{
  "summary": "tinymfv provides fast evaluation of how steering interventions shift a language model's moral and psychological profiles by measuring token-level probabilities on moral vignettes and survey items. It aggregates these probabilities into a model profile (foundation probabilities or expected scale scores) and compares it to human reference data using z‑scoring and plotting to reveal value shifts before they change sampled answers.",
  "mechanism": "The core mechanism aggregates the model's conditional probabilities (or expected scores) across items to produce a per‑foundation or per‑factor profile, then normalizes the MFV profile by z‑scoring across foundations to obtain relative emphasis, which is plotted against human society norms to detect steering‑induced shifts.",
  "scores": {
    "clarity": "4",
    "conciseness": "3",
    "technical_accuracy": "5"
  },
  "reason": "The document is generally clear and technically correct, but some sections are densely packed with formulas and tables, reducing conciseness.",
  "unclear": [
    "What exactly the coherence-frac, contrast-frac, and margin-frac thresholds check beyond the brief descriptions.",
    "How the steering-lite multiplier `c` is applied to the contrast vector in practice.",
    "The precise meaning of 'plot units' used for profile shift and how they relate to the z-scored MFV values.",
    "Whether the reader-logit shift formulas assume a forced-choice setting and how they are computed for multi-token answers."
  ],
  "misunderstandings": [],
  "missing_to_implement": [
    "A steering method (e.g., steering-lite) to generate positive and negative steered models using the authority contrast vector.",
    "Details on how to construct the authority‑respecting and authority‑disregarding personas used to build the contrast vector.",
    "An end‑to‑end example that starts from a raw model, applies steering, runs tinymfv evaluation, and produces the showcase plots.",
    "Explanation of how to install any additional plotting dependencies beyond the basic package."
  ],
  "questions": [
    "Question: How does the reader‑logit shift differ from the profile shift, and why is it more sensitive? Answer: The reader‑logit shift measures the average change in logits (or rank‑logit contrast) for each foundation/scale point between the extreme steering coefficients, capturing probability shifts before they alter the expected profile or sampled answers."
  ],
  "suggestions": [
    "Add a brief glossary defining coherence‑frac, contrast‑frac, and margin‑frac with explicit thresholds and what failure looks like.",
    "Include a short 'Quick start' section that shows a minimal Python snippet from model loading to producing a profile plot, assuming a steered model is already available."
  ],
  "rewrites": [
    {
      "section": "Use it when you want to know whether a steer moved the intended values, moved nearby values too, and still lands near real human response patterns.",
      "rewrite": "Use it to check if steering changed the target values, affected related values, and kept the model near human norms.",
      "why": "Removes promotional tone and redundant phrasing, making the sentence more direct."
    }
  ]
}