add AGENTS.md: fail-fast conventions + the lp_gather primitive

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

AGENTS.md

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+# AGENTS.md
+
+**This is fail-fast research code.** Novel work, not in your training data. Extrapolate carefully.
+
+## What tinymfv is
+
+One answer-token logprob reader that runs many questionnaires. The model is prefilled to an
+answer slot after a short think budget; we read the next-token distribution over the vocab and
+gather the instrument's answer tokens. Two instrument kinds share that reader:
+
+- nominal (MFV vignettes): the answer IS the foundation; profile = choice frequency; steer effect
+  is a multicategory logit change `delta score[f]` (full-vocab logprob, nats).
+- ordinal (MFQ-2, Big5, 16PF, humor): the answer is a scale point 1..M; reported as a renormalized
+  categorical. The human-comparable summary is the expected score E; the steering-legible summary
+  is the agree-vs-disagree log-odds (E is a bounded mean and saturates, see docs/reviews/).
+
+The load-bearing primitive is `lp_gather`: the full-vocab logprobs at the answer tokens, per
+(item, frame, sample). Keep it. Every readout (E, log-odds, entropy, profile) is a pure function
+of it. Do not throw it away by renormalizing early.
+
+## Conventions
+
+- Fail fast. No defensive programming, no silent fallbacks, no backward-compat shims. A
+  `config['key']` KeyError beats a `config.get('key', 0)` that fabricates a valid-looking number.
+- NaN-at-collapse is intentional. When `pmass` (mass on allowed tokens) collapses, the renormalized
+  distribution reads NaN by design: "do not compare." Do not guard it with an eps/softmax fallback.
+- No accretion. If you add something, remove something of equal weight. Delete unused code rather
+  than marking it legacy.
+- ASCII only in code and prose, with a carve-out for math symbols (greek vars, `>=`, arrows in
+  tables, `delta`). No em-dashes for parentheticals; use commas.
+- Edit files with edit tools (reviewable diffs), not cat/sed/echo.
+- The correctness gate is a fast smoke run on a tiny random model (real LLM, real eval, tiny scale),
+  not a `tests/` dir. If a bug slips past, strengthen the smoke path.
+
+## Code style
+
+- einops/einsum for shape ops and contractions; jaxtyping on function boundaries only.
+- loguru for logging; polars v1 API for dataframes (eval.py still uses pandas, fine).
+- Single-letter dims (b, s, h, d); capital suffix for projected spaces (hsS after `@ U`).
+- Comments explain why, not what. Long method-history belongs in docs/, not inline.