Close librarian: full archive promote-survey + parallel dispatch

Second survey processed the remaining 12 archives via gemini after
the first batch of 3 was accepted. 12 drafts, 0 skips, 0 errors.
Every draft carries a deterministic Last-verified header
(2026-05-08, commit 5c221fb) thanks to the post-processing fix
landed in the previous commit. All 12 accepted into docs/research/
verbatim:

  deep-cfr-evaluation-profile-plan
  deep-cfr-legacy-experiment-reproduction
  deep-cfr-legacy-runtime-comparison
  deep-cfr-performance-experiments
  deep-cfr-profile-advantage-memory-split
  deep-cfr-profile
  deep-cfr-regret-fallback-audit
  deep-cfr-v0-gap-vs-coolrl
  deep-cfr-v0-plan
  fast-engine-next-optimizations
  post-a-optimization-calculus
  test-coverage-notes

docs/archive/ is now fully covered: every entry either has a
research counterpart by stem or by tail-match.

Also extracts _dispatch_one and adds --parallel N to
scripts/librarian_survey.py. ThreadPoolExecutor over the per-archive
work is safe because subprocess.run is network-bound (no GIL fight)
and each thread writes to its own output filename. Default stays
1 (sequential); --parallel 4 is the recommended speedup for large
surveys. The two surveys above ran sequentially; future runs can
opt in.

Plan declares librarian closed for new feature work. MEMORY drift
fixup and duplicate-merge modes stay deferred until a real input
surfaces. Stage 1 (5 deterministic checks) and Stage 2 (promote +
survey) remain operational.

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

docs/research/deep-cfr-legacy-experiment-reproduction.md

@@ -0,0 +1,46 @@
+# Deep CFR Legacy Parity and Hyperparameter Mapping
+
+**Last verified:** 2026-05-08, commit `5c221fb`
+
+Source: `docs/archive/deep-cfr-legacy-experiment-reproduction.md`
+
+## Question
+
+How are legacy `coolrl` Deep CFR hyperparameters and feature semantics mapped to the current implementation to ensure parity and meaningful experiment reproduction?
+
+## Code reference
+
+- `src/coolrl_lost_cities/games/classic/deep_cfr/config.py`, lines 63–64 (`EncodingConfig`) and 98–99 (`TraversalConfig`): Definition of parity-critical flags and traversal parameters.
+- `src/coolrl_lost_cities/games/classic/deep_cfr/encoding.pyx`, lines 156 and 209: Implementation of `_append_derived_playability_features_c` and `_append_slot_aware_playability_features_c`.
+- `src/coolrl_lost_cities/games/classic/deep_cfr/networks.py`, lines 21–33: `_build_mlp` implementation that supports the legacy 3-layer, 256-hidden-unit architecture.
+- `src/coolrl_lost_cities/games/classic/deep_cfr/traversal.pyx`, lines 408–411 and 606–607: Logic for outcome-sampling mixture and value clipping.
+
+## Analysis
+
+Reproduction of legacy Deep CFR experiments requires strict adherence to both hyperparameter values and specific feature engineering. The mapping between the legacy `coolrl` environment and this repository is achieved through the following core components:
+
+### 1. Information-State Encoding
+The legacy experiment relied on specialized features beyond the raw board state. These are preserved through two critical flags in `EncodingConfig`:
+- **`derived_playability`**: Color-level features that calculate the utility or risk of playing into specific expeditions.
+- **`slot_aware_playability`**: Hand-slot local features that anchor actions to specific hand positions. This allows the model to distinguish between identical cards in different slots or empty slots, which is critical for high-tier play.
+
+### 2. Network Architecture
+The current `DeepCFRMLP` supports configurable `hidden_size` and `num_layers`. To match legacy performance, the model must be configured with a 3-layer ReLU MLP (hidden size 256). The `_build_mlp` helper in `networks.py` ensures the layer stack is constructed identically to the legacy Torch implementation.
+
+### 3. Traversal and Optimization
+Deep CFR performance is highly sensitive to the traversal mechanism. The current implementation matches legacy semantics via:
+- **Outcome Sampling Mixture**: On-policy sampling mixed with ε-uniform exploration (`outcome_sampling_epsilon: 0.2`).
+- **Value Clipping**: Restricting the range of sampled values (`outcome_sampling_value_clip: 500`) to prevent gradient instability.
+- **Batching**: Separate advantage and strategy batch sizes (legacy default: 1024) and update counts (legacy default: 256 per iteration).
+
+## Practical implication
+
+Maintaining this mapping allows for direct comparison between modern runs and legacy baselines. The **slot-aware encoding** is identified as the single most critical factor for parity in tier3 rulesets; without it, the information-state is insufficiently descriptive for the policy network to replicate legacy performance.
+
+When evaluating current performance against legacy reports, ensure the `encoding.slot_aware_playability` flag is enabled and the `traversal` parameters match the 0.2/500 epsilon/clip baseline.
+
+## References
+
+- `docs/archive/deep-cfr-legacy-experiment-reproduction.md` (Reproduction plan)
+- `configs/archive/deep-cfr-selfplay-full-depth-slot-playability.yaml` (Reference configuration)
+- `docs/research/deep-cfr-v0-feature-parity.md` (General subsystem coverage)