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Paper Citation Record · LEDGER

G-RAG: Knowledge Expansion in Material Science

As of 19 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 2 inbound Pith citation observations for arXiv:2411.14592.

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

pith.paper-citation-record.v1
2411.14592 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:10:09.329642Z

measured 20 of 20 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:24:46.639516Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:16:04.680576Z

Reference resolution

18 of 18 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 58865842-ed65-49cb-8614-7b853e09ea54 · outbound

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

G-RAG: Knowledge Expansion in Material Science Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.237541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.237541Z digest=sha256:99ae5647516c1cde18cac45ba1f44b515fc8705b03150164abcd6ec39f6a23c8

Observation ca0673a6-2c9e-4acb-9a88-e0a3ae121696 · outbound

This paper cites Retrieval Augmented Generation for Domain-specific Question Answering.

G-RAG: Knowledge Expansion in Material Science Retrieval Augmented Generation for Domain-specific Question Answering

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.243412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.243412Z digest=sha256:bbb48ce847c621b44b81f5495b124dfac18057c6e674771183cc0605fe2afee2

Observation 90d660a6-2316-4d5f-821e-1c9d637895da · outbound

This paper cites Think-on-graph 2.0: Deep and interpretable large language model reasoning with knowledge graph-guided retrieval.

G-RAG: Knowledge Expansion in Material Science Think-on-graph 2.0: Deep and interpretable large language model reasoning with knowledge graph-guided retrieval

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:10:09.682351Z

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.

source=pdf_text observed=2026-08-12T15:10:09.248819Z digest=sha256:8c3c234d89e29ad281781854cd1264f7680d3e8b414472adfe926fde97fecec0

Observation 5e72ede2-e7e7-4946-98f7-5bc46316b5ee · outbound

This paper cites Exploration of word embeddings with graph-based context adaptation for en- hanced word vectors.

G-RAG: Knowledge Expansion in Material Science Exploration of word embeddings with graph-based context adaptation for en- hanced word vectors

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:10:09.663980Z

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.

source=pdf_text observed=2026-08-12T15:10:09.254132Z digest=sha256:7899a47389e220fc967b9f869c72938fd04ee869c3e2e1889199a119af4fa8f2

Observation ea4d31b3-04e0-4a98-88e4-289c25d1ba7e · outbound

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

G-RAG: Knowledge Expansion in Material Science From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.259813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.259813Z digest=sha256:4ffed026713a12d55343cb68c7b2e49afa6a138cac4187b1f1b7d92e38354e16

Observation ea5091f9-98a1-4f9e-9b61-a1dd0abb93b9 · outbound

This paper cites Leveraging medical knowledge graphs and large language models for enhanced mental disorder information extraction.

G-RAG: Knowledge Expansion in Material Science Leveraging medical knowledge graphs and large language models for enhanced mental disorder information extraction

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:10:09.646460Z

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.

source=pdf_text observed=2026-08-12T15:10:09.265830Z digest=sha256:e33108bb219aac18043ffed94965e5084da241630f84fb51cbc8fd2722f9cc4a

Observation 519cf3fc-0ea1-44bc-ab24-c62e462dcf3c · outbound

This paper cites Generative retrieval-augmented ontologic graph and multiagent strategies for interpretive large language model-based materials design.

G-RAG: Knowledge Expansion in Material Science Generative retrieval-augmented ontologic graph and multiagent strategies for interpretive large language model-based materials design

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:10:09.628837Z

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.

source=pdf_text observed=2026-08-12T15:10:09.271541Z digest=sha256:70a7a2d32576188ebafb02db87cd4aa545dda091f342d2db961dfef0c763c7ff

Observation 07cdecff-7f87-4359-a37e-136ce7a4a499 · outbound

This paper cites Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge.

G-RAG: Knowledge Expansion in Material Science Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.276317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.276317Z digest=sha256:82bd59f12c50d0e35e224baadb218c3e32f1b01b17b05ccb2d16a8b26bbb3b01

Observation 78f7c7e0-4e2b-41bc-ab2c-3f108fbd32a1 · outbound

This paper cites Knowledge graphs: Opportunities and challenges.

G-RAG: Knowledge Expansion in Material Science Knowledge graphs: Opportunities and challenges

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:10:09.610947Z

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.

source=pdf_text observed=2026-08-12T15:10:09.281804Z digest=sha256:6a8a9cc5e6fc96788918d688eca348528aeef9b1e18871ca0a719a3af22a7c2d

Observation d94b858c-9c77-473c-91a1-f7f6f8fc59c5 · outbound

This paper cites AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents.

G-RAG: Knowledge Expansion in Material Science AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.286865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.286865Z digest=sha256:1010ff89a4566a700195431513f5b9070958c00163b6bc8a3aa37291f76c761c

Observation 27513143-73ca-43ef-8ec1-a1849a70bfb1 · outbound

This paper cites Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation.

G-RAG: Knowledge Expansion in Material Science Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.292146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.292146Z digest=sha256:bca3ae53d3732d445b136a9943983cb43134c3ecdffa22258ac70fffbe15979c

Observation bb7e1c3b-8b58-4f7b-9fa1-35d14b15b906 · outbound

This paper cites Extending Context Window of Large Language Models via Positional Interpolation.

G-RAG: Knowledge Expansion in Material Science Extending Context Window of Large Language Models via Positional Interpolation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.297804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.297804Z digest=sha256:9c2a6d1de2e1576f15715591e43f2f7f46097a4d9b743151dea9bbe0f2cd5d5c

Observation d1f60daa-be13-4f54-b8bc-3dbccfa4fcce · outbound

This paper cites MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool.

G-RAG: Knowledge Expansion in Material Science MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.302933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.302933Z digest=sha256:034ed7212b9934bb4548d6a001dbcd30e36f3f8f0f0840e412ee7deba1408452

Observation 754b9f84-ed15-4f08-b36d-ab92116fbbdf · outbound

This paper cites Searching for best practices in retrieval augmented generation.

G-RAG: Knowledge Expansion in Material Science Searching for best practices in retrieval augmented generation

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:10:09.592599Z

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.

source=pdf_text observed=2026-08-12T15:10:09.308067Z digest=sha256:b7332ebb86425e74ddf5b7e7fbfaf105bfb2f6d64d06e31c6d7b2f39857d93a4

Observation dd17f341-a7c8-413a-b126-1c828f08e29d · outbound

This paper cites Lost in the middle: How language models use long contexts.

G-RAG: Knowledge Expansion in Material Science Lost in the middle: How language models use long contexts

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.313180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.313180Z digest=sha256:00197c8d7dafe81eff986d3eba3ea473d694238b20e1a31d7292f0323ef36756

Observation 3cafe3d7-43fe-4d31-9ab0-cd036a984573 · outbound

This paper cites Graph Retrieval-Augmented Generation: A Survey.

G-RAG: Knowledge Expansion in Material Science Graph Retrieval-Augmented Generation: A Survey

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.319129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.319129Z digest=sha256:afc217a165d3030a7f0d8871e81f75651d3cdd2ee343c637212c3574a365bbc6

Observation 8c6fa310-6155-40a3-b73e-2b4ac2d6116f · outbound

This paper cites Named entity recognition for entity linking: What works and what’s next.

G-RAG: Knowledge Expansion in Material Science Named entity recognition for entity linking: What works and what’s next

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:10:09.563939Z

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.

source=pdf_text observed=2026-08-12T15:10:09.324653Z digest=sha256:f47b1b5f24f5b72272944a78c5a96a3f7f44a886b930813a23712d8c473f3709

Observation 083fb863-1b4f-4074-a584-7f1708ca3454 · outbound

This paper cites ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget.

G-RAG: Knowledge Expansion in Material Science ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-12T15:10:09.329642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:10:09.329642Z digest=sha256:74919414734121dee4f032d95f50b8eba00568fdb415efc8a3397b06ec0d046f

Pith citing papers

Observation 923cd8bd-2b44-465a-b1a4-806f7977328d · inbound

HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights cites this paper.

HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights G-RAG: Knowledge Expansion in Material Science

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-15T23:24:46.639516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:24:46.639516Z digest=sha256:960b9e05979a36534f10a287f4e21b4f21917ee262e776e798c22930d48d3fa7

Observation 5212a617-e581-46d8-a01e-7514f1804f03 · inbound

RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering cites this paper.

RECIPER: A Dual-View Retrieval Pipeline for Procedure-Oriented Materials Question Answering G-RAG: Knowledge Expansion in Material Science

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:16:04.684880Z

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.

source=pdf_text observed=2026-05-10T16:07:18.732576Z digest=sha256:3b376ad83e3e6f6d859537876e4325d70530fc969753f1360fee84e884d21846