Paper Citation Record · LEDGER

RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2205.12548.

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

pith.paper-citation-record.v1
2205.12548 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:01:27.950864Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T09:07:48.430556Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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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 88d78e69-6e92-4862-96d1-121383366bc7 · inbound

Large Language Models as Optimizers cites this paper.

Large Language Models as Optimizers RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:04:31.254934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe76dec6-689a-4f2a-8034-f114824f7bde · inbound

Robust Adaptation of Foundation Models with Black-Box Visual Prompting cites this paper.

Robust Adaptation of Foundation Models with Black-Box Visual Prompting RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:23:36.387152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9ccbe5c2-51d1-40ca-8019-d0a459647cc6 · inbound

The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories cites this paper.

The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T17:01:27.950864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9af4f4f4-7b99-482f-bb15-ba266f347735 · inbound

LLM-AutoDiff: Auto-Differentiate Any LLM Workflow cites this paper.

LLM-AutoDiff: Auto-Differentiate Any LLM Workflow RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T11:33:24.599668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc7e9b01-f2a8-4054-9ba0-f5b095ca68cf · inbound

Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks cites this paper.

Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T04:37:28.378324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d579cd0e-5493-453e-8983-a637d4941938 · inbound

Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering cites this paper.

Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:02:10.501676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d8ed3ec-49bf-4b51-9ed1-26ae0b82aff0 · inbound

Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search cites this paper.

Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:39.500635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 224d88e8-ab61-4fb8-93bf-d059a9da626b · inbound

SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models cites this paper.

SI-Agent: An Agentic Framework for Feedback-Driven Generation and Tuning of Human-Readable System Instructions for Large Language Models RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T20:21:22.259301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75487985-9309-43ef-b8a0-89b8d87f7ec6 · inbound

DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning cites this paper.

DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T16:42:30.841622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8344a737-9e84-4b6f-b48d-f63975534ed0 · inbound

TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards cites this paper.

TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T14:35:46.534226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bd1bca7-01ff-4995-ab4f-ae97d6c44e1b · inbound

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data cites this paper.

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T23:18:40.114020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 55da2032-0889-4030-b6ed-b493c56ee86f · inbound

PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses cites this paper.

PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:00:03.031641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 395c8fda-1aa3-435d-928c-c3640072b345 · inbound

Learning, Fast and Slow: Towards LLMs That Adapt Continually cites this paper.

Learning, Fast and Slow: Towards LLMs That Adapt Continually RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:18.437010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 803935cc-b03b-412d-a9e3-80369770b8cb · inbound

Learning, Fast and Slow: Towards LLMs That Adapt Continually cites this paper.

Learning, Fast and Slow: Towards LLMs That Adapt Continually RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:19:45.771542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 139d43b9-f050-42c1-b168-10468afda8a5 · inbound

Prompt Optimization for LLM Code Generation via Reinforcement Learning cites this paper.

Prompt Optimization for LLM Code Generation via Reinforcement Learning RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T08:53:10.355874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8f7e93f8-0978-4034-aa9a-110e29e52fd2 · inbound

Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning cites this paper.

Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 103

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T09:07:48.432137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.