Martin Gubri

LLM agents struggle to translate general capabilities into cost-efficient, reusable solutions, often failing to outperform simple distillation baselines despite significant zero-shot prowess.

Multi-party conversations expose LLMs to a staggering increase in privacy leaks, with models revealing sensitive information far more than previously understood.

Framework choice in multi-agent systems matters just as much as the LLM itself, a fact obscured by existing model-centric benchmarks.