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Paper Citation Record · LEDGER

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence

As of 16 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2608.12290.

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

pith.paper-citation-record.v1
2608.12290 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:13:08.029353Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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 fuzzy17
  • unresolved24
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8f95c0a-b0df-47b5-8523-e93a074d6f35 · outbound

This paper cites Sora as an agi world model? a complete survey on text-to-video generation.arXiv preprint arXiv:2403.05131, 2024.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Sora as an agi world model? a complete survey on text-to-video generation.arXiv preprint arXiv:2403.05131, 2024

Reference 1

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Observation c9103f11-a681-445b-a44b-e9ff486e56e0 · outbound

This paper cites From Sora What We Can See: A Survey of Text-to-Video Generation.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence From Sora What We Can See: A Survey of Text-to-Video Generation

Reference 2

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Observation 9f41bf44-4b78-4733-aaf6-c4a0cdb945f8 · outbound

This paper cites Video-to-Video Synthesis.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Video-to-Video Synthesis

Reference 3

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Observation 22da8293-2197-4be9-8596-cd3f910ca4a8 · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffusion models.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Videocrafter2: Overcoming data limitations for high-quality video diffusion models

Reference 4

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source=pdf_text observed=2026-08-16T00:13:07.897803Z digest=sha256:b0d49637659767baf5453cef4bd3607466382d741e23aa13c603b288065b3e4a

Observation 97a86b19-cdfb-4996-bb06-5373d77e36a3 · outbound

This paper cites Vpo: Aligning text-to-video generation models with prompt optimization.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Vpo: Aligning text-to-video generation models with prompt optimization

Reference 5

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:07.901492Z digest=sha256:aaca5794aeb61ebab6fd798ad36283cbc3d16c88baf8bbfdc48edaf8a842ed19

Observation 29ccdd61-46d4-4268-88a1-48cbaa67c17e · outbound

This paper cites Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k

Reference 6

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source=pdf_text observed=2026-08-16T00:13:07.905862Z digest=sha256:96899dc00bcdfcfa0bef99345eb9b84e01224f618ffc805b65348035561d505a

Observation fafbe7e1-a240-4e7d-b560-1aea411dec71 · outbound

This paper cites Make it move: controllable image-to- video generation with text descriptions.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Make it move: controllable image-to- video generation with text descriptions

Reference 7

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source=pdf_text observed=2026-08-16T00:13:07.910874Z digest=sha256:f8200c91866d27f8c82b4c6dae0f9c78910b3fff58dd73a7d775e21deefee64e

Observation 7b047e86-1dfb-43be-9482-3ea1a00badca · outbound

This paper cites Prompt-a-video: Prompt your video diffusion model via preference-aligned llm.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Prompt-a-video: Prompt your video diffusion model via preference-aligned llm

Reference 8

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source=pdf_text observed=2026-08-16T00:13:07.915715Z digest=sha256:6ce82c0fc0bf20952034a910d1a5ac1687a064b21ce5b7be4d191d06fbbff181

Observation aeee1728-edb0-48c4-94da-a78d5a726bd7 · outbound

This paper cites We need to talk about random seeds.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence We need to talk about random seeds

Reference 9

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source=pdf_text observed=2026-08-16T00:13:07.919177Z digest=sha256:0437e0c890713f3cf301c5d56b84f6134fc7882bb5367df48ffd3b6c33e36aec

Observation e20c3403-42fd-4898-843d-e43c073d6a2a · outbound

This paper cites Rethink- ing the spatial inconsistency in classifier-free diffusion guidance.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Rethink- ing the spatial inconsistency in classifier-free diffusion guidance

Reference 10

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:07.922856Z digest=sha256:39ad41e0105a5c4515baecc5bf0fe6f1dfcd6519864eb2c2c71c57583efc87a3

Observation 095499db-1823-487a-a1b2-216db7450e39 · outbound

This paper cites Davidsonian scene graph: Im- proving reliability in fine-grained evaluation for text-to-image generation.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Davidsonian scene graph: Im- proving reliability in fine-grained evaluation for text-to-image generation

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:07.926505Z digest=sha256:f4893f793698d739332ffe41f2279aed59086b6c45ccbe63f7f45f1702e75681

Observation b47020f3-6d8d-4be6-8676-2b8c562c350f · outbound

This paper cites Generative adversarial nets.Ad- vances in neural information processing systems, 27, 2014.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Generative adversarial nets.Ad- vances in neural information processing systems, 27, 2014

Reference 12

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source=pdf_text observed=2026-08-16T00:13:07.931198Z digest=sha256:d34baeb2d393c27b302987bbdbaaee0b0a96c86fbde858b58285b090ccdd8234

Observation 5b715188-d148-489c-826f-2a3c1806c92c · outbound

This paper cites An introduction to variational autoencoders.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence An introduction to variational autoencoders

Reference 13

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:07.935930Z digest=sha256:2bb6cc61c62f678c2a74de5e2e5204c3465de7e6ced7f3cc3d693877039b2a1c

Observation c38568ca-0a37-4b95-8a3d-d2c9a9a7f250 · outbound

This paper cites Denoising diffusion probabilistic models.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Denoising diffusion probabilistic models

Reference 14

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source=pdf_text observed=2026-08-16T00:13:07.939843Z digest=sha256:17346c9990df8a05c38460ab2fad988e80b503f218cd19b23c3da6531f8b788e

Observation 3504e273-0b37-4512-a8e4-28d86f5fa033 · outbound

This paper cites Generating videos with scene dynamics.Advances in neural information processing systems, 29, 2016.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Generating videos with scene dynamics.Advances in neural information processing systems, 29, 2016

Reference 15

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 870864cb-e4b4-4db8-9f0b-3b9bfeae0dba · outbound

This paper cites Video generation models as world simulators.OpenAI Blog, 1:8, 2024.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Video generation models as world simulators.OpenAI Blog, 1:8, 2024

Reference 16

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Observation 19413c11-d6c3-4c2a-adf6-f9d954f0e54c · outbound

This paper cites Scalable diffusion models with transformers.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Scalable diffusion models with transformers

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 4737dac9-b01a-4c71-9a3c-ef395f62f854 · outbound

This paper cites Videomar: Autoregressive video generation with continuous tokens.Advances in neural information processing systems, 38:56928– 56958, 2026.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Videomar: Autoregressive video generation with continuous tokens.Advances in neural information processing systems, 38:56928– 56958, 2026

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 009f7f40-00ea-4dd0-ad96-c17889aa96ba · outbound

This paper cites Adding conditional control to text- to-image diffusion models.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Adding conditional control to text- to-image diffusion models

Reference 19

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Observation fdb5da39-2e25-4b74-a9d5-0d86b3400b09 · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 20

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Observation 4677660a-20ef-4f54-9cb2-771010c3c817 · outbound

This paper cites Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning

Reference 21

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Observation ce4a85ef-1689-4ad1-b500-2808bb780b32 · outbound

This paper cites Align your latents: High-resolution video synthesis with latent diffusion models.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Align your latents: High-resolution video synthesis with latent diffusion models

Reference 22

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Observation a16255e4-5a38-47a6-868c-f8b2a87dbe2e · outbound

This paper cites Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery.Advances in Neural Information Processing Systems, 36:51008– 51025, 2023.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery.Advances in Neural Information Processing Systems, 36:51008– 51025, 2023

Reference 23

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source=pdf_text observed=2026-08-16T00:13:07.968906Z digest=sha256:ab241428cd9fe26a57913827b818e2f2397095aa17d70a673e6e09fec88af6b9

Observation 3b41dc66-33b7-48d9-9fc6-882ea0627777 · outbound

This paper cites Training diffusion models with reinforcement learning.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Training diffusion models with reinforcement learning

Reference 24

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source=pdf_text observed=2026-08-16T00:13:07.972276Z digest=sha256:9e7a7fff3fa0dfa41c772dc213cfea6d5c6014a19a990ac091311b3497173a8b

Observation e9a07f51-2cec-4925-9e0d-c4b3d4a540fb · outbound

This paper cites Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation

Reference 25

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source=pdf_text observed=2026-08-16T00:13:07.975903Z digest=sha256:a93a301b32140e91838ff58adcfd4eacdf4c4b5a5271640f25cd6bba7d33917c

Observation 3e0d2840-b9bf-455c-baeb-499a845dc5ab · outbound

This paper cites Optimizing prompts for text-to-image generation.Advances in Neural Information Processing Systems, 36:66923–66939, 2023.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Optimizing prompts for text-to-image generation.Advances in Neural Information Processing Systems, 36:66923–66939, 2023

Reference 26

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source=pdf_text observed=2026-08-16T00:13:07.980321Z digest=sha256:d0bed4163bfb24aee80fb278090a387e8d67234d1024ee085a7bd094e867e863

Observation 9935308e-1195-4139-a07a-5979e6c46037 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Classifier-Free Diffusion Guidance

Reference 27

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source=pdf_text observed=2026-08-16T00:13:07.984156Z digest=sha256:ad4f1515f010dbc09a2d5ecef6f5278b8c55efd77d2167c9d1ae6a10ab57b7c2

Observation 387431e4-0d33-4e2f-b543-1e8862808797 · outbound

This paper cites Practical bayesian optimization of machine learning algorithms.Advances in neural information processing systems, 25, 2012.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Practical bayesian optimization of machine learning algorithms.Advances in neural information processing systems, 25, 2012

Reference 28

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source=pdf_text observed=2026-08-16T00:13:07.987991Z digest=sha256:f859e85f16da2c7c0df19e15c92ed7d15fb20dfbccdb7dc3d4132ec5a662afd0

Observation ff1845b9-acc5-4ae8-8f29-449d4484db46 · outbound

This paper cites MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

Reference 29

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Observation 48d9777c-f819-4fe7-a286-e40bdf9d490d · outbound

This paper cites DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation

Reference 30

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source=pdf_text observed=2026-08-16T00:13:07.994952Z digest=sha256:c4c291d746ae67f338e3fb1ab5d9b7b81c29961b4ab8e95a80c44ee304399355

Observation 313c2745-76c7-4f70-b0cf-006006b4bee8 · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 31

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source=pdf_text observed=2026-08-16T00:13:07.998387Z digest=sha256:c16fc202e30c0e3d0078414d17ab983e0402201fa40567287f7714342c71209a

Observation 56392c04-b1fc-47e5-8da4-bec93729cc01 · outbound

This paper cites A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024

Reference 32

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source=pdf_text observed=2026-08-16T00:13:08.001886Z digest=sha256:ece06a01f22d3e605ea414e6f85ad5b73e2995c7c24e18973083218d0ae76f77

Observation 3f1bbdf3-e0eb-485a-8f2f-49d6de19cbbb · outbound

This paper cites Gemini 2.5 pro (gemini-2.5-pro-preview-06-05): Our most advanced intel- ligent ai model, 2025.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Gemini 2.5 pro (gemini-2.5-pro-preview-06-05): Our most advanced intel- ligent ai model, 2025

Reference 33

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:08.004964Z digest=sha256:d554bc0bf2dc6ab574ffab3d2a6f17e6cf32702db2641feb2aee56c55be6a387

Observation 5648e3d6-94d6-4b73-8f81-68340ff34574 · outbound

This paper cites The Vizier Gaussian Process Bandit Algorithm.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence The Vizier Gaussian Process Bandit Algorithm

Reference 34

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source=pdf_text observed=2026-08-16T00:13:08.008273Z digest=sha256:a7516629250e11ff5c73ba68ffae4d254e05be52f31299dc4b14d98aeb006173

Observation ad494e1e-9919-4adb-88bf-3662665d8126 · outbound

This paper cites Rich human feedback for text-to-image generation.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Rich human feedback for text-to-image generation

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:13:08.293221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:08.012232Z digest=sha256:8d634e437480a72d23a222db2f6db813a7ca5c95a6dc3fcc1f410449eb71b494

Observation 5fe38027-2de5-4eea-aa3f-edc6530300c9 · outbound

This paper cites Rich features for perceptual quality assessment of ugc videos.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Rich features for perceptual quality assessment of ugc videos

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:13:08.284853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:08.014817Z digest=sha256:72264d56caa50c8a65b3f0a0f40d5f456c00be1b165fedd86d333a7f4f7cdf3f

Observation a43342f7-5eb8-404a-9421-f8e41af0258f · outbound

This paper cites VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-16T00:13:08.017256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:13:08.017256Z digest=sha256:9f7e4142d7a0921808a6c749a4eb3ac98e6ff9a4243ae52d976badef188d10b2

Observation 77ac2cfb-e69e-4380-903e-8e912f731cc0 · outbound

This paper cites The devil is in the prompts: Retrieval-augmented prompt optimiza- tion for text-to-video generation.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence The devil is in the prompts: Retrieval-augmented prompt optimiza- tion for text-to-video generation

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:13:08.276333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:08.019856Z digest=sha256:cb45970a89a424219e8d5d7a4968bf67156c8d2b2edf69b496e72dd7901b4b49

Observation a20b9f1b-bf11-4e22-bff7-e6d5ed9f040e · outbound

This paper cites Q-bench-video: Benchmark the video quality understanding of lmms.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Q-bench-video: Benchmark the video quality understanding of lmms

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:13:08.267250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:08.022536Z digest=sha256:8157e1dc03558b3111f842a395b981626f7c1926c8a99730c0121c3382d9981d

Observation 31b2333c-fe95-4ddc-8ffb-fb3b95fc08b0 · outbound

This paper cites Gemini Robotics: Bringing AI into the Physical World.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence Gemini Robotics: Bringing AI into the Physical World

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-16T00:13:08.025654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:13:08.025654Z digest=sha256:c92f8fab7864ee0423e075a6c5d5a0292cd380ca95312e2153d4bc68dc562fcd

Observation 7a5df1fe-2ad0-4c0d-96a6-ca21014bf6a1 · outbound

This paper cites scene_graph.

Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence scene_graph

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:13:08.256619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T00:13:08.029353Z digest=sha256:65c14c24eae9ba6520b2c98a21b2fb1480b16f7c465cdb7108fa1b0867b5d32f

Pith citing papers

No inbound Pith citation observations are available.