Rename bot family, curate analyze plots, tier evaluation cadence

Three coordinated hygiene changes; none target the diagnosed
selection-bias bottleneck. They make the codebase honestly reflect the
pure-self-play stance and reduce dashboard noise.

Bot rename (drop the unhelpful safe_ prefix; suffixes describe behaviour):
- safe_heuristic_loose -> heuristic_aggressive
- safe_heuristic       -> heuristic_balanced
- safe_heuristic_strict -> heuristic_cautious
- noisy_safe           -> heuristic_noisy
- passive_discard      -> discard_only

Class renames in bots/: SafeHeuristicBot -> HeuristicBot,
SafeHeuristicParams -> HeuristicParams, PassiveDiscardBot -> DiscardOnlyBot,
plus loose/strict parameter constants. Backwards compatibility was dropped
intentionally per user instruction; no aliases. Active configs, docs,
scripts, tests updated. Archive directories (configs/archive,
docs/archive, runs/archive) left intact and may still reference old
names per their read-only policy. The src/.../bots/passive.py module was
renamed to discard_only.py via git mv.

Analyze plot curation (deep_cfr/analyze.py):
- Added analysis_00_core.png as the canonical daily dashboard with 10
  heuristic-free metrics (loss/{advantage,strategy}; vs heuristic_cautious:
  avg_score_diff0, win_rate0, avg_opened_colors, positive_expedition_rate,
  bonus_expedition_rate, score_per_opened_color, policy_entropy; vs random:
  win_rate0).
- Removed analysis_05_open_quality.png (bad/weak/good open rates,
  recoverable score) and analysis_07_calibration.png (calibration gap,
  recoverable mean) - both relied on the heuristic recoverable_score
  classifier already dropped from inputs.
- Removed SELECTIVITY_PLOTS and plot_selectivity (heuristic-laden).
- SUMMARY_EVAL_METRICS no longer includes bad_open_rate or
  calibration_gap.
- PlotSpec gained an opponents allowlist so the new core section can pin
  a specific opponent per panel without restructuring plot_section.

Tiered evaluation cadence (EvaluationConfig):
- Added extended_opponents and extended_eval_every (default 0 = disabled).
- opponents_for_iteration(iteration) returns the core list every
  eval_every and appends extended_opponents (de-duplicated) when
  iteration is also a multiple of extended_eval_every.
- default.yaml now uses 3 core opponents (random, discard_only,
  heuristic_cautious) every 5 iterations and 3 extended opponents
  (heuristic_balanced, heuristic_aggressive, heuristic_noisy) every 50
  iterations. random is the floor sanity. discard_only is the
  zero-pit detector / absolute-score reference (its score is always 0,
  so eval/discard_only/avg_score_diff0 directly equals the model's raw
  average score). heuristic_cautious is the ceiling and the
  archive-comparable benchmark used in the prior diagnostic sections.

Net eval cost reduction: roughly 50% (3 opponents x every 5 iter, plus
6 opponents x every 50 iter, vs the prior 6 x every 5).

Documented in docs/plans/deep-cfr-selectivity.md section 9.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

docs/plans/torch_compile.md

@@ -26,7 +26,7 @@ Re-enable `torch.compile` on the Deep CFR trainer's networks at a model size whe
 
 1. **Model-size precondition met.** The active `default.yaml` (or the targeted variant) has `network.hidden_size ≥ 1024` *or* `network.num_layers ≥ 6`, *or* an architecture (e.g. `color_shared` with non-trivial `color_attention_layers`) whose per-call forward time exceeds ~150 μs at the trainer's training batch size on the target GPU. If neither condition holds, this plan is **not merged**; the branch is parked.
 2. **Iter-time improvement.** With `compile.trainer.enabled: true` on the chosen larger model and the same seed, the 1000-iter projection improves by at least **5%** vs the no-compile baseline on the same machine (measured on `home`). Eval and checkpointing should be disabled for the bench window, matching the protocol used in the 2026-05-07 experiment.
-3. **No learning-curve drift.** Over at least 100 iterations with `compile.trainer.enabled: true` vs `false` (same seed, same config), the eval win-rate trajectories against `random` and `safe_heuristic` are within seed noise. If trajectories visibly diverge, the plan does not ship even if iter time improves.
+3. **No learning-curve drift.** Over at least 100 iterations with `compile.trainer.enabled: true` vs `false` (same seed, same config), the eval win-rate trajectories against `random` and `heuristic_balanced` are within seed noise. If trajectories visibly diverge, the plan does not ship even if iter time improves.
 4. **No checkpoint-format break.** Checkpoints saved with compile enabled must load cleanly when compile is disabled, and vice versa. (Handled via `_clean_state_dict()`; see Risks.)
 5. **No multiprocessing-worker break.** Whether `inference_backend` is `local` or `server`, traversal workers must continue to receive uncompiled `state_dict`s without `_orig_mod.` prefixes.
 6. **(Secondary) Inference-server forward.** If step B below is taken, the server's `policy_network_seconds` decreases by at least 20% at the chosen model size, with no traversal-path correctness regression. If step B does not produce a measurable win, it is left disabled and the plan still ships with step A only.