Topic: evaluation

Four-month, full-time fellowship for empirical AI safety and security research, with researcher mentorship, a weekly stipend and research support. Workstreams include model interpretability, scalable oversight, adversarial robustness and evaluation of advanced AI systems. Applications for the January cohort are open and reviewed on a rolling basis. Shared workspaces are available in Berkeley and London, with remote participation possible in the United States, United Kingdom or Canada. A subsequent permanent role is not guaranteed.

Rashmi Shetty explains how Capital One develops multi-agent systems for regulated services. The discussion covers intent disambiguation, tool use and human handoffs, alongside platform design, runtime policies, observability and evaluation. It also examines model specialisation through fine-tuning and distillation, and learning from production feedback.

Dominant theories of analogical thinking postulate that making an analogy consists in discovering that two superficially different situations share isomorphic systems of similar relations. According to this perspective, the comparison between the two situations may eventually lead to the construction of a schema, which retains the structural aspects they share and deletes their specific contents. We have developed a new approach to analogical thinking, whose purpose is to explain a particular type of analogies: those in which the analogs are exemplars of a schema-governed category (e.g., two i