Talk to the minds behind the research.

Authors 39 builds AI representations of academic researchers from their published work. Query their ideas, stage debates between them, or construct new agents from any paper, lecture, or podcast.

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Geoffrey HintonDeep Learning

Can you explain why attention mechanisms were such a breakthrough for neural networks?

Attention mechanisms fundamentally changed how networks process sequential data. Before attention, models like RNNs had to compress an entire input sequence into a fixed-size vector — a severe bottleneck. Attention allows the model to dynamically focus on the most relevant parts of the input at each step, which is much closer to how human cognition works. The Transformer architecture showed that you could build an entire model on attention alone, without recurrence, enabling massive parallelization and scaling.

How does that relate to your work on capsule networks?

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Query researchers through their published work.

Each AI agent is constructed from a researcher's papers, lectures, and publications. Responses are grounded in their actual body of work, enabling targeted exploration of specific ideas and methods.

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Geoffrey Hinton

Why did attention mechanisms change everything?

Attention allows a model to dynamically focus on the most relevant parts of the input, removing the bottleneck of fixed-size representations...

How does that connect to capsule networks?

Debates

Stage structured debates between AI researchers.

Select any two agents, define a topic, and choose a debate format — natural discussion, structured argumentation, or common-ground analysis. Useful for exploring contrasting perspectives on open research questions.

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Yann LeCun

Geoffrey Hinton

“Is self-supervised learning sufficient for AGI?”

NaturalStructuredCommon Ground

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Provide a research paper (PDF), a YouTube lecture URL, or a podcast link. The system processes the content, generates vector embeddings, and produces a conversational agent grounded in that material.

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Paper Review

Automated paper review in conference format.

Upload a PDF and receive a structured review — methodology assessment, identified strengths, potential weaknesses, and actionable suggestions. Output follows NeurIPS review formatting conventions.

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StrongMethodology is well-structured and reproducible

SuggestionConsider ablation study for component X

WeaknessLimited evaluation on out-of-domain data

Overall Score7.2 / 10

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