Symbolic regression solvers use a variety of search mechanisms, but according to a new arXiv preprint, most share a common weakness: they rely on the same low-information scalar fitness score. That score tells the solver how well a candidate expression performs overall, but it gives little guidance about why the expression fails or which part of its structure should be changed.
The paper, titled "From Score to Diagnosis: Search-Independent Post-Diagnosis for Symbolic Regression," proposes adding a post-diagnosis step that is independent of the underlying search method. Rather than feeding the solver only a number, the approach aims to provide a diagnosis of the candidate skeleton, pointing toward how it should be revised.
The abstract is truncated in the source, so the specific diagnostic mechanism and any benchmark results are not described in the available text. The contribution, as stated, is a move away from scalar fitness toward more informative feedback that could be applied across different symbolic regression solvers. Where the source is explicit, it frames this as a general limitation of current solvers and a search-independent remedy.```json // valid JSON, no comments in real output; shown here for clarity only. // Actually, I must return strict JSON. I'll output the JSON object directly without code fences or comments. // But the instruction says