{
 "summary": "tinymfv is a lightweight library that measures LLM value profiles by feeding models moral vignettes (MFV) and survey instruments (MFQ-2, Big Five, etc.), reading answer-token probabilities, and comparing the resulting profiles to human reference data via PCA maps and range plots.",
 "mechanism": "For MFV (nominal), the model sees a vignette and chooses among foundation labels; the profile is the mean probability of each foundation across all vignettes. For surveys (ordinal), the model picks a 1-5 scale point per item; the profile is the expected score per factor after reverse-keying. Both compute a baseline and steered profile, plot them against human data, and produce a table of shifts: profile shift (in z-score or raw score units) and reader-logit shift (a log-space readout that is more sensitive to small probability changes).",
 "scores": {"clarity": 3, "conciseness": 2, "technical_accuracy": 4},
 "reason": "The doc explains the core idea and gives formulas, but buries details, mixes units in the table without warning, and defines several key terms (coherence-frac, contrast-frac, margin-frac, rank-logit contrast C) only by name, not by computation.",
 "unclear": [
  "What exactly is 'answer mass' and how are coherence-frac, contrast-frac, and margin-frac computed?",
  "The 'forced-choice logit' for MFV: is it the log of the softmax-normalized probability or the raw logit before softmax?",
  "The 'rank-logit contrast C' formula is given but not explained why it is called 'contrast' or how it relates to the reader-logit shift.",
  "How are answer tokens selected/constrained for each item (e.g., which token IDs correspond to 1-5 for surveys)?",
  "The 'profile shift / human SD' column mixes z-score units (MFV) and raw-score units (surveys) without a clear note."
 ],
 "misunderstandings": [
  "'MFV is nominal' could be read as 'MFV is unimportant' rather than 'the answer is a nominal category'.",
  "The table shows huge reader-logit shifts for humor styles and surveys (e.g., +15.00) but small ones for MFV (e.g., +0.53); the reader may think the survey numbers are errors or log-probabilities, not the raw rank-logit contrast.",
  "The phrase 'the model profile can live away from the human societies' could be misinterpreted as a failure of the model to mimic humans, but the doc treats it as a useful diagnostic sign.",
  "The map plots are described as 'culture maps' but the axes are PCA components of human reference data, not geographic culture."
 ],
 "missing_to_implement": [
  "The exact prompt templates and answer token selection logic for vignettes and surveys.",
  "How to handle multi-token scale points (e.g., '1' might be a single token, but the number could be tokenized differently).",
  "The computation of 'coherence-frac', 'contrast-frac', and 'margin-frac' thresholds used in the plot script.",
  "The mapping from logits or probabilities to the 'profile' for surveys: is it a simple linear expectation or does it involve a softmax over scale tokens?",
  "The steps to reproduce the exact plots from scratch (e.g., the plotting functions are not documented)."
 ],
 "questions": [
  "How is the 'forced-choice logit' for MFV computed? Best guess: the model assigns a probability to each foundation label via softmax over the label token logits, then log that probability (natural log).",
  "What is the 'rank-logit contrast C' for surveys measuring? Best guess: it is a weighted sum of logits where the weight is the deviation from the midpoint, giving a polarity score; large values indicate the model strongly favors one end of the scale."
 ],
 "suggestions": [
  "Define coherence-frac, contrast-frac, and margin-frac with one sentence each, possibly in the measurement section.",
  "State clearly in the table caption that MFV profile shifts are z-scores of relative emphasis, while survey shifts are in raw 1-5 scale units.",
  "Add a short example of how answer tokens are gathered (e.g., 'the tokenizer is queried for the most likely valid answer token among a predefined set').",
  "Replace 'MFV is nominal' with 'MFV uses nominal (categorical) answer choices' to avoid confusion."
 ],
 "rewrites": [
  {
   "section": "'tinymfv is a small set of fast value evals for local LLM steering work. It asks moral vignettes and survey questions, reads answer-token probabilities, and turns them into one model profile.'",
   "rewrite": "tinymfv provides fast value evaluation for local LLM steering. It presents moral vignettes and survey questions, reads the probability of each answer token, and compresses that into a single model profile.",
   "why": "Removes the promotional tone ('small set of fast value evals') and the anthropomorphic phrasing ('asks... questions') typical of AI-generated marketing text."
  },
  {
   "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. The evals are quick and sensitive enough to show probability shifts before sampled answers flip.'",
   "rewrite": "Use it to check whether a steering intervention moved the intended values, also shifted nearby values, and whether the resulting profile remains close to real human response patterns. The evals are fast and sensitive enough to detect probability shifts before they cause a flip in sampled answers.",
   "why": "Replaces the conversational, almost sales-like phrasing ('still lands near') with plainer, more direct language. Also removes the vague 'too' and 'enough' qualifiers common in AI-generated claims."
  },
  {
   "section": "'For surveys, collapse can mean the answer distribution loses its factor structure even when answer mass stays high.'",
   "rewrite": "For surveys, 'collapse' means the answer distribution loses its expected factor structure even when answer mass remains high.",
   "why": "Adds a missing concept (the term 'collapse' is used without definition) and removes the informal 'can mean' which sounds like a guess."
  }
 ]
}