arXiv Computer Vision
Moore, Escher, Penrose: A Conformal Golden Braid
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arXiv Machine Learning
Sep 17
Transformation Laws in Neural Representations: Structure, Realisability, and Construction
The paper investigates how neural representations maintain the structure of input changes, linking representation analysis with internal interventions. It characterises when transformations can be applied through an encoder, providing linear settings where defects depend on discarded information and detailing failure modes for rectifiers and harmonic carriers. Using colour as a case study, the authors show that hue orbits in frozen visual features concentrate most energy in the first two harmonics, that this structure is inherited from input and architecture, and that a compact, fixed‑action interface can read hue zero‑shot with low error on unseen shapes.
Hugging Face Trending Papers
Sep 16
Transformation Laws in Neural Representations: Structure, Realisability, and Construction
How neural representations preserve the structure of input changes connects representation analysis with internal intervention. We study operable representational content through compatible actions of...
arXiv Machine Learning
Jul 10
Computation, Condensation, and the Incompleteness Between Them: A Coupled Foundation of Intelligence
arXiv:2303. 04203v4 Announce Type: replace Abstract: The theory of computation was built to answer Turing's question: what is effectively calculable by an unbounded, immortal, disembodied agent following rules?
arXiv Machine Learning
Jun 19
Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems
arXiv:2606. 19802v1 Announce Type: new Abstract: Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithful images.
arXiv Machine Learning
Jun 24
The Cost Geometry of Belief: finite-resource inference under noisy observation
arXiv:2606. 21585v2 Announce Type: replace Abstract: A finite machine's digital twin of a system observes the territory through finite, noisy sensors; we model its coherent output as a belief, a probability density over states, the Bayes posterior, never a point.
arXiv AI
Sep 3
Looped Transformers under the Jacobian Lens: Does the Global Workspace Survive Recurrence?
The paper investigates whether a global workspace—a set of verbalisable, causally potent representations—emerges in transformer models that use recurrence instead of a stack of distinct layers. Using a Jacobian lens extended with a virtual‑unrolling adapter, the authors analyze two recurrent transformer architectures, Ouro‑2.6B and Huginn‑0125, and compare them to a standard Qwen3.6‑27B baseline. They find that a workspace does form in the iterated parts of both models, but recurrence alters how it can be accessed: Ouro reconstructs workspace content in every loop and requires writes and ablations across all loops, whereas Huginn forwards content across all recurrences but limits reads, writes, and ablations to a sliding window of about two recurrences.