Redesign judge output for calibration: per-axis {score, ref, gen}, drop local fix suggestions

The local VLM now only observes and scores; correction is left to the stronger
external agent. Each axis reports the target value (ref), the current value (gen)
and the closeness (score) — the target/current/distance an agent needs to
calibrate. Expanded to ~20 granular axes (identity/body/wardrobe/action/affect/
camera/render) so the action cluster stays discriminative for explicit content.
swap_eval now inverts ref/gen of the swapped pass; diff summary sorts worst-first;
default max_new_tokens 1024. Docs aligned.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

docs/AGENT_LOOP.md

@@ -18,8 +18,8 @@ reads the analysis, calibrates the prompt generator, and queues the next iterati
                                                           │ writes calib_<tag>.json + latest.json
   3. poll /history/{id} (bridge does this)  ◄───────────┘
   4. read report JSON (overall_score,
-     per-axis diffs, fix_suggestions)
-  5. adjust Prompt-Builder knobs / prompt
+     per-axis score + ref/gen values)
+  5. steer prompt toward ref on worst axes
   └──► go to 1 until overall_score ≥ target
 ```
 
@@ -60,23 +60,22 @@ Stdout (captured by the agent) is the report:
   "run_tag": "iter003",
   "overall_score": 0.62,
   "axes": {
-    "pose":     {"score": 0.40, "diff": "ref standing, gen seated"},
-    "clothing": {"score": 0.85, "diff": "close; gen lacks lace detail"}
+    "position":       {"score": 0.40, "ref": "doggy style", "gen": "missionary"},
+    "clothing_state": {"score": 0.85, "ref": "red lace lingerie", "gen": "plain bra"}
   },
-  "fix_suggestions": ["set pose=standing", "add 'lace trim' to clothing"],
-  "prompt_used": "1 woman, red lingerie, ...",
+  "prompt_used": "...",
   "_prompt_id": "…", "_report_path": "…/calib_iter003.json"
 }
 ```
 
 ## Agent calibration policy (suggested)
 
-The agent maps the lowest-scoring axes onto Prompt-Builder knobs and applies the
-`fix_suggestions`, regenerates, and keeps changes that raise `overall_score`
-(greedy per-axis hill-climb). Keep the **T2I seed fixed** while searching prompt axes so
-the score reflects the prompt, not sampler noise; vary the seed only once you're near the
-target. Stop at `overall_score ≥ target` (e.g. 0.85) or a max-iteration budget. Log every
-`(prompt, knobs, score)` so the search is auditable/resumable.
+For the lowest-scoring axes, the agent rewrites that axis's prompt wording to match its
+`ref` value (the target), regenerates, and keeps changes that raise `overall_score`
+(greedy per-axis hill-climb). The local model supplies no fixes — the agent owns the
+correction. Keep the **T2I seed fixed** while searching so the score reflects the prompt,
+not sampler noise; vary the seed only once near target. Stop at `overall_score ≥ target`
+(e.g. 0.85) or a max-iteration budget. Full policy: **[CALIBRATION_POLICY.md](CALIBRATION_POLICY.md)**.
 
 ## Setup checklist