Expert attention map

The Who's Who of AI

What credible people across AI noticed, why it matters, and where the field is converging or disagreeing.

2,397 searchable experts 4,280 tracked across all sources
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Showing signals surfaced by Artificial Intelligence Papers ×
New & accelerating

What is moving across the network now

One card per development. Sources are clustered; reaction bundles describe these posts, never the people behind them.

Developing AI research Research 2d ago
⚡ 142 h early
SPIRAL learning search aggregate

SPIRAL: Learning to Search and Aggregate

3 directory members surfaced this signal.

3 experts 1 community 1 sources clustered
Chelsea Finn Artificial Intelligence Papers AI Firehose

“LLM RL optimizes for sequential reasoning We also optimize over the reasoning strategy, incl parallel trains of thought, aggregation of parallel traces, & sequential reasoning This allows the model to better explore & allocate compute at test time h…”

“SPIRAL: Learning to Search and Aggregate Jubayer Ibn Hamid, Ifdita Hasan Orney, Michael Y. Li, Omar Shaikh, Yoonho Lee, Dorsa Sadigh, Chelsea Finn, Noah Goodman https://t.co/CRBpj1Mjhk [𝚌𝚜.𝙰𝙸] https://t.co/kVEHyMHKpK”

Primary source arxiv.org ↗
Established Policy & governance Research 3d ago
⚡ 1027 h early
policy iteration human feedback in-context RL

Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning

2 directory members surfaced this signal.

2 experts 1 community 1 sources clustered
Artificial Intelligence Papers AI Firehose

“Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning Minh-Ha Nguyen, Cathy Shyr https://t.co/8OgE5J45Jp [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙲𝙻] https://t.co/OurY1BxFEX”

“A new study unveils Policy Iteration with Human Feedback (PIHF-MCP), enhancing large language models' efficiency in rare-disease diagnostics. This method accelerates learning while keeping humans engaged, paving a path for safer, critical applications. http…”

Primary source arxiv.org ↗