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Paper Citation Record · LEDGER

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops

As of 17 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 4 inbound Pith citation observations for arXiv:2412.17149.

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

pith.paper-citation-record.v1
2412.17149 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:47:25.771298Z

measured 18 of 18 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 2026-08-15T15:55:13.550702Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:35:43.864558Z

Reference resolution

14 of 14 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 59fd81f1-de03-4dbb-ad9d-508029775403 · outbound

This paper cites online" 'onlinestring :=.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops online" 'onlinestring :=

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T05:47:25.700143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:47:25.700143Z digest=sha256:9e4243eb64f43a68576d9acdfeb053de208b8dacb2d976b0e341db92ef1b178d

Observation 83fa7cd9-e609-478f-8d5d-604199c9e842 · outbound

This paper cites write newline.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops write newline

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T05:47:25.706424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:47:25.706424Z digest=sha256:35a821de0be376d6602c36d8527b77c260b1ea6866816e125ab6c91e341ebd42

Observation 998198a4-b478-478d-8614-274fa82680b1 · outbound

This paper cites Automated Design of Agentic Systems.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops Automated Design of Agentic Systems

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T05:47:25.711971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:47:25.711971Z digest=sha256:5d2f0b494c39e8f17d58ad9553b7c3db5d1ea0aa6b084639e3dfb5a76a58a5e5

Observation cce98787-741b-4933-91ed-85e7c0555f58 · outbound

This paper cites an unresolved cited work.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:47:25.993647Z

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.

source=arxiv_source observed=2026-08-11T05:47:25.717388Z digest=sha256:6cb44cc322f2c0f26d446be86d2b762102e239b66dbf73f03a3487933df152ec

Observation 36c952e7-55aa-45ed-a608-519b07d73c8a · outbound

This paper cites an unresolved cited work.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:47:25.976711Z

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.

source=arxiv_source observed=2026-08-11T05:47:25.723585Z digest=sha256:f4a4409dd19c3afdb831ed26f06f0b59c3295c8a086312af281b317aef84617e

Observation 80cf0cd6-4cec-4b58-a3e8-2a6dd9e145e9 · outbound

This paper cites The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T05:47:25.728481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:47:25.728481Z digest=sha256:39332f8c1ea64a877b98f42081cd5f60bf6f1f5e657688ef3489dff99bbdf1d5

Observation b583745f-9921-494d-a6d7-0f89d74697c4 · outbound

This paper cites an unresolved cited work.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:47:25.960806Z

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.

source=arxiv_source observed=2026-08-11T05:47:25.733788Z digest=sha256:2d90417f991f3a39e8edd8fe758319824e4dd954e2ae025a73b6ecf6faaaf8cd

Observation 9968fb31-5f08-4fdb-a3ae-4f850efc3636 · outbound

This paper cites AgentInstruct: Toward Generative Teaching with Agentic Flows.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops AgentInstruct: Toward Generative Teaching with Agentic Flows

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T05:47:25.739286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:47:25.739286Z digest=sha256:dcfa47cce35f5a79e5be1ad4ef80859ee32553caad63fe948ad5e8741b506477

Observation cafc8b8c-707b-4662-a46b-89424dff79a9 · outbound

This paper cites Feedback Loops With Language Models Drive In-Context Reward Hacking.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops Feedback Loops With Language Models Drive In-Context Reward Hacking

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-11T05:47:25.745361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:47:25.745361Z digest=sha256:8208c4cca12cee08ce64f5512c6ea8d22b44268998245b9005f1f413eae57d6d

Observation 9b700709-b1b5-48e2-9dfd-1626f6d547c1 · outbound

This paper cites an unresolved cited work.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:47:25.944866Z

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.

source=arxiv_source observed=2026-08-11T05:47:25.750973Z digest=sha256:70686e625dc692b81587451cc362540a2a9f9b78f644893d5432c771f3661175

Observation 7e0e2c1b-4ae5-47e5-a7fe-dfc8816384d2 · outbound

This paper cites an unresolved cited work.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:47:25.928829Z

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.

source=arxiv_source observed=2026-08-11T05:47:25.756154Z digest=sha256:6cebfd116d31b977e44ff5ced6011cb2cbee213b175c20ead499cfc80b674eaf

Observation baf5501d-c5e7-4c35-8c76-e6fff3a3da75 · outbound

This paper cites Towards Agentic AI on Particle Accelerators.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops Towards Agentic AI on Particle Accelerators

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T05:47:25.760998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:47:25.760998Z digest=sha256:dc568a19e3ebb29ceeba62456177853bd604bcdcf58ce132ae93c9e3347f60df

Observation 2f4ab687-9a3c-4292-a9cb-7fa7ba44bdfa · outbound

This paper cites an unresolved cited work.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:47:25.912552Z

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.

source=arxiv_source observed=2026-08-11T05:47:25.766385Z digest=sha256:24d68217e7ccefc2876ebe0d575b5f11831fd9742705ab7881cd7f2634bf6b4b

Observation 315bd876-a5fd-4726-9ff1-3db28bed851b · outbound

This paper cites ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning.

A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T05:47:25.771298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:47:25.771298Z digest=sha256:4e8053c013383210455e6cf5747b7840c0e4cd9612a51c1a74fce8da0acb7b9a

Pith citing papers

Observation 2574e3ca-5520-4400-8517-2c96421be459 · inbound

A Survey on LLM-as-a-Judge cites this paper.

A Survey on LLM-as-a-Judge A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops

Reference 201

Resolution
verified exact
arxiv_id, observed 2026-05-23T17:35:43.866874Z

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.

source=pdf_text observed=2026-05-23T17:33:13.394338Z digest=sha256:81b51ca84ce8852d3c2cefbbdfb73a5ce37706ddbf18c4194e061d10e7dfb49d

Observation a22af73a-49c5-4b5f-8e9d-b149f6e20768 · inbound

Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems? cites this paper.

Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems? A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-15T15:55:13.550702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:55:13.550702Z digest=sha256:c11f894357a563c8bafd0e4b4a44bdfd6ec60fbf21c1f2f177d5c743abc515d2

Observation 03f04f47-e4c6-45eb-b5aa-6345b1bfac17 · inbound

Security Considerations for Multi-agent Systems cites this paper.

Security Considerations for Multi-agent Systems A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops

Reference 210

Resolution
verified exact
arxiv_id, observed 2026-05-15T14:15:55.992072Z

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.

source=pdf_text observed=2026-05-15T14:12:14.160789Z digest=sha256:bef02077245091f33c15fb4eda3eb9a83f4c54fabee64b59352cc0ba778d2ca2

Observation b155e3f2-b548-4388-9193-76e6f87b1722 · inbound

CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation cites this paper.

CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops

Reference 30

Resolution
unresolved
no resolver link, observed 2026-07-13T11:58:11.319217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T11:58:11.319217Z digest=sha256:a90fdfba330c73653c2d1a31ab795ab03ae4b7bc7d93940d8bb70dacaa8c9357