T-FIX: Text-Based Explanations with Features Interpretable to eXperts

ArXi:2511.04070v2 Announce Type: replace As LLMs are deployed in knowledge-intensive settings (e.g., surgery, astronomy, therapy), users are often domain experts who expect not just answers, but explanations that mirror professional reasoning. However, most automatic evaluations of explanations prioritize plausibility or faithfulness, rather than testing whether an LLM thinks like an expert. Existing approaches to evaluating professional reasoning rely heavily on per-example expert annotation, making such evaluations costly and difficult to scale. To address this gap, we