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Paper Citation Record · LEDGER

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation

As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2502.01692.

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

pith.paper-citation-record.v1
2502.01692 v5

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:35:58.366847Z

measured 36 of 36 standing notices

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

36 of 36 outbound references displayed

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  • unresolved26
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External citation measurements

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Outbound references

Observation 6cb896d7-9d17-4908-b964-3fe10d7c4fc5 · outbound

This paper cites An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization

Reference 2

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Observation 52f265f5-680c-45b0-a88f-f0a507f32ec9 · outbound

This paper cites DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Reference 5

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source=pdf_text observed=2026-08-09T17:35:58.274899Z digest=sha256:1bd8733f560f614be6a51d7ffaf6021ac29a72b7daf5b9a4f7a2f794d3b21392

Observation 0e049bfa-ed85-4f7e-bf7d-9cb150273a72 · outbound

This paper cites Laurianne David, Amol Thakkar, Roc´ıo Mercado, and Ola Engkvist.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Laurianne David, Amol Thakkar, Roc´ıo Mercado, and Ola Engkvist

Reference 6

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Observation af9d8f21-eaa8-4bde-9416-8a0933d9778c · outbound

This paper cites 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction

Reference 9

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source=pdf_text observed=2026-08-09T17:35:58.287398Z digest=sha256:2da3f30605ce556b7bce8774a1c3c2039e142cfd2e28ef1bbaf88c47fa96eb2b

Observation cc1adb01-cc63-48db-b696-681ddf397003 · outbound

This paper cites Manifold Preserving Guided Diffusion.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Manifold Preserving Guided Diffusion

Reference 10

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Observation 637e4874-f072-4953-ad96-14a4c811f5a4 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation LoRA: Low-Rank Adaptation of Large Language Models

Reference 11

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Observation 70b4b7be-466e-4766-ad3d-d8a5e4be6413 · outbound

This paper cites Vanilla Bayesian Optimization Performs Great in High Dimensions.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Vanilla Bayesian Optimization Performs Great in High Dimensions

Reference 12

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Observation b67d9572-aede-41cb-8efe-b41dd2251eef · outbound

This paper cites Diffusion Models for Black-Box Optimization.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Diffusion Models for Black-Box Optimization

Reference 13

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Observation aaf0fae5-180c-4a38-94b7-d45b10aed825 · outbound

This paper cites Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding

Reference 14

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Observation 162be374-201c-4137-974f-2b61de64727a · outbound

This paper cites SDXL-Lightning: Progressive Adversarial Diffusion Distillation.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation SDXL-Lightning: Progressive Adversarial Diffusion Distillation

Reference 15

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Observation 1f325a5a-5ed2-4222-b611-3ae13dc57048 · outbound

This paper cites Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation

Reference 16

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Observation 50ba317a-462a-407f-89b9-4b77bcf264d1 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 17

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Observation 1d76ee12-788b-4dac-ba78-fae0f2870b28 · outbound

This paper cites Aligning Text-to-Image Diffusion Models with Reward Backpropagation.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 18

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Observation b53660be-401a-4642-be27-1efeee7132c1 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36,.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36,

Reference 19

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Observation 83fd2a7a-87e5-457e-aa13-d900329da8a9 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 20

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Observation bd552552-8d53-4e15-bc9a-dbfd9822eb28 · outbound

This paper cites Equivalence Between Policy Gradients and Soft Q-Learning.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Equivalence Between Policy Gradients and Soft Q-Learning

Reference 21

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Observation 705dd4ee-775b-435e-9c16-36219f743b76 · outbound

This paper cites Inference-Time Alignment of Diffusion Models with Direct Noise Optimization.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Inference-Time Alignment of Diffusion Models with Direct Noise Optimization

Reference 23

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Observation 2e0388e4-e48b-467d-8fd2-90e7c6da6657 · outbound

This paper cites Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review

Reference 24

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Observation 4a589ed5-8063-4232-ac24-c3d5cf2f07a5 · outbound

This paper cites We follow DDPO (Black et al.,.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation We follow DDPO (Black et al.,

Reference 25

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Observation c6d9eb79-7ccf-4c0d-b980-26c7eb4bb22c · outbound

This paper cites Objective Function.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Objective Function

Reference 26

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Observation 20e0d885-4bc1-46b7-9f1d-ccf195e117dd · outbound

This paper cites We set N = 50 batch query budget for compressibility and incompressibility, and N = 100 for aesthetic quality.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation We set N = 50 batch query budget for compressibility and incompressibility, and N = 100 for aesthetic quality

Reference 27

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Observation cdeccb40-ec79-4154-9d1a-7a937dc7b9fb · outbound

This paper cites Right column: The objective score of the images generated byunseen prompts, which demonstrates the generalization capability.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Right column: The objective score of the images generated byunseen prompts, which demonstrates the generalization capability

Reference 28

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Observation c2364150-696c-4791-a7f1-6a8cb4bce809 · outbound

This paper cites 7For D3PO, to evaluate 64 images only requires 32 queries calls, see Appendix G.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation 7For D3PO, to evaluate 64 images only requires 32 queries calls, see Appendix G

Reference 30

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Observation 0809043f-24a0-4d5b-8ff3-0ed9e78d6dc2 · outbound

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Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Unresolved cited work

Reference 31

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Observation c935e5a4-3111-4af7-9acb-31939a39598a · outbound

This paper cites an unresolved cited work.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Unresolved cited work

Reference 32

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Observation c23ec8ef-9866-4cad-95b2-5599718793a2 · outbound

This paper cites an unresolved cited work.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Unresolved cited work

Reference 33

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Observation d1528f95-8f79-4e34-89a7-c2902ede22f1 · outbound

This paper cites Answer in the format: Score=(score), Reason=(reason).

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Answer in the format: Score=(score), Reason=(reason)

Reference 34

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Observation a71442aa-ffe2-462c-8822-a7188387600b · outbound

This paper cites The accumulated plot displays the maximum Gemini rating achieved for each number of batch queries.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation The accumulated plot displays the maximum Gemini rating achieved for each number of batch queries

Reference 35

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Observation 2e020de1-2e44-4c0d-b6e3-74ee85a2b98c · outbound

This paper cites 22 Published as a conference paper at ICLR 2025 Figure 15: The generated images over each number of batch queries on the prompt ”cyber-dog” for each algorithm.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation 22 Published as a conference paper at ICLR 2025 Figure 15: The generated images over each number of batch queries on the prompt ”cyber-dog” for each algorithm

Reference 36

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Observation c182e75a-2d12-4881-bdb4-5841cb24f759 · outbound

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Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Unresolved cited work

Reference 500

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Observation d7166dba-1c1e-4a25-91ca-c977b3f00aca · outbound

This paper cites Denoising Diffusion Implicit Models.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Denoising Diffusion Implicit Models

Reference 2004

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Observation 72048b02-c972-4585-9a25-a286b7eabbfd · outbound

This paper cites Aligning optimization trajec- tories with diffusion models for constrained design generation.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Aligning optimization trajec- tories with diffusion models for constrained design generation

Reference 2020

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Observation b93548a9-19da-44f2-af77-8cece48317a6 · outbound

This paper cites ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization

Reference 2021

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Observation 73708102-e331-40c3-aec9-ef2b9e3bac73 · outbound

This paper cites Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 2022

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Observation efd89a95-9da0-4d23-bba5-2be99a9b257c · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Training Diffusion Models with Reinforcement Learning

Reference 2023

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This paper cites Diffusion Posterior Sampling for General Noisy Inverse Problems.

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation Diffusion Posterior Sampling for General Noisy Inverse Problems

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