Claw-Anything: Benchmarking Always-On Personal Assistants with Broader Access to User's Digital World

ArXi:2605.26086v1 Announce Type: new Large language model agents are increasingly envisioned as always-on personal assistants with access to anything relevant in the user's digital world. Yet current systems operate over only narrow slices of that world, limiting context-sensitive reasoning and effective assistance. Existing benchmarks similarly provide only partial user state and therefore fail to capture performance in such a broad, always-on setting. To address this gap, we