arXiv:2607. 10203v2 Announce Type: replace-cross Abstract: Adaptive-compute world models -- early-exit or mixture-of-depths predictors that spend variable depth per step -- assume depth buys better predictions and can be routed adaptively.
By Achyuthan Sivasankar
arXiv:2607. 06925v1 Announce Type: new Abstract: Compact world models that condition on a language goal promise to ground relations such as ``put the red block left of the blue block'' using a sparse set of explicit \emph{reference anchors}.
By Yufeng Wang, Lu Wei, Haibin Ling
arXiv:2608.23094v1 Announce Type: new
Abstract: One implicit DDIM inversion step is the cheapest probe of whether a pretrained diffusion model encodes local manifold geometry. It is the stationarity...
By Gordei Verbii
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.
By Irina Piontkovskaia, Sergey Nikolenko
The paper investigates how the dynamical regime of recurrent-depth reasoners—whether they settle, drift, or remain marginal—affects the reliability of test‑time depth. It establishes a depth‑safety condition based on per‑step displacement relative to the decoder margin, showing that operators in a settling regime can safely increase depth without degrading performance and can even improve accuracy on harder unseen tasks such as Sudoku. The authors provide empirical evidence from algorithmic tasks trained on limited data, demonstrate the impact of a terminal fixed‑point objective on depth behavior, and offer operational criteria to identify useful test‑time depth while cataloguing failure modes.
By Ivan Viakhirev, Kirill Borodin, Amirah Almutairi, Serguei Barannikov, Maxim Abramov, Grach Mkrtchian
arXiv:2608.22347v1 Announce Type: new
Abstract: A cognitive architecture is more than the module that reasons: it must also decide how long to think and what deserves the effort. We built a minimal b...
By Francisco M. Arrabal-Campos, Francisco G. Montoya, Alfredo Alcayde, Ignacio Fern\'andez