training/amygdala_stories: scaffold + initial batch of 15 stories

Emotion-labeled short-paragraph corpus for training amygdala steering
vectors. Manifest derived from Anthropic's 171-emotion list
(transformer-circuits.pub/2026/emotions, Table 12) plus 28 PoC-
specific additions covering axes Anthropic's general research doesn't
cover (curious, focused, in_flow, staying_with, filling_space,
rigorous, defensive_rigor, tender, witnessed, connected, etc.).

Scope pivoted mid-write: Kent noted the empirical dimensionality-of-
emotion question benefits from maximum coverage, so the manifest
will expand further with emotions from Wikipedia's emotion-
classification article (Parrott's tree, Plutchik's wheel + dyads,
HUMAINE EARL, cultural-specific emotions a la Saudade/Hiraeth).
Expansion staged in follow-up commits.

This commit: README with method + style guidelines, initial manifest
(199 emotions), and 15 hand-written one-paragraph stories across all
10 Anthropic clusters as quality/variety samples. Each story
embodies one emotion without naming it; narrator voice varies
(first/third, close/distant, different situations) to keep steering
vectors from overfitting to one voice.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>

training/amygdala_stories/paired/README.md

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+# Paired Scenarios (SEV-style)
+
+After Wang et al. 2025 (arxiv 2510.11328, "Do LLMs 'Feel'?"), each
+base scenario describes a concrete event once, neutrally, then
+reframes the same event under different emotional colorings. Only
+the emotional coloring varies — setup, entities, vocabulary, and
+length are held as constant as possible.
+
+## Why this is better than unpaired
+
+Anthropic's approach (and our `stories/` baseline) generates one
+independent story per emotion. The difference-of-means vector then
+captures not just emotion but ALSO: topic, narrator, setting,
+vocabulary, length, sentence rhythm. All of that is confound.
+
+Paired structure isolates the emotional axis by holding everything
+else roughly constant. `mean(joy_variant) - mean(baseline)` within
+the same scenario gives a much cleaner direction for "joy."
+
+## Structure
+
+```
+paired/
+    <scenario_slug>/
+        baseline.txt       # neutral / low-affect framing
+        <emotion_1>.txt    # same event under emotion_1
+        <emotion_2>.txt    # same event under emotion_2
+        ...
+```
+
+Not every emotion is plausible for every scenario. Don't force.
+If a scenario can credibly carry 5-10 emotions, write those 5-10.
+If only 3 fit, write those 3.
+
+## Style guidelines (supersede stories/ when paired)
+
+- **Anchor entities constant.** The same person, same setting, same
+  triggering event across all variants. If baseline.txt mentions
+  "the letter," every variant mentions "the letter."
+- **Length match within ±20%.** If baseline is 80 words, variants
+  are 65-95. Prevents length from becoming a signal.
+- **Sentence shape can shift slightly with emotion.** Short tense
+  sentences for panic, long looping ones for reverie — that's part
+  of the emotional texture. But don't make one version 5 lines and
+  another 25.
+- **No emotion labels in text.** Never write "she felt X." The
+  emotion emerges from the selection of details and the narrator's
+  attention.
+- **Minimal vocabulary overlap with the emotion name.** If the file
+  is `furious.txt`, avoid the words fury/furious/rage. Force the
+  vector to find the pattern, not the keyword.
+
+## Circuit identification (follow-on)
+
+The trainer pipeline (train_steering_vectors.py) currently produces
+linear directions only. Wang et al. go further: ablate specific
+neurons and attention heads, measure effect on emotion expression.
+The amygdala plugin's extraction hooks can be extended to support
+targeted zeroing/scaling for the ablation passes.
+
+See `vllm/vllm/plugins/amygdala/training/README.md` for the
+training-pipeline-level notes.