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Paper Citation Record · LEDGER

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries

As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.10151.

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

pith.paper-citation-record.v1
2607.10151 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T13:55:16.160002Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 81d09d2e-3f57-4580-840c-fa4dc1dd213d · outbound

This paper cites Improving language models by retrieving from trillions of tokens.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Improving language models by retrieving from trillions of tokens

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:26a2cc72df71cbd7d92631bc07d3e71c46fad0b2f452391c34b1ec4cddfb98ea

Observation 40439415-dd10-40e0-968b-4cb33e1fc707 · outbound

This paper cites Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento

Reference 2

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:ed703b435b4ac390c3519caa4408a80601258c5a2cbf33f201d8f8c478f4ab1f

Observation ca664ec5-8728-45d4-95c8-995786450573 · outbound

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

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 3

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:5f2bd00e0ed15534e4315fd37d147627578c08a8077de5fefe1605cab36dd487

Observation 645825ed-95f7-425c-ba7b-5f258b33326c · outbound

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

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries A survey on RAG meeting llms: Towards retrieval-augmented large language models

Reference 4

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:8df48d62db3287b0f17ed86d65b7831e6e0ee5be4758d93d6638a38bdd13f0fd

Observation 165b6956-381f-48e2-af8d-8df16725fb9e · outbound

This paper cites Lightrag: Simple and fast retrieval-augmented generation.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Lightrag: Simple and fast retrieval-augmented generation

Reference 5

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:13f33efd7c16f2e2bebe8d1074cd009627e4383a85905c871e743f1a1227624f

Observation 82377efa-a3cd-445e-9a45-f3f6aeaaf319 · outbound

This paper cites Turboiso: towards ultrafast and robust sub- graph isomorphism search in large graph databases.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Turboiso: towards ultrafast and robust sub- graph isomorphism search in large graph databases

Reference 6

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:71295dc00172e36d8a198ca7d7b1d417ec0628b9170a1a5e9de5d57517986ae0

Observation b509c4d1-40da-4768-aa7b-13bf61cd82da · outbound

This paper cites G-retriever: Retrieval-augmented gener- ation for textual graph understanding and question answer- ing.Advances in Neural Information Processing Systems, 37:132876–132907,.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries G-retriever: Retrieval-augmented gener- ation for textual graph understanding and question answer- ing.Advances in Neural Information Processing Systems, 37:132876–132907,

Reference 7

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:90dbe0855853a37adb77a6b25239e1472fb5e872286304d446877e4cb3523489

Observation 520b4c8c-0b15-4e92-903a-3626c93cf995 · outbound

This paper cites Leveraging passage retrieval with generative mod- els for open domain question answering.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Leveraging passage retrieval with generative mod- els for open domain question answering

Reference 8

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:e0189696d1c21d6b42ee3e15c51264f938096db2548cc661a38495213db745c1

Observation 1e476eae-5bc4-46b2-8744-0955dbe67f70 · outbound

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

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Dense passage retrieval for open-domain question answering

Reference 9

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:f640693a3891829417e1b266041d2aecc945ccfcd3decb9a333589d397470bf8

Observation 72011e19-cdf4-4422-8770-bb2d06acc225 · outbound

This paper cites Colbert: Efficient and effective passage search via contextualized late interaction over bert.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Colbert: Efficient and effective passage search via contextualized late interaction over bert

Reference 10

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:712a9f66004b0341fd5ed79c8764378045b8f30710b184073bc413b09541c1d0

Observation 4100946e-f2c2-45d7-9e6d-242a4d07b15d · outbound

This paper cites Turboflux: A fast continuous subgraph matching system for streaming graph data.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Turboflux: A fast continuous subgraph matching system for streaming graph data

Reference 11

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:e37fda128a5f26e6f39243a52082ac955ce992d82272a34cbf184519391758d7

Observation 00da0432-05f7-448f-9a7a-8a983d34618e · outbound

This paper cites Natural questions: A benchmark for question answering research.Transactions of the Association for Computational Linguistics, 7:453– 466,.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Natural questions: A benchmark for question answering research.Transactions of the Association for Computational Linguistics, 7:453– 466,

Reference 12

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:db86bcd8b3b0c67019b77e2215f911b07f72c1d48e6ef2db544fe6206c7c2697

Observation 9b88e152-b62b-4ee3-9333-f3e94c63dd25 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 13

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:5951fdb625b8da06de43cb3e3267a42b60e7d5b02fc0d4b4d923e1ca8094fbd9

Observation 37b827d7-c06c-47ce-91ca-806691d1bf87 · outbound

This paper cites KAG: boosting llms in professional do- mains via knowledge augmented generation.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries KAG: boosting llms in professional do- mains via knowledge augmented generation

Reference 14

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:2748abe11829fe5433c7f33b5f9d5987ef4b8c01e93f661c209aad3af76e7d7e

Observation 036d5a91-4433-427f-ba69-5c2e5246088e · outbound

This paper cites Bhowmick, Gao Cong, and Qing Wang.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Bhowmick, Gao Cong, and Qing Wang

Reference 15

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:05068764094af91cf4aa1dc788c8db2d1fe3e485c493e7bcc32738beff89099f

Observation fff23aeb-3157-483b-9ec5-4d3450a7d0ab · outbound

This paper cites Panda: a system for par- tial topology-based search on large networks.Proc.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Panda: a system for par- tial topology-based search on large networks.Proc

Reference 16

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:2915e687ea0888ee5613d32a7241b5ee2fc5821b3c4ed91c313b4bd861f6bffe

Observation af5d6bc4-4236-4e1f-ab37-123fc67b10a7 · outbound

This paper cites Structure guided retrieval-augmented generation for factual queries,.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Structure guided retrieval-augmented generation for factual queries,

Reference 17

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:e1bac7e2a4b5ffa0effc1296c00083f1cf07ce8a500db16c7d0e9b783c2f920e

Observation b7eefbed-9a03-46dc-a234-443b776d8010 · outbound

This paper cites Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation

Reference 18

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:68ab9411e6c4b745b35c00d340d68031642050bf1584532276d6c0e71cca1c31

Observation 2fbb397c-a7f4-46fb-9658-ef424546a741 · outbound

This paper cites Efficient exact subgraph matching via gnn- based path dominance embedding.Proc.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries Efficient exact subgraph matching via gnn- based path dominance embedding.Proc

Reference 19

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:24a471735fc598a19dc55ca4fd6d0c08be49522697030022e285ae76106ce7a3

Observation f43bfee7-ef3c-4b10-8d71-f98bc5bc7efa · outbound

This paper cites A comprehensive survey and experimen- tal study of subgraph matching: trends, unbiasedness, and interaction.Proceedings of the ACM on Management of Data, 2(1):1–29, 2024.

MC-RAG System: A Structure-Driven RAG System for Multi-Constraint Queries A comprehensive survey and experimen- tal study of subgraph matching: trends, unbiasedness, and interaction.Proceedings of the ACM on Management of Data, 2(1):1–29, 2024

Reference 20

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source=pdf_text observed=2026-07-14T13:55:16.160002Z digest=sha256:8853b31f77a9db9270deba4a4c9f177671643a4b74a43265e17c76b93a8262b7

Pith citing papers

No inbound Pith citation observations are available.