Paper Citation Record · LEDGER

Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2306.03604.

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

pith.paper-citation-record.v1
2306.03604 v8

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:56:12.846313Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7d5fbd5d-bd6a-4454-84ec-9f7094f3af82 · inbound

A Survey on Large Language Model based Autonomous Agents cites this paper.

A Survey on Large Language Model based Autonomous Agents Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Reference 132

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T04:03:00.722878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9f36f5d0-8746-4814-9402-eefbc739152f · inbound

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines cites this paper.

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:57:47.360949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 816bbec4-5615-45bc-931a-225c14f2aea5 · inbound

ClausewitzGPT Framework: A New Frontier in Theoretical Large Language Model Enhanced Information Operations cites this paper.

ClausewitzGPT Framework: A New Frontier in Theoretical Large Language Model Enhanced Information Operations Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-24T05:58:57.537157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0f6bed36-8039-4375-91b8-473f1a3387cb · inbound

Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches cites this paper.

Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Reference 213

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:12.846313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3f4d9ff-8b58-421a-aa92-464e82c02794 · inbound

Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting cites this paper.

Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T23:42:00.090363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc315893-5824-43fa-83cd-ec954e3c8ebb · inbound

Application of LLMs to Multi-Robot Path Planning and Task Allocation cites this paper.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:48:17.837436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97a8c8b4-fb66-45fe-b343-a074b072e702 · inbound

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment cites this paper.

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:55:50.685819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.