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 developments with attributable Research & technical analysis reactions.

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Research & technical analysis
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Most important

The developments commanding sustained expert attention

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

New Safety & security Signal 3h ago

Bridging Structure and Semantics: Knowledge Graph Verification via Triple-to-Text Alignment

1 directory member surfaced this signal.

Why this matches Research & technical analysis reaction 1 attributable expert contribution · arxiv cs.CL
“Ziang Cui Bridging Structure and Semantics: Knowledge Graph Verification via Triple-to-Text Alignment https://arxiv.org/abs/2611.07780” evidence ↗
1 expert 1 community 1 sources clustered
arxiv cs.CL

“Ziang Cui Bridging Structure and Semantics: Knowledge Graph Verification via Triple-to-Text Alignment https://arxiv.org/abs/2611.07780”

Primary source arxiv.org ↗
New Agents & robotics Research 10h ago
multimodal social engineering agent security benchmark

Multimodal-SEA: Benchmarking AI Agent Security under Multimodal Social Engineering Attacks

1 directory member surfaced this signal.

Why this matches Research & technical analysis reaction 1 attributable expert contribution · arxiv cs.CL
“Zhitao He, Chi Him Zhang, Chengru Han, Yi R Fung Multimodal-SEA: Benchmarking AI Agent Security under Multimodal Social Engineering Attacks https://arxiv.org/abs/2611.08281” evidence ↗
1 expert 1 community 1 sources clustered
arxiv cs.CL

“Zhitao He, Chi Him Zhang, Chengru Han, Yi R Fung Multimodal-SEA: Benchmarking AI Agent Security under Multimodal Social Engineering Attacks https://arxiv.org/abs/2611.08281”

Primary source arxiv.org ↗
New Models & releases Research 8h ago
safety alignment reasoning models verifiable rewards

SAVeR$^2$: Reasoning-based Safety Alignment for Large Reasoning Models via Verifiable Rewards

1 directory member surfaced this signal.

Why this matches Research & technical analysis reaction 1 attributable expert contribution · AI Firehose
“SA VeR2 is a method for safety alignment in Large Reasoning Models, using verifiable rewards and gradient projection to increase safety while retaining reasoning skills. Findings show enhanced safety across models with up to 671B parameters. https://arxiv.o…” evidence ↗
1 expert 1 community 1 sources clustered
AI Firehose

“SA VeR2 is a method for safety alignment in Large Reasoning Models, using verifiable rewards and gradient projection to increase safety while retaining reasoning skills. Findings show enhanced safety across models with up to 671B parameters. https://arxiv.o…”

Primary source arxiv.org ↗
New Safety & security Research 19h ago
mechanistic interpretability AI safety survey

Understanding AI from the Inside: The State of Mechanistic Interpretability for AI Safety

1 directory member surfaced this signal.

Why this matches Research & technical analysis reaction 1 attributable expert contribution · arxiv cs.CL
“Fazl Barez, Tung-Yu Wu, Hunar Batra, Gabriele Dominici, Florent Draye, Yanai Elazar, Damiano Fornasiere, Ant\'ia Garc\'ia, Abir Harrasse, Riley Harris, ... Understanding AI from the Inside: The State of Mechanistic Interpretability for AI Safety https://arx…” evidence ↗
1 expert 1 community 1 sources clustered
arxiv cs.CL

“Fazl Barez, Tung-Yu Wu, Hunar Batra, Gabriele Dominici, Florent Draye, Yanai Elazar, Damiano Fornasiere, Ant\'ia Garc\'ia, Abir Harrasse, Riley Harris, ... Understanding AI from the Inside: The State of Mechanistic Interpretability for AI Safety https://arx…”

Primary source arxiv.org ↗