Move team_prior, lhood_lose, lhood_win, inv_buf into ScratchArena so
their Vec capacity is reused across games in a Batch. Eliminates 5
per-game heap allocations (the trunc Vec remains local due to borrow
constraints with arena.vars).
Batch::iteration: 23.0 µs (down from 27.0 µs with naive local Vecs;
8% above T0 21.253 µs baseline due to TruncFactor propagate overhead).
Batch::iteration: 29.840 µs → 21.253 µs (1.40×)
Gaussian::mul: 1.568 ns → 218.69 ps (7.17×)
Gaussian::div: 1.572 ns → 218.64 ps (7.19×)
Gaussian arithmetic hit target (7×+ vs 1.5–2× expected). Batch::iteration
reached 1.40× vs the 3× target. Post-mortem: the bench exercises 100 tiny
2-team events and the dominant cost is still Vec allocation in within_priors,
sort_perm, and Game::likelihoods. The HashMap→Vec win shows at the History
level (forward/backward sweep) which this bench doesn't exercise.
Remediation plan documented in benches/baseline.txt: arena-ify sort_perm,
within_priors, and Game::likelihoods in T1 when Game's internals are
redesigned around the new factor graph.
38/38 tests passing. Closes T0 tier.
AgentStore<D> is a Vec<Option<Agent<D>>>-backed store indexed directly
by Index.0, eliminating per-iteration hashing in the cross-history
forward/backward sweep. Implements Index<Index>/IndexMut<Index> for
ergonomic agent access.
AgentStore is public (so benches/batch.rs can use it). SkillStore
remains pub(crate) since Skill is pub(crate) in batch.rs.
HashMap<Index, _> is now only used for the posteriors() return value
(temporary; will be replaced in T2 with a proper typed return) and
for the add_events_with_prior(priors: HashMap<Index, Player<D>>) API
(also T2 target).
Part of T0 engine redesign.
- Promotes Gaussian::pi and Gaussian::tau to public so benches/gaussian.rs
compiles, then captures baseline numbers for the T0 acceptance gate.
- Fixes the divide bench: g1/g2 panicked (g1 has lower precision than g2;
cavity requires pi_num >= pi_den). Swapped to g2/g1 (well-defined).
Baseline on Apple M5 Pro:
Batch::iteration 29.840 µs
Gaussian::mul 1.568 ns (vs ~220 ps for add/sub — hot path)
Gaussian::div 1.572 ns
