Research on algorithms, machine learning, AI evaluation, and accountable systems. Explore computational experiments, software, and technical evidence interfaces.
SSRN Working Papers present developed research. Some are maintained as standalone working papers by design. Research maturity and publication status are recorded separately.
10 papers · Academic acceptance first · Records updated 2026-10-11 · Preliminary drafts are grouped separately.
An empirical regime map of which structural constraints a Minecraft voxel generator can learn.
FDG 2026 17th PCG Workshop · Accepted 2026-05-02 · Published 2026-08-09
Separates candidate generation, computational verification and empirical evidence when evaluating end-to-end AI science, and examines how the evaluation interface affects the conclusion.
A portable evidence interface linking AI-agent logging obligations to independent audit.
These working papers include early ideas, initial drafts, and work awaiting further development or verification. They are shared as work in progress.
We report covariance-induced irreversible drift, a failure mode of CMA-ES in barrier-constrained optimization.
Formalizes the natural-proofs barrier under the assumption that pseudorandom function generators exist, together with Shannon’s counting argument.
We study the geometry of circuit-SAT solution spaces over the complete binary basis B₂.
Proposes an open schema for recording when AI agents expose sensitive data, including exposure events, agent attribution and differences in platform coverage.
Measures whether machine-learning models exploit artifacts introduced by choosing a canonical representative under symmetry, using a diagnostic protocol and a matched circuit-complexity experiment.
Studies reinforcement learning for dynamically configuring MaxSAT local search, reporting transfer results and limits on generated benchmarks.
Can a fixed, user-deployable tactic string — requiring no solver modification — materially improve Z3's performance on nonlinear integer arithmetic? We show that the answer is yes.
