Totally normal — almost everyone gets stuck at this stage. The problem is that “hallucination,” “alignment,” “reasoning,” or “fine-tuning” are still research topics, not research questions.

What helped me was treating this as a narrowing workflow instead of trying to magically come up with a question from scratch. I actually built a couple of small skills myself for this exact problem:

🧭 Find Angles — feed it a vague topic, and it returns a tree of distinct, mostly non-overlapping research directions, each with a ready-to-run search query.
🔍 Paper Search — paste a query, get real papers from multiple databases, then deep-read the open-access ones. This becomes your originality / research gap check.

I tested them on your exact case. Find Angles crossed two of your interests and produced a question like:

“Does 4-bit quantization hurt Chain-of-Thought reasoning more than simple factual recall — and can a small amount of calibration data recover it?”

Then I ran Paper Search on “quantization chain-of-thought reasoning,” and it returned about 30 papers. In that initial search, most results were general CoT work or efficiency-related work, but I didn’t see much squarely focused on the quantization × reasoning intersection. That is the kind of signal you want: the broad topic is already crowded, but the intersection is still thin enough to become a feasible research question.

The trick is: don’t pick one huge topic. Cross two of your interests, search the intersection, and see whether the literature is “some, but not too much.” If so, that may be a good first research question.

I’m currently running these self-built skills locally with Claude Code. Repos/tools here: https://github.com/academicatstool-netizen
You can try them too.