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Paper Citation Record · LEDGER

RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.11132.

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

pith.paper-citation-record.v1
2406.11132 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:20:55.786633Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:28:58.981049Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 98035dd5-0125-4f69-a14d-cc0004c189f0 · inbound

Federated In-Context LLM Agent Learning cites this paper.

Federated In-Context LLM Agent Learning RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-11T18:20:55.786633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:20:55.786633Z digest=sha256:cb5b14ec2fac28a2e6a994654c79331b8ec1d289ce9d61223409299761d7104f

Observation f453a22c-6776-4d7f-be79-beeb3aae3237 · inbound

Script-Based Dialog Policy Planning for LLM-Powered Conversational Agents: A Basic Architecture for an "AI Therapist" cites this paper.

Script-Based Dialog Policy Planning for LLM-Powered Conversational Agents: A Basic Architecture for an "AI Therapist" RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-11T16:25:55.896315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T16:25:55.896315Z digest=sha256:6cd8f82a28c8d400c16b6d27d0b22a0246102cdf85f63b8848cc666a39307982

Observation fd05ab1f-b1c7-452e-a023-64c57dacae3b · inbound

Iterative Deepening Sampling as Efficient Test-Time Scaling cites this paper.

Iterative Deepening Sampling as Efficient Test-Time Scaling RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-08T19:23:26.286580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:23:26.286580Z digest=sha256:c75511581b34ab16a7c69fea88da562e357aee4c1f6a2f7cbae3e2bf1e4aa451

Observation 17af7e53-82ef-4714-a73d-0502b397e090 · inbound

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence cites this paper.

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:23:15.577864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-14T22:23:14.621091Z digest=sha256:640b465365d3262643baaa668608ac2bd9bba2dcfec24d6bf16ca49421082743

Observation 203b735c-7071-4d86-ad12-9f6b38c0d130 · inbound

Environment-Grounded Automated Prompt Optimization for LLM Game Agents cites this paper.

Environment-Grounded Automated Prompt Optimization for LLM Game Agents RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-07-03T21:28:58.982799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-27T00:30:42.133192Z digest=sha256:846695b24f75f244daed31f5d28b66b85a2d5e3a4a289b1f407fcda748b554dd