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>

README.md

@@ -7,7 +7,7 @@ The current implementation starts with the classic two-player card game:
 - classic 5-expedition rules by default
 - Python/Cython game engine
 - env wrapper
-- random, passive-discard, and safe-heuristic bots
+- random, discard-only, and safe-heuristic bots
 - core rule, scoring, mask, env, canonical-state, bot, and GUI smoke tests
 
 Training code, Deep CFR, learned-policy evaluation, GUI, and web client are

configs/deep_cfr/default.yaml

@@ -92,11 +92,13 @@ evaluation:
   games: 100
   opponents:
     - random
-    - passive_discard
-    - safe_heuristic
-    - safe_heuristic_loose
-    - safe_heuristic_strict
-    - noisy_safe
+    - discard_only
+    - heuristic_cautious
+  extended_eval_every: 50
+  extended_opponents:
+    - heuristic_balanced
+    - heuristic_aggressive
+    - heuristic_noisy
   max_steps: 10000
   on_max_steps: score_diff
   batch_size: 64

configs/deep_cfr/default_server.yaml

@@ -89,11 +89,11 @@ evaluation:
   games: 100
   opponents:
     - random
-    - passive_discard
-    - safe_heuristic
-    - safe_heuristic_loose
-    - safe_heuristic_strict
-    - noisy_safe
+    - discard_only
+    - heuristic_balanced
+    - heuristic_aggressive
+    - heuristic_cautious
+    - heuristic_noisy
   max_steps: 10000
   on_max_steps: score_diff
   batch_size: 64

docs/performance.md

@@ -182,7 +182,7 @@ eval_<opponent>_<metric>
 
 For that run, iteration 75 had `evaluation_seconds = 18.29s`. Evaluation was
 parallelized by opponent, so per-opponent `elapsed_seconds` values overlap and
-must not be summed as wall-clock time. The slow safe-heuristic opponents
+must not be summed as wall-clock time. The slow heuristic opponents
 dominated the eval wall-clock.
 
 Representative per-opponent breakdown:
@@ -190,13 +190,13 @@ Representative per-opponent breakdown:
 | Opponent | Elapsed | Network | Postprocess | Opponent act |
 | --- | ---: | ---: | ---: | ---: |
 | `random` | 0.57s | 0.18s | 0.25s | 0.06s |
-| `passive_discard` | 0.36s | 0.13s | 0.17s | 0.00s |
-| `safe_heuristic` | 14.54s | 2.65s | 3.34s | 7.57s |
-| `safe_heuristic_loose` | 11.20s | 2.55s | 3.30s | 4.63s |
-| `safe_heuristic_strict` | 15.94s | 2.51s | 3.14s | 9.22s |
-| `noisy_safe` | 1.68s | 0.40s | 0.58s | 0.57s |
+| `discard_only` | 0.36s | 0.13s | 0.17s | 0.00s |
+| `heuristic_balanced` | 14.54s | 2.65s | 3.34s | 7.57s |
+| `heuristic_aggressive` | 11.20s | 2.55s | 3.30s | 4.63s |
+| `heuristic_cautious` | 15.94s | 2.51s | 3.14s | 9.22s |
+| `heuristic_noisy` | 1.68s | 0.40s | 0.58s | 0.57s |
 
-The important read is that safe-heuristic evaluation is not primarily GPU
+The important read is that heuristic evaluation is not primarily GPU
 network forward time. `opponent_act_seconds` and policy post-processing are
 larger than `policy_network_seconds` for the slowest opponents.
 
@@ -236,7 +236,7 @@ The practical eval tuning levers are:
    this would require a feature change.
 
 4. Reduce frequent opponents.
-   The safe-heuristic opponents dominate wall-clock in the inspected run. For
+   The heuristic opponents dominate wall-clock in the inspected run. For
    frequent checks, evaluate against one or two representative opponents and run
    the full suite less often.
 
@@ -278,7 +278,7 @@ already implemented. It would replace or wrap the strategy-network forward pass
 with a precompiled inference engine. Its maximum impact is bounded by
 `policy_network_seconds`, not by total eval time.
 
-In the inspected eval row, the slow safe-heuristic opponents spent about
+In the inspected eval row, the slow heuristic opponents spent about
 2.5-2.6s in policy-network forward but 4.6-9.2s in opponent action selection and
 about 3.1-3.3s in policy post-processing. That means TensorRT could help eval,
 especially for larger `evaluation.games`, but it is not expected to collapse the
@@ -319,7 +319,7 @@ Based on the current metrics, the more plausible performance work is:
    but it requires changing traversal scheduling, not just swapping the network
    backend.
 
-6. For eval-heavy runs, optimize the safe-heuristic opponents and policy
+6. For eval-heavy runs, optimize the heuristic opponents and policy
    post-processing before assuming TensorRT is the main lever.
    The inspected eval row shows those costs dominate the slowest opponents.
 

docs/plans/deep-cfr-selectivity.md

@@ -17,7 +17,7 @@ first-open advantage target.
 ## Baseline symptoms
 
 The 512x3 dense-eval baseline showed improving training losses, but the main
-game-quality metrics against `safe_heuristic_strict` did not improve enough to
+game-quality metrics against `heuristic_cautious` did not improve enough to
 indicate a useful policy.
 
 Observed pattern:
@@ -176,7 +176,7 @@ against the current policy's best non-open action from the same state.
 
 `delta_open = value(force open) - value(best non-open)`.
 
-Counterfactual summary against `safe_heuristic_strict`:
+Counterfactual summary against `heuristic_cautious`:
 
 | checkpoint | bucket | candidates | delta mean | delta median | delta positive | policy prob | selected rate |
 | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
@@ -257,7 +257,7 @@ first-open sampling scanned the full replay memory and pushed iteration time
 above 60 seconds. The indexed-memory version kept first-open sampling near
 0.25 seconds per player at 4M advantage samples and completed 500 iterations.
 
-Final `safe_heuristic_strict` comparison:
+Final `heuristic_cautious` comparison:
 
 | Run | Iter | Score diff | Win rate | Bad open | Score/opened |
 | --- | ---: | ---: | ---: | ---: | ---: |
@@ -331,15 +331,15 @@ Result (2026-05-10):
 - W&B: synced online to group `first-open-prior-v1` (run `teuh915r`)
 - Commit: see HEAD at run start
 
-`safe_heuristic_strict` at iter 200:
+`heuristic_cautious` at iter 200:
 
 | Run | Score diff | Win rate | Bad open | Score/opened |
 | --- | ---: | ---: | ---: | ---: |
 | baseline `confirm-eps-005-zero` (iter 200) | -40.01 | 0.12 | 0.893 | -6.25 |
 | **A1 prior α=5.0 (iter 200)** | **-67.54** | **0.02** | **0.796** | **-8.85** |
 
-Other opponents at iter 200: random +32.51 / 0.86, noisy_safe -71.52 / 0.07,
-safe_heuristic -81.81 / 0.02, safe_heuristic_loose -81.24 / 0.05.
+Other opponents at iter 200: random +32.51 / 0.86, heuristic_noisy -71.52 / 0.07,
+heuristic_balanced -81.81 / 0.02, heuristic_aggressive -81.24 / 0.05.
 
 Conclusion: **mixed result, net regression.** The prior did shift behavior
 in the intended direction on one axis — `bad_open_rate` dropped from 0.893
@@ -382,7 +382,7 @@ post-action play (selection bias)?
 
 Method: re-ran `analyze_first_open_counterfactual.py` on the
 `confirm-eps-005-zero-512x3-det-500` baseline checkpoints (iter 200, iter
-500), but with `--post-policy safe_heuristic_strict`. The opponent and
+500), but with `--post-policy heuristic_cautious`. The opponent and
 state-collection policy stayed the same; only the policy_player's actions
 *after* the forced first action used the strong fixed bot.
 
@@ -434,7 +434,7 @@ Implications:
 
 Candidate next directions (decision pending):
 
-- (E1) Train with `cutoff_rollout_policy=safe_heuristic` instead of `random`.
+- (E1) Train with `cutoff_rollout_policy=heuristic_balanced` instead of `random`.
   Already a config option; gives leaf nodes stronger value estimates
   during traversal. Trades some pure-self-play purity for a stronger
   bootstrap signal. Cheap to test.
@@ -510,7 +510,7 @@ This combined with D1 means:
 
 Updated next-step priorities:
 
-- **(E1) `cutoff_rollout_policy=safe_heuristic` training ablation.**
+- **(E1) `cutoff_rollout_policy=heuristic_balanced` training ablation.**
   Strongest single lever: gives traversal leaves stronger value estimates
   during training, which should ripple back to "open + follow up" signal.
   Pure-self-play purity dented, but only at cutoff leaves.
@@ -635,7 +635,7 @@ Fix:
 
 Also bumped `traversal.outcome_sampling_epsilon` in `default.yaml` from
 0.2 to 0.05. The 200-iteration sweep (section 1) showed 0.05 produced the
-best short-run safe_heuristic_strict score diff (-40.01 vs -57.87 for
+best short-run heuristic_cautious score diff (-40.01 vs -57.87 for
 0.20). All recent experimental runs already used 0.05; the default now
 matches actual experimental practice.
 
@@ -643,7 +643,71 @@ These changes do not target the diagnosed selection-bias bottleneck. They
 align config intent with actual scheduler behaviour and make the default
 config reproduce known-best knob settings out of the box.
 
-### 9. Short open-selectivity ablation
+### 9. Naming, plot curation, and tiered eval cadence (2026-05-10)
+
+Hygiene changes — none target the diagnosed selection-bias bottleneck,
+but they make the codebase honestly reflect the pure-self-play stance
+and reduce dashboard noise.
+
+Bot family rename (drop the unhelpful `safe_` prefix; suffixes now
+describe behaviour):
+
+| Old | New |
+| --- | --- |
+| `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 the loose/strict parameter constants. Backwards
+compatibility was dropped intentionally — no aliases. Active configs,
+docs, scripts, tests updated; archive files (read-only by policy)
+left intact and may still reference old names.
+
+Analyze plot curation (`deep_cfr/analyze.py`):
+
+- New `analysis_00_core.png` 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 we 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 optional `opponents` allowlist so the new core
+section can pin a specific opponent per panel without restructuring the
+existing `plot_section` plumbing.
+
+Tiered evaluation cadence (`EvaluationConfig`):
+
+- Added `extended_opponents: tuple[str, ...]` and `extended_eval_every:
+  int = 0`.
+- Method `opponents_for_iteration(iteration)` returns the core list at
+  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 our model's
+  raw average score). `heuristic_cautious` is the ceiling and the
+  archive-comparable benchmark used in sections 1–6.
+
+Net effect on ongoing eval cost: ~50% reduction (3 opponents × every
+5 iter, plus 6 opponents × every 50 iter, vs the prior 6 opponents
+× every 5).
+
+### 10. Short open-selectivity ablation
 
 Run a 200-300 iteration ablation only after the target audit identifies a
 specific change. Candidate changes include:
@@ -656,10 +720,10 @@ specific change. Candidate changes include:
 
 Primary metrics:
 
-- `eval/safe_heuristic_strict/avg_score_diff0`
-- `eval/safe_heuristic_strict/win_rate0`
-- `eval/safe_heuristic_strict/bad_open_rate`
-- `eval/safe_heuristic_strict/score_per_opened_color`
+- `eval/heuristic_cautious/avg_score_diff0`
+- `eval/heuristic_cautious/win_rate0`
+- `eval/heuristic_cautious/bad_open_rate`
+- `eval/heuristic_cautious/score_per_opened_color`
 
 Do not promote to 500+ iterations unless bad-open rate and score/opened color
 both improve without degrading score diff.

docs/plans/model_size_experiment.md

@@ -39,7 +39,7 @@ The experiment must produce either a recommended new `network` config or a docum
 
 ## Success criteria
 
-1. At least one tested config produces win-rate trajectories vs `safe_heuristic_strict` that are **clearly outside seed noise** compared to the current baseline at iteration 200 — OR a clear documented null result (no size in the tested range improves the curve).
+1. At least one tested config produces win-rate trajectories vs `heuristic_cautious` that are **clearly outside seed noise** compared to the current baseline at iteration 200 — OR a clear documented null result (no size in the tested range improves the curve).
 2. `iteration_seconds`, `traversal_seconds`, `advantage_train_seconds`, `strategy_train_seconds`, and `policy_network_seconds` (eval) are captured for each tested size and written to `docs/performance.md`.
 3. A recommended `network` config emerges from the data, OR the experiment documents why the current size should be kept, with specific rationale.
 
@@ -72,7 +72,7 @@ configs/deep_cfr/model-size-1536x8.yaml
   - `traversal_seconds` — traversal phase.
   - `advantage_train_seconds` — advantage network optimization.
   - `strategy_train_seconds` — strategy network optimization.
-  - At eval iterations {50, 100, 150, 200}: `eval/<opponent>/win_rate` for all opponents, with special attention to `safe_heuristic_strict`.
+  - At eval iterations {50, 100, 150, 200}: `eval/<opponent>/win_rate` for all opponents, with special attention to `heuristic_cautious`.
   - At eval iterations: `eval/<opponent>/policy_network_seconds` — needed for the AMP/TRT prerequisite check.
 - **Memory monitoring:** watch GPU VRAM during the 1024x6 and 1536x8 runs. If a run OOMs or VRAM > 20 GB, reduce `optimization.advantage_batch_size` and `optimization.strategy_batch_size` by half (1024 → 512) and note the change in the results table. Do not adjust traversal settings.
 
@@ -259,8 +259,8 @@ if non_eval:
 eval_rows = {r['iteration']: r for r in rows if r.get('evaluation_seconds')}
 for it in [50, 100, 150, 200]:
     if it in eval_rows:
-        wr = eval_rows[it].get('eval/safe_heuristic_strict/win_rate', 'n/a')
-        print(f'  iter={it} safe_heuristic_strict win_rate={wr}')
+        wr = eval_rows[it].get('eval/heuristic_cautious/win_rate', 'n/a')
+        print(f'  iter={it} heuristic_cautious win_rate={wr}')
 "
 done
 ```
@@ -269,7 +269,7 @@ done
 
 After the grid completes, append a date-stamped experiment subsection to `docs/performance.md` under the "Experiments" heading. The subsection must include:
 
-- A results table with `iteration_seconds` mean (non-eval) and win-rate vs `safe_heuristic_strict` at {50, 100, 150, 200} for each config.
+- A results table with `iteration_seconds` mean (non-eval) and win-rate vs `heuristic_cautious` at {50, 100, 150, 200} for each config.
 - A `policy_network_seconds` column from eval rows — this is the key data for the AMP/compile/TRT prerequisite check.
 - The recommendation that follows from the decision tree below.
 
@@ -302,7 +302,7 @@ Apply this logic after the grid completes:
 
 ### Branch A — a size unlocks the curve AND iter time is acceptable
 
-**Condition:** at least one config at or above 768x4 shows win-rate trajectories vs `safe_heuristic_strict` that are clearly outside seed noise vs 512x3 baseline at iteration 200, AND `iteration_seconds` at that size is ≤ 3× the baseline (i.e., ≤ ~54s/iter).
+**Condition:** at least one config at or above 768x4 shows win-rate trajectories vs `heuristic_cautious` that are clearly outside seed noise vs 512x3 baseline at iteration 200, AND `iteration_seconds` at that size is ≤ 3× the baseline (i.e., ≤ ~54s/iter).
 
 **Action:**
 1. Recommend that config as the new `network` default.

docs/plans/option_b_interleaved_traversal.md

@@ -152,7 +152,7 @@ Cython production rewrite. It intentionally uses per-context RNG so interleaved
 execution order does not change the random stream for another context. That
 lets the prototype assert value/stat/sample parity against a recursive prototype
 while measuring realized batch size. Production Cython parity is a later Phase 2
-gate because the real path also has safe-heuristic opponents, average-strategy
+gate because the real path also has heuristic-balanced opponents, average-strategy
 opponents, self-play league snapshots, deck-draw chance sampling, external
 sampling, and cutoff rollouts.
 
@@ -312,7 +312,7 @@ Required feature expansion:
 
 - Support `opponent_policy: average_strategy`, matching the default config's
   opponent branch.
-- Keep unsupported branches guarded (`self_play_league`, `safe_heuristic`,
+- Keep unsupported branches guarded (`self_play_league`, `heuristic_balanced`,
   random rollout cutoffs, external sampling).
 - Add parity tests for the average-strategy fixed-opponent branch.
 - Verify a default-policy interleaved run starts and emits batch metrics.

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.

docs/reports/cost_cython_nogil_audit_2026-05-07.md

@@ -157,9 +157,9 @@ Python-object touch가 깔려 있다 (per-node, per-iteration):
    `self.strategy_samples.append(...)` (file:903, 937, 969). list의
    PyObject reference 갱신은 free-threaded Python에서도 atomic refcount
    비용을 추가로 부담한다.
-6. **`SafeHeuristicBot.act(state)`** — `_fixed_opponent_action`
+6. **`HeuristicBot.act(state)`** — `_fixed_opponent_action`
    (file:633, 652), `_rollout_value` (file:841). Python class
-   메서드 호출. `safe_heuristic` 옵션 사용 시만 핫.
+   메서드 호출. `heuristic_balanced` 옵션 사용 시만 핫.
 7. **`league_advantage_networks` indexing** — `_self_play_snapshot_
    networks` (file:802), `[-recent_count:]`, `[:max(0, ...)]` slicing
    = Python list slicing.
@@ -267,15 +267,15 @@ candidates = self.league_advantage_networks[:max(0, len(self.league_advantage_ne
 - 빈도: traversal 진입 시 한 번 (`traverse`에서 미리 픽), 재귀 안에서는
   `active_self_play_networks`만 본다. 따라서 cold path. 변환 불필요.
 
-### B6. `SafeHeuristicBot.act(state)` — Python bot
+### B6. `HeuristicBot.act(state)` — Python bot
 
 ```python
 # traversal.pyx:633, 652, 841
-return int(self.safe_heuristic_opponent_bot.act(state))
+return int(self.heuristic_opponent_bot.act(state))
 ```
 
-- 빈도: `opponent_policy=safe_heuristic` 또는 `cutoff_rollout_policy=
-  safe_heuristic`일 때만. 현 default는 self_play_league + score_diff
+- 빈도: `opponent_policy=heuristic_balanced` 또는 `cutoff_rollout_policy=
+  heuristic_balanced`일 때만. 현 default는 self_play_league + score_diff
   cutoff (per memory의 opponent_policy_network_divergence note + AGENTS).
 - 변환 난이도: **Medium-High** (Python class 전체를 cython화). 현 default
   config에서는 핫 아님 — 시도하지 않는 게 합리.

docs/research/deep-cfr-architecture.md

@@ -48,7 +48,7 @@ src/coolrl_lost_cities/games/classic/deep_cfr/
   traversal_stats.py — structured traversal diagnostic metrics
 
   # Auxiliary training modes
-  imitation.py       — safe-heuristic imitation pretraining
+  imitation.py       — heuristic imitation pretraining
   policy_gradient.py — policy-gradient fine-tuning
 ```
 

docs/research/deep-cfr-baseline-2000-analysis.md

@@ -59,23 +59,23 @@ Evaluation performance peaks early and then degrades.
 | Opponent | First 20 evals `win_rate0` | Last 20 evals `win_rate0` | First 20 evals score diff | Last 20 evals score diff |
 | --- | ---: | ---: | ---: | ---: |
 | random | 0.808 | 0.665 | +35.1 | +12.4 |
-| passive_discard | 0.026 | 0.012 | -30.8 | -43.0 |
-| safe_heuristic | 0.070 | 0.011 | -68.7 | -94.2 |
-| safe_heuristic_loose | 0.078 | 0.014 | -69.3 | -95.0 |
-| safe_heuristic_strict | 0.072 | 0.011 | -57.6 | -85.0 |
-| noisy_safe | 0.120 | 0.033 | -49.4 | -78.8 |
+| discard_only | 0.026 | 0.012 | -30.8 | -43.0 |
+| heuristic_balanced | 0.070 | 0.011 | -68.7 | -94.2 |
+| heuristic_aggressive | 0.078 | 0.014 | -69.3 | -95.0 |
+| heuristic_cautious | 0.072 | 0.011 | -57.6 | -85.0 |
+| heuristic_noisy | 0.120 | 0.033 | -49.4 | -78.8 |
 
 The best strict-heuristic point appears around iteration 70:
 
-- `eval/safe_heuristic_strict/win_rate0=0.15`
-- `eval/safe_heuristic_strict/avg_score_diff0=-41.09`
+- `eval/heuristic_cautious/win_rate0=0.15`
+- `eval/heuristic_cautious/avg_score_diff0=-41.09`
 
 The final point at iteration 2000 is worse:
 
-- `eval/safe_heuristic_strict/win_rate0=0.02`
-- `eval/safe_heuristic_strict/avg_score_diff0=-81.64`
+- `eval/heuristic_cautious/win_rate0=0.02`
+- `eval/heuristic_cautious/avg_score_diff0=-81.64`
 
-Selectivity does not meaningfully emerge. Against `safe_heuristic_strict`:
+Selectivity does not meaningfully emerge. Against `heuristic_cautious`:
 
 | Metric | First 20 evals | Last 20 evals | Interpretation |
 | --- | ---: | ---: | --- |

docs/research/deep-cfr-batched-evaluation.md

@@ -19,7 +19,7 @@ The primary bottleneck in CUDA-based evaluation is the overhead of launching sma
 
 A critical refinement in the batched implementation was the handling of policy entropy. Initial versions that calculated entropy per-row on the CPU incurred significant synchronization penalties because each row required a GPU-to-CPU transfer. Moving the entropy calculation into the Torch post-processing pipeline—specifically calculating it directly on the `probs_tensor` (line 220)—ensures that the computation remains on the device and only the final results are transferred back to the host in bulk.
 
-Once network inference is batched, the remaining bottleneck often shifts to the CPU-bound logic of heuristic opponents (e.g., `safe_heuristic_strict`). Parallelizing the evaluation across multiple workers allows the trainer to evaluate against multiple opponents simultaneously. For a single iteration profile, using 4 parallel workers reduced wall-clock evaluation time from 14.8 seconds to 6.4 seconds, achieving a ~2.3x speedup.
+Once network inference is batched, the remaining bottleneck often shifts to the CPU-bound logic of heuristic opponents (e.g., `heuristic_cautious`). Parallelizing the evaluation across multiple workers allows the trainer to evaluate against multiple opponents simultaneously. For a single iteration profile, using 4 parallel workers reduced wall-clock evaluation time from 14.8 seconds to 6.4 seconds, achieving a ~2.3x speedup.
 
 ## Practical implication
 

docs/research/deep-cfr-evaluation-profile.md

@@ -37,7 +37,7 @@ On CPU, the `network / turn` cost is approximately **0.074 ms**. On CUDA, this r
 
 ### Secondary Bottlenecks
 - **Post-processing:** Moving tensors back to CPU (`.cpu().numpy()`) and calculating entropy adds measurable overhead on CUDA that is largely absent on CPU.
-- **Opponent Logic:** Heuristic opponents (e.g., `safe_heuristic`) contribute significant `opponent_act_seconds` (up to 3.5s per eval iteration). Since this logic is pure Python/Cython and runs on the CPU, it does not benefit from GPU acceleration, further diluting any potential CUDA wins.
+- **Opponent Logic:** Heuristic opponents (e.g., `heuristic_balanced`) contribute significant `opponent_act_seconds` (up to 3.5s per eval iteration). Since this logic is pure Python/Cython and runs on the CPU, it does not benefit from GPU acceleration, further diluting any potential CUDA wins.
 
 ## Practical Implications
 

docs/research/deep-cfr-reproducibility.md

@@ -45,7 +45,7 @@ Two temporary runs used:
 - `run.max_iterations=3`
 - `evaluation.eval_every=1`
 - `evaluation.games=20`
-- `evaluation.opponents=[random,safe_heuristic_strict]`
+- `evaluation.opponents=[random,heuristic_cautious]`
 - W&B disabled
 
 Runs:
@@ -55,7 +55,7 @@ Runs:
 
 Core metrics matched exactly:
 
-| Iteration | `traversal/nodes` | `memory/advantage` | `loss/advantage` | `eval/random/win_rate0` | `eval/safe_heuristic_strict/win_rate0` |
+| Iteration | `traversal/nodes` | `memory/advantage` | `loss/advantage` | `eval/random/win_rate0` | `eval/heuristic_cautious/win_rate0` |
 | --- | ---: | ---: | ---: | ---: | ---: |
 | 1 | 170822 | 85096 | 803.9475702643394 | 0.75 | 0.10 |
 | 2 | 206225 | 187987 | 821.3355012834072 | 0.50 | 0.05 |

docs/research/deep-cfr-v0-feature-parity.md

@@ -56,7 +56,7 @@ registered classic bots (`evaluate.py`), training CLI and evaluation CLI
 (`cli.py`), traversal benchmark CLI (`benchmark.py`), `metrics.jsonl` /
 `runtime_progress.json` / `train.log` run artifacts, self-play league with
 snapshot pool and weighted current/recent/older/anchor bucket sampling, safe-
-heuristic anchor opponent, safe-heuristic imitation pretraining (`imitation.py`),
+heuristic anchor opponent, heuristic imitation pretraining (`imitation.py`),
 and policy-gradient fine-tuning (`policy_gradient.py`).
 
 As of `ad0be89`, the package also includes `inference_server.py`,

docs/research/julia_port_evaluation.md

@@ -31,7 +31,7 @@ Decision criteria for going forward:
 
 ### 2026-05-07 — Safe heuristic single-thread parity (criterion 1)
 
-Path: `experiments/julia_safe_heuristic/`.
+Path: `experiments/julia_heuristic/`.
 
 1,838 snapshots in 157.471 ms median (~85.6 μs/call). Action-sequence
 parity vs Python. Same order of magnitude as the Cython port of the

docs/research/lost_cities_selectivity.md

@@ -28,7 +28,7 @@ Deep CFR은 최종적으로 average strategy가 수렴 대상. current advantage
 Visible-score predictor의 ceiling은 있을 수 있음. 다만 selectivity 자체의 ceiling은 아님. selectivity는 visible-score prediction 말고 option value, irreversible cost 회피, opponent dynamics 대응 등 다른 경로로도 emerge 가능.
 
 **Self-play attractor 가설**
-5-color가 stable equilibrium. 한쪽이 selectivity 시도하면 즉시 손해. safe_heuristic이 3.7에서 강하면 진짜 NE는 아닐 듯. 다만 self-play 안에서는 stable.
+5-color가 stable equilibrium. 한쪽이 selectivity 시도하면 즉시 손해. heuristic_balanced이 3.7에서 강하면 진짜 NE는 아닐 듯. 다만 self-play 안에서는 stable.
 
 **Lost Cities NE 자체가 5-color 가설**
 이론적 가능성 0 아님. self-play가 발견한 게 진짜 NE면 transition 영원히 안 옴. 검증 가능한 형태로는 tabular oracle 또는 BR 진단.

scripts/analyze_first_open_advantage.py

@@ -240,7 +240,7 @@ def main() -> None:
     parser.add_argument("--output", type=Path, required=True)
     args = parser.parse_args()
 
-    opponents = args.opponent or ["safe_heuristic_strict"]
+    opponents = args.opponent or ["heuristic_cautious"]
     args.output.parent.mkdir(parents=True, exist_ok=True)
     rows = []
     for checkpoint in args.checkpoints:

scripts/analyze_first_open_counterfactual.py

@@ -367,7 +367,7 @@ def analyze_checkpoint(
 def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("checkpoints", nargs="+", type=Path)
-    parser.add_argument("--opponent", default="safe_heuristic_strict")
+    parser.add_argument("--opponent", default="heuristic_cautious")
     parser.add_argument("--games", type=int, default=100)
     parser.add_argument("--seed", type=int, default=231_000)
     parser.add_argument("--device", default="cuda")
@@ -379,7 +379,7 @@ def main() -> None:
         help=(
             "Policy used for the policy_player during forced-action rollouts. "
             "'model' uses the trained advantage network; any other value is "
-            "treated as a bot name (e.g. 'safe_heuristic_strict')."
+            "treated as a bot name (e.g. 'heuristic_cautious')."
         ),
     )
     parser.add_argument("--output", type=Path, required=True)

scripts/analyze_first_open_followup.py

@@ -273,7 +273,7 @@ def analyze_checkpoint(
 def main() -> None:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("checkpoints", nargs="+", type=Path)
-    parser.add_argument("--opponent", default="safe_heuristic_strict")
+    parser.add_argument("--opponent", default="heuristic_cautious")
     parser.add_argument("--games", type=int, default=100)
     parser.add_argument("--seed", type=int, default=232_000)
     parser.add_argument("--device", default="cpu")

scripts/eval_current_vs_average.py

@@ -265,7 +265,7 @@ def main() -> None:
     parser.add_argument("--output", type=Path, required=True)
     args = parser.parse_args()
 
-    opponents = args.opponent or ["random", "safe_heuristic_strict"]
+    opponents = args.opponent or ["random", "heuristic_cautious"]
     args.output.parent.mkdir(parents=True, exist_ok=True)
 
     rows: list[dict] = []

scripts/export_safe_heuristic_snapshots.py

@@ -7,22 +7,22 @@ from typing import Any
 
 from coolrl_lost_cities.games.classic.game import GameState, LostCitiesConfig
 
-from coolrl_lost_cities.games.classic.bots.heuristic_py import SafeHeuristicBot
+from coolrl_lost_cities.games.classic.bots.heuristic_py import HeuristicBot
 from coolrl_lost_cities.games.classic.bots.registry import (
-    LOOSE_SAFE_HEURISTIC_PARAMS,
-    STRICT_SAFE_HEURISTIC_PARAMS,
+    AGGRESSIVE_HEURISTIC_PARAMS,
+    CAUTIOUS_HEURISTIC_PARAMS,
 )
 
 VARIANTS = {
     "default": None,
-    "loose": LOOSE_SAFE_HEURISTIC_PARAMS,
-    "strict": STRICT_SAFE_HEURISTIC_PARAMS,
+    "loose": AGGRESSIVE_HEURISTIC_PARAMS,
+    "strict": CAUTIOUS_HEURISTIC_PARAMS,
 }
 
 
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
-        description="Export safe-heuristic bot parity snapshots for external implementations."
+        description="Export heuristic bot parity snapshots for external implementations."
     )
     parser.add_argument("--output", required=True, help="JSONL output path.")
     parser.add_argument("--seeds", type=int, default=50, help="Number of seeds per config.")
@@ -71,7 +71,7 @@ def main() -> None:
         for config_name, config in _configs():
             for variant_name, params in VARIANTS.items():
                 for seed in range(args.seeds):
-                    bot = SafeHeuristicBot(params)
+                    bot = HeuristicBot(params)
                     state = GameState.new_game(config, seed=seed)
                     for turn in range(args.max_steps):
                         if state.terminal:

src/coolrl_lost_cities/games/classic/bots/__init__.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
 from ..policy import LostCitiesPolicy, PolicyInput
-from .heuristic import SafeHeuristicBot
-from .passive import PassiveDiscardBot
+from .discard_only import DiscardOnlyBot
+from .heuristic import HeuristicBot
 from .random import RandomBot
 from .registry import DEFAULT_BOT, available_bot_names, build_bot
 
@@ -10,9 +10,9 @@ __all__ = [
     "PolicyInput",
     "DEFAULT_BOT",
     "LostCitiesPolicy",
-    "PassiveDiscardBot",
+    "DiscardOnlyBot",
     "RandomBot",
-    "SafeHeuristicBot",
+    "HeuristicBot",
     "available_bot_names",
     "build_bot",
 ]

src/coolrl_lost_cities/games/classic/bots/discard_only.py

@@ -6,7 +6,7 @@ from ..snapshots import Snapshot
 from .base import first_legal, legal_from_obs
 
 
-class PassiveDiscardBot(LostCitiesPolicy):
+class DiscardOnlyBot(LostCitiesPolicy):
     """Baseline that avoids opening expeditions whenever discarding is legal."""
 
     def act(self, obs_or_state: PolicyInput) -> int:

src/coolrl_lost_cities/games/classic/bots/heuristic.py

@@ -4,8 +4,8 @@ from .heuristic_cy import (
     DRAW_FROM_DECK_ACTION,
     PLAY_OR_DISCARD_ACTIONS_PER_SLOT,
     DerivedHeuristicConfig,
-    SafeHeuristicBot,
-    SafeHeuristicParams,
+    HeuristicBot,
+    HeuristicParams,
     derive_heuristic_config,
     discard_action,
     draw_from_discard_action,
@@ -16,8 +16,8 @@ __all__ = [
     "DRAW_FROM_DECK_ACTION",
     "PLAY_OR_DISCARD_ACTIONS_PER_SLOT",
     "DerivedHeuristicConfig",
-    "SafeHeuristicBot",
-    "SafeHeuristicParams",
+    "HeuristicBot",
+    "HeuristicParams",
     "derive_heuristic_config",
     "discard_action",
     "draw_from_discard_action",

src/coolrl_lost_cities/games/classic/bots/heuristic_cy.pyx

@@ -31,7 +31,7 @@ def draw_from_discard_action(color: int) -> int:
     return 1 + color
 
 
-LOGGER = logging.getLogger("coolrl_lost_cities.games.classic.bots.safe_heuristic")
+LOGGER = logging.getLogger("coolrl_lost_cities.games.classic.bots.heuristic")
 
 
 class _CachedState:
@@ -66,7 +66,7 @@ class _CachedState:
 
 
 @dataclass(frozen=True)
-class SafeHeuristicParams:
+class HeuristicParams:
     # Expedition opening.
     open_target_ratio: float = 0.50
     open_min_card_ratio: float = 0.40
@@ -124,7 +124,7 @@ class DerivedHeuristicConfig:
 @lru_cache(maxsize=64)
 def derive_heuristic_config(
     config: LostCitiesConfig,
-    params: SafeHeuristicParams,
+    params: HeuristicParams,
 ) -> DerivedHeuristicConfig:
     max_color_sum = sum(config.min_rank + rank - 1 for rank in range(1, config.n_ranks + 1))
     break_even_sum = -config.expedition_penalty
@@ -178,20 +178,20 @@ def derive_heuristic_config(
     )
 
 
-class SafeHeuristicBot(LostCitiesPolicy):
-    def __init__(self, params: SafeHeuristicParams | None = None):
-        self.params = params or SafeHeuristicParams()
+class HeuristicBot(LostCitiesPolicy):
+    def __init__(self, params: HeuristicParams | None = None):
+        self.params = params or HeuristicParams()
 
     def act(self, obs_or_state: PolicyInput) -> int:
         if not isinstance(obs_or_state, GameState) and not hasattr(obs_or_state, "legal_mask"):
             LOGGER.debug(
-                "SafeHeuristicBot fallback to first legal: input_type=%s",
+                "HeuristicBot fallback to first legal: input_type=%s",
                 type(obs_or_state).__name__,
             )
             return first_legal(legal_from_obs(obs_or_state))
 
         LOGGER.debug(
-            "SafeHeuristicBot heuristic path: player=%s phase=%s turn=%s",
+            "HeuristicBot heuristic path: player=%s phase=%s turn=%s",
             obs_or_state.current_player,
             obs_or_state.phase,
             obs_or_state.turn_count,

src/coolrl_lost_cities/games/classic/bots/heuristic_py.py

@@ -30,11 +30,11 @@ def draw_from_discard_action(color: int) -> int:
     return 1 + color
 
 
-LOGGER = logging.getLogger("coolrl_lost_cities.games.classic.bots.safe_heuristic")
+LOGGER = logging.getLogger("coolrl_lost_cities.games.classic.bots.heuristic")
 
 
 @dataclass(frozen=True)
-class SafeHeuristicParams:
+class HeuristicParams:
     # Expedition opening.
     open_target_ratio: float = 0.50
     open_min_card_ratio: float = 0.40
@@ -92,7 +92,7 @@ class DerivedHeuristicConfig:
 @lru_cache(maxsize=64)
 def derive_heuristic_config(
     config: LostCitiesConfig,
-    params: SafeHeuristicParams,
+    params: HeuristicParams,
 ) -> DerivedHeuristicConfig:
     max_color_sum = sum(config.min_rank + rank - 1 for rank in range(1, config.n_ranks + 1))
     break_even_sum = -config.expedition_penalty
@@ -146,20 +146,20 @@ def derive_heuristic_config(
     )
 
 
-class SafeHeuristicBot(LostCitiesPolicy):
-    def __init__(self, params: SafeHeuristicParams | None = None):
-        self.params = params or SafeHeuristicParams()
+class HeuristicBot(LostCitiesPolicy):
+    def __init__(self, params: HeuristicParams | None = None):
+        self.params = params or HeuristicParams()
 
     def act(self, obs_or_state: PolicyInput) -> int:
         if not isinstance(obs_or_state, GameState) and not hasattr(obs_or_state, "legal_mask"):
             LOGGER.debug(
-                "SafeHeuristicBot fallback to first legal: input_type=%s",
+                "HeuristicBot fallback to first legal: input_type=%s",
                 type(obs_or_state).__name__,
             )
             return first_legal(legal_from_obs(obs_or_state))
 
         LOGGER.debug(
-            "SafeHeuristicBot heuristic path: player=%s phase=%s turn=%s",
+            "HeuristicBot heuristic path: player=%s phase=%s turn=%s",
             obs_or_state.current_player,
             obs_or_state.phase,
             obs_or_state.turn_count,

src/coolrl_lost_cities/games/classic/bots/registry.py

@@ -3,8 +3,8 @@ from __future__ import annotations
 from collections.abc import Callable
 
 from ..policy import LostCitiesPolicy, PolicyInput
-from .heuristic import SafeHeuristicBot, SafeHeuristicParams
-from .passive import PassiveDiscardBot
+from .discard_only import DiscardOnlyBot
+from .heuristic import HeuristicBot, HeuristicParams
 from .random import RandomBot
 
 BotName = str
@@ -30,7 +30,7 @@ class NoisyPolicy(LostCitiesPolicy):
         return self.base.act(obs_or_state)
 
 
-LOOSE_SAFE_HEURISTIC_PARAMS = SafeHeuristicParams(
+AGGRESSIVE_HEURISTIC_PARAMS = HeuristicParams(
     open_target_ratio=0.42,
     open_min_card_ratio=0.30,
     handshake_target_multiplier=1.00,
@@ -38,7 +38,7 @@ LOOSE_SAFE_HEURISTIC_PARAMS = SafeHeuristicParams(
     late_open_block_ratio=0.12,
 )
 
-STRICT_SAFE_HEURISTIC_PARAMS = SafeHeuristicParams(
+CAUTIOUS_HEURISTIC_PARAMS = HeuristicParams(
     open_target_ratio=0.62,
     open_min_card_ratio=0.50,
     handshake_target_multiplier=1.35,
@@ -49,12 +49,12 @@ STRICT_SAFE_HEURISTIC_PARAMS = SafeHeuristicParams(
 
 BOT_REGISTRY: dict[BotName, PolicyFactory] = {
     DEFAULT_BOT: RandomBot,
-    "passive-discard": lambda seed: PassiveDiscardBot(),
-    "safe-heuristic": lambda seed: SafeHeuristicBot(),
-    "safe-heuristic-loose": lambda seed: SafeHeuristicBot(LOOSE_SAFE_HEURISTIC_PARAMS),
-    "safe-heuristic-strict": lambda seed: SafeHeuristicBot(STRICT_SAFE_HEURISTIC_PARAMS),
-    "noisy-safe": lambda seed: NoisyPolicy(
-        SafeHeuristicBot(),
+    "discard-only": lambda seed: DiscardOnlyBot(),
+    "heuristic-balanced": lambda seed: HeuristicBot(),
+    "heuristic-aggressive": lambda seed: HeuristicBot(AGGRESSIVE_HEURISTIC_PARAMS),
+    "heuristic-cautious": lambda seed: HeuristicBot(CAUTIOUS_HEURISTIC_PARAMS),
+    "heuristic-noisy": lambda seed: NoisyPolicy(
+        HeuristicBot(),
         RandomBot(seed),
     ),
 }

src/coolrl_lost_cities/games/classic/deep_cfr/analyze.py

@@ -18,6 +18,7 @@ class PlotSpec:
     scale: float = 1.0
     kind: str = "eval"
     fixed_ylim: tuple[float, float] | None = None
+    opponents: tuple[str, ...] | None = None
 
 
 @dataclass(frozen=True)
@@ -28,6 +29,71 @@ class SectionSpec:
 
 
 SECTIONS: tuple[SectionSpec, ...] = (
+    SectionSpec(
+        "Core",
+        "analysis_00_core.png",
+        (
+            PlotSpec("Advantage Loss", ("loss/advantage",), "loss", kind="train"),
+            PlotSpec("Strategy Loss", ("loss/strategy",), "loss", kind="train"),
+            PlotSpec(
+                "Avg Score Diff (heuristic_cautious)",
+                ("avg_score_diff0",),
+                "score diff",
+                opponents=("heuristic_cautious",),
+            ),
+            PlotSpec(
+                "Win Rate (heuristic_cautious)",
+                ("win_rate0",),
+                "rate (%)",
+                scale=100.0,
+                fixed_ylim=(0, 100),
+                opponents=("heuristic_cautious",),
+            ),
+            PlotSpec(
+                "Win Rate (random)",
+                ("win_rate0",),
+                "rate (%)",
+                scale=100.0,
+                fixed_ylim=(0, 100),
+                opponents=("random",),
+            ),
+            PlotSpec(
+                "Avg Opened Colors (heuristic_cautious)",
+                ("avg_opened_colors",),
+                "colors",
+                fixed_ylim=(0, 5),
+                opponents=("heuristic_cautious",),
+            ),
+            PlotSpec(
+                "Positive Expedition Rate (heuristic_cautious)",
+                ("positive_expedition_rate",),
+                "rate (%)",
+                scale=100.0,
+                fixed_ylim=(0, 100),
+                opponents=("heuristic_cautious",),
+            ),
+            PlotSpec(
+                "Bonus Expedition Rate (heuristic_cautious)",
+                ("bonus_expedition_rate",),
+                "rate (%)",
+                scale=100.0,
+                fixed_ylim=(0, 100),
+                opponents=("heuristic_cautious",),
+            ),
+            PlotSpec(
+                "Score per Opened Color (heuristic_cautious)",
+                ("score_per_opened_color",),
+                "score / color",
+                opponents=("heuristic_cautious",),
+            ),
+            PlotSpec(
+                "Policy Entropy (heuristic_cautious)",
+                ("policy_entropy",),
+                "entropy",
+                opponents=("heuristic_cautious",),
+            ),
+        ),
+    ),
     SectionSpec(
         "Loss",
         "analysis_01_loss.png",
@@ -107,48 +173,6 @@ SECTIONS: tuple[SectionSpec, ...] = (
             PlotSpec("Expedition Cards", ("avg_expedition_cards",), "cards"),
         ),
     ),
-    SectionSpec(
-        "OpenQuality",
-        "analysis_05_open_quality.png",
-        (
-            PlotSpec(
-                "Bad Open Rate",
-                ("bad_open_rate",),
-                "rate (%)",
-                scale=100.0,
-                fixed_ylim=(0, 100),
-            ),
-            PlotSpec(
-                "Weak Open Rate",
-                ("weak_open_rate",),
-                "rate (%)",
-                scale=100.0,
-                fixed_ylim=(0, 100),
-            ),
-            PlotSpec(
-                "Bad or Weak Open Rate",
-                ("bad_or_weak_open_rate",),
-                "rate (%)",
-                scale=100.0,
-                fixed_ylim=(0, 100),
-            ),
-            PlotSpec(
-                "Good Open Rate",
-                ("good_open_rate",),
-                "rate (%)",
-                scale=100.0,
-                fixed_ylim=(0, 100),
-            ),
-            PlotSpec("Bad Open per Game", ("bad_open_per_game",), "opens / game"),
-            PlotSpec(
-                "Bad or Weak Open per Game",
-                ("bad_or_weak_open_per_game",),
-                "opens / game",
-            ),
-            PlotSpec("Opening Play Actions", ("opening_play_actions",), "actions / game"),
-            PlotSpec("Opening Recoverable p25", ("opening_recoverable_score_p25",), "score"),
-        ),
-    ),
     SectionSpec(
         "ExpeditionOutcomes",
         "analysis_06_expedition_outcomes.png",
@@ -188,23 +212,6 @@ SECTIONS: tuple[SectionSpec, ...] = (
             PlotSpec("Score per Opened Color", ("score_per_opened_color",), "score / color"),
         ),
     ),
-    SectionSpec(
-        "Calibration",
-        "analysis_07_calibration.png",
-        (
-            PlotSpec(
-                "First Open Recoverable Score",
-                (
-                    "first_open_recoverable_score_mean_for_positive_final",
-                    "first_open_recoverable_score_mean_for_negative_final",
-                ),
-                "score",
-            ),
-            PlotSpec("Opening Recoverable Mean", ("opening_recoverable_score_mean",), "score"),
-            PlotSpec("Opening Recoverable p25", ("opening_recoverable_score_p25",), "score"),
-            PlotSpec("Calibration Gap", ("calibration_gap",), "score"),
-        ),
-    ),
     SectionSpec(
         "Traversal",
         "analysis_08_traversal.png",
@@ -311,41 +318,23 @@ SECTIONS: tuple[SectionSpec, ...] = (
     ),
 )
 
-SELECTIVITY_PLOTS: tuple[PlotSpec, ...] = (
-    PlotSpec("Opened Colors", ("avg_opened_colors",), "colors"),
-    PlotSpec("5-Color Open Count", ("5_color_open_count",), "games / eval"),
-    PlotSpec("Opening Recoverable Mean", ("opening_recoverable_score_mean",), "score"),
-    PlotSpec("Calibration Gap", ("calibration_gap",), "score"),
-    PlotSpec(
-        "First Open Recoverable Score",
-        (
-            "first_open_recoverable_score_mean_for_positive_final",
-            "first_open_recoverable_score_mean_for_negative_final",
-        ),
-        "score",
-    ),
-    PlotSpec("Opening Play Actions", ("opening_play_actions",), "actions / game"),
-)
-
 SUMMARY_EVAL_METRICS: tuple[tuple[str, str, float], ...] = (
     ("win_rate0", "win rate (%)", 100.0),
     ("avg_score_diff0", "avg score diff", 1.0),
     ("avg_score0", "avg score", 1.0),
     ("play_action_rate", "play rate (%)", 100.0),
     ("avg_opened_colors", "opened colors", 1.0),
-    ("bad_open_rate", "bad open (%)", 100.0),
     ("score_per_opened_color", "score / opened color", 1.0),
-    ("calibration_gap", "calibration gap", 1.0),
     ("bonus_contribution_per_game", "bonus / game", 1.0),
 )
 
 OPPONENT_COLORS: dict[str, str] = {
-    "noisy_safe": "tab:blue",
-    "passive_discard": "tab:orange",
+    "heuristic_noisy": "tab:blue",
+    "discard_only": "tab:orange",
     "random": "tab:green",
-    "safe_heuristic": "tab:red",
-    "safe_heuristic_loose": "tab:purple",
-    "safe_heuristic_strict": "tab:brown",
+    "heuristic_balanced": "tab:red",
+    "heuristic_aggressive": "tab:purple",
+    "heuristic_cautious": "tab:brown",
 }
 
 TRAVERSAL_COLORS: dict[str, str] = {
@@ -428,7 +417,14 @@ def plot_section(
         if spec.kind == "train":
             plotted = _plot_train_spec(ax, rows, spec, smoothing_window=smoothing_window)
         else:
-            plotted = _plot_eval_spec(ax, rows, opponents, spec, smoothing_window=smoothing_window)
+            filtered_opponents = (
+                [o for o in opponents if o in spec.opponents]
+                if spec.opponents is not None
+                else opponents
+            )
+            plotted = _plot_eval_spec(
+                ax, rows, filtered_opponents, spec, smoothing_window=smoothing_window
+            )
         _finish_axis(
             ax, spec.title, ylabel=spec.ylabel, plotted=plotted, fixed_ylim=spec.fixed_ylim
         )
@@ -539,12 +535,6 @@ def analyze_run(
         if plot_section(rows, section, path, smoothing_window=smoothing_window):
             written.append(path)
 
-    selectivity_path = output_dir / _with_filename_suffix(
-        "analysis_09_selectivity.png", filename_suffix
-    )
-    if plot_selectivity(rows, selectivity_path, smoothing_window=smoothing_window):
-        written.append(selectivity_path)
-
     final_eval_path = output_dir / _with_filename_suffix(
         "analysis_final_eval_summary.png", filename_suffix
     )
@@ -566,45 +556,6 @@ def _with_filename_suffix(filename: str, suffix: str) -> str:
     return f"{path.stem}{suffix}{path.suffix}"
 
 
-def plot_selectivity(
-    rows: list[dict[str, Any]],
-    output: Path,
-    *,
-    smoothing_window: int,
-) -> bool:
-    import matplotlib.pyplot as plt
-
-    opponents = opponent_names(rows)
-    if not opponents:
-        return False
-
-    fig, axes = plt.subplots(3, 2, figsize=(16, 12), squeeze=False)
-    axes_flat = list(axes.flat)
-    plotted_any = False
-
-    for ax, spec in zip(axes_flat, SELECTIVITY_PLOTS, strict=True):
-        plotted = _plot_eval_spec(ax, rows, opponents, spec, smoothing_window=smoothing_window)
-        _finish_axis(ax, spec.title, ylabel=spec.ylabel, plotted=plotted)
-        plotted_any = plotted_any or plotted
-
-    handles, labels = _legend_items(axes_flat)
-    if handles:
-        fig.legend(handles, labels, loc="upper center", ncols=min(len(labels), 6), fontsize="small")
-    suffix = f" ({smoothing_window}-iter moving average)" if smoothing_window > 1 else ""
-    fig.suptitle(
-        f"Lost Cities Deep CFR selectivity metrics{suffix}",
-        fontsize=14,
-        fontweight="bold",
-    )
-    fig.tight_layout(rect=(0, 0, 1, 0.95))
-    if not plotted_any:
-        plt.close(fig)
-        return False
-    fig.savefig(output, dpi=150)
-    plt.close(fig)
-    return True
-
-
 def _all_eval_metrics() -> set[str]:
     metrics: set[str] = set()
     for section in SECTIONS:

src/coolrl_lost_cities/games/classic/deep_cfr/config.py

@@ -143,16 +143,16 @@ class TraversalConfig(StrictModel):
     @field_validator("cutoff_rollout_policy")
     @classmethod
     def _validate_cutoff_rollout_policy(cls, value: str) -> str:
-        if value not in {"random", "safe_heuristic"}:
-            raise ValueError("must be 'random' or 'safe_heuristic'")
+        if value not in {"random", "heuristic_balanced"}:
+            raise ValueError("must be 'random' or 'heuristic_balanced'")
         return value
 
     @field_validator("opponent_policy")
     @classmethod
     def _validate_opponent_policy(cls, value: str) -> str:
-        if value not in {"network", "safe_heuristic", "self_play_league", "average_strategy"}:
+        if value not in {"network", "heuristic_balanced", "self_play_league", "average_strategy"}:
             raise ValueError(
-                "must be 'network', 'safe_heuristic', 'self_play_league', or 'average_strategy'"
+                "must be 'network', 'heuristic_balanced', 'self_play_league', or 'average_strategy'"
             )
         return value
 
@@ -294,13 +294,35 @@ class CheckpointConfig(StrictModel):
 class EvaluationConfig(StrictModel):
     eval_every: int = 50
     games: int = 10
-    opponents: tuple[str, ...] = ("random", "safe_heuristic")
+    opponents: tuple[str, ...] = ("random", "heuristic_balanced")
+    extended_eval_every: int = 0
+    extended_opponents: tuple[str, ...] = ()
     max_steps: int = 10_000
     on_max_steps: str = "score_diff"
     batch_size: int = 64
     device: str = "trainer"
     num_workers: int = 4
 
+    def opponents_for_iteration(self, iteration: int) -> tuple[str, ...]:
+        """Return the opponent set to evaluate against at ``iteration``.
+
+        - Always returns ``opponents`` when ``iteration % eval_every == 0``.
+        - When ``extended_eval_every > 0`` and ``iteration`` is also a multiple
+          of ``extended_eval_every``, appends ``extended_opponents`` (de-duplicated)
+          to the core list.
+        - Returns ``()`` when no eval is scheduled this iteration.
+        """
+        if self.eval_every <= 0 or iteration <= 0:
+            return ()
+        if iteration % self.eval_every != 0:
+            return ()
+        result = list(self.opponents)
+        if self.extended_eval_every > 0 and iteration % self.extended_eval_every == 0:
+            for opponent in self.extended_opponents:
+                if opponent not in result:
+                    result.append(opponent)
+        return tuple(result)
+
     @field_validator("on_max_steps")
     @classmethod
     def _validate_on_max_steps(cls, value: str) -> str:

src/coolrl_lost_cities/games/classic/deep_cfr/imitation.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 from torch import nn
 
-from coolrl_lost_cities.games.classic.bots import SafeHeuristicBot
+from coolrl_lost_cities.games.classic.bots import HeuristicBot
 from coolrl_lost_cities.games.classic.deep_cfr.encoding import encode_info_state, input_dim
 from coolrl_lost_cities.games.classic.deep_cfr.networks import DeepCFRMLP
 from coolrl_lost_cities.games.classic.game import GameState, LostCitiesConfig, classic_config
@@ -18,7 +18,7 @@ class ImitationMetrics:
     loss: float
 
 
-def collect_safe_heuristic_samples(
+def collect_heuristic_samples(
     config: LostCitiesConfig | None = None,
     *,
     games: int = 4,
@@ -28,7 +28,7 @@ def collect_safe_heuristic_samples(
     game_config = config or classic_config(seed=seed)
     probe = GameState.new_game(game_config, seed=seed)
     action_size = 2 * probe.config.hand_size + 1 + probe.config.n_colors
-    bot = SafeHeuristicBot()
+    bot = HeuristicBot()
     infos: list[np.ndarray] = []
     targets: list[np.ndarray] = []
     masks: list[np.ndarray] = []
@@ -67,7 +67,7 @@ def pretrain_strategy_network(
     learning_rate: float = 1.0e-3,
     device: torch.device | str = "cpu",
 ) -> ImitationMetrics:
-    x_np, y_np, legal_np = collect_safe_heuristic_samples(config, games=games, seed=seed)
+    x_np, y_np, legal_np = collect_heuristic_samples(config, games=games, seed=seed)
     device = torch.device(device)
     strategy_network.to(device)
     strategy_network.train()

src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py

@@ -894,17 +894,15 @@ class DeepCFRTrainer:
         self.tracker.log_event(_format_iteration_summary(metrics, data))
 
     def _evaluate(self, iteration: int) -> dict[str, float | int]:
-        if (
-            self.config.evaluation.eval_every <= 0
-            or iteration % self.config.evaluation.eval_every != 0
-        ):
+        opponents = self.config.evaluation.opponents_for_iteration(iteration)
+        if not opponents:
             return {}
         results: dict[str, float | int] = {}
         eval_device = self._evaluation_device()
-        if self.config.evaluation.resolved_num_workers(len(self.config.evaluation.opponents)) > 1:
-            return self._evaluate_parallel(iteration, eval_device)
+        if self.config.evaluation.resolved_num_workers(len(opponents)) > 1:
+            return self._evaluate_parallel(iteration, eval_device, opponents)
         eval_network = self._evaluation_network(eval_device)
-        for opponent in self.config.evaluation.opponents:
+        for opponent in opponents:
             result = evaluate_strategy_network(
                 eval_network,
                 self.game_config,
@@ -925,8 +923,8 @@ class DeepCFRTrainer:
         self,
         iteration: int,
         eval_device: torch.device,
+        opponents: tuple[str, ...],
     ) -> dict[str, float | int]:
-        opponents = self.config.evaluation.opponents
         max_workers = self.config.evaluation.resolved_num_workers(len(opponents))
         self.tracker.log_event(
             f"Evaluation multiprocessing enabled iteration={iteration} "

src/coolrl_lost_cities/games/classic/deep_cfr/traversal.pyx

@@ -7,7 +7,7 @@ from libc.stdlib cimport free, malloc
 import numpy as np
 import torch
 
-from coolrl_lost_cities.games.classic.bots import SafeHeuristicBot
+from coolrl_lost_cities.games.classic.bots import HeuristicBot
 from coolrl_lost_cities.games.classic.deep_cfr.cfr_math cimport regret_matching_c
 from coolrl_lost_cities.games.classic.deep_cfr.encoding cimport (
     _encode_info_state_with_flags_c,
@@ -70,8 +70,8 @@ cdef class CythonDeepCFRTraverser:
     cdef object device
     cdef object encoding
     cdef object league_advantage_networks
-    cdef object safe_heuristic_rollout_bot
-    cdef object safe_heuristic_opponent_bot
+    cdef object heuristic_rollout_bot
+    cdef object heuristic_opponent_bot
     cdef int action_size
     cdef int input_dim
     cdef float epsilon
@@ -185,15 +185,15 @@ cdef class CythonDeepCFRTraverser:
             raise ValueError("cutoff_value_mode must be 'score_diff' or 'random_rollout'")
         self.cutoff_random_rollout = cutoff_value_mode == "random_rollout"
         self.cutoff_rollouts = max(0, cutoff_rollouts)
-        if cutoff_rollout_policy not in {"random", "safe_heuristic"}:
-            raise ValueError("cutoff_rollout_policy must be 'random' or 'safe_heuristic'")
+        if cutoff_rollout_policy not in {"random", "heuristic_balanced"}:
+            raise ValueError("cutoff_rollout_policy must be 'random' or 'heuristic_balanced'")
         self.cutoff_rollout_max_steps = max(1, cutoff_rollout_max_steps)
-        self.safe_heuristic_rollout_bot = (
-            SafeHeuristicBot() if cutoff_rollout_policy == "safe_heuristic" else None
+        self.heuristic_rollout_bot = (
+            HeuristicBot() if cutoff_rollout_policy == "heuristic_balanced" else None
         )
         if opponent_policy == "network":
             self.opponent_policy_id = 0
-        elif opponent_policy == "safe_heuristic":
+        elif opponent_policy == "heuristic_balanced":
             self.opponent_policy_id = 1
         elif opponent_policy == "self_play_league":
             self.opponent_policy_id = 2
@@ -205,7 +205,7 @@ cdef class CythonDeepCFRTraverser:
                 )
         else:
             raise ValueError(
-                "opponent_policy must be 'network', 'safe_heuristic', 'self_play_league', or 'average_strategy'"
+                "opponent_policy must be 'network', 'heuristic_balanced', 'self_play_league', or 'average_strategy'"
             )
         if all_negative_fallback == "uniform":
             self.all_negative_fallback_id = 0
@@ -225,8 +225,8 @@ cdef class CythonDeepCFRTraverser:
         self.self_play_recent_window = max(0, self_play_recent_window)
         self.active_self_play_bucket = 0
         self.active_self_play_networks = None
-        self.safe_heuristic_opponent_bot = (
-            SafeHeuristicBot()
+        self.heuristic_opponent_bot = (
+            HeuristicBot()
             if self.opponent_policy_id == 1 or self.self_play_anchor_probability > 0.0
             else None
         )
@@ -630,9 +630,9 @@ cdef class CythonDeepCFRTraverser:
         if player == traverser or self.opponent_policy_id == 0:
             return -1
         if self.opponent_policy_id == 1:
-            if self.safe_heuristic_opponent_bot is None:
-                self.safe_heuristic_opponent_bot = SafeHeuristicBot()
-            return int(self.safe_heuristic_opponent_bot.act(state))
+            if self.heuristic_opponent_bot is None:
+                self.heuristic_opponent_bot = HeuristicBot()
+            return int(self.heuristic_opponent_bot.act(state))
         if self.opponent_policy_id == 3:
             self._policy_from_strategy_network(state, player, legal, policy)
             for i in range(self.action_size):
@@ -649,9 +649,9 @@ cdef class CythonDeepCFRTraverser:
         if bucket == 0:
             return -1
         if bucket == 3:
-            if self.safe_heuristic_opponent_bot is None:
-                self.safe_heuristic_opponent_bot = SafeHeuristicBot()
-            return int(self.safe_heuristic_opponent_bot.act(state))
+            if self.heuristic_opponent_bot is None:
+                self.heuristic_opponent_bot = HeuristicBot()
+            return int(self.heuristic_opponent_bot.act(state))
         networks = self.active_self_play_networks
         if networks is None:
             return -1
@@ -839,8 +839,8 @@ cdef class CythonDeepCFRTraverser:
         if swapped_indices == NULL:
             raise MemoryError()
         while not state.terminal and steps < self.cutoff_rollout_max_steps:
-            if self.safe_heuristic_rollout_bot is not None:
-                local_action = int(self.safe_heuristic_rollout_bot.act(state))
+            if self.heuristic_rollout_bot is not None:
+                local_action = int(self.heuristic_rollout_bot.act(state))
                 unified_action = self._to_unified_action_c(state, local_action)
             else:
                 count = state._unified_legal_actions_c(actions)

src/coolrl_lost_cities/games/classic/evaluation.py

@@ -277,7 +277,7 @@ def evaluate_policy(
 
 def main(argv: list[str] | None = None) -> None:
     parser = argparse.ArgumentParser(description="Evaluate Lost Cities classic bots.")
-    parser.add_argument("--bot0", default="safe-heuristic", choices=available_bot_names())
+    parser.add_argument("--bot0", default="heuristic-balanced", choices=available_bot_names())
     parser.add_argument("--bot1", default="random", choices=available_bot_names())
     parser.add_argument("--games", type=int, default=100)
     parser.add_argument("--seed", type=int, default=1)

tests/games/classic/test_bots.py

@@ -1,9 +1,9 @@
 from coolrl_lost_cities.games.classic.game import Card, GameState, LostCitiesConfig
 
 from coolrl_lost_cities.games.classic.bots import (
+    HeuristicBot,
     LostCitiesPolicy,
     RandomBot,
-    SafeHeuristicBot,
 )
 from coolrl_lost_cities.games.classic.bots.heuristic import draw_from_discard_action
 from coolrl_lost_cities.games.classic.evaluation import play_game_for_evaluation
@@ -19,13 +19,13 @@ def _expeditions(config: LostCitiesConfig) -> list[list[list[Card]]]:
 
 def test_builtin_bots_implement_lost_cities_policy() -> None:
     assert isinstance(RandomBot(1), LostCitiesPolicy)
-    assert isinstance(SafeHeuristicBot(), LostCitiesPolicy)
+    assert isinstance(HeuristicBot(), LostCitiesPolicy)
 
 
-def test_safe_heuristic_mirror_match_finishes() -> None:
+def test_heuristic_mirror_match_finishes() -> None:
     state, result = play_game_for_evaluation(
-        SafeHeuristicBot(),
-        SafeHeuristicBot(),
+        HeuristicBot(),
+        HeuristicBot(),
         LostCitiesConfig(n_colors=3, n_ranks=5, n_handshakes=1, hand_size=5),
         seed=2000,
         max_steps=200,
@@ -34,9 +34,9 @@ def test_safe_heuristic_mirror_match_finishes() -> None:
     assert result.timed_out is False
 
 
-def test_safe_heuristic_opponent_value_ignores_hidden_hand() -> None:
+def test_heuristic_opponent_value_ignores_hidden_hand() -> None:
     config = LostCitiesConfig(n_colors=2, n_ranks=8, hand_size=3)
-    bot = SafeHeuristicBot()
+    bot = HeuristicBot()
     discard_card = Card(color=0, rank=6)
 
     expeditions_a = _expeditions(config)
@@ -76,9 +76,9 @@ def test_safe_heuristic_opponent_value_ignores_hidden_hand() -> None:
     assert value_a == value_b
 
 
-def test_safe_heuristic_started_expedition_value_ignores_invalid_lower_followup() -> None:
+def test_heuristic_started_expedition_value_ignores_invalid_lower_followup() -> None:
     config = LostCitiesConfig(n_colors=2, n_ranks=8, hand_size=3)
-    bot = SafeHeuristicBot()
+    bot = HeuristicBot()
     high_card = Card(color=0, rank=8)
 
     base_expeditions = _expeditions(config)
@@ -115,9 +115,9 @@ def test_safe_heuristic_started_expedition_value_ignores_invalid_lower_followup(
     assert lower_followup_value == base_value
 
 
-def test_safe_heuristic_draws_playable_discard_instead_of_deck() -> None:
+def test_heuristic_draws_playable_discard_instead_of_deck() -> None:
     config = LostCitiesConfig(n_colors=2, n_ranks=8, hand_size=3)
-    bot = SafeHeuristicBot()
+    bot = HeuristicBot()
 
     expeditions = _expeditions(config)
     expeditions[0][0] = [Card(color=0, rank=4)]
@@ -132,9 +132,9 @@ def test_safe_heuristic_draws_playable_discard_instead_of_deck() -> None:
     assert bot._act_draw(state) == draw_from_discard_action(0)
 
 
-def test_safe_heuristic_can_draw_discard_to_deny_opponent_when_losing() -> None:
+def test_heuristic_can_draw_discard_to_deny_opponent_when_losing() -> None:
     config = LostCitiesConfig(n_colors=2, n_ranks=8, hand_size=4)
-    bot = SafeHeuristicBot()
+    bot = HeuristicBot()
 
     expeditions = _expeditions(config)
     expeditions[0][1] = [Card(color=1, rank=8)]
@@ -158,9 +158,9 @@ def test_safe_heuristic_can_draw_discard_to_deny_opponent_when_losing() -> None:
     assert bot._act_draw(state) == draw_from_discard_action(0)
 
 
-def test_safe_heuristic_classic_self_play_opens_expeditions() -> None:
+def test_heuristic_classic_self_play_opens_expeditions() -> None:
     state = GameState.new_game(LostCitiesConfig(), seed=1)
-    bot = SafeHeuristicBot()
+    bot = HeuristicBot()
     player0_actions: list[int] = []
 
     for _ in range(60):
@@ -182,9 +182,9 @@ def test_safe_heuristic_classic_self_play_opens_expeditions() -> None:
     assert any(state.expeditions[0][color] for color in range(state.config.n_colors))
 
 
-def test_safe_heuristic_avoids_opening_weak_fifth_color() -> None:
+def test_heuristic_avoids_opening_weak_fifth_color() -> None:
     config = LostCitiesConfig(n_colors=5, n_ranks=8, hand_size=8)
-    bot = SafeHeuristicBot()
+    bot = HeuristicBot()
     expeditions = _expeditions(config)
     expeditions[0][0] = [Card(color=0, rank=4)]
     expeditions[0][1] = [Card(color=1, rank=4)]
@@ -211,9 +211,9 @@ def test_safe_heuristic_avoids_opening_weak_fifth_color() -> None:
     )
 
 
-def test_safe_heuristic_prefers_followup_on_started_expedition() -> None:
+def test_heuristic_prefers_followup_on_started_expedition() -> None:
     config = LostCitiesConfig(n_colors=3, n_ranks=8, hand_size=5)
-    bot = SafeHeuristicBot()
+    bot = HeuristicBot()
     expeditions = _expeditions(config)
     expeditions[0][0] = [Card(color=0, rank=4)]
     state = make_state(
@@ -233,9 +233,9 @@ def test_safe_heuristic_prefers_followup_on_started_expedition() -> None:
     assert chosen.color == 0
 
 
-def test_safe_heuristic_avoids_unopened_discard_draw_after_four_opens() -> None:
+def test_heuristic_avoids_unopened_discard_draw_after_four_opens() -> None:
     config = LostCitiesConfig(n_colors=5, n_ranks=8, hand_size=8)
-    bot = SafeHeuristicBot()
+    bot = HeuristicBot()
     expeditions = _expeditions(config)
     expeditions[0][0] = [Card(color=0, rank=4)]
     expeditions[0][1] = [Card(color=1, rank=4)]

tests/games/classic/test_deep_cfr_imitation.py

@@ -3,13 +3,13 @@ from __future__ import annotations
 from coolrl_lost_cities.games.classic.game import LostCitiesConfig
 
 from coolrl_lost_cities.games.classic.deep_cfr.imitation import (
-    collect_safe_heuristic_samples,
+    collect_heuristic_samples,
     new_pretrained_strategy_network,
 )
 
 
-def test_collect_safe_heuristic_samples_shapes() -> None:
-    x, y, legal = collect_safe_heuristic_samples(LostCitiesConfig(seed=61), games=1, seed=61)
+def test_collect_heuristic_samples_shapes() -> None:
+    x, y, legal = collect_heuristic_samples(LostCitiesConfig(seed=61), games=1, seed=61)
 
     assert len(x) == len(y) == len(legal)
     assert x.ndim == 2

tests/games/classic/test_deep_cfr_trainer.py

@@ -1494,3 +1494,83 @@ def test_interleaved_regret_matching_no_fallback_unchanged_by_mode() -> None:
     assert fallback_a is False
     assert np.allclose(policy_uniform, policy_argmax)
     assert np.allclose(policy_uniform.sum(), 1.0)
+
+
+def test_evaluation_opponents_for_iteration_core_only_when_extended_disabled() -> None:
+    from coolrl_lost_cities.games.classic.deep_cfr.config import EvaluationConfig
+
+    cfg = EvaluationConfig(
+        eval_every=5,
+        opponents=("random", "discard_only", "heuristic_cautious"),
+        extended_eval_every=0,
+        extended_opponents=("heuristic_balanced",),
+    )
+    assert cfg.opponents_for_iteration(0) == ()
+    assert cfg.opponents_for_iteration(3) == ()
+    assert cfg.opponents_for_iteration(5) == (
+        "random",
+        "discard_only",
+        "heuristic_cautious",
+    )
+    assert cfg.opponents_for_iteration(50) == (
+        "random",
+        "discard_only",
+        "heuristic_cautious",
+    )
+
+
+def test_evaluation_opponents_for_iteration_extends_on_extended_cadence() -> None:
+    from coolrl_lost_cities.games.classic.deep_cfr.config import EvaluationConfig
+
+    cfg = EvaluationConfig(
+        eval_every=5,
+        opponents=("random", "discard_only", "heuristic_cautious"),
+        extended_eval_every=50,
+        extended_opponents=(
+            "heuristic_balanced",
+            "heuristic_aggressive",
+            "heuristic_noisy",
+        ),
+    )
+    assert cfg.opponents_for_iteration(5) == (
+        "random",
+        "discard_only",
+        "heuristic_cautious",
+    )
+    assert cfg.opponents_for_iteration(45) == (
+        "random",
+        "discard_only",
+        "heuristic_cautious",
+    )
+    assert cfg.opponents_for_iteration(50) == (
+        "random",
+        "discard_only",
+        "heuristic_cautious",
+        "heuristic_balanced",
+        "heuristic_aggressive",
+        "heuristic_noisy",
+    )
+    assert cfg.opponents_for_iteration(100) == (
+        "random",
+        "discard_only",
+        "heuristic_cautious",
+        "heuristic_balanced",
+        "heuristic_aggressive",
+        "heuristic_noisy",
+    )
+
+
+def test_evaluation_opponents_for_iteration_dedupes_overlap() -> None:
+    from coolrl_lost_cities.games.classic.deep_cfr.config import EvaluationConfig
+
+    cfg = EvaluationConfig(
+        eval_every=5,
+        opponents=("random", "heuristic_cautious"),
+        extended_eval_every=10,
+        extended_opponents=("heuristic_cautious", "heuristic_balanced"),
+    )
+    assert cfg.opponents_for_iteration(10) == (
+        "random",
+        "heuristic_cautious",
+        "heuristic_balanced",
+    )

tests/games/classic/test_evaluation.py

@@ -13,7 +13,7 @@ def test_play_game_for_evaluation_finishes_small_match() -> None:
 
     state, result = play_game_for_evaluation(
         build_bot("random", seed=1),
-        build_bot("passive-discard", seed=2),
+        build_bot("discard-only", seed=2),
         config,
         seed=3,
         max_steps=200,
@@ -30,7 +30,7 @@ def test_play_match_alternates_seats_and_reports_rates() -> None:
 
     result = play_match(
         make_policy_factory("random"),
-        make_policy_factory("passive-discard"),
+        make_policy_factory("discard-only"),
         config,
         games=4,
         seed=10,
@@ -50,7 +50,7 @@ def test_evaluation_cli_smoke_json(capsys) -> None:
             "--bot0",
             "random",
             "--bot1",
-            "passive-discard",
+            "discard-only",
             "--games",
             "2",
             "--seed",

tests/games/classic/test_public_api.py

@@ -43,10 +43,10 @@ def test_classic_package_exports_bot_registry_helpers() -> None:
 def test_classic_bot_registry_accepts_reproduction_opponent_names() -> None:
     for name in [
         "random",
-        "passive_discard",
-        "safe_heuristic",
-        "safe_heuristic_loose",
-        "safe_heuristic_strict",
-        "noisy_safe",
+        "discard_only",
+        "heuristic_balanced",
+        "heuristic_aggressive",
+        "heuristic_cautious",
+        "heuristic_noisy",
     ]:
         assert isinstance(classic.build_bot(name, seed=1), classic.LostCitiesPolicy)

tests/games/classic/test_pygame_pvp.py

@@ -11,7 +11,7 @@ def test_gui_argparser_accepts_classic_options() -> None:
             "--mode",
             "pvc",
             "--bot",
-            "safe-heuristic",
+            "heuristic-balanced",
             "--seed",
             "7",
             "--width",
@@ -22,7 +22,7 @@ def test_gui_argparser_accepts_classic_options() -> None:
     )
 
     assert args.mode == "pvc"
-    assert args.bot == "safe-heuristic"
+    assert args.bot == "heuristic-balanced"
     assert args.seed == 7
     assert args.width == 1024
     assert args.height == 768

tests/games/classic/test_safe_heuristic_equivalence.py

@@ -3,19 +3,19 @@ from __future__ import annotations
 import pytest
 from coolrl_lost_cities.games.classic.game import GameState, LostCitiesConfig
 
-from coolrl_lost_cities.games.classic.bots.heuristic import SafeHeuristicBot
+from coolrl_lost_cities.games.classic.bots.heuristic import HeuristicBot
 from coolrl_lost_cities.games.classic.bots.heuristic_py import (
-    SafeHeuristicBot as PythonSafeHeuristicBot,
+    HeuristicBot as PythonHeuristicBot,
 )
 from coolrl_lost_cities.games.classic.bots.registry import (
-    LOOSE_SAFE_HEURISTIC_PARAMS,
-    STRICT_SAFE_HEURISTIC_PARAMS,
+    AGGRESSIVE_HEURISTIC_PARAMS,
+    CAUTIOUS_HEURISTIC_PARAMS,
 )
 
 VARIANTS = (
     ("default", None),
-    ("loose", LOOSE_SAFE_HEURISTIC_PARAMS),
-    ("strict", STRICT_SAFE_HEURISTIC_PARAMS),
+    ("loose", AGGRESSIVE_HEURISTIC_PARAMS),
+    ("strict", CAUTIOUS_HEURISTIC_PARAMS),
 )
 
 CONFIGS = (
@@ -28,14 +28,14 @@ CONFIGS = (
 @pytest.mark.parametrize(("variant_name", "params"), VARIANTS)
 @pytest.mark.parametrize("config", CONFIGS)
 @pytest.mark.parametrize("seed", range(2))
-def test_cython_safe_heuristic_matches_python_action_sequence(
+def test_cython_heuristic_matches_python_action_sequence(
     variant_name: str,
     params,
     config: LostCitiesConfig,
     seed: int,
 ) -> None:
-    py_bot = PythonSafeHeuristicBot(params)
-    cy_bot = SafeHeuristicBot(params)
+    py_bot = PythonHeuristicBot(params)
+    cy_bot = HeuristicBot(params)
     py_state = GameState.new_game(config, seed=seed)
     cy_state = GameState.new_game(config, seed=seed)