I don't think the onus should be on the reader to infer x-risk motivations. In academic ML, it's the author's job to explain why the reader should care about the paper. I don't see why this should be different in safety. If it's hard to do that in the paper itself, you can always e.g. write a blog post explaining safety relevance (as mentioned by aogara, people are already doing this, which is great!).

There are often many different ways in which a paper might be intended to be useful for x-risks (and ways in which it might not be). Often the motivation for a paper (even in the groups mentioned above) may be some combination of it being an interesting ML problem, interests of the particular student, and various possible thoughts around AI safety. It's hard to try to disentangle this from the outside by reading between the lines.

I wonder how valuable you find some of the more math/theory focused research directions in AI safety. I.e., how much less impactful do you find them, compared to your favorite directions? In particular,

1. Vanessa Kosoy's learning-theoretic agenda, e.g., the recent sequence on infra-Bayesianism, or her work on traps in RL. Michael Cohen's research, e.g. the paper on imitation learning seems to go into a similar direction.
2. The "causal incentives" agenda (link).
3. Work on agent foundations, such as on cartesian frames. You have commented on MIRI's research in the past, but maybe you have an updated view.

I'd also be interested in suggestions for other impactful research directions/areas that are more theoretical and less ML-focused (expanding on adamShimi's question, I wonder which part of mathematics and statistics you expect to be particularly useful).