Systems

The Big Idea Operating systems have always struggled with a silent mismatch: the kernel’s scheduler doesn’t know what your application actually wants. SchedCP proposes a clean solution—turn scheduling into a semantic control plane. AI agents reason about what a workload needs; the system safely handles how to observe and act via eBPF-based schedulers. This division keeps LLMs out of the hot path while letting them generate and refine policies that actually fit the job. ...

The “right to be forgotten” (GDPR Art. 17) has always seemed like kryptonite for large language models. Once a trillion-parameter system memorizes personal data, how can it truly be erased without starting training from scratch? Most prior attempts—whether using influence functions or alignment-style fine-tuning—felt like damage control: approximate, unverifiable, and too fragile to withstand regulatory scrutiny. This new paper, Unlearning at Scale, turns the problem on its head. It argues that forgetting is not a mathematical optimization problem, but a systems engineering challenge. If training can be made deterministic and auditable, then unlearning can be handled with the same rigor as database recovery or transaction rollbacks. ...