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Paper Citation Record · LEDGER

RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 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 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:37:28.378324Z

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
  • 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 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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T00:04:31.212102Z digest=sha256:4b016bc245562a66abe5138ed0aef8e5464f8639492d5e1f8ac2b78c6c1c419f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T23:23:03.562550Z digest=sha256:54210208ec965fc3484da2889b33583dfee0d533f7c80f721297dd76d98a2295

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.

source=pdf_text observed=2026-08-08T04:37:28.378324Z digest=sha256:7a09e6b4997c5f2de3957a4057982da95d4a2a71230f5cab5a86d9c38ddcad91

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.

source=arxiv_source observed=2026-08-07T12:02:10.501676Z digest=sha256:d454669c19187a03dc64d29bda916cba7827017f6cf03883435522c73536db0e

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.

source=arxiv_source observed=2026-08-07T05:10:39.500635Z digest=sha256:9e1ae611b2a3874dcf266a9fa7c468f03ce400dacde2c53319f2bb6dea362644

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.

source=pdf_text observed=2026-08-06T20:21:22.259301Z digest=sha256:a4cb3bf2b01eac9669da1ad0cd605b93ef1de3b8732b82ab7d053173376421ad

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.

source=pdf_text observed=2026-08-06T16:42:30.841622Z digest=sha256:cd9b84ab8dbc07fbb81561743c69529074662718068eea140860027813b17352

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.

source=pdf_text observed=2026-08-06T14:35:46.534226Z digest=sha256:d983c911b88355f3368ab8d2941b0f8b4fc6460c06b903c3e418aef7df166686

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T23:15:52.444217Z digest=sha256:00328e62b9cbac34964a68dbe63f3d74ed4271086aab4bde00ead3bbf21eef6f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T13:57:41.428695Z digest=sha256:2c61b872e7ba919a3be5cbf50a62c66d9fdda716f40bfb879c3580100877c21d

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T05:00:31.452781Z digest=sha256:19bd60fa8c6ebf48279b938685205d5d66a41d4712dad840a06b78aef48eeb94

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T05:19:05.368681Z digest=sha256:cd9430fd21f05ace66faab8627be77206c226898b000180f72769cfa23cd244d

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T08:49:36.986452Z digest=sha256:910d6e9ce30a03ca4c8557832b720f94c81203b4f4b75d0cd8e26b9c7b7cf343

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-27T10:28:11.440915Z digest=sha256:7b0313f076483b4c602e33c33e125dcef649c173f89acfd791c73128ae499041