Add Dirichlet root noise + standalone eval CLI, fix self-play stall trap

Trap diagnosis: agent learned to stall (avoid opening expeditions, draw
from discard pile to extend deck) until max_steps timeout, then squeak by
on opponents' negative scores. All eval wins were from timeouts; agent
never won a naturally-terminating game. Self-play reinforced this because
timeout games still got a positive value target.

Fixes (no algorithm change, all MCTS hyperparameters or signal shaping):

- Dirichlet noise at root prior (AlphaZero standard, was missing):
  mcts.pyx `_expand_with_prior` takes `is_root` flag; root expansion
  mixes prior with Dirichlet(α). Callers in interleaved_self_play and
  the internal evaluate_and_backup pass `not item.path`.
- Default config strengthens exploration on the 50-sim batched search:
  c_puct 1.5 -> 3.0, virtual_loss_value 1.0 -> 5.0, plus new
  root_dirichlet_alpha=0.3 / root_dirichlet_epsilon=0.25.
- Self-play timeout signal zeroed: `_finalize_context` sets v_target=0
  if context.state is not terminal. Stops the network from learning
  "stall = positive value".

New standalone evaluator:
- `lost-cities-ismcts eval` subcommand (eval_checkpoint.py): loads a
  checkpoint, runs N games per opponent across a parallel pool, reports
  win/score with 95% CIs plus per-game logging via --verbose. Defaults
  cover heuristic-balanced/aggressive/cautious (rollout policy isn't in
  the training-eval opponent list, so this is the natural way to compare
  the trained policy against its rollout target).

Tests (19) still pass; .so rebuilt.

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

configs/ismcts/default.yaml

@@ -1,8 +1,8 @@
 run:
   experiment_name: ismcts-default
-  max_iterations: 100
+  max_iterations: 500
   seed: 1
-  device: auto
+  device: cuda
 rules:
   n_colors: 5
   n_ranks: 9
@@ -22,10 +22,14 @@ network:
   activation: relu
 mcts:
   n_simulations: 50
-  c_puct: 1.5
+  c_puct: 3.0
   max_depth: 200
-  parallel_simulations: 8
-  virtual_loss_value: 1.0
+  parallel_simulations: 64
+  virtual_loss_value: 5.0
+  eval_n_simulations: 16
+  rollout_policy: heuristic_balanced
+  root_dirichlet_alpha: 0.3
+  root_dirichlet_epsilon: 0.25
 temperature:
   training: 1.0
   eval: 0.0
@@ -36,15 +40,17 @@ training:
   replay_capacity: 100000
   interleave_games: 8
   interleave_max_batch: 64
+  num_workers: 8
+  worker_device: cuda
 optimization:
   learning_rate: 0.0003
   grad_clip: 5.0
 checkpoint:
-  save_every: 10
+  save_every: 20
   save_latest: true
 evaluation:
-  eval_every: 10
-  games: 20
+  eval_every: 5
+  games: 5
   opponents: [random, discard-only, heuristic-cautious]
-  max_steps: 10000
-  num_workers: 1
+  max_steps: 500
+  num_workers: 8