Paper Citation Record · LEDGER

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

As of 17 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 4 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 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:06:54.398974Z

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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+00:00.

Observation ae7571d7-1ac3-4c8c-b8d0-9e1d03b198a6 · inbound

Large Language Model Powered Automated Modeling and Optimization of Active Distribution Network Dispatch Problems cites this paper.

Large Language Model Powered Automated Modeling and Optimization of Active Distribution Network Dispatch Problems Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T18:06:54.398974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-17T06:30:58.91139+00:00.