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Paper Citation Record · LEDGER

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems

As of 3 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2604.09666.

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

pith.paper-citation-record.v1
2604.09666 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-13T22:35:18.954951Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T08:38:01.144528Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-31T12:16:09.833992Z

Reference resolution

47 of 47 outbound references displayed

  • verified exact30
  • verified fuzzy3
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch8

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ce408473-e056-4889-9a23-b835028fc07e · outbound

This paper cites Pathrag: Pruning graph-based re- trieval augmented generation with relational paths.CoRR, abs/2502.14902.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Pathrag: Pruning graph-based re- trieval augmented generation with relational paths.CoRR, abs/2502.14902

Reference 1

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verified exact
arxiv_id, observed 2026-05-13T22:38:22.409854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:657a68eff2fac06602505195604787ad56ced39a5a4bffaf0bf53a866201ecd4

Observation 956dfaa2-1c46-46a1-9565-75e631d82d02 · outbound

This paper cites Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning

Reference 2

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verified exact
arxiv_id, observed 2026-05-13T22:38:22.412324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:7e58f86bdece371e0358f822eea8aebb595b6af331024ec202cec82b3e0062aa

Observation 747b08b1-a710-43cb-abce-754988501d8d · outbound

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

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 3

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verified exact
local_arxiv, observed 2026-05-13T22:38:22.370967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:494d0770b2952fd7deb1422c0627ccf967f18772f0e1b7a049eab82b68bad3b8

Observation dfbed170-7fb4-46aa-a1db-1a6f8e304f0a · outbound

This paper cites Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-13T22:38:22.404205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:badd57f37bb68ebe487e3eef76255fff789a4671b3147c1ad7ffc9632eb8b6ef

Observation d7f734ed-1e18-4859-8984-038972f79fa4 · outbound

This paper cites LightRAG: Simple and Fast Retrieval-Augmented Generation.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems LightRAG: Simple and Fast Retrieval-Augmented Generation

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-05-13T22:38:22.401720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:c056e23d6f70cffb55fd50c24d626771b27b4c84620cb3051bc0bc3b792fb157

Observation 985e9322-5b03-4f38-a216-afd5b6da7f6f · outbound

This paper cites an unresolved cited work.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-05-13T22:58:24.914352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:33417037787a20bc67f497bf0f34b80d3daea0a957aab39771646a5e164cda36

Observation ab97d9d4-41b2-442f-a6b6-a7a239fab845 · outbound

This paper cites HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

Reference 7

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metadata mismatch
arxiv_id, observed 2026-05-13T22:38:22.399352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:9f1d5c5998a94d08f166a73d9427dedfe16feef4b0cdc8f3636c3a94663f69c6

Observation b749e419-b103-4f4b-9bee-5e622369f0a6 · outbound

This paper cites From RAG to Memory: Non-Parametric Continual Learning for Large Language Models.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Reference 8

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verified exact
arxiv_id, observed 2026-05-17T01:36:19.859440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:7e34aa3da050c9a6840386f1bcca38e70e2fa0b24e0e3b8686459504aae544c3

Observation 76f9b480-afc0-495e-aaec-3a54e4aeefe9 · outbound

This paper cites an unresolved cited work.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Unresolved cited work

Reference 9

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unresolved
raw_fallback, observed 2026-05-13T22:58:24.917783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:5bad8a6c40a028b6fb88b75fb07637cab7149fe2c75845afe92c3135dc9614d6

Observation 5f50eca5-078f-484a-8569-6582586a18b5 · outbound

This paper cites Retrieval-Augmented Generation with Graphs (GraphRAG).

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Retrieval-Augmented Generation with Graphs (GraphRAG)

Reference 10

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verified exact
arxiv_id, observed 2026-05-18T04:33:40.134243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:d8b42f9acd7181a66faa02d637f98cc55c128835a09cff30a5696f736c31e26d

Observation 78e61182-f5b0-40bf-bc14-85fd90ec666f · outbound

This paper cites Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation

Reference 11

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verified exact
arxiv_id, observed 2026-05-13T22:38:22.388769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:2ebcde6c033cbc3d5856629446197cdb280ddb4a911ce799b3542139da25dfb8

Observation b5a8fb4a-775d-4a07-9485-b124c6aff8ce · outbound

This paper cites G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T22:38:22.386287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:faaec212ecb9f6190d7b98cb609d9f6719dcd9831c4a41b44997ea41420e91d6

Observation 3eacbc76-875d-4df7-ac24-333baa2b351a · outbound

This paper cites Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Reference 13

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verified exact
arxiv_id, observed 2026-05-18T07:39:58.458269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:1ede39fb1c1839783b1d6c75fbb721ffed63974da7edf1f75be1d9985397e84f

Observation 35e66fc6-1b49-4080-8e2e-d6aeabb9634b · outbound

This paper cites Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models

Reference 14

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verified exact
arxiv_id, observed 2026-05-13T22:38:22.379602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:165d139e10763272122f71330770b732bbe05f5d115e26e693cc76ded5393b64

Observation 4836db50-9a17-4572-a9dc-54aadf92eff9 · outbound

This paper cites an unresolved cited work.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-05-13T22:58:24.910788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:bf63fc8f03ed84c936aac86e6d32b29fe96857d86d000ff0323e714befaa4ba9

Observation ef6972bb-48d1-49cd-af0b-cbb6f871029a · outbound

This paper cites Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

Reference 16

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metadata mismatch
arxiv_id, observed 2026-05-13T22:38:22.407067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:ecc94f8765b17d641d89eea7984f8d1ff9b825de1e02a370378ac306a60e3baf

Observation 24a39b4a-d854-47f3-bf0c-b03eea885f53 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-05-13T22:38:22.376601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:8eab5bda21d2021ee5cd299f75b5b481a21aec2e7d8064c5b248d568b6f5495c

Observation 1fd0812c-daa5-4d67-8336-5083422d4b3b · outbound

This paper cites emnlp-main.495/.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems emnlp-main.495/

Reference 18

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metadata mismatch
arxiv_id, observed 2026-05-13T22:38:22.100367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:b444edadad44a887d481375c998de3589a0dbb7795d8261d75565db4264b86f4

Observation 04449034-4cc9-4ffb-9b43-a75bc08fdacf · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 19

Resolution
metadata mismatch
local_arxiv, observed 2026-05-13T22:38:22.393824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:9351ea8793ed609465bfe2ff1a245fb1421b96ab6ae659ef4c795073ad6715a2

Observation aa4548ae-e850-4068-81fc-c9207f1df54c · outbound

This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Dense Passage Retrieval for Open-Domain Question Answering

Reference 20

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verified exact
arxiv_id, observed 2026-05-15T21:24:42.733368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:da53364f59d80698c227fd6ad3c992c4d9ab36f63bd465f7f523cb1110a82e9e

Observation 9091c157-341b-4873-8ece-d197ea2263c8 · outbound

This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 21

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doi, observed 2026-05-13T22:38:22.097101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:76ca197e20fdb8aae004255b9e8772f0f8491b46cd40576cffec5490cf43e5dd

Observation 6dc36248-dda4-4531-91df-bbc74ffa84d5 · outbound

This paper cites an unresolved cited work.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Unresolved cited work

Reference 22

Resolution
unresolved
raw_fallback, observed 2026-05-13T22:58:24.906278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:42b69a73250c578efcd3a198e4c10f632fb98e1c6bd4b2a8c4b811f8709d3e45

Observation 8e07af79-3ad6-4eec-9530-ced846bb0a28 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-05-13T22:38:22.414723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:d57fb1fb8d1da309db05ce1bd12de8e3434cea316787810fbd80e0ca7afdb814

Observation 4f9df527-88d1-46fa-909e-8acb733c1af9 · outbound

This paper cites Search-o1: Agentic Search-Enhanced Large Reasoning Models.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Search-o1: Agentic Search-Enhanced Large Reasoning Models

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-05-13T22:38:22.340397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:7d294b72e16998c3b872d34f5278b0e00b1f3693ffd800c3f87133a4921dcced

Observation 038cf100-5c98-4230-8a22-3c98083cf378 · outbound

This paper cites Yuyan Liu, Sirui Ding, Sheng Zhou, Wenqi Fan, and Qiaoyu Tan.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Yuyan Liu, Sirui Ding, Sheng Zhou, Wenqi Fan, and Qiaoyu Tan

Reference 25

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verified exact
arxiv_id, observed 2026-05-13T22:38:22.332864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:2c1d1ca70c6481d9d4ee92f60c463e63ed06c9bcb8ed66335b27d98f99417581

Observation d36cfe31-d688-4fd3-9621-cede8b50e75f · outbound

This paper cites Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-06-04T02:06:52.238547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:7146559aa55b041d360b211ac981233865bffe6aa1e140d92b8aefb29b7f2308

Observation 5c78a183-441a-4406-9d2a-9a5dc3965de0 · outbound

This paper cites HyperGraphRAG: Retrieval-Augmented Generation with Hypergraph-Structured Knowledge Representation //.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems HyperGraphRAG: Retrieval-Augmented Generation with Hypergraph-Structured Knowledge Representation //

Reference 27

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verified exact
arxiv_id, observed 2026-05-13T22:38:22.338028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:b8ea1108705457fa3596897884e229a26d1854ab9b9ead1250460f72a9e5a512

Observation 3e8f5f77-7645-4118-8e02-44a35d615754 · outbound

This paper cites KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search

Reference 28

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verified exact
arxiv_id, observed 2026-05-13T22:38:22.342963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:50f49e40d053922dbf24c4a8cc2099bd90490bf2ec6448e0f9445761d220a8aa

Observation 24c86add-c098-44fd-ba85-bf1d49892b25 · outbound

This paper cites When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

Reference 29

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verified exact
arxiv_id, observed 2026-05-18T11:33:08.656299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:0428bb7042c64676a7ba418d1c4971ad1f0e273436113b188da0e19b58250572

Observation c424a095-4258-433a-93a0-4d34542e5077 · outbound

This paper cites OpenAI o1 System Card.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems OpenAI o1 System Card

Reference 30

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verified exact
local_arxiv, observed 2026-05-13T22:38:22.327505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:e86a441469ccf8bd1f989b5ea1c51e745975f8a240c4af4cb31d39ba008cde32

Observation 3fba57f9-f9e5-434c-a85d-58dd59817a08 · outbound

This paper cites Qwen2.5 Technical Report.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Qwen2.5 Technical Report

Reference 31

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verified exact
local_arxiv, observed 2026-05-13T22:38:22.368201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:37c55d5e9071e5105bbc5102b09d5bf1e5ec198f466ebfd8183db04f7c76b7f0

Observation 5f66da50-8b16-4418-a2a4-414ec433f790 · outbound

This paper cites RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Reference 32

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verified exact
arxiv_id, observed 2026-05-15T13:07:16.607810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:cd15680561d8678ddc143b3d58f304cf01dc522b4c28c7fccb21e2fedc7fd016

Observation 9b1cf53b-a378-4433-bd48-8f43b2560a4d · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-05-13T22:38:22.358130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:7f128c704cfd143896aaa5bbb94c4a5a267f7c0d9e579d815cab086977279192

Observation a288e271-19fb-4f2f-aac6-26f6ad177236 · outbound

This paper cites an unresolved cited work.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Unresolved cited work

Reference 34

Resolution
unresolved
raw_fallback, observed 2026-05-13T22:58:24.920744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:2f1565f4eee18dc6b18466fb832d2e6e41429b51c3466fcf49ce323f5a234475

Observation 49f692ea-ec99-47e9-935b-ab005da40f63 · outbound

This paper cites MuSiQue: Multihop Questions via Single-hop Question Composition.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems MuSiQue: Multihop Questions via Single-hop Question Composition

Reference 35

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T22:38:22.347975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:eccfa64772259ce1ad0de1c99a64955be09572340f6337d6b18bc24c89317a4f

Observation d3d4ebfb-3875-4edd-b3e5-c6e93585c902 · outbound

This paper cites KBLaM: Knowledge Base augmented Language Model.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems KBLaM: Knowledge Base augmented Language Model

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-13T22:38:22.350464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:6e20923a5862bc4328060e5c086b15ae7c0c039db243e6e6fd95a019485e6c1b

Observation 8d8d9bc2-999a-44bd-b15d-0228d44e7235 · outbound

This paper cites MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-13T22:38:22.355780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:5e8121e59871bd5c2106f8da29d389b026b217591bfbc86483b309130c7dfef9

Observation fb74bcf0-bdbe-4f50-9f82-bca875ba3f7f · outbound

This paper cites 2509.22009 , archivePrefix=.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems 2509.22009 , archivePrefix=

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-13T22:38:22.335482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:7289529339f9f1e5d3b0cd628e3b12f03918a8bfc7a7f21ea6219633e1522284

Observation 1ee51542-b524-4f3c-b37d-e7b39a978b5a · outbound

This paper cites HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Reference 39

Resolution
metadata mismatch
local_arxiv, observed 2026-05-13T22:38:22.352910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:a9477a960393cd18ecaf6fa54cef929d90b8cb0f322797c8d98998a8e0d54e26

Observation 8042f2d6-0873-48ab-9356-0ca6d001c2d9 · outbound

This paper cites an unresolved cited work.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-05-13T22:58:24.926873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:6ce68b0834845f0edcc0439b2d1ed0479a0929b4d4754dd36a64ec280c075894

Observation 67b37521-a5fa-4229-a72e-9e92a8af2e1e · outbound

This paper cites arXiv preprint arXiv:2507.23581 , year=.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems arXiv preprint arXiv:2507.23581 , year=

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-13T22:38:22.345415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:dfe7b321d9a1dcc8d3c4365e0f888feb4e57db49262afddf44db5d8f862ae7b1

Observation cb7c477e-6567-409b-806d-cce55ea06701 · outbound

This paper cites RAFT: Adapting Language Model to Domain Specific RAG.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems RAFT: Adapting Language Model to Domain Specific RAG

Reference 42

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T22:38:22.360816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:69379b014085b3deff2a8c21bac3d0f35d02d853850d3d26b8547dc6614720f1

Observation 4caf040b-288f-4ddb-9d46-e8f7c13406c0 · outbound

This paper cites ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-13T22:38:22.365793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:c5f39a7d8fe94f87527d6b46fd3d0f3a578f0abbffa2153bf04d1ea1affba337

Observation d0e50e40-95b7-49c5-93dc-e5937f9b0b40 · outbound

This paper cites Tri-Graph.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems Tri-Graph

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-13T22:38:22.322139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:0b7bea5d2b3ad164c0a27c02f95f1fa44ae94ad1e42228e4d969a2d0283827dd

Observation bd08d125-e684-47ac-a6c0-eaab1a2176e2 · outbound

This paper cites - Identify factual information that is relevant to the Current Search Query and can aid in the reasoning process for the original question.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems - Identify factual information that is relevant to the Current Search Query and can aid in the reasoning process for the original question

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-05-13T22:58:24.930722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:41920f28852c2bca29516c5e3df2844af9ed8c13c90f6968022a8ae8c7ddc020

Observation 9cfcc225-429b-4f26-b2da-6853c06bcdb4 · outbound

This paper cites -Ensure that the extracted information is accurate and relevant.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems -Ensure that the extracted information is accurate and relevant

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-05-13T22:58:24.934237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:8410815577dab327515d5e66a2038cc239763a47f79af1de506ba1fc635550fc

Observation 80c8c014-bbf9-4a4f-8855-ef9556679fcc · outbound

This paper cites {search_query}.

Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems {search_query}

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-05-13T22:58:24.923577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-05-13T22:35:18.954951Z digest=sha256:73eca4279031f0992cfb3b8bc27fe47eea85981eca98535391a5dbb9e97bc5f1

Pith citing papers

Observation bea17c10-656f-4bfd-9f3a-38ef5e2b510a · inbound

TRIAGE: Trustworthy Retrieval Instrumentation And Graph Evaluation cites this paper.

TRIAGE: Trustworthy Retrieval Instrumentation And Graph Evaluation Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-12T02:25:06.265447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T02:25:06.265447Z digest=sha256:9ff6d33b8f6fe50fe4ecec5efd55db7b7b75562d424c41401b92dd4e85aaa8c3

Observation 22673a67-4477-427f-9ddf-8e5ed9120ca8 · inbound

A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility cites this paper.

A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-07-31T08:41:06.988524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-07-31T08:38:01.144528Z digest=sha256:79766059d4539df2452f1fd7b8873186aed80b6cecbd4c0b304ca3e86c4d2a1c