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Paper Citation Record · LEDGER

G-RAG: Knowledge Expansion in Material Science

As of 12 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation 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 19 of 19 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T16:07:18.732576Z

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:b4c350fc0d857964b105c62f1caf5a12f867225b79a8c1864b161456f8619560

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=sha256:64adc39c65d1e95da04694bd29b1a9a9284418c86db4879e8c0040cbf4d9fad9

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T15:10:09.248819Z digest=sha256:8bd094e639aacc6ed394fb0b40b2d72d73cb597a01a46402bb5782dd89d4eca4

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T15:10:09.254132Z digest=sha256:668e5ebb902564e208e54891a01861a01336e865c4a9d34517869d5431ee6ff9

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=sha256:6b88c4eeb5c5195aea3508da6c89ff07669f031599039a921263aaa7b9dc99e0

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T15:10:09.265830Z digest=sha256:c5809fe39b431749fb175109c12056b032eca2eac4edd70a4305a6ee8e147595

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T15:10:09.271541Z digest=sha256:df9eb44d1bd35c5ab09319b6502f393fe56a47104bdb60aaa150505dae48101f

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:c86308bc68a50372ba5c4e75e87202a1015fc911c3307a0ccc3cc6aa14abf57c

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T15:10:09.281804Z digest=sha256:5b73ab783692a3293b065ed0ef7e634cc5e62ef4fa9494286ff23e96e14438de

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:de09e4a7dc0cc0f5cf08a0156172015e409026534452c3abb2c4a073179eded3

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:e9516c190db1c6fb9e96e8ddd743dc4a6fe35845ee8231baffb3f6a2854e9569

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=sha256:7e853a3b3e38d49ab665ab133a14edc0d954d7bbcad3b86cffb98758d8a89990

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=sha256:570d3b7b9e123be6bff27ad22924398248ee6c2087560ca304150cb1adf94718

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T15:10:09.308067Z digest=sha256:45475a86127ae62b0b2d7309ab3cd33191320c834c909d5c46a879f409c5b40a

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=sha256:531a70d26a940e4e519c50b2b17291f952e3175e549d3c6e16d8e3e12b39c1c8

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=sha256:9548ac1ecc1b3f61aa545ff41842c6f710db4716e4ab1fe68718e245f773f330

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T15:10:09.324653Z digest=sha256:88e3791bb060e6ce92ce6b3f04f18329ab06088bb4473d45c236404b0ddea965

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:ce6a0430e57ba690c5eaf6ca9e9a1879694624935dafa1b8ca44a81e6c79938c

Pith citing papers

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-10T16:07:18.732576Z digest=sha256:5424721afc4b64237a9bc83b345661f9c7c6bf76dfe2d88935f1ecb793ee0516