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Paper Citation Record · LEDGER

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge

As of 13 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2608.07994.

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

pith.paper-citation-record.v1
2608.07994 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:37:51.929201Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

  • verified exact3
  • verified fuzzy3
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f307d3e-c777-44ed-9441-6e26f6e6fbfa · outbound

This paper cites From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-12T00:37:52.592644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:37:51.675317Z digest=sha256:a3e8d355340eb759318d662a7d89588a9db5817360617a59a524b45373989eb7

Observation 64261203-5740-4602-b874-aee69254516d · outbound

This paper cites Context-as-AI-Service: Surfacing Cross-File Dependency Chains for LLM-Generated Developer Documentation.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Context-as-AI-Service: Surfacing Cross-File Dependency Chains for LLM-Generated Developer Documentation

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-12T00:37:52.317593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:37:51.707642Z digest=sha256:c2fdded5fffa5cd7ffb39f1a7a15635ffa094922a20c38b6a178f918dc2fefaa

Observation 2bee2907-32a0-498d-8f2a-40f2999e9cae · outbound

This paper cites Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:37:52.646338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:37:51.715124Z digest=sha256:5b2c13017ef8dd35c9ae72d4656a4e44c540c116ff15c4db1959399e908806c4

Observation 4ad0584b-57bd-42e0-967f-38dd3123211e · outbound

This paper cites Dense passage retrieval for open-domain question answering.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Dense passage retrieval for open-domain question answering

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:37:52.628591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:37:51.720720Z digest=sha256:15506642b89aa4c7a7d0ba3b715192469366bcd19b088d8a8654028a7c961f3f

Observation caf808b3-38c1-42f1-9f4f-0c97b3289827 · outbound

This paper cites Bookrag: A hierarchical structure-aware index- based approach for retrieval-augmented generation on complex documents.arXiv preprint arXiv:2512.03413,.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Bookrag: A hierarchical structure-aware index- based approach for retrieval-augmented generation on complex documents.arXiv preprint arXiv:2512.03413,

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.751736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.751736Z digest=sha256:15bb63daebe0f25f9e3bac60da92d1375e01baa79e2e12beea93e776053ca680

Observation 2d42bb3a-a16c-49e9-a5b0-cd567fe97628 · outbound

This paper cites NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.763542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.763542Z digest=sha256:5b502d0ade7533e0bc95dab8c46eae576bac26528e95bec30c3f3586785592e0

Observation 06f048e7-b120-451d-a1a0-fdf1fba06741 · outbound

This paper cites Qwen3 Technical Report.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Qwen3 Technical Report

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.769906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.769906Z digest=sha256:e7c05bbe4f376de22ecda18a33632b27ee84c3422a7354492264e4e68ce2fb09

Observation 7e88deb5-f8e2-4755-aaba-11a6c21de1e6 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge ReAct: Synergizing Reasoning and Acting in Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.776018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.776018Z digest=sha256:a30632f88428596a402cf8a03fed8f39b38bb0881e7dd73552665bcc39acf5e2

Observation 11b65e7e-2d8b-4dba-9c9a-27df06306fbd · outbound

This paper cites Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.787209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.787209Z digest=sha256:c37f45a7b9cbb535a7cd788caca6aa85314cedbdeb87a800799797404159967a

Observation d79dda69-0c60-4e8a-8045-2d7698fa88dc · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.796646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.796646Z digest=sha256:c376f24d9df871a79b168e868842eb723824d4800532c782b2f5fcbea7837911

Observation 936ecae1-b8b4-46e1-8b2a-dae539362ed8 · outbound

This paper cites Linearrag: Linear graph retrieval augmented generation on large-scale corpora.arXiv preprint arXiv:2510.10114,.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Linearrag: Linear graph retrieval augmented generation on large-scale corpora.arXiv preprint arXiv:2510.10114,

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.929201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.929201Z digest=sha256:f5c5586a71ba1b1e75c6d90cabcc8af6ecbed4b57fab3dcb2498f79ef0542c82

Observation 009ebd31-74ea-4777-8a8a-5318b3e38fd3 · outbound

This paper cites Complex qa and language models hybrid architectures, survey.arXiv preprint arXiv:2302.09051,.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Complex qa and language models hybrid architectures, survey.arXiv preprint arXiv:2302.09051,

Reference 2009

Resolution
verified exact
raw_fallback, observed 2026-08-12T00:37:52.567621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:37:51.683147Z digest=sha256:f7bf9f57fe9947ffb89591c00dea4af6edf7e6309d72165307565c52d3018d2b

Observation 22a0e4f3-bcd5-469c-b28b-9aa313cfa8c2 · outbound

This paper cites A Survey of Multimodal Retrieval-Augmented Generation.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge A Survey of Multimodal Retrieval-Augmented Generation

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.733711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.733711Z digest=sha256:a5a217dd36b551af1e6c35c2668dc954e093284d11f37911908d91331a6e8121

Observation 422662a3-bf11-4c19-9fc9-49058e5eb80e · outbound

This paper cites Simple is effective: The roles of graphs and large language models in knowledge-graph-based retrieval-augmented generation.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Simple is effective: The roles of graphs and large language models in knowledge-graph-based retrieval-augmented generation

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:37:52.609833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:37:51.727108Z digest=sha256:2dfb6e30f0a28a295878511676e43a7276a0e27a325dccb2dbc5ace836b63d79

Observation 904b0f51-f10d-400c-83c0-b30afc7fcae7 · outbound

This paper cites Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.740267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.740267Z digest=sha256:cca6b891ac35e73580cc8f014bc064e8d136cde3eef3df060c1ca93031a8b087

Observation 32654309-ed39-48d0-84e7-43fda3c21911 · outbound

This paper cites A Survey of Large Language Model Agents for Question Answering.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge A Survey of Large Language Model Agents for Question Answering

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.781191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.781191Z digest=sha256:078be53b8bc6bf51e7809ae9520e3b2c6910ca40c741306f65067e62e05c61a3

Observation 614aa4a0-10c9-4632-8dc8-c4ee22611e76 · outbound

This paper cites A-rag: Scaling agentic retrieval-augmented generation via hierarchical retrieval interfaces.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge A-rag: Scaling agentic retrieval-augmented generation via hierarchical retrieval interfaces

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.688980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.688980Z digest=sha256:101bb50a70ad459d02e356f676a049c418c871f99c2a36827993ffaa46d0cf62

Observation abc18c8b-c51f-4ea2-b855-5cc1a248fa77 · outbound

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

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.701013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.701013Z digest=sha256:9cd4231b21790be7a60aa46c6210cc9faae3e9fab215af0a6cf1493c1284059f

Observation 65c65097-b68f-4d3a-a7a9-a2e339f4c8f8 · outbound

This paper cites C-Pack: Packed Resources For General Chinese Embeddings.

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge C-Pack: Packed Resources For General Chinese Embeddings

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.757988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.757988Z digest=sha256:22734c32221ab5988a2585c8fa3d0383ceee48e2db92d38c4491326a7451353f

Observation 2f0aa7a6-c208-4b6d-8e69-f26a153661e9 · outbound

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

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 2026

Resolution
unresolved
no resolver link, observed 2026-08-12T00:37:51.694696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:37:51.694696Z digest=sha256:431fda617ea52010323840a9de2be2bd7adff10dea1b4e43522f3e3ff2249d36

Pith citing papers

No inbound Pith citation observations are available.