Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings | Paper Citation Record

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings write newline

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings ROBEL : R obotics benchmarks for learning with low-cost robots

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This paper cites Physics of Language Models: Part 1, Learning Hierarchical Language Structures.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Physics of Language Models: Part 1, Learning Hierarchical Language Structures

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This paper cites The Claude 3 model family: Opus, Sonnet, Haiku.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings The Claude 3 model family: Opus, Sonnet, Haiku

Reference 4

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms

Reference 5

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Transfer Learning for Bayesian Optimization: A Survey

Reference 6

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings R., Daulton, S., Letham, B., Wilson, A

Reference 7

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This paper cites A., Vedenko, A., Kurland, J.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings A., Vedenko, A., Kurland, J

Reference 8

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Deb , K

Reference 9

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings OpenAI Gym

Reference 10

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Unresolved cited work

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Linear algebra with T ransformers

Reference 12

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings N., and Doppa, J

Reference 13

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Robust guided diffusion for offline black-box optimization

Reference 14

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This paper cites S., Zhang, Y., Fu, J., Liu, X.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings S., Zhang, Y., Fu, J., Liu, X

Reference 15

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings S., Beckham, C., Liu, Z., Liu, X

Reference 16

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings S., Zhang, Y., Liu, X

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings A simple framework for contrastive learning of visual representations

Reference 18

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Towards learning universal hyperparameter optimizers with T ransformers

Reference 19

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings G rad N orm: G radient normalization for adaptive loss balancing in deep multitask networks

Reference 20

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings C., Nguyen, P

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings C., Nguyen, P

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings S., Babu, C

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 24

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings M., Friesen, A

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Transfer learning for B ayesian optimization on heterogeneous search spaces

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Listgarten, J

Reference 27

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings A Tutorial on Bayesian Optimization

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Levine, S

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Metadata Conditioning Accelerates Language Model Pre-training

Reference 30

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings S., and Valiant, G

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings W., Rezende, D., and Eslami, S

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings B ayesian Optimization

Reference 33

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings A., and Hennig, P

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings J., Bento, A

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings S.-H., Shi, Y.-H., and Zhang, J

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Generative adversarial networks

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Soft Actor-Critic Algorithms and Applications

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings A data-driven statistical model for predicting the critical temperature of a superconductor

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings The CMA Evolution Strategy: A Tutorial

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings V., and Auger, A

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings MetaBalance : I mproving multi-task recommendations via adapting gradient magnitudes of auxiliary tasks

Reference 42

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Improving neural networks by preventing co-adaptation of feature detectors

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Denoising diffusion probabilistic models

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Unresolved cited work

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings u ller, S., Purucker, L., Krishnakumar, A., K \

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings BO : A ugmenting acquisition functions with user beliefs for B ayesian optimization

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings A general framework for user-guided B ayesian optimization

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Global Optimization and Space Pruning for Spacecraft Trajectory Design , pp.\ 178--199

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Manuel L \' o pez-Ib \' a \ n ez, M

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings LLMOPT : L earning to define and solve general optimization problems from scratch

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Eberhart, R

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings I., Grosnit, A., Deik, D.-G.-X., Robert, P

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Bootstrapped training of score-conditioned generator for offline design of biological sequences

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Offline model-based optimization: C omprehensive review

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings D., Wu, D., Wang, H., Ferber, A., Ma, Y.-A., Gomes, C

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings M., and Grover, A

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings G., Abbeel, P., and Levine, S

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings G., Uehara, M., Levine, S., and Abbeel, P

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Richardson, J

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Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Levine, S

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This paper cites Data-driven offline optimization for architecting hardware accelerators.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Data-driven offline optimization for architecting hardware accelerators

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.653509Z

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Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:52.636623Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 68353828-f2c0-4f92-b66b-94e0f0749519 · outbound

This paper cites an unresolved cited work.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:52.620466Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8d55afd6-eaf3-4680-b49b-00328cc37c0a · outbound

This paper cites an unresolved cited work.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:52.605686Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 861b1b3b-d807-4ef4-bd47-14ecb1ac7cf2 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings The power of scale for parameter-efficient prompt tuning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.590621Z

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Observation f9938b15-e455-4cbc-b6ee-b3c34182c8e8 · outbound

This paper cites C., Hutter, F., Feurer, M., and Bischl, B.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings C., Hutter, F., Feurer, M., and Bischl, B

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.575189Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c1e485b2-c358-43cc-8fe7-2ffd0ba06a0d · outbound

This paper cites Evolution of heuristics: T owards efficient automatic algorithm design using large language model.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Evolution of heuristics: T owards efficient automatic algorithm design using large language model

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.560508Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6e91c608-ac11-486c-9b4a-8e2852057a75 · outbound

This paper cites A systematic survey on large language models for algorithm design.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings A systematic survey on large language models for algorithm design

Reference 69

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Observation a933e565-db59-4f93-a425-9cc5835deb84 · outbound

This paper cites LLM4AD : A platform for algorithm design with large language model.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings LLM4AD : A platform for algorithm design with large language model

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T05:46:50.822431Z

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Observation aeb409e7-9e10-4186-b45a-e2ebe42a93a9 · outbound

This paper cites J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, L., and Shazeer, N.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, L., and Shazeer, N

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.546016Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8719976b-779d-4b2c-ae53-ee6b0e88ef6a · outbound

This paper cites Large language models to enhance B ayesian optimization.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Large language models to enhance B ayesian optimization

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.531725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 655d6ab9-46a9-4e70-91cd-c8ea4bdcc9d6 · outbound

This paper cites and Hutter, F.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Hutter, F

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.516817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 895cd0c5-7d9e-460a-b00b-82037f85d083 · outbound

This paper cites Degradation-resistant offline optimization via accumulative risk control.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Degradation-resistant offline optimization via accumulative risk control

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.501478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d50dbff0-6b9a-42f6-9d44-21fda8af08f0 · outbound

This paper cites LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T05:46:50.845870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5442e2df-620f-41c8-94ce-1b2f8f7f5c44 · outbound

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Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:52.486338Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fc04e0f7-bfef-47fd-8843-4464f0f41af1 · outbound

This paper cites an unresolved cited work.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:52.471371Z

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Observation f5254b0d-8726-4b53-82d6-5afaf682d47c · outbound

This paper cites M., Krishnamoorthy, S., and Grover, A.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings M., Krishnamoorthy, S., and Grover, A

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.456183Z

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Observation 5eb5f13b-4a05-4095-96cf-fb0eec83c283 · outbound

This paper cites P., Grabocka, J., and Hutter, F.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings P., Grabocka, J., and Hutter, F

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.440905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9a91adfd-f83d-428e-ac97-efa72b8d0581 · outbound

This paper cites PFNs4BO : I n-context learning for B ayesian optimization.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings PFNs4BO : I n-context learning for B ayesian optimization

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.425571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 03e37889-f957-455d-8e6d-0bd94a8e06c8 · outbound

This paper cites L., Zhang, F., and Barak, B.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings L., Zhang, F., and Barak, B

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.410902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bb32b67c-5ca4-4245-8348-fba42889cf61 · outbound

This paper cites and Grover, A.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Grover, A

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.395832Z

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Observation 05d11ff9-b6f8-41cd-baec-49dfa2c3c0fa · outbound

This paper cites and Grover, A.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings and Grover, A

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.380606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fe775602-b00e-4735-b197-21bc6832613b · outbound

This paper cites ExPT : S ynthetic pretraining for few-shot experimental design.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings ExPT : S ynthetic pretraining for few-shot experimental design

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.366687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0f6101b1-9450-4a16-927c-450132fe9bf6 · outbound

This paper cites Predicting from strings: L anguage model embeddings for B ayesian optimization.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Predicting from strings: L anguage model embeddings for B ayesian optimization

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T05:46:50.894274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2aa5e46-f99e-463f-8170-5ff2a2b9dc12 · outbound

This paper cites Z., Shirobokov, S., Kozlovskii, B., Ruiz, F.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Z., Shirobokov, S., Kozlovskii, B., Ruiz, F

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.351674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 59dc732a-ef14-4376-b167-95d8afcff708 · outbound

This paper cites Introduction to Game Physics with Box2D.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Introduction to Game Physics with Box2D

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.336167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 827333c0-06c2-4a11-8cc3-704b9e3dc85f · outbound

This paper cites Data-driven offline decision-making via invariant representation learning.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Data-driven offline decision-making via invariant representation learning

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.320584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 15a4d381-c9e2-499e-9606-786918bd8965 · outbound

This paper cites SOO - B ench: B enchmarks for evaluating the stability of offline black-box optimization.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings SOO - B ench: B enchmarks for evaluating the stability of offline black-box optimization

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.304256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 86ff406b-9fb9-4384-9c9f-2217554f14de · outbound

This paper cites Qwen2.5 Technical Report.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Qwen2.5 Technical Report

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T05:46:50.919390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2c2356f-f154-471f-8a1a-cca78ad7bc00 · outbound

This paper cites an unresolved cited work.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Unresolved cited work

Reference 91

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:52.288832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6ed1f301-adcb-4dce-a75e-9af698f296d5 · outbound

This paper cites an unresolved cited work.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:46:52.274176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 89ae224e-c449-4518-b98d-864ae52395b6 · outbound

This paper cites P., Dupont, E., Ruiz, F.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings P., Dupont, E., Ruiz, F

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.259400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f722d3a1-beff-4b26-8942-f4dc6f7a212d · outbound

This paper cites GTOPX space mission benchmarks.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings GTOPX space mission benchmarks

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.244719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation be807eec-56be-4dee-a164-1d261a98d61d · outbound

This paper cites P., and de Freitas, N.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings P., and de Freitas, N

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.229935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 554c0a99-3880-4f29-8197-2c84d1ccea3e · outbound

This paper cites B., and Reddy, C.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings B., and Reddy, C

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.215438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 99c3f16a-8434-4d2c-9795-40655495dcf9 · outbound

This paper cites Reinforced in-context black-box optimization.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Reinforced in-context black-box optimization

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.200262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8a8d8ac8-09a7-4679-89a7-6310e4519f1c · outbound

This paper cites Decoding-based Regression.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings Decoding-based Regression

Reference 98

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:46:51.194156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b23dbbc6-5058-4b81-afff-516627c131b7 · outbound

This paper cites OmniPred : L anguage models as universal regressors.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings OmniPred : L anguage models as universal regressors

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.184663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 756053c7-3e79-41b6-bafe-dcdb0467f2f2 · outbound

This paper cites T., Lee, C., Tang, Y., and Chen, Y.

Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings T., Lee, C., Tang, Y., and Chen, Y

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:46:52.169613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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