The execution tiers answer 'how to run a tactic', not 'which tactic is best'
nor 'where is the danger'. Adds two decision layers: an optimizer over
StrategySpec (objectives incl. tail risk, coarse-to-fine search, LLM as
candidate seeder) and an adversarial cartography (AdversarySpec + alternating
min-max + scenario sweeps + danger frontier + sensitivity), giving the danger
view for BOTH sides. Reframes the LLM as hypothesis generator / red team /
explicator / reporter, never the optimizer or per-turn executor in sweeps.
Adds D12-D14 and phases P3 (optimizer) / P4 (robustness).
Key correction: the LLM must not sit on the per-turn hot path of a Monte
Carlo sweep (runs x rounds x combatants calls, no cross-run cache reuse).
Adopts a two-tier design - a compiled, parameterised deterministic Policy for
quantitative sweeps (tier A) and a per-turn NLPolicy for a handful of showcase
battles (tier B). Also fixes: 'engine unchanged' -> rules unchanged/additive
boundary; replay keyed by a monotonic call_seq (delay re-queues, summons);
composite response-cache key incl. model id; ObservationView as a structured
object; candidates derived from pairs instead of a second legality engine;
per-run PolicyFactory; cost guardrails in P1; concurrency; D1 to settle in P0.
Design-only document (no code): target architecture (compact versioned
observation view, legal-actions API, LLM client + cache, NL policy with
parse/validate/repair/fallback, strategy sweep, replay/eval harness), the
concrete gaps in the current runner/CLI wiring, the decisions to settle and a
phased implementation plan (P0 foundations, P1 client+policy, P2 strategy
sweep, P3 provider+report) with exit criteria and risks.
ctan