# Overview

This candidate addresses OpenReview paper `l35QweVxgn`, *On the Theory of Continual Learning with Gradient Descent for Neural Networks*. It is deliberately static: the useful artefact is a claim-to-source record for six active, unverified theorem claims. It does not assert that the paper's experiments were reproduced.

The first admission gate pinned the current challenge-space revision and selected the anchored claim map. The paper's author-released arXiv v2 TeX source was inspected alongside the repository named in the paper. The source discusses sequential learning on stylised XOR-cluster data, bounds on forgetting, and numerical corroboration. That scope is suitable for a non-executing theory audit.

The remaining pages distinguish direct source checks from inference and from empirical evidence. No result in this directory should be interpreted as a successful run, an independent proxy experiment, or a new theorem proof.