbound

A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking cites this paper.

A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 39

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T01:03:21.632740Z digest=sha256:cf9a2e14917bb96819a8861984e775500712d9dfdc52f32356b227ce953ad9ee

Observation 68a37181-778a-433b-9146-c06d2937629f · inbound

SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration cites this paper.

SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 37

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source=arxiv_source observed=2026-08-16T00:56:55.836060Z digest=sha256:9064a97f8884696cc254015f58de9b100e7c4275912f9bbc561250963221ca93

Observation c669cec0-ec15-4d60-8ae9-76c9d2aa2743 · inbound

DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral cites this paper.

DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 35

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source=arxiv_source observed=2026-08-15T23:59:04.300217Z digest=sha256:4d1d9a197d6022a7c2a6b57541af54b59e02dba8c78adfffe87e6031e8332877

Observation 92b7ce05-1f14-4620-a9e9-c6e4d6827562 · inbound

Survey of Filtered Approximate Nearest Neighbor Search over the Vector-Scalar Hybrid Data cites this paper.

Survey of Filtered Approximate Nearest Neighbor Search over the Vector-Scalar Hybrid Data Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 104

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source=pdf_text observed=2026-08-15T22:45:39.735551Z digest=sha256:88822c4bef7f84df81aa8e9fdac74d54c99d5958ab9b3ed7b995efb6c28b85c5

Observation 4df75833-cb40-4c0c-aed4-e18d020f28e8 · inbound

Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents cites this paper.

Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 11

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source=pdf_text observed=2026-08-15T20:48:22.714953Z digest=sha256:69323da8bd186e2898bf9b4e1f789a350df78c079244d73ff54daee82b977254

Observation 252c035a-fe45-41e2-80c2-4547273f67ef · inbound

Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval cites this paper.

Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 52

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source=pdf_text observed=2026-08-07T15:15:48.790118Z digest=sha256:41893ef46bec7d71416ce99f1979867fedf5ad6ab2be570ef51d2cca9844cc87

Observation 452f652b-4b97-44c0-8433-23efb5b0d4a3 · inbound

Private GPTs for LLM-driven testing in software development and machine learning cites this paper.

Private GPTs for LLM-driven testing in software development and machine learning Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 2

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source=arxiv_source observed=2026-08-07T05:58:15.403271Z digest=sha256:68d2b45224d3b2361dde186dad24e9633a9ddd17689f7ca15cfdfc66043be6fd

Observation cbb81562-206d-4cae-8a9a-f1826387d5c4 · inbound

FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation cites this paper.

FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 48

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:51:21.255177Z digest=sha256:717b71562d4af84976e14bf38a2a8c4711c1f844bff5487b8b6e84813a596bd2

Observation 48056f53-88b4-4252-9b74-57141a8c9a6a · inbound

Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation cites this paper.

Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 2008

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source=pdf_text observed=2026-08-06T23:20:34.827663Z digest=sha256:c034cf6847ae56981a176b8a17045857a1f03f132fd2633615b9ed60ad599c8f

Observation 87cfb609-01c4-4750-a836-71ea6394694f · inbound

Enterprise Large Language Model Evaluation Benchmark cites this paper.

Enterprise Large Language Model Evaluation Benchmark Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 45

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no resolver link, observed 2026-08-06T22:56:33.954784Z

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source=pdf_text observed=2026-08-06T22:56:33.954784Z digest=sha256:1ee216be357cd3262eff7783a07abf78846e2d339cd176573dca2f3224f2dfcc

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

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora cites this paper.

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:44:01.343918Z digest=sha256:7dbb4ae121585e0136853cc6f26befaceda815b6a5610f325a96d45dce376e5d

Observation 6f106aec-25a4-4601-84df-ceb3c5e3c690 · inbound

BLOCKS: Blockchain-supported Cross-Silo Knowledge Sharing for Efficient LLM Services cites this paper.

BLOCKS: Blockchain-supported Cross-Silo Knowledge Sharing for Efficient LLM Services Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 2

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source=pdf_text observed=2026-08-06T22:40:38.172760Z digest=sha256:f73fd4d51791dd6b47ec481c3d33d3744f1863151662e7c86075835542aed5ac

Observation f7b1c942-96c1-4f92-8e73-3774b9388592 · inbound

Assessing RAG and HyDE on 1B vs. 4B-Parameter Gemma LLMs for Personal Assistants Integretion cites this paper.

Assessing RAG and HyDE on 1B vs. 4B-Parameter Gemma LLMs for Personal Assistants Integretion Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 10

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source=pdf_text observed=2026-08-07T04:22:14.707396Z digest=sha256:a221ac68336d7d297eaee76c5c0ea0ed093b65e9b3f48ce207c92fb4b5023a54

Observation 163dc024-5d5a-45b1-8965-bf32c46768fa · inbound

UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization cites this paper.

UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 92

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source=pdf_text observed=2026-08-06T19:45:56.964590Z digest=sha256:3d30c099728605a57007f96e970fcdb4449f7ea3179cec0cea8abf16cc78296c

Observation cc5088c0-02a8-41a4-a143-2228b5762267 · inbound

Knowledge Conceptualization Impacts RAG Efficacy cites this paper.

Knowledge Conceptualization Impacts RAG Efficacy Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 52

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source=pdf_text observed=2026-08-06T18:01:20.325769Z digest=sha256:87320730f75327f6daa209a983157230f75c14e246a2257177ad032dc5db9473

Observation 1672b298-ddfa-48c1-8d1e-88ae6677fa66 · inbound

Accelerating Prefilling via Decoding-time Contribution Sparsity cites this paper.

Accelerating Prefilling via Decoding-time Contribution Sparsity Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 17

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arxiv_id, observed 2026-05-19T03:06:59.972943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-19T03:05:34.843274Z digest=sha256:cca9a4f3e9e97ade36adf03a6dab2ef723d166baa9001ffff859766559c14965

Observation 7608af1f-383a-4b2d-abc9-25048b82d771 · inbound

Systematic Evaluation of Knowledge Graph Repair with Large Language Models cites this paper.

Systematic Evaluation of Knowledge Graph Repair with Large Language Models Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 49

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source=pdf_text observed=2026-08-06T11:49:18.929150Z digest=sha256:94e97d5face1f621975ce289294fd7b525cbafb96c30d0d2c330daa48d05c969

Observation 20bb4d8e-8b1f-45a4-b430-b8c64de4c505 · inbound

MetaAgent: Toward Self-Evolving Agent via Tool Meta-Learning cites this paper.

MetaAgent: Toward Self-Evolving Agent via Tool Meta-Learning Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 36

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no resolver link, observed 2026-08-06T10:17:13.715397Z

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source=arxiv_source observed=2026-08-06T10:17:13.715397Z digest=sha256:9233bc4123a8bc73e30a2160aa100ed3bea89a0362cda6d2ee48284241a7dfe9

Observation 820d409b-6712-44d7-831c-74df8ce6956a · inbound

Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement cites this paper.

Method-Based Reasoning for Large Language Models: Extraction, Reuse, and Continuous Improvement Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 12

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source=pdf_text observed=2026-08-06T00:48:07.524592Z digest=sha256:b1c65a88b2ff682af9af219d5eff0be596c766448ce6d1bf5b655bf253a2638e

Observation 2426b466-2af9-471d-96fc-44ec5fdbff8d · inbound

GPL-SLAM: A Laser SLAM Framework with Gaussian Process Based Extended Landmarks cites this paper.

GPL-SLAM: A Laser SLAM Framework with Gaussian Process Based Extended Landmarks Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 41

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source=pdf_text observed=2026-08-05T17:18:32.062885Z digest=sha256:8dc508cd2efcd0f7fbce839e955db2e1ad8a58589ef0cd4f872ac5b25dafb42d

Observation d51a29c2-c421-448f-8281-6e5b4431d019 · inbound

Open Data Synthesis For Deep Research cites this paper.

Open Data Synthesis For Deep Research Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 33

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source=pdf_text observed=2026-08-05T13:45:13.613918Z digest=sha256:cdaa96b9511d82f63ce0b82b197a90e623bd67dd441b60a6bb6985655b6d45cc

Observation 7bc73867-52b3-49f7-9949-918902458bdb · inbound

PG-Agent: An Agent Powered by Page Graph cites this paper.

PG-Agent: An Agent Powered by Page Graph Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 46

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no resolver link, observed 2026-08-05T15:29:45.142535Z

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source=pdf_text observed=2026-08-05T15:29:45.142535Z digest=sha256:19cfc07a537835dbf7a2ef7f96ab233ce8d822740c53b038fb66f859bac81f76

Observation a36e3f06-bd95-4907-a8ef-2a7f7df0a1fd · inbound

Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction cites this paper.

Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 41

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arxiv_id, observed 2026-05-18T19:26:47.237844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-18T19:26:35.001429Z digest=sha256:2d84d710dca137e69d6d60456c165d7aa3ac49d40e5b049387b6ba9c61953831

Observation 865aad26-c4e4-4a6b-b41e-28740144a684 · inbound

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial cites this paper.

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 269

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source=pdf_text observed=2026-08-05T04:50:32.540167Z digest=sha256:c1e20e88813d6666f1e000f4c1c21ae834eb0d32aeeb0a637d4659902716d27a

Observation 3b201037-1f8b-437f-995d-4af0f1553901 · inbound

DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent cites this paper.

DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 2024

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no resolver link, observed 2026-08-02T19:46:45.069746Z

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source=pdf_text observed=2026-08-02T19:46:45.069746Z digest=sha256:94994e41f3b787a77c1d67180c15984cc6570f64d42396eeaa6f7d264f6f11f8

Observation 90ed30bf-8d6e-483d-9703-f031ef266de1 · inbound

Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda cites this paper.

Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 47

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arxiv_id, observed 2026-05-10T06:31:30.820771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T06:27:23.580445Z digest=sha256:9cfd29c4f4c36ee28b682686bd0c2803d565a69303ac9484be09e9f9465138af

Observation 93401f21-0d5e-48b9-a1b4-1aeaaceabb9e · inbound

Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA cites this paper.

Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 32

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arxiv_id, observed 2026-05-11T21:01:11.968809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-08T07:23:56.105372Z digest=sha256:4b58260d5bdf77edb4ce14ef8d7b541a683082d7c4899fd8461154aeec5b2f35

Observation d9b49e5a-c84f-4398-a7f4-adee9e3e7ec1 · inbound

Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA cites this paper.

Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 32

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arxiv_id, observed 2026-05-14T21:18:00.247267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-14T21:09:39.821912Z digest=sha256:26677a80b46cca18b185d667ba938ee19e1837eb572d6695aa0e378cab86522d

Observation 8040fbe9-62b5-4689-8d85-7f6d4af01e4a · inbound

VikingMem: A Memory Base Management System for Stateful LLM-based Applications cites this paper.

VikingMem: A Memory Base Management System for Stateful LLM-based Applications Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

Reference 79

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verified exact
arxiv_id, observed 2026-06-29T07:33:13.583875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-29T07:30:42.104455Z digest=sha256:ee6b3d1e842849277af4e5644954260779fa5bbfa9694748801c85246677e56d

Observation ea10d174-fde9-49a5-8004-a51fb32c5b8e · inbound

Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory cites this paper.

Light-Omni: Reflex over Reasoning in Agentic Video Understanding with Long-Term Memory Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

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