Proof Review for Math Thesis in Predictive Compression and KL Divergence

I am looking for a mathematically trained reviewer to provide a proof-structure and rigor review of my mathematical thesis manuscript. The project involves a theoretical mathematics manuscript using tools from probability, information theory, stochastic processes, and mathematical analysis. The review will focus on theorem statements, assumptions, proof logic, and notation consistency. This is not a request for ghostwriting or generic proofreading. I am looking for technical feedback on mathematical correctness and exposition. The ideal reviewer has experience with probability theory or stochastic processes, measure-theoretic notation, conditional distributions or Markov-type models, KL divergence or information-theoretic quantities, mathematical proof-writing, LaTeX / Overleaf, and academic manuscript review or thesis editing. Deliverables requested: 1. Annotated comments on the manuscript or selected sections. 2. A short referee-style report summarizing the main strengths, weaknesses, and risks. 3. A theorem-by-theorem assessment where appropriate: solid / needs clarification / missing assumption / should be weakened / not checked. 4. Suggestions for improving rigor, clarity, and advisor-readiness. The manuscript is unpublished, so I prefer a reviewer who is comfortable treating the material as private and using it only for the purpose of this review. Because I am a private student working with a limited budget, I would prefer to agree on a flat-fee milestone for a clearly defined scope rather than an open-ended hourly arrangement. Thanks! DT