"Query-Efficient Planning with Language Models"

Diffusion models reconstruct past cultural data while generating new visual content, exposing dataset biases.

Deep generative models like diffusion systems can reconstruct data from the past while generating future content, revealing inherent cultural biases in dataset records.

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https://arxiv.org/abs/2412.06162v1

🤔 Original Problem:

→ Current deep generative models lack frameworks to understand how they reconstruct and represent cultural data through time, especially in multimodal contexts like film and audiovisual arts.

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🔧 Solution in this Paper:

→ The paper proposes using virtual timelines and event schedulers to reveal how diffusion models reconstruct visual content.

→ It implements a cross-modal representation system combining image diffusion with language guidance through text prompts.

→ The approach uses parameter sequencing and embedding coordination to control frame generation while exposing model biases.

→ Frame generation involves variable frame-skip-steps, 3D field of view planes, and language-guidance ratios.

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💡 Key Insights:

→ Datasets contain temporal and cultural markers that influence model outputs

→ Video production through diffusion models creates both documentary and experimental content

→ Parameter scheduling can reveal model biases and reconstruction capabilities

→ Multimodal inference exposes cultural timestamps in learned representations

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📊 Results:

→ System enables reproducible cross-modal film coordination across different model architectures

→ Achieves semantic and formal parameter control through virtual timeline templates

→ Successfully demonstrates both documentary and abstract video generation capabilities