Kaggle

Why Frontier Models Fail as Buy-Side Analysts - Janie Gu, Google DeepMind & Suyeol Yun, LinqAlpha

Why Frontier Models Fail as Buy-Side Analysts - Janie Gu, Google DeepMind & Suyeol Yun, LinqAlpha

Janie Gu of Google DeepMind and Kaggle explains why most AI benchmarks are unreliable: they don't reflect real work, they are costly to keep current, and their scores are hard to verify. Drawing on a reproduction of 65 industry benchmarks, she details the failure modes and how Kaggle is building auditable, containerized evaluation infrastructure. Suyeol Yun of LinqAlpha then presents MandateBench, which tests whether models can carry out buy-side equity analyst work from a fund mandate to a full investment memo.

Agentic Evaluations at Scale, For Everybody — Nicholas Kang & Michael Aaron, Google DeepMind

Agentic Evaluations at Scale, For Everybody — Nicholas Kang & Michael Aaron, Google DeepMind

Nicholas Kang and Michael Aaron from Google DeepMind's Kaggle team discuss the broken state of AI evaluations—scattered, non-transparent, and created by a homogenous group. They present their solutions: a community-driven benchmarks platform, a PvP Game Arena for non-saturating ELO ratings, standardized agent exams, and hackathons to crowdsource novel evals and address the limitations of current benchmarking practices.