Paper Citation Record · LEDGER

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models

As of 18 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2608.10471.

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

pith.paper-citation-record.v1
2608.10471 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:26:30.330510Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy18
unresolved23
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 638a525f-2864-4c82-b2c9-1c69e093de63 · outbound

This paper cites GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.188421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d166e139-46b7-47c1-858c-03dd797f72de · outbound

This paper cites Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.193115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb57501d-d22d-44de-8655-ed96ce006d17 · outbound

This paper cites Large Language Models as Optimizers.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Large Language Models as Optimizers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.196997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdf0d1ce-eef0-4f7e-b399-4a77fb137fd4 · outbound

This paper cites an unresolved cited work.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:26:30.746741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed8843cb-c760-417b-8822-748efd971738 · outbound

This paper cites TextGrad: Automatic "Differentiation" via Text.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models TextGrad: Automatic "Differentiation" via Text

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.203821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de79cd07-8441-4fb4-82bc-0f8b2651735e · outbound

This paper cites Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.207020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55440776-fc97-4ff5-83e7-7d290f295d9a · outbound

This paper cites EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.210364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9babd9a9-c319-4bfb-84ca-aa706b768f2d · outbound

This paper cites RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.213550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad55ee85-c9b7-4f99-ba15-b5c00e5b7c61 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.217121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0645c6ca-1570-4968-9415-e9e0cc661c9b · outbound

This paper cites Automated Design of Agentic Systems.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Automated Design of Agentic Systems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.220720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57182a43-8cfc-4dfd-b863-28dd1a586c82 · outbound

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

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.224219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91559be6-cd38-40ac-af16-2e379081cbc5 · outbound

This paper cites Recursive Language Models.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Recursive Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.228045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4301cea-f2b9-4c85-87d4-830d2ffd6bbb · outbound

This paper cites SkillOpt: Executive Strategy for Self-Evolving Agent Skills.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models SkillOpt: Executive Strategy for Self-Evolving Agent Skills

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.232028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f3b340d-62cc-49ed-928a-1927d446fd6d · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.235648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66f344ff-f5d2-4d9a-83bb-805785fe929d · outbound

This paper cites Christiano, J.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Christiano, J

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.737037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 94c2c675-e94e-4988-b5a0-13e64ec83ac9 · outbound

This paper cites Ouyang, J.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Ouyang, J

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.727714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9bc6aec3-0590-4fc0-9cc7-3c37e609f118 · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.247223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66a2986d-ef64-41df-8be3-c6a24eaaa7e3 · outbound

This paper cites an unresolved cited work.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:26:30.718509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 870da879-4de2-4409-90c0-f0f9f0e17dd4 · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.254943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 239440e6-f545-4ce9-94cf-357bf160c06f · outbound

This paper cites an unresolved cited work.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:26:30.710174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5099acf5-6e07-4b3f-b60a-a8ecea7a7896 · outbound

This paper cites Zheng, W.-L.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Zheng, W.-L

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.701774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f09585e0-219b-4402-938e-29f3e567814d · outbound

This paper cites an unresolved cited work.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:26:30.692528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f09da6e0-ed0d-4b74-b2e5-ed9779256aa5 · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Instruction-Following Evaluation for Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:30.266740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb3a5adc-ba8a-473c-8f42-21d3ee46d9b7 · outbound

This paper cites an unresolved cited work.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:26:30.683569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 93096b78-c4de-4e2a-a587-fe21f920b0b5 · outbound

This paper cites Benchmark suite (58-verifier registry), 2025.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Benchmark suite (58-verifier registry), 2025

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.673140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b62bb3cf-abb8-4e09-bf43-fe04315aa228 · outbound

This paper cites Call describe_task() + dataset_overview() + peek_examples() a single time each, then STOP – they are static.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Call describe_task() + dataset_overview() + peek_examples() a single time each, then STOP – they are static

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.663295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 139082b5-9720-4ff7-aaa7-1370f79fd9ad · outbound

This paper cites Read describe_task()[’known_rules’] (rules promoted from earlier runs of THIS task) AND scratchpad_read(...).

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Read describe_task()[’known_rules’] (rules promoted from earlier runs of THIS task) AND scratchpad_read(...)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.654360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e112e896-bfcf-4a0f-9976-7edf638b9ae4 · outbound

This paper cites an unresolved cited work.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:26:30.644738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 64ef524a-16bb-401c-a52e-577907bc2ba2 · outbound

This paper cites Make ONE targeted change, then run_candidate(prompt) WITHOUT example_ids (the host’s representative minibatch).

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Make ONE targeted change, then run_candidate(prompt) WITHOUT example_ids (the host’s representative minibatch)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.635395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b7a11e0c-84bb-42e8-84b3-bb6eb9b7159c · outbound

This paper cites Build only on REAL_GAIN.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Build only on REAL_GAIN

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.624834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 88e0c93d-d85b-4b5a-b1cd-20c358e1424a · outbound

This paper cites When a change earns a REAL_GAIN, record the durable, transferable rule via scratchpad_add(kind="rule", ...) – it is promoted to the cross-run library so future runs start ahead.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models When a change earns a REAL_GAIN, record the durable, transferable rule via scratchpad_add(kind="rule", ...) – it is promoted to the cross-run library so future runs start ahead

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.613571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8eb9daa4-7f92-45f8-969b-774a79f553fa · outbound

This paper cites You are a helpful assistant.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models You are a helpful assistant

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.603128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 576fdc2b-0765-4525-8011-7e8a1526554f · outbound

This paper cites an unresolved cited work.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:26:30.592321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation df5eee8d-41a8-4148-af5a-fc3d1474875c · outbound

This paper cites bigger is not better.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models bigger is not better

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.582865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 78a18ea2-7f34-42ba-a43b-921af3bd756e · outbound

This paper cites output nothing.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models output nothing

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.574374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 18ded130-f873-40c4-8e99-4087f7040915 · outbound

This paper cites output nothing.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models output nothing

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.564336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 925f9d06-2ed7-41eb-8066-53834c2396b5 · outbound

This paper cites start with.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models start with

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.555841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 268d599d-6e3d-4035-a802-fcda101b167f · outbound

This paper cites - Include every required keyword or phrase verbatim and exclude every forbidden keyword or phrase, respecting case sensitivity when specified.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models - Include every required keyword or phrase verbatim and exclude every forbidden keyword or phrase, respecting case sensitivity when specified

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.546384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f7c23747-3600-44da-9621-56fe6f99841e · outbound

This paper cites - Do NOT define concepts, explain laws, or provide background information unless the instruction explicitly asks for an explanation.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models - Do NOT define concepts, explain laws, or provide background information unless the instruction explicitly asks for an explanation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.533822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 232318da-8970-4179-be3c-df5a6421f7a9 · outbound

This paper cites - Do not relax or ignore any stated constraint; choose the interpretation that best satisfies all explicit requirements without adding new assumptions.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models - Do not relax or ignore any stated constraint; choose the interpretation that best satisfies all explicit requirements without adding new assumptions

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.521580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 151b8418-0b20-4c2a-939c-5124391b4990 · outbound

This paper cites no explanation.

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models no explanation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:30.505934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.