Paper Citation Record · LEDGER

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework

As of 13 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 3 inbound Pith citation observations for arXiv:2411.16707.

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

pith.paper-citation-record.v1
2411.16707 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:14:51.386128Z

measured 30 of 30 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:05.801927Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:16:11.863597Z

Reference resolution

27 of 27 outbound references displayed

verified exact3
verified fuzzy16
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3e259881-3b37-417f-aa33-caad09c6e112 · outbound

This paper cites Autonomous chemical research with large language models,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Autonomous chemical research with large language models,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:51.263832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 269e1ab8-3582-480f-aca7-471a91cd53fb · outbound

This paper cites Mathematical discoveries from program search with large language models,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Mathematical discoveries from program search with large language models,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:51.269028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bc8b46d-36de-4775-aed7-5660f9ea348e · outbound

This paper cites Solving olympiad geometry without human demonstrations,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Solving olympiad geometry without human demonstrations,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.883779Z

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.

Observation deca7e0b-259e-43ef-8297-f7a7aea04c08 · outbound

This paper cites Large language models streamline automated machine learning for clinical studies,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Large language models streamline automated machine learning for clinical studies,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.867943Z

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.

Observation 08f508ca-792f-438c-8f37-911c15488793 · outbound

This paper cites On the potential of chatgpt to generate distribution systems for load flow studies using opendss,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework On the potential of chatgpt to generate distribution systems for load flow studies using opendss,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.852653Z

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.

Observation 4d1eb540-dc56-4fe4-ac48-686d0f3003d9 · outbound

This paper cites Exploring the capabilities and limitations of large language models in the electric energy sector,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Exploring the capabilities and limitations of large language models in the electric energy sector,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:51.285634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff5cdc55-4642-4dfa-92e9-98834f101dbe · outbound

This paper cites How do large language models acquire factual knowledge during pretraining?.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework How do large language models acquire factual knowledge during pretraining?

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.827337Z

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.

Observation 38a49eef-8e8e-4f60-a9d9-ecf368766f9f · outbound

This paper cites Real-time optimal power flow with linguistic stipulations: integrating gpt-agent and deep reinforcement learning,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Real-time optimal power flow with linguistic stipulations: integrating gpt-agent and deep reinforcement learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.811895Z

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.

Observation 4a4d7681-5709-4a57-8fe6-340d65979eb4 · outbound

This paper cites Large Language Model Assisted Optimal Bidding of BESS in FCAS Market: An AI-agent based Approach.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Large Language Model Assisted Optimal Bidding of BESS in FCAS Market: An AI-agent based Approach

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:14:51.513420Z

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.

Observation 66226590-101e-42b7-8d05-1901d38da699 · outbound

This paper cites Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:51.307543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21a9af4e-b7ef-4f0c-821c-bfe7d3caaa63 · outbound

This paper cites Chatgpt and other large language models for cybersecurity of smart grid applications,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Chatgpt and other large language models for cybersecurity of smart grid applications,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.785434Z

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.

Observation 26e2be80-496f-478a-9faf-0d309b7437e3 · outbound

This paper cites From news to forecast: Integrating event analysis in llm-based time series forecasting with reflection,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework From news to forecast: Integrating event analysis in llm-based time series forecasting with reflection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.768063Z

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.

Observation d343623b-40a0-4ab5-b11c-33494c88b7ef · outbound

This paper cites Global, regional, and local acceptance of solar power,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Global, regional, and local acceptance of solar power,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.705900Z

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.

Observation c577c8a5-80ad-45fd-b4d7-30e9d6f60778 · outbound

This paper cites ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:51.325714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faaa44bf-6a88-4612-9878-7714748d5cd4 · outbound

This paper cites Applying large language models to power systems: Potential security threats,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Applying large language models to power systems: Potential security threats,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.655648Z

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.

Observation 3ef12803-bf69-43f8-af71-ac39a6a6f2e3 · outbound

This paper cites Exploration of generative intelligent application mode for new power systems based on large language models,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Exploration of generative intelligent application mode for new power systems based on large language models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.640096Z

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.

Observation ff7f8a89-87e5-4881-a10a-3e89541f9aae · outbound

This paper cites Large foundation models for power systems,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Large foundation models for power systems,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:51.339209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e0b3ece-7db9-4949-a02e-cfcf6afeb31c · outbound

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

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Retrieval-augmented generation for knowledge-intensive NLP tasks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.624436Z

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.

Observation 5fdddbc2-1888-4cde-a77b-7c516bf7d35f · outbound

This paper cites Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Daline.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Daline

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:51.347955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2acceee-f8d0-4dad-9158-80b1c10f9a29 · outbound

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

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Chain-of-thought prompting elicits reasoning in large language models,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:51.352798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebcaaf4c-25d3-419c-ae05-9b2173397a90 · outbound

This paper cites Language models are few-shot learners,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Language models are few-shot learners,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.597271Z

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.

Observation 29ac7ef1-ec0f-4964-8f12-b8883bb5892f · outbound

This paper cites Daline: A data-driven power flow linearization toolbox for power systems research and education,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Daline: A data-driven power flow linearization toolbox for power systems research and education,

Reference 23

Resolution: verified exact; doi, observed 2026-08-12T15:14:51.440681Z

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.

Observation b0eac0bc-48bf-420b-b9dd-d8857f731970 · outbound

This paper cites Matpower: Steady-state operations, planning, and analysis tools for power systems research and education,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Matpower: Steady-state operations, planning, and analysis tools for power systems research and education,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.579684Z

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.

Observation effd2483-2d83-44fe-bdf4-27b21d87baec · outbound

This paper cites User manual for daline 1.1.5,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework User manual for daline 1.1.5,

Reference 25

Resolution: verified exact; doi, observed 2026-08-12T15:14:51.423138Z

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.

Observation d1220227-6144-46c9-a338-97f7268864c7 · outbound

This paper cites Matpower 8.0 user’s manual,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Matpower 8.0 user’s manual,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.562924Z

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.

Observation 401086c2-bdb2-4647-838c-f157f17d8aa1 · outbound

This paper cites Finetuned language models are zero-shot learners,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Finetuned language models are zero-shot learners,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.547239Z

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.

Observation 051ce874-8d23-41fb-9897-6bc563c34d4d · outbound

This paper cites Chatgpt-4o,.

Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework Chatgpt-4o,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:51.531084Z

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.

Pith citing papers

Observation 7281f076-d6cc-4024-a3ad-942f392b1ce8 · inbound

Large Language Model-Empowered Interactive Load Forecasting cites this paper.

Large Language Model-Empowered Interactive Load Forecasting Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:03:05.801927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f9788ea-229e-4fde-8a1b-9714f923e41e · inbound

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading cites this paper.

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:12:59.610006Z

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.

Observation 93ec93f3-d923-4cd9-b57f-a40292bd4e11 · inbound

Knowledge Boundary Probing and Demand-Guided Intervention for LLM-Based Power System Code Generation cites this paper.

Knowledge Boundary Probing and Demand-Guided Intervention for LLM-Based Power System Code Generation Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:16:11.865186Z

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.