  Scientific writing is a challenging task, particularly for novice researchers
who often rely on feedback from experienced peers. Recent work has primarily
focused on improving surface form and style rather than manuscript content. In
this paper, we propose a novel task: automated focused feedback generation for
scientific writing assistance. We present SWIF$^{2}$T: a Scientific WrIting
Focused Feedback Tool. It is designed to generate specific, actionable and
coherent comments, which identify weaknesses in a scientific paper and/or
propose revisions to it. Our approach consists of four components - planner,
investigator, reviewer and controller - leveraging multiple Large Language
Models (LLMs) to implement them. We compile a dataset of 300 peer reviews
citing weaknesses in scientific papers and conduct human evaluation. The
results demonstrate the superiority in specificity, reading comprehension, and
overall helpfulness of SWIF$^{2}$T's feedback compared to other approaches. In
our analysis, we also identified cases where automatically generated reviews
were judged better than human ones, suggesting opportunities for integration of
AI-generated feedback in scientific writing.
