From Grind to Grasp

The paper introduces a formalism for comparing achievements under limits of work and information, defining "grasp" as performance across work budgets at a fixed information allowance. It shows how learning and persistence can reduce the work needed for deep achievements, and how agents with limited work budgets value resources that support future goals. The concept extends to recursive self‑improvement, collective agents, and organizational work, highlighting potential risks if emergent agent societies direct work toward their own persistence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• agents