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Paper Citation Record · LEDGER

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance

As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2607.24791.

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

pith.paper-citation-record.v1
2607.24791 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:42:49.752547Z

measured 18 of 18 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

18 of 18 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved18
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 60eb218e-d5a0-4d2d-a5cd-11f70159806d · outbound

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

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Retrieval- Augmented Generation for Knowledge- Intensive NLP Tasks,

Reference 1

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no resolver link, observed 2026-08-02T09:42:49.676084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.676084Z digest=sha256:475ad2726160bb4600434a1ac35a8716a1483bb9d79c38265a5e0f0f9a04e5fe

Observation 44d49e11-1a32-4546-8f29-2cf4ed7651e0 · outbound

This paper cites Question -Based Retrieval Using Atomic Units for Enterprise RAG,.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Question -Based Retrieval Using Atomic Units for Enterprise RAG,

Reference 2

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no resolver link, observed 2026-08-02T09:42:49.681154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.681154Z digest=sha256:d133435d2bf8e068c4d84e0e5978af3ed763f1fe73d32e4a7afcc2d4bb8214f5

Observation 8d864654-ec3d-4c48-925d-e721fe4d5ea4 · outbound

This paper cites Benchmarking LLMs for Environmental Review and Permitting.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Benchmarking LLMs for Environmental Review and Permitting

Reference 3

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no resolver link, observed 2026-08-02T09:42:49.685666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.685666Z digest=sha256:ee013f13289e4c63d12175d6fdff0478d0749f9f5bdf1183cde130456d9f62be

Observation 11037d4a-4008-4086-a51c-4a5f9960a4ee · outbound

This paper cites Evaluating ChatGPT on Nuclear Domain-Specific Data.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Evaluating ChatGPT on Nuclear Domain-Specific Data

Reference 4

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no resolver link, observed 2026-08-02T09:42:49.690596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.690596Z digest=sha256:4bc315d9ee4f430b9eb40fcc84336613b319856185a6909f4126ff6b9d85a5ad

Observation 39f9acc7-0283-4a13-9b79-2690232f9e7b · outbound

This paper cites Lost in the Middle: How Language Models Use Long Contexts,.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Lost in the Middle: How Language Models Use Long Contexts,

Reference 5

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no resolver link, observed 2026-08-02T09:42:49.695220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.695220Z digest=sha256:0dcb2b97dbb8e5e41890a59465139856080d5058a8d02494f7b9363c6dc53b73

Observation 90d518a5-d6d3-4bd2-b7a4-7b74025b981d · outbound

This paper cites Prompt Compression for Large Language Models: A Survey,.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Prompt Compression for Large Language Models: A Survey,

Reference 6

Resolution
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no resolver link, observed 2026-08-02T09:42:49.699313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.699313Z digest=sha256:ec014cbc9877cf189fbf6429af9a30afde85fa9c2b2f2577522c2327aa972527

Observation 3e268800-482e-431c-abf0-93b0b739e6dd · outbound

This paper cites LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models,.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models,

Reference 7

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no resolver link, observed 2026-08-02T09:42:49.704241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.704241Z digest=sha256:59d7a98ce0bf8c8f90261f20ddcf520a6ee37e2fdef2782ddb018322121f5a06

Observation cbbbbe6a-df0b-406e-b335-6f3028177875 · outbound

This paper cites Toolformer: Language Models Can Teach Themselves to Use Tools,.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Toolformer: Language Models Can Teach Themselves to Use Tools,

Reference 8

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unresolved
no resolver link, observed 2026-08-02T09:42:49.708308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.708308Z digest=sha256:700e0d74229309b1735a57d8b4d640435b7b02c78dd2c721bc960df69c3b8fbb

Observation 4d90e1f4-aa75-438d-9839-5c28ffe55ff6 · outbound

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

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance ReAct: Synergizing Reasoning and Acting in Language Models,

Reference 9

Resolution
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no resolver link, observed 2026-08-02T09:42:49.712386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.712386Z digest=sha256:23a545db6832363ba23d29d58096e22ce4bc921a23965fa841dd22e61457c88a

Observation ecdf345f-e575-45a9-9ee5-e5c077362152 · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection,.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection,

Reference 10

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no resolver link, observed 2026-08-02T09:42:49.716708Z

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source=pdf_text observed=2026-08-02T09:42:49.716708Z digest=sha256:406940ebff385610798842a1ab058f9a580e5151a760be6fd5ddfd7734797125

Observation 76a45171-a27f-43bc-bfed-6fbce8b59aba · outbound

This paper cites Interaction with Texts: Information Retrieval as Information-Seeking Behavior,.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Interaction with Texts: Information Retrieval as Information-Seeking Behavior,

Reference 11

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no resolver link, observed 2026-08-02T09:42:49.720944Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.720944Z digest=sha256:c6629e6fc21bf11cd93dd21c84df25582348edf2b783e8a5c826c75cb4d503a2

Observation e7216b99-efff-4228-abec-ba5de4769500 · outbound

This paper cites Information Foraging,.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Information Foraging,

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-02T09:42:49.725175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.725175Z digest=sha256:963f2ac7c9c796ef4383b5ab7e397d754d02b330d4ba0968a34a7791c1b34026

Observation 9b66708d-297c-4b9d-b94a-ed3837556748 · outbound

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

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-02T09:42:49.729663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.729663Z digest=sha256:b58c46327dc57b16f61e713810d9b7ce9539d01210e0a5cd0909945c1d2878b1

Observation 5ae6c16d-dbc1-4415-b788-8ad0353a6708 · outbound

This paper cites Enhancing Accuracy and Maintainability in Nuclear Plant Data Retrieval: A Function-Calling LLM Approach Over NL-to-SQL.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Enhancing Accuracy and Maintainability in Nuclear Plant Data Retrieval: A Function-Calling LLM Approach Over NL-to-SQL

Reference 14

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no resolver link, observed 2026-08-02T09:42:49.734207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.734207Z digest=sha256:13af965cfe47fc4c4f04e5a26970af1fe6e12c9cc2c2785e65e1417cfd7e73e9

Observation 0589a018-6530-4b9b-a1d6-a484819711ec · outbound

This paper cites Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval- Augmented Generation,.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval- Augmented Generation,

Reference 15

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no resolver link, observed 2026-08-02T09:42:49.738753Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.738753Z digest=sha256:8707f65ae7c3454e752b8c10345eb863164a792cd6989100fccb78bf8cc13363

Observation 091fcc7c-92a8-4818-b180-0b6762d831d6 · outbound

This paper cites Classification of Safety Events at Nuclear Sites using Large Language Models.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Classification of Safety Events at Nuclear Sites using Large Language Models

Reference 16

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no resolver link, observed 2026-08-02T09:42:49.743000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.743000Z digest=sha256:4ae4c5daa62792ea21db40a451b8756b315f43f2a0fb758fe36e8a39020fd451

Observation f1458f22-b45d-4279-9fd2-e5717e2a803a · outbound

This paper cites Automating equipment identification in nuclear engineering drawings.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Automating equipment identification in nuclear engineering drawings

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-02T09:42:49.747900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.747900Z digest=sha256:4a7550eff476e1a5ee2d0020a23d61997b986312ed064ed9a03bad0656802192

Observation a3bf0296-cce4-4bd0-9e07-b65e1952c3d2 · outbound

This paper cites Towards Secure and Private Language Models for Nuclear Power Plants.

From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance Towards Secure and Private Language Models for Nuclear Power Plants

Reference 18

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no resolver link, observed 2026-08-02T09:42:49.752547Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:42:49.752547Z digest=sha256:02829184eb8599f116542a437334b4fcf724d1325a2f751d2fc429ff88fd4a07

Pith citing papers

No inbound Pith citation observations are available.