Jacob Steinhardt
Krishna Patel
Expanding proven isolation techniques to high-risk capability domains in Mixture of Expert models
Xyra Sinclair
Unlocking the paradigm of agents + SQL + compositional vector search
Lawrence Wagner
Preeti Ravindra
AI Safety Camp 2026 project: Bidirectional Failure modes between security and safety
Anthony Ware
Identifying operational bottlenecks and cruxes between alignment proposals and executable governance.
Finn Metz
Funding 5–10 AI security startups through Seldon’s second SF cohort.
Sean Peters
Measuring attack selection as an emergent capability, and extending offensive cyber time horizons to newer models and benchmarks
João Medeiros da Fonseca
Phenomenological Fine-tuning for Medical AI Alignment
Parker Whitfill
Mirco Giacobbe
Developing the software infrastructure to make AI systems safe, with formal guarantees
Gergő Gáspár
Help us solve the talent and funding bottleneck for EA and AIS.
Muhammad Ahmad
A pilot to build policy and technical capacity for governing high-risk AI systems in Africa
Centre pour la Sécurité de l'IA
Leveraging 12 Nobel signatories to harmonize lab safety thresholds and secure an international agreement during the 2026 diplomatic window.
L
Sandy Tanwisuth
We reframe the alignment problem as the problem of governing meaning and intent when they cannot be fully expressed.
Miles Tidmarsh
Training AI to generalize compassion for all sentient beings using pretraining-style interventions as a more robust alternative to instruction tuning
Chris Canal
Enabling rapid deployment of specialized engineering teams for critical AI safety evaluation projects worldwide
Brian McCallion
A mechanistic, testable framework explaining LLM failure modes via boundary writes and attractor dynamics
Christopher Kuntz
A bounded protocol audit and implementation-ready mitigation for intent ambiguity and escalation in deployed LLM systems.