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arXiv:2607. 16821v1 Announce Type: cross Abstract: Task arithmetic, sequential fine-tuning, activation steering, and first-order random search all operate through relatively small perturbations around an already trained checkpoint, and they rely on different local approximations: individual perturbations should be first-order predictable, task updates should compose with controlled interference, useful tangent structure should be stable and possible to estimate, and weight edits should have counterparts in representation space.
arXiv:2610. 27628v1 Announce Type: new Abstract: Looped language models can run more iterations at test time, but additional iterations need not help them solve longer problems.
The study investigates whether a neural network’s training history can better predict its future learning than its current state. Using small multilayer perceptrons trained under 42 different history regimes, the authors measured future learning at four checkpoints via a short probe that replicated the network for 100 updates on a new task. Results show that a low‑dimensional history state does not outperform a calibrated model of the current state for short‑term prediction, but in a companion screen of 1,560 synthetic regression runs, history models forecast final error better than current validation error after 12 epochs and remain comparable until 48 epochs.
arXiv:2607. 27422v2 Announce Type: replace Abstract: Goal-conditioned action generators can produce many plausible actions.
arXiv:2609.36375v1 Announce Type: new Abstract: Continual learning is usually studied through mechanisms that preserve old knowledge. We develop Successional Learning Theory (SLT), a mesoscopic accou...
The paper investigates how implicit assumptions about tasks in continual learning affect reported performance metrics. It distinguishes between the underlying data, the partitioning of that data into tasks, and the learner’s experience, showing that different granularities, orders, and similarity structures can lead to markedly different outcomes. The authors use controlled teacher–student experiments to illustrate these effects and review existing benchmarks to assess which properties are explicitly stated.