Paper Citation Record · LEDGER

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits

As of 5 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2605.14553.

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

pith.paper-citation-record.v1
2605.14553 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-15T01:50:21.013336Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

33 of 33 outbound references displayed

verified exact6
verified fuzzy21
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac2abbae-81f9-4468-b204-7ae3267a5c5c · outbound

This paper cites Gepa: Reflective prompt evolution can outperform reinforcement learning.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Gepa: Reflective prompt evolution can outperform reinforcement learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.972667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a265d99e-d00a-457f-8b14-d1dc890e5e40 · outbound

This paper cites Best arm identification in multi-armed bandits.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Best arm identification in multi-armed bandits

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.967761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation aba489c4-eb97-4df5-b038-695a82017d5d · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.970396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c84e5a03-b9fe-4b33-b5e9-03701ad70099 · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits LEAF: A Benchmark for Federated Settings

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T01:53:29.078725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 014dea16-a4dd-4af2-b065-487116c14bf0 · outbound

This paper cites Discrete prompt optimization via constrained generation for zero-shot re-ranker.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Discrete prompt optimization via constrained generation for zero-shot re-ranker

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.921458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5daa4583-fcb2-4a20-bedf-d20d139d0ccb · outbound

This paper cites Rlprompt: Optimizing discrete text prompts with reinforcement learning.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Rlprompt: Optimizing discrete text prompts with reinforcement learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.957041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 880ef7b5-42d0-4a98-8cf7-9a99fa300174 · outbound

This paper cites Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:53:29.088035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 36fa0f86-9814-47a1-bbcf-4aec30e08724 · outbound

This paper cites Connecting large language models with evolutionary algorithms yields powerful prompt optimizers.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Connecting large language models with evolutionary algorithms yields powerful prompt optimizers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.923973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a5d8aa67-73ed-487b-a436-2a04af6ec472 · outbound

This paper cites Morl-prompt: An em- pirical analysis of multi-objective reinforcement learning for discrete prompt optimization.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Morl-prompt: An em- pirical analysis of multi-objective reinforcement learning for discrete prompt optimization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.926195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation becde7a7-e613-4c92-90a6-30cc8297ccad · outbound

This paper cites Prompt-RAG: Pioneering Vector Embedding-Free Retrieval-Augmented Generation in Niche Domains, Exemplified by Korean Medicine.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Prompt-RAG: Pioneering Vector Embedding-Free Retrieval-Augmented Generation in Niche Domains, Exemplified by Korean Medicine

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:53:29.097534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9b6b264f-bb7e-48f8-a1fa-90d1ca9744f5 · outbound

This paper cites Bounded archiving using the lebesgue measure.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Bounded archiving using the lebesgue measure

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.945945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a04f48bb-bf70-4602-a6f2-e75af73d9438 · outbound

This paper cites Bandit Pareto set identification in a multi- output linear model.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Bandit Pareto set identification in a multi- output linear model

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.952727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 35148e7c-5146-41f1-b58a-41da6c34345f · outbound

This paper cites Meta-prompt optimization for LLM- based sequential decision making.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Meta-prompt optimization for LLM- based sequential decision making

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.928324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 95b29c6a-b6e5-4728-b23d-e76ea094ccd0 · outbound

This paper cites Prompt optimization with human feedback.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Prompt optimization with human feedback

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.959147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f07e2739-d4d6-4929-a33b-8df2188e29c0 · outbound

This paper cites Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T01:53:29.075357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b9b3b0d5-5cd2-41e6-959e-b70e9e98e73f · outbound

This paper cites gradient descent.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits gradient descent

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.963431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 63bc68b5-5084-431e-bd1f-3c6034b3f7a7 · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-15T01:53:29.094523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 39016120-146d-4124-b252-106ab70d1ad1 · outbound

This paper cites The Prompt Report: A Systematic Survey of Prompt Engineering Techniques.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:16:17.988307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 93a0d0bd-11be-4306-a0ee-4b674f827caf · outbound

This paper cites Autoprompt: Eliciting knowledge from language models with automatically generated prompts.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Autoprompt: Eliciting knowledge from language models with automatically generated prompts

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.930461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1db96bb4-3e6a-44cf-9890-249e41f1e3a3 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Gemma: Open Models Based on Gemini Research and Technology

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-15T01:53:29.084605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation aa5b0daf-b655-42da-845d-f676fd58d717 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.943446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c2da5128-f804-4b81-bdbd-f4f90322c51e · outbound

This paper cites A Survey of Attacks on Large Language Models.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits A Survey of Attacks on Large Language Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:53:29.081821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a246509f-ab48-4110-ba97-433d37d9ad27 · outbound

This paper cites Backdooring instruction-tuned large language models with virtual prompt injection.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Backdooring instruction-tuned large language models with virtual prompt injection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.948169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e6837e30-c243-4867-8e4c-29ba5cf861d2 · outbound

This paper cites Instoptima: Evolutionary multi-objective instruction optimization via large language model-based instruction operators.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Instoptima: Evolutionary multi-objective instruction optimization via large language model-based instruction operators

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.954982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7a44ea58-731e-42d2-bfbb-88e3fe1c03ea · outbound

This paper cites an unresolved cited work.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-15T08:30:17.965452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 51433411-0d5a-4712-b4c0-de7041ed367f · outbound

This paper cites For the multi-objective bandit setting, we have the prompt or arm setXwith|X |=K, and the expected performance or reward vector µ(x), x∈ X.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits For the multi-objective bandit setting, we have the prompt or arm setXwith|X |=K, and the expected performance or reward vector µ(x), x∈ X

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.961233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cbb164f1-72f8-4122-b299-d9ae0ac31d6d · outbound

This paper cites an unresolved cited work.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-05-15T08:30:17.938962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7124e729-03d6-41ed-9978-e177f21a5b44 · outbound

This paper cites In roundr, given the active arm setA r−1, the arm pulled at steptsatisfiesx (t) ∈A r−1 and yields the observed outcomef (t).

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits In roundr, given the active arm setA r−1, the arm pulled at steptsatisfiesx (t) ∈A r−1 and yields the observed outcomef (t)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.950582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5ab7226a-e076-4e30-9c6c-1cd3a0f53580 · outbound

This paper cites an unresolved cited work.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-05-15T08:30:17.936717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0244d1b1-39db-403f-812f-ef9e74a90a67 · outbound

This paper cites an unresolved cited work.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-05-15T08:30:17.941158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 59fe84ed-ea82-41b9-821a-d6c74c5f8ab0 · outbound

This paper cites For the instruction models, we adopt the recommended system template as Figure.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits For the instruction models, we adopt the recommended system template as Figure

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.934653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d37313f5-2a9c-43eb-aa83-664c4bbe4646 · outbound

This paper cites It captures the token-wise similarity between two texts.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits It captures the token-wise similarity between two texts

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.919239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 49cb6f07-521a-4f28-9742-0031623c6118 · outbound

This paper cites (a) Constraint = 0.6 (b) Constraint = 0.5 (c) Constraint = 0.4 Figure 9: Feasible average reward vs.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits (a) Constraint = 0.6 (b) Constraint = 0.5 (c) Constraint = 0.4 Figure 9: Feasible average reward vs

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T08:30:17.932535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

No inbound Pith citation observations are available.