Evolution strategies (ES) provide a vital gradient-free alternative for solving complex, high-dimensional optimization problems in fields like neuroscience and LLM fine-tuning where traditional backpropagation is often unavailable or inefficient. Binxu Wang will explore the geometric properties that enable these methods to succeed and demonstrate how identifying task-irrelevant parameter directions can be leveraged to further accelerate optimization in modern large-scale models.

Staged metaprogramming offers an alternative to traditional macros by using annotations to control execution timing, allowing programs to be specialized for known inputs by delaying specific computations. Cameron Wong will demonstrate how this technique can derive compilers from interpreters and reify low-level hardware behaviors to detect side-channel vulnerabilities, while also discussing future applications in address sanitization and decompilation.

While modern data is governed by strict privacy policies, developers often lack the practical abstractions needed to ensure their code complies, leading to frequent manual errors and costly violations. Malte Schwarzkopf will introduce Sesame, a framework that utilizes policy containers and static analysis to automate end-to-end privacy enforcement with minimal developer effort and low performance overhead.