Regression

Thesis: When the job is to read text, reason carefully, and return a precise number (not just a label), ordinary regression heads and vanilla prompting often fail in opposite ways. The paper introduces MENTAT, a lightweight recipe that marries batch‑reflective prompt evolution with a small MLP aggregator over multiple LLM rollouts. The result: tighter calibration and better ranking on tasks where each example demands real reasoning, not surface features. What counts as “Reasoning‑Intensive Regression” (RiR)? RiR tasks look like this: the model must (1) think through the input with step‑wise analysis, and then (2) score it on a real‑valued scale. The paper frames three such tasks: ...