arXiv:2609.13197v1 Announce Type: new
Abstract: Algorithmic Information Dynamics (AID) studies systems by perturbing them and measuring changes in algorithmic complexity, but its usual estimator, the...
By Luan Ozelim, Hector Zenil
arXiv:2606. 13092v3 Announce Type: replace Abstract: Scale buys interpolation; structure buys certifiable transfer.
By Hongbo Wang
arXiv:2606. 30705v1 Announce Type: cross Abstract: Deterministic few-step generation succeeds on continuous image latents but collapses to incoherent text on continuous text latents, and we show the cause is geometric rather than a training or scaling deficiency: a smooth, regularity-limited deterministic map cannot resolve a discrete branch choice before a sharp categorical readout, so few-step failure is governed by decoder sharpness, not transport accuracy.
By Zhongyao Wang
The paper studies how a code‑world model can be perfectly accurate on the portion of the state space that a sampling gate can observe while potentially being arbitrarily wrong elsewhere. By treating the unobservable interior as an annular freeze mode, the authors formalize the notion of reach and show that acceptance with certainty fixes the model only on the reachable query set, leaving the rest as a gauge. Experiments on a minimal ring instrument demonstrate that a single channel width parameter can move the model through regimes of being unfalsifiable and harmless, falsifiable and costly, or instantly falsified, illustrating how topology relative to reach governs danger, repair, and mitigation strategies.
By Javier Aguilar Mart\'in
The paper introduces MACCHIATO, a training algorithm that builds a ReLU‑MLP from partial truth‑table data while simultaneously constructing an explicit Boolean circuit over AND, OR, and XOR gates that certifies the network’s computation. The method iteratively projects residuals onto low‑dimensional Boolean classes, compiles the resulting circuit into a ReLU‑MLP, and uses logic minimization and influence‑based variable selection to achieve a six‑layer network with provable truth‑table error bounds. Experiments on synthetic random‑junta tasks show that these certified networks outperform Adam‑trained MLPs in data‑sparse or projection‑aligned regimes and complete faster than flat ESPRESSO in certain settings.
By Hrad Ghoukasian, Anastasis Kratsios
arXiv:2509. 11208v3 Announce Type: replace-cross Abstract: Transformers used for evidence-grounded binary adjudication (e.
By Leon Chlon, Ahmed Karim, Maggie Chlon, MarcAntonio Awada