Prospective selection and contemporary agent alternatives [ftip-00NN]

Prospective Compression in Human Abstraction Learning studies reusable-helper choices while a latent curriculum changes. Its two controlled Pattern Builder experiments involve 60 participants in total and six computational comparison models. The results support sensitivity to future reusable structure beyond the tested retrospective and LLM-based accounts. They motivate studying prospective acquisition rather than only compression of earlier solutions.

The paper's limitations are decisive for the comparison here. It does not implement a full learner that infers the latent task-generating process, and individual tasks can be solved without reproducing human helper choices. Its restricted helper-optimization hardness result does not bound arbitrary learning agents. The proposed construction therefore evaluates checked future performance and total work, rather than similarity to human-selected helpers, and admits agents that jointly learn a process model and a library.

Such agents may forecast by Bayesian inference, learned world models, sequence prediction or simulation; select structure by search, expected utility or learned value estimates; and retain experience in memory or model updates. ProPlay supplies one contemporary procedural-memory mechanism, while BREW constructs reusable recipes from trajectories and optimizes their correctness and retrieval usefulness. These particular systems do not establish prospective learning for the public rewrite process, but their mechanisms are available components of an alternative campaign.

Theoretical active-inference guarantees also require attention to what is already supplied. Curiosity is Knowledge proves results under stated identifiability and regularity conditions; its discussion identifies idealizations including a discrete hypothesis setting and exact mutual-information computation. Providing an adequate hypothesis class or an exact acquisition calculation can remove work that the developmental construction is meant to explain. Conversely, these restrictions do not establish that a more general agent cannot find an affordable approximation or a different sufficient method.