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Paper Citation Record · LEDGER

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction

As of 8 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.09713.

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

pith.paper-citation-record.v1
2607.09713 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T17:21:18.459380Z

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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  • verified fuzzy0
  • unresolved20
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4deafa04-5a72-4b31-936a-3c138a073804 · outbound

This paper cites Leveraging llm agents and digital twins for fault handling in process plants,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Leveraging llm agents and digital twins for fault handling in process plants,

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:a30c105c7c1b0bf6eeec8d116d793fbc0c884f70f94e19eba1ae560a38235822

Observation ddbf7902-704f-4265-8987-b6fd6db8f2ea · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 2

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:a10b6a15d44248165e786f4469655489ddabc5c50eedb8824e99d4bce740de67

Observation 44b4ad37-82f7-4fb6-b359-42ca8293e862 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Chain-of-thought prompting elicits reasoning in large language models,

Reference 3

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:7b61c359608beb16901737ead77eb2a286e449f64e12cfa81e7e7a54c8ac61dc

Observation 00aca0ae-b3e9-4cbc-ae65-b205e14e2d84 · outbound

This paper cites Edge computing: Vision and challenges,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Edge computing: Vision and challenges,

Reference 4

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:3b0f3371d21e343182827df3c53aa60404900ef00232847e29c2febd50ae7d78

Observation aa9b4edf-b350-4251-b7b3-d7a0ccfb92df · outbound

This paper cites A review of attacks, vulnerabilities, and defenses in industry 4.0 with new challenges on data sovereignty ahead,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction A review of attacks, vulnerabilities, and defenses in industry 4.0 with new challenges on data sovereignty ahead,

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:01ad91bcc7b8484136f3db09b793ec95e6a1b7e4f197ddc92d4c03024266cf03

Observation d9a7618c-5a7e-458f-80dc-984f69773a0b · outbound

This paper cites Qwen2.5-Coder Technical Report.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Qwen2.5-Coder Technical Report

Reference 6

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:da509a68e2ea2fb342b6b64b8ec90c9721c662f556abee86e108b1b0f0ce617d

Observation 59fafce3-23f0-44b2-87ac-786f57ee5ded · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:d520751b2cd3b12fe984aaf01020f76f03fd05ff5d8cc7b7be7c3d1f39b69065

Observation a311a00b-0509-4e63-af7b-3f521eb026d9 · outbound

This paper cites From automated to autonomous process operations,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction From automated to autonomous process operations,

Reference 8

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:19518259ecb46aca9238d07a1a7872fb29a338826269851f15a937bef1b17827

Observation 8ab205b7-2d01-4a28-bc5f-bf9287e0ca1c · outbound

This paper cites A comparative review of large language models in engineering with emphasis on chemical engineering applications,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction A comparative review of large language models in engineering with emphasis on chemical engineering applications,

Reference 9

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:c8cfd3f935f27dedf7fdc30e7213cdfed860c569508eaf6af8533fd0d59c659b

Observation 90419300-9436-4151-bdbc-98afb8ea786e · outbound

This paper cites Eureka: Human-Level Reward Design via Coding Large Language Models.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 10

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:35595b3bb84e060cf47f2e31f3fc48443800e5c587376ebb1f94190728062454

Observation 7b367414-12e3-4090-bd7b-5b4fb6c8675a · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 11

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:bbe10a55d0a00b3b0bb518281924111435cb5f7c0ff1adaf80999035563059da

Observation dff9ee1c-60a1-4dbe-af23-84224dbc9668 · outbound

This paper cites ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise

Reference 12

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no resolver link, observed 2026-07-14T17:21:18.459380Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:e8cc831b840a5ef14921bf0ed1d5760b2135451015a7e8effd9e739b0890a4b0

Observation 453196f1-5719-4d4d-9c8a-3a5125970c88 · outbound

This paper cites Autonomous industrial control using an agentic framework with large language models,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Autonomous industrial control using an agentic framework with large language models,

Reference 13

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:cb3a44eafd57dcff810010525511581c4f2e32819b83e3d9223c554330073c90

Observation e427072e-b0d0-4566-bc00-66c67de056ae · outbound

This paper cites Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation

Reference 14

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:ae6a5c659bd9b26235b9dc8d2c000a787e9bde10ccf78c806b59ea83df2b7922

Observation 0fada225-845d-4fdc-adf4-d1fb349fd6c3 · outbound

This paper cites Star: Bootstrapping reasoning with reasoning,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Star: Bootstrapping reasoning with reasoning,

Reference 15

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:262d0fa6e8ec88a3d964237912fe9375dbc04b15cc238bbca0e363c242a7157e

Observation 7418241b-a99d-45f7-801c-2f81bd516874 · outbound

This paper cites Agentic ai for intent-based industrial automation,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Agentic ai for intent-based industrial automation,

Reference 16

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:5e0977a7aed612c8c6fe559e926072be554a44e49cb241ea2d78d154d2b24d91

Observation 0366430c-8b81-411c-9272-a24860b6b2c6 · outbound

This paper cites Autocontrol: An end-to-end fully automated workflow for control design of building energy systems,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Autocontrol: An end-to-end fully automated workflow for control design of building energy systems,

Reference 17

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:961931a1cc48ceea2217875aaf11b0c8cbd6f3299f7d28fa265c983ca0693267

Observation 0a3938d0-dc2d-4f3f-8962-9173b115dc94 · outbound

This paper cites Small Language Models are the Future of Agentic AI.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Small Language Models are the Future of Agentic AI

Reference 18

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:a456dd5ab59ef3e171595169a70e0655bf65b219e1bef9fa58edd0d2ed90cb84

Observation 8e8bea40-ca8f-462a-a86d-b44236f600e2 · outbound

This paper cites DeepSeek-V3 Technical Report.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction DeepSeek-V3 Technical Report

Reference 19

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source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:e329497d6c57472ccc0fedd99dde311c84fb3afd0832f4fab9bfbf73fc47d08e

Observation a9ddfd56-73b8-4a5a-953b-eaff8887929b · outbound

This paper cites Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes,.

Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes,

Reference 20

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:21:18.459380Z digest=sha256:d1c83678adb840adc5d2abb4553de127ae545caaa8fc37c0c00bf123fd4ed0d5

Pith citing papers

No inbound Pith citation observations are available.