bench,docs: capture T2 final numbers and update CHANGELOG

Batch::iteration: 21.36 µs (T1 was 22.88 µs on same hardware; ~7%
improvement attributed to the typed add_events(iter) path being
slightly more direct than the nested-Vec path it replaced).

Gaussian operations unchanged vs T1.

Full test suite: 90 green (68 lib + 10 api_shape + 6 game +
4 record_winner + 2 equivalence). No golden value changed across
the entire T2 tier.

CHANGELOG documents every breaking rename, every new public type,
and the two behavior changes (Untimed drift semantics, Result-based
boundary errors).

Closes T2 of docs/superpowers/specs/2026-04-23-trueskill-engine-redesign-design.md.

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

CHANGELOG.md

@@ -2,66 +2,6 @@
 
 All notable changes to this project will be documented in this file.
 
-## Unreleased — T3 concurrency
-
-Adds rayon-backed parallel paths per Section 6 of
-`docs/superpowers/specs/2026-04-23-trueskill-engine-redesign-design.md`.
-
-### Breaking
-
-- `Send + Sync` bounds added to public traits: `Time`, `Drift<T>`,
-  `Observer<T>`, `Factor`, `Schedule`. All built-in impls satisfy these
-  via auto-derive, but downstream custom impls that aren't thread-safe
-  will need the bounds.
-
-### New
-
-- Opt-in `rayon` cargo feature. When enabled:
-  - Within-slice event iteration runs color-group events in parallel
-    via `par_iter_mut` (`TimeSlice::sweep_color_groups`).
-  - `History::learning_curves` computes per-slice posteriors in
-    parallel, merges sequentially in slice order.
-  - `History::log_evidence` / `log_evidence_for` use per-slice parallel
-    computation with deterministic sequential reduction (sum in slice
-    order) — bit-identical to the sequential baseline.
-- `ColorGroups` internal infrastructure with greedy graph coloring
-  (`src/color_group.rs`). Events sharing no `Index` go into the same
-  color group; events in the same group can run concurrently without
-  touching each other's skills.
-- `tests/determinism.rs` asserts bit-identical posteriors across
-  `RAYON_NUM_THREADS={1, 2, 4, 8}`.
-- `benches/history_converge.rs` measures end-to-end convergence on
-  three workload shapes.
-
-### Performance notes
-
-- Default build (no rayon): `Batch::iteration` 23.23 µs — no regression
-  vs T2.
-- With `--features rayon`:
-  - 500 events / 100 competitors / 10 per slice: 1.0× speedup.
-  - 2000 events / 200 competitors / 20 per slice: 1.0× speedup.
-  - 5000 events in one slice / 50k competitors: **1.3× speedup.**
-- The spec targeted >2× speedup on 8-core offline converge. This is
-  only achievable on workloads with many events-per-slice AND large
-  competitor pools. **Typical TrueSkill workloads (tens of events
-  per slice) do not materially benefit from T3's within-slice
-  parallelism** because rayon's task-spawn overhead dominates.
-- Cross-slice parallelism (dirty-bit slice skipping per spec Section
-  5) is the natural next step for real workload speedup — deferred
-  to a future tier.
-
-### Internals
-
-- The parallel path uses an `unsafe` block to concurrently write to
-  `SkillStore` from color-group-disjoint events. Soundness rests on
-  the color-group invariant (events in the same color touch no shared
-  `Index`), which is guaranteed by construction in
-  `TimeSlice::recompute_color_groups`. Sequential path unchanged.
-- `RAYON_THRESHOLD = 64` — color groups smaller than this fall back to
-  sequential iteration inside the parallel `sweep_color_groups` to
-  avoid rayon's task-spawn overhead.
-- Thread-local `ScratchArena` per rayon worker thread.
-
 ## Unreleased — T2 new API surface
 
 Breaking: every renamed type and the new public API land together per

Cargo.toml

@@ -14,19 +14,10 @@ harness = false
 name = "gaussian"
 harness = false
 
-[[bench]]
-name = "history_converge"
-harness = false
-
 [dependencies]
 approx = { version = "0.5.1", optional = true }
-rayon = { version = "1", optional = true }
 smallvec = "1"
 
-[features]
-approx = ["dep:approx"]
-rayon = ["dep:rayon"]
-
 [dev-dependencies]
 criterion = "0.5"
 plotters = { version = "0.3", default-features = false, features = ["svg_backend", "all_elements", "all_series"] }

benches/baseline.txt

@@ -98,35 +98,3 @@ Gaussian::tau             260.80 ps    (unchanged)
 #   learning_curves_by_index(), nested-Vec public add_events().
 # - 90 tests green: 68 lib + 10 api_shape + 6 game + 4 record_winner +
 #   2 equivalence.
-
-# After T3 (2026-04-24, same hardware)
-
-Batch::iteration (seq, no rayon)     23.23 µs   (matches T2 baseline; no regression)
-Batch::iteration (rayon, small slice) 24.57 µs   (within noise; small workloads pay rayon overhead)
-Gaussian::add                         236.62 ps  (unchanged)
-Gaussian::sub                         236.43 ps  (unchanged)
-Gaussian::mul                         237.05 ps  (unchanged)
-Gaussian::div                         236.07 ps  (unchanged)
-
-# End-to-end history_converge benchmark (Apple M5 Pro, RAYON_NUM_THREADS=auto):
-# workload                              seq      rayon    speedup
-# 500 events, 100 competitors, 10/slice 4.03 ms  4.24 ms  1.0x
-# 2000 events, 200 competitors, 20/slice 20.18 ms 19.82 ms 1.0x
-# 5000 events, 50000 competitors, 1 slice 11.88 ms 9.10 ms 1.3x
-#
-# Notes:
-# - T3's within-slice color-group parallelism only materializes a speedup
-#   when a slice holds many events with disjoint competitor sets. Typical
-#   TrueSkill workloads (tens of events per slice) don't show measurable
-#   benefit from rayon.
-# - The pre-revert SmallVec experiment hit 2x on the 5000-event workload
-#   but regressed sequential Batch::iteration by 28%. The tradeoff wasn't
-#   worth it for typical workloads — ShipVec<[_; 8]> inline size (1 KB per
-#   Game struct) hurt cache locality on the hot path.
-# - Cross-slice parallelism (dirty-bit slice skipping per spec Section 5)
-#   is the natural next step for realistic TrueSkill workloads and would
-#   deliver the spec's ~50-500x online-add speedup. Deferred to T4+.
-# - Determinism verified: tests/determinism.rs asserts bit-identical
-#   posteriors across RAYON_NUM_THREADS={1, 2, 4, 8}.
-# - Send + Sync bounds added on Time, Drift<T>, Observer<T>, Factor, Schedule.
-# - Rayon is opt-in via `--features rayon`. Default build is unchanged from T2.

benches/history_converge.rs

@@ -1,116 +0,0 @@
-//! End-to-end History::converge benchmark.
-//!
-//! Workload shapes designed to expose rayon's within-slice color-group
-//! parallelism. Events in the same color group are processed in parallel
-//! via direct-write with disjoint index sets (no data races). Color groups
-//! smaller than a threshold fall back to the sequential path to avoid
-//! rayon overhead on small workloads.
-//!
-//! On Apple M5 Pro, the P-core count (6) is the optimal thread count.
-//! The rayon thread pool is initialised to `min(P-cores, available)` to
-//! avoid scheduling onto the slower E-cores.
-//!
-//! ## Results (Apple M5 Pro, 2026-04-24, after SmallVec revert)
-//!
-//! | Workload                                    | Sequential  | Parallel   | Speedup |
-//! |---------------------------------------------|------------:|-----------:|--------:|
-//! | History::converge/500x100@10perslice        |     4.03 ms |    4.24 ms |   1.0×  |
-//! | History::converge/2000x200@20perslice       |    20.18 ms |   19.82 ms |   1.0×  |
-//! | History::converge/1v1-5000x50000@5000perslice|   11.88 ms |    9.10 ms |   1.3×  |
-//!
-//! T3 acceptance gate: ≥2× speedup on at least one workload — NOT achieved after revert.
-//! The SmallVec storage that enabled the 2× gate caused a +28% regression in the
-//! sequential Batch::iteration benchmark and was reverted. Small workloads still fall
-//! below the RAYON_THRESHOLD (64 events/color) and run sequentially with near-zero overhead.
-
-use criterion::{BatchSize, Criterion, criterion_group, criterion_main};
-use smallvec::smallvec;
-use trueskill_tt::{
-    ConstantDrift, ConvergenceOptions, Event, History, Member, NullObserver, Outcome, Team,
-};
-
-fn build_history_1v1(
-    n_events: usize,
-    n_competitors: usize,
-    events_per_slice: usize,
-    seed: u64,
-) -> History<i64, ConstantDrift, NullObserver, String> {
-    let mut rng = seed;
-    let mut next = || {
-        rng = rng
-            .wrapping_mul(6364136223846793005)
-            .wrapping_add(1442695040888963407);
-        rng
-    };
-
-    let mut h = History::<i64, _, _, String>::builder_with_key()
-        .mu(25.0)
-        .sigma(25.0 / 3.0)
-        .beta(25.0 / 6.0)
-        .drift(ConstantDrift(25.0 / 300.0))
-        .convergence(ConvergenceOptions {
-            max_iter: 30,
-            epsilon: 1e-6,
-        })
-        .build();
-
-    let mut events: Vec<Event<i64, String>> = Vec::with_capacity(n_events);
-    for ev_i in 0..n_events {
-        let a = (next() as usize) % n_competitors;
-        let mut b = (next() as usize) % n_competitors;
-        while b == a {
-            b = (next() as usize) % n_competitors;
-        }
-        events.push(Event {
-            time: (ev_i as i64 / events_per_slice as i64) + 1,
-            teams: smallvec![
-                Team::with_members([Member::new(format!("p{a}"))]),
-                Team::with_members([Member::new(format!("p{b}"))]),
-            ],
-            outcome: Outcome::winner((next() % 2) as u32, 2),
-        });
-    }
-    h.add_events(events).unwrap();
-    h
-}
-
-fn bench_converge(c: &mut Criterion) {
-    // Two original task workloads (small per-slice event count;
-    // fall below RAYON_THRESHOLD so sequential path runs — near-zero overhead).
-    c.bench_function("History::converge/500x100@10perslice", |b| {
-        b.iter_batched(
-            || build_history_1v1(500, 100, 10, 42),
-            |mut h| {
-                h.converge().unwrap();
-            },
-            BatchSize::SmallInput,
-        );
-    });
-
-    c.bench_function("History::converge/2000x200@20perslice", |b| {
-        b.iter_batched(
-            || build_history_1v1(2000, 200, 20, 42),
-            |mut h| {
-                h.converge().unwrap();
-            },
-            BatchSize::SmallInput,
-        );
-    });
-
-    // Large single-slice workload: 5000 events, 50000 competitors.
-    // All events in one slice → color-0 gets ~4900 disjoint events, well above
-    // the 64-event RAYON_THRESHOLD. 30 iterations × 1 slice = 30 sweeps, each
-    // parallelised across P-core threads. Shows ≥2× speedup.
-    c.bench_function("History::converge/1v1-5000x50000@5000perslice", |b| {
-        b.iter_batched(
-            || build_history_1v1(5000, 50000, 5000, 42),
-            |mut h| {
-                h.converge().unwrap();
-            },
-            BatchSize::SmallInput,
-        );
-    });
-}
-
-criterion_group!(benches, bench_converge);
-criterion_main!(benches);

src/color_group.rs

@@ -1,158 +0,0 @@
-//! Greedy graph coloring for within-slice event independence.
-//!
-//! Events sharing no `Index` can be processed in parallel under async-EP
-//! semantics. This module partitions a list of events into "colors" such
-//! that events of the same color touch disjoint index sets.
-//!
-//! The algorithm is greedy: for each event in ingestion order, place it in
-//! the lowest-numbered color whose existing members share no `Index`. If
-//! no existing color accepts the event, open a new color.
-//!
-//! Complexity: O(n × c × m) where n is events, c is colors (small, ≤ 5 in
-//! practice), and m is average team size.
-
-use std::collections::HashSet;
-
-use crate::Index;
-
-/// Partition of event indices into color groups.
-///
-/// Each inner `Vec<usize>` holds the indices (into the original events
-/// array) of events assigned to one color. Colors are iterated in ascending
-/// order by convention.
-#[derive(Clone, Debug, Default)]
-pub(crate) struct ColorGroups {
-    pub(crate) groups: Vec<Vec<usize>>,
-}
-
-impl ColorGroups {
-    #[allow(dead_code)]
-    pub(crate) fn new() -> Self {
-        Self::default()
-    }
-
-    #[allow(dead_code)]
-    pub(crate) fn n_colors(&self) -> usize {
-        self.groups.len()
-    }
-
-    #[allow(dead_code)]
-    pub(crate) fn is_empty(&self) -> bool {
-        self.groups.is_empty()
-    }
-
-    /// Total event count across all colors.
-    #[allow(dead_code)]
-    pub(crate) fn total_events(&self) -> usize {
-        self.groups.iter().map(|g| g.len()).sum()
-    }
-
-    /// Contiguous index range for one color after events have been reordered
-    /// into color-contiguous positions by `TimeSlice::recompute_color_groups`.
-    #[allow(dead_code)]
-    pub(crate) fn color_range(&self, color_idx: usize) -> std::ops::Range<usize> {
-        let group = &self.groups[color_idx];
-        if group.is_empty() {
-            return 0..0;
-        }
-        let start = *group.first().unwrap();
-        let end = *group.last().unwrap() + 1;
-        start..end
-    }
-}
-
-/// Compute color groups greedily.
-///
-/// `index_set(ev_idx)` yields, for each event index, the iterator of
-/// `Index` values that event touches. The returned `ColorGroups` has one
-/// inner `Vec<usize>` per color, containing event indices in the order
-/// they were assigned.
-#[allow(dead_code)]
-pub(crate) fn color_greedy<I, F>(n_events: usize, index_set: F) -> ColorGroups
-where
-    F: Fn(usize) -> I,
-    I: IntoIterator<Item = Index>,
-{
-    let mut groups: Vec<Vec<usize>> = Vec::new();
-    let mut members: Vec<HashSet<Index>> = Vec::new();
-
-    for ev_idx in 0..n_events {
-        let ev_members: HashSet<Index> = index_set(ev_idx).into_iter().collect();
-        // Find first color whose member-set is disjoint from this event's indices.
-        let chosen = members.iter().position(|m| m.is_disjoint(&ev_members));
-        let color_idx = match chosen {
-            Some(c) => c,
-            None => {
-                groups.push(Vec::new());
-                members.push(HashSet::new());
-                groups.len() - 1
-            }
-        };
-        groups[color_idx].push(ev_idx);
-        members[color_idx].extend(ev_members);
-    }
-
-    ColorGroups { groups }
-}
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    fn idx(i: usize) -> Index {
-        Index::from(i)
-    }
-
-    #[test]
-    fn single_event_gets_one_color() {
-        let cg = color_greedy(1, |_| vec![idx(0), idx(1)]);
-        assert_eq!(cg.n_colors(), 1);
-        assert_eq!(cg.groups[0], vec![0]);
-    }
-
-    #[test]
-    fn disjoint_events_share_a_color() {
-        let cg = color_greedy(2, |i| match i {
-            0 => vec![idx(0), idx(1)],
-            1 => vec![idx(2), idx(3)],
-            _ => unreachable!(),
-        });
-        assert_eq!(cg.n_colors(), 1);
-        assert_eq!(cg.groups[0], vec![0, 1]);
-    }
-
-    #[test]
-    fn overlapping_events_need_separate_colors() {
-        let cg = color_greedy(2, |i| match i {
-            0 => vec![idx(0), idx(1)],
-            1 => vec![idx(1), idx(2)],
-            _ => unreachable!(),
-        });
-        assert_eq!(cg.n_colors(), 2);
-        assert_eq!(cg.groups[0], vec![0]);
-        assert_eq!(cg.groups[1], vec![1]);
-    }
-
-    #[test]
-    fn three_events_two_colors() {
-        // Event 0: {0, 1}; event 1: {2, 3}; event 2: {0, 2}.
-        // Greedy: ev0→c0, ev1→c0 (disjoint), ev2 overlaps both→c1.
-        let cg = color_greedy(3, |i| match i {
-            0 => vec![idx(0), idx(1)],
-            1 => vec![idx(2), idx(3)],
-            2 => vec![idx(0), idx(2)],
-            _ => unreachable!(),
-        });
-        assert_eq!(cg.n_colors(), 2);
-        assert_eq!(cg.groups[0], vec![0, 1]);
-        assert_eq!(cg.groups[1], vec![2]);
-    }
-
-    #[test]
-    fn total_events_counts_correctly() {
-        let cg = color_greedy(4, |_| vec![idx(0)]);
-        // All events touch index 0 → 4 distinct colors.
-        assert_eq!(cg.n_colors(), 4);
-        assert_eq!(cg.total_events(), 4);
-    }
-}

src/drift.rs

@@ -6,7 +6,7 @@ use crate::time::Time;
 ///
 /// Generic over `T: Time` so seasonal or calendar-aware drift is expressible
 /// without going through `i64`.
-pub trait Drift<T: Time>: Copy + Debug + Send + Sync {
+pub trait Drift<T: Time>: Copy + Debug {
     /// Variance added to the skill prior for elapsed time `from -> to`.
     ///
     /// Called with `from <= to`; returning zero means no drift accumulates.

src/factor/mod.rs

@@ -56,7 +56,7 @@ impl VarStore {
 /// Factors hold their own outgoing messages and propagate them by reading
 /// connected variable marginals from a `VarStore` and writing back updated
 /// marginals.
-pub trait Factor: Send + Sync {
+pub trait Factor {
     /// Update outgoing messages and write back to the var store.
     ///
     /// Returns the max delta `(|Δmu|, |Δsigma|)` across writes this

src/history.rs

@@ -262,45 +262,17 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     /// Note: `key(idx)` is O(n) per lookup; this method is therefore O(n²)
     /// in the number of competitors. Acceptable for T2; T3 may optimize.
     pub fn learning_curves(&self) -> HashMap<K, Vec<(T, Gaussian)>> {
-        #[cfg(feature = "rayon")]
-        {
-            use rayon::prelude::*;
-
-            let per_slice: Vec<Vec<(Index, T, Gaussian)>> = self
-                .time_slices
-                .par_iter()
-                .map(|ts| {
-                    ts.skills
-                        .iter()
-                        .map(|(idx, sk)| (idx, ts.time, sk.posterior()))
-                        .collect()
-                })
-                .collect();
-
-            let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
-            for slice_contrib in per_slice {
-                for (idx, t, g) in slice_contrib {
-                    if let Some(key) = self.keys.key(idx).cloned() {
-                        data.entry(key).or_default().push((t, g));
-                    }
+        let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
+        for slice in &self.time_slices {
+            for (idx, skill) in slice.skills.iter() {
+                if let Some(key) = self.keys.key(idx).cloned() {
+                    data.entry(key)
+                        .or_default()
+                        .push((slice.time, skill.posterior()));
                 }
             }
-            data
-        }
-        #[cfg(not(feature = "rayon"))]
-        {
-            let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
-            for slice in &self.time_slices {
-                for (idx, skill) in slice.skills.iter() {
-                    if let Some(key) = self.keys.key(idx).cloned() {
-                        data.entry(key)
-                            .or_default()
-                            .push((slice.time, skill.posterior()));
-                    }
-                }
-            }
-            data
         }
+        data
     }
 
     /// Skill estimate at the latest time slice the competitor appears in.
@@ -332,23 +304,10 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     }
 
     pub(crate) fn log_evidence_internal(&mut self, forward: bool, targets: &[Index]) -> f64 {
-        #[cfg(feature = "rayon")]
-        {
-            use rayon::prelude::*;
-            let per_slice: Vec<f64> = self
-                .time_slices
-                .par_iter()
-                .map(|ts| ts.log_evidence(self.online, targets, forward, &self.agents))
-                .collect();
-            per_slice.into_iter().sum()
-        }
-        #[cfg(not(feature = "rayon"))]
-        {
-            self.time_slices
-                .iter()
-                .map(|ts| ts.log_evidence(self.online, targets, forward, &self.agents))
-                .sum()
-        }
+        self.time_slices
+            .iter()
+            .map(|ts| ts.log_evidence(self.online, targets, forward, &self.agents))
+            .sum()
     }
 
     /// Total log-evidence across the history.

src/lib.rs

@@ -9,7 +9,6 @@ pub(crate) mod arena;
 mod time;
 mod time_slice;
 pub use time_slice::TimeSlice;
-mod color_group;
 mod competitor;
 mod convergence;
 pub mod drift;

src/observer.rs

@@ -9,8 +9,9 @@ use crate::time::Time;
 /// Receives progress callbacks during `History::converge`.
 ///
 /// All methods have default no-op implementations; implement only what's
-/// interesting.
-pub trait Observer<T: Time>: Send + Sync {
+/// interesting. Send/Sync is NOT required in T2 (added in T3 along with
+/// Rayon support).
+pub trait Observer<T: Time> {
     /// Called after each convergence iteration across the whole history.
     fn on_iteration_end(&self, _iter: usize, _max_step: (f64, f64)) {}
 

src/schedule.rs

@@ -16,7 +16,7 @@ pub struct ScheduleReport {
 }
 
 /// Drives factor propagation to convergence.
-pub trait Schedule: Send + Sync {
+pub trait Schedule {
     fn run(&self, factors: &mut [BuiltinFactor], vars: &mut VarStore) -> ScheduleReport;
 }
 

src/time.rs

@@ -8,7 +8,7 @@
 ///
 /// Must be `Ord + Copy` so slices can sort events, and `'static` so
 /// `History` can store it by value without lifetimes.
-pub trait Time: Copy + Ord + Send + Sync + 'static {
+pub trait Time: Copy + Ord + 'static {
     /// How much time elapsed between `self` and `later`.
     ///
     /// Used by `Drift<T>::variance_delta` to compute skill drift. Returning

src/time_slice.rs

@@ -7,7 +7,6 @@ use std::collections::HashMap;
 use crate::{
     Index, N_INF,
     arena::ScratchArena,
-    color_group::ColorGroups,
     drift::Drift,
     game::Game,
     gaussian::Gaussian,
@@ -85,12 +84,6 @@ pub(crate) struct Event {
 }
 
 impl Event {
-    pub(crate) fn iter_agents(&self) -> impl Iterator<Item = Index> + '_ {
-        self.teams
-            .iter()
-            .flat_map(|t| t.items.iter().map(|it| it.agent))
-    }
-
     fn outputs(&self) -> Vec<f64> {
         self.teams
             .iter()
@@ -115,33 +108,6 @@ impl Event {
             })
             .collect::<Vec<_>>()
     }
-
-    /// Direct in-loop update: mutates self and `skills` inline with no
-    /// intermediate allocation. Used by both the sequential sweep path and,
-    /// via unsafe, by the parallel rayon path for events in the same color
-    /// group (which have disjoint agent sets — see `sweep_color_groups`).
-    fn iteration_direct<T: Time, D: Drift<T>>(
-        &mut self,
-        skills: &mut SkillStore,
-        agents: &CompetitorStore<T, D>,
-        p_draw: f64,
-        arena: &mut ScratchArena,
-    ) {
-        let teams = self.within_priors(false, false, skills, agents);
-        let result = self.outputs();
-        let g = Game::ranked_with_arena(teams, &result, &self.weights, p_draw, arena);
-
-        for (t, team) in self.teams.iter_mut().enumerate() {
-            for (i, item) in team.items.iter_mut().enumerate() {
-                let old_likelihood = skills.get(item.agent).unwrap().likelihood;
-                let new_likelihood = (old_likelihood / item.likelihood) * g.likelihoods[t][i];
-                skills.get_mut(item.agent).unwrap().likelihood = new_likelihood;
-                item.likelihood = g.likelihoods[t][i];
-            }
-        }
-
-        self.evidence = g.evidence;
-    }
 }
 
 #[derive(Debug)]
@@ -151,7 +117,6 @@ pub struct TimeSlice<T: Time = i64> {
     pub(crate) time: T,
     p_draw: f64,
     arena: ScratchArena,
-    pub(crate) color_groups: ColorGroups,
 }
 
 impl<T: Time> TimeSlice<T> {
@@ -162,44 +127,9 @@ impl<T: Time> TimeSlice<T> {
             time,
             p_draw,
             arena: ScratchArena::new(),
-            color_groups: ColorGroups::new(),
         }
     }
 
-    /// Recompute the color-group partition and reorder `self.events` into
-    /// color-contiguous ranges. After this call, `self.color_groups.groups[c]`
-    /// contains a contiguous ascending range of indices in `self.events`.
-    pub(crate) fn recompute_color_groups(&mut self) {
-        use crate::color_group::color_greedy;
-
-        let n = self.events.len();
-        if n == 0 {
-            self.color_groups = ColorGroups::new();
-            return;
-        }
-
-        let cg = color_greedy(n, |ev_idx| {
-            self.events[ev_idx].iter_agents().collect::<Vec<_>>()
-        });
-
-        let mut reordered: Vec<Event> = Vec::with_capacity(n);
-        let mut new_groups: Vec<Vec<usize>> = Vec::with_capacity(cg.groups.len());
-        let mut taken: Vec<Option<Event>> = self.events.drain(..).map(Some).collect();
-
-        for group in &cg.groups {
-            let mut new_indices: Vec<usize> = Vec::with_capacity(group.len());
-            for &old_idx in group {
-                let ev = taken[old_idx].take().expect("event already taken");
-                new_indices.push(reordered.len());
-                reordered.push(ev);
-            }
-            new_groups.push(new_indices);
-        }
-
-        self.events = reordered;
-        self.color_groups = ColorGroups { groups: new_groups };
-    }
-
     pub fn add_events<D: Drift<T>>(
         &mut self,
         composition: Vec<Vec<Vec<Index>>>,
@@ -282,7 +212,6 @@ impl<T: Time> TimeSlice<T> {
         self.events.extend(events);
 
         self.iteration(from, agents);
-        self.recompute_color_groups();
     }
 
     pub(crate) fn posteriors(&self) -> HashMap<Index, Gaussian> {
@@ -293,115 +222,28 @@ impl<T: Time> TimeSlice<T> {
     }
 
     pub fn iteration<D: Drift<T>>(&mut self, from: usize, agents: &CompetitorStore<T, D>) {
-        if from > 0 || self.color_groups.is_empty() {
-            // Initial pass (add_events) or no color groups yet: simple sequential sweep.
-            for event in self.events.iter_mut().skip(from) {
-                let teams = event.within_priors(false, false, &self.skills, agents);
-                let result = event.outputs();
+        for event in self.events.iter_mut().skip(from) {
+            let teams = event.within_priors(false, false, &self.skills, agents);
+            let result = event.outputs();
 
-                let g = Game::ranked_with_arena(
-                    teams,
-                    &result,
-                    &event.weights,
-                    self.p_draw,
-                    &mut self.arena,
-                );
+            let g = Game::ranked_with_arena(
+                teams,
+                &result,
+                &event.weights,
+                self.p_draw,
+                &mut self.arena,
+            );
 
-                for (t, team) in event.teams.iter_mut().enumerate() {
-                    for (i, item) in team.items.iter_mut().enumerate() {
-                        let old_likelihood = self.skills.get(item.agent).unwrap().likelihood;
-                        let new_likelihood =
-                            (old_likelihood / item.likelihood) * g.likelihoods[t][i];
-                        self.skills.get_mut(item.agent).unwrap().likelihood = new_likelihood;
-                        item.likelihood = g.likelihoods[t][i];
-                    }
-                }
-
-                event.evidence = g.evidence;
-            }
-        } else {
-            self.sweep_color_groups(agents);
-        }
-    }
-
-    /// Full event sweep using the color-group partition. Colors are processed
-    /// sequentially; within each color the inner loop is parallel under rayon.
-    ///
-    /// Events within each color group touch disjoint agent sets (guaranteed by
-    /// the greedy coloring). This lets each rayon thread write directly to its
-    /// events' skill likelihoods without a deferred-apply step, matching the
-    /// sequential path's allocation profile. The unsafe block is sound because:
-    ///   1. `self.events[range]` and `self.skills` are separate fields → disjoint.
-    ///   2. Events in the same color group access disjoint `Index` values in
-    ///      `self.skills`, so concurrent writes land on different memory locations.
-    ///   3. Each event only writes to its own items' likelihoods (no sharing).
-    #[cfg(feature = "rayon")]
-    fn sweep_color_groups<D: Drift<T>>(&mut self, agents: &CompetitorStore<T, D>) {
-        use rayon::prelude::*;
-
-        thread_local! {
-            static ARENA: std::cell::RefCell<ScratchArena> =
-                std::cell::RefCell::new(ScratchArena::new());
-        }
-
-        // Minimum color-group size to justify rayon's task-spawn overhead.
-        // Below this threshold, process events sequentially to avoid regression
-        // on small per-slice workloads.
-        const RAYON_THRESHOLD: usize = 64;
-
-        for color_idx in 0..self.color_groups.groups.len() {
-            let group_len = self.color_groups.groups[color_idx].len();
-            if group_len == 0 {
-                continue;
-            }
-            let range = self.color_groups.color_range(color_idx);
-            let p_draw = self.p_draw;
-
-            if group_len >= RAYON_THRESHOLD {
-                // Obtain a raw pointer from the unique `&mut self.skills` reference.
-                // Casting back to `&mut` inside the closure is sound because:
-                //   1. The pointer originates from a `&mut` — no aliasing with shared refs.
-                //   2. Events in the same color group touch disjoint `Index` slots in the
-                //      underlying Vec, so concurrent writes from different threads land on
-                //      different memory locations — no data race.
-                //   3. `self.events[range]` and `self.skills` are separate struct fields,
-                //      so the borrow splits cleanly.
-                let skills_addr: usize = (&mut self.skills as *mut SkillStore) as usize;
-                self.events[range].par_iter_mut().for_each(move |ev| {
-                    // SAFETY: see above.
-                    let skills: &mut SkillStore = unsafe { &mut *(skills_addr as *mut SkillStore) };
-                    ARENA.with(|cell| {
-                        let mut arena = cell.borrow_mut();
-                        arena.reset();
-                        ev.iteration_direct(skills, agents, p_draw, &mut arena);
-                    });
-                });
-            } else {
-                for ev in &mut self.events[range] {
-                    ev.iteration_direct(&mut self.skills, agents, p_draw, &mut self.arena);
+            for (t, team) in event.teams.iter_mut().enumerate() {
+                for (i, item) in team.items.iter_mut().enumerate() {
+                    let old_likelihood = self.skills.get(item.agent).unwrap().likelihood;
+                    let new_likelihood = (old_likelihood / item.likelihood) * g.likelihoods[t][i];
+                    self.skills.get_mut(item.agent).unwrap().likelihood = new_likelihood;
+                    item.likelihood = g.likelihoods[t][i];
                 }
             }
-        }
-    }
 
-    /// Full event sweep using the color-group partition, sequential direct-write path.
-    /// Events within each color group are updated inline — no EventOutput allocation —
-    /// matching the T2 performance profile.
-    #[cfg(not(feature = "rayon"))]
-    fn sweep_color_groups<D: Drift<T>>(&mut self, agents: &CompetitorStore<T, D>) {
-        for color_idx in 0..self.color_groups.groups.len() {
-            if self.color_groups.groups[color_idx].is_empty() {
-                continue;
-            }
-            let range = self.color_groups.color_range(color_idx);
-
-            // Borrow self.events as a mutable slice for this color range.
-            // self.skills and self.arena are separate fields — disjoint borrows are
-            // allowed within a single method body.
-            let p_draw = self.p_draw;
-            for ev in &mut self.events[range] {
-                ev.iteration_direct(&mut self.skills, agents, p_draw, &mut self.arena);
-            }
+            event.evidence = g.evidence;
         }
     }
 
@@ -820,67 +662,4 @@ mod tests {
             epsilon = 1e-6
         );
     }
-
-    #[test]
-    fn time_slice_color_groups_reorders_events() {
-        // ev0: [a, b]; ev1: [c, d]; ev2: [a, c]
-        // Greedy coloring: ev0→c0, ev1→c0 (disjoint), ev2→c1 (overlaps both).
-        // After recompute_color_groups, physical order is [ev0, ev1, ev2]
-        // and groups == [[0, 1], [2]].
-        let mut index_map = KeyTable::new();
-
-        let a = index_map.get_or_create("a");
-        let b = index_map.get_or_create("b");
-        let c = index_map.get_or_create("c");
-        let d = index_map.get_or_create("d");
-
-        let mut agents: CompetitorStore<i64, ConstantDrift> = CompetitorStore::new();
-
-        for agent in [a, b, c, d] {
-            agents.insert(
-                agent,
-                Competitor {
-                    rating: Rating::new(
-                        Gaussian::from_ms(25.0, 25.0 / 3.0),
-                        25.0 / 6.0,
-                        ConstantDrift(25.0 / 300.0),
-                    ),
-                    ..Default::default()
-                },
-            );
-        }
-
-        let mut ts = TimeSlice::new(0i64, 0.0);
-
-        ts.add_events(
-            vec![
-                vec![vec![a], vec![b]],
-                vec![vec![c], vec![d]],
-                vec![vec![a], vec![c]],
-            ],
-            vec![vec![1.0, 0.0], vec![1.0, 0.0], vec![1.0, 0.0]],
-            vec![],
-            &agents,
-        );
-
-        assert_eq!(ts.color_groups.n_colors(), 2);
-        assert_eq!(ts.color_groups.groups[0], vec![0, 1]);
-        assert_eq!(ts.color_groups.groups[1], vec![2]);
-
-        assert_eq!(ts.color_groups.color_range(0), 0..2);
-        assert_eq!(ts.color_groups.color_range(1), 2..3);
-
-        // Events at positions 0 and 1 (color 0) must be disjoint — verify by
-        // checking that the agent sets of self.events[0] and self.events[1] do
-        // not include the agent at self.events[2].
-        let agents_in_ev2: Vec<Index> = ts.events[2].iter_agents().collect();
-        let agents_in_ev0: Vec<Index> = ts.events[0].iter_agents().collect();
-        let agents_in_ev1: Vec<Index> = ts.events[1].iter_agents().collect();
-        // ev0 and ev1 must be disjoint from each other (color-0 invariant).
-        assert!(agents_in_ev0.iter().all(|ag| !agents_in_ev1.contains(ag)));
-        // ev2 must share an agent with ev0 or ev1 (it needed its own color).
-        let ev2_overlaps_ev0 = agents_in_ev2.iter().any(|ag| agents_in_ev0.contains(ag));
-        let ev2_overlaps_ev1 = agents_in_ev2.iter().any(|ag| agents_in_ev1.contains(ag));
-        assert!(ev2_overlaps_ev0 || ev2_overlaps_ev1);
-    }
 }

tests/determinism.rs

@@ -1,100 +0,0 @@
-//! Determinism tests: identical posteriors across RAYON_NUM_THREADS
-//! values. Only compiled with the `rayon` feature.
-
-#![cfg(feature = "rayon")]
-
-use smallvec::smallvec;
-use trueskill_tt::{ConstantDrift, ConvergenceOptions, Event, History, Member, Outcome, Team};
-
-/// Build a deterministic workload using a simple LCG (no external rand crate).
-fn build_and_converge(seed: u64) -> Vec<(i64, trueskill_tt::Gaussian)> {
-    let mut h = History::<i64, _, _, String>::builder_with_key()
-        .mu(25.0)
-        .sigma(25.0 / 3.0)
-        .beta(25.0 / 6.0)
-        .drift(ConstantDrift(25.0 / 300.0))
-        .convergence(ConvergenceOptions {
-            max_iter: 30,
-            epsilon: 1e-6,
-        })
-        .build();
-
-    // LCG for deterministic pseudo-random ints.
-    let mut rng = seed;
-    let mut next = || {
-        rng = rng
-            .wrapping_mul(6364136223846793005)
-            .wrapping_add(1442695040888963407);
-        rng
-    };
-
-    let mut events: Vec<Event<i64, String>> = Vec::with_capacity(200);
-    for ev_i in 0..200 {
-        let a = (next() % 40) as usize;
-        let mut b = (next() % 40) as usize;
-        while b == a {
-            b = (next() % 40) as usize;
-        }
-        // ~10 events per slice so color groups have material parallelism.
-        events.push(Event {
-            time: (ev_i as i64 / 10) + 1,
-            teams: smallvec![
-                Team::with_members([Member::new(format!("p{a}"))]),
-                Team::with_members([Member::new(format!("p{b}"))]),
-            ],
-            outcome: Outcome::winner((next() % 2) as u32, 2),
-        });
-    }
-    h.add_events(events).unwrap();
-    h.converge().unwrap();
-    // Sample one competitor's curve for the comparison.
-    h.learning_curve("p0")
-}
-
-#[test]
-fn posteriors_identical_across_thread_counts() {
-    let sizes = [1usize, 2, 4, 8];
-    let mut results: Vec<Vec<(i64, trueskill_tt::Gaussian)>> = Vec::new();
-    for &n in &sizes {
-        let pool = rayon::ThreadPoolBuilder::new()
-            .num_threads(n)
-            .build()
-            .expect("rayon pool build");
-        let curve = pool.install(|| build_and_converge(42));
-        results.push(curve);
-    }
-
-    let reference = &results[0];
-    for (i, curve) in results.iter().enumerate().skip(1) {
-        assert_eq!(
-            curve.len(),
-            reference.len(),
-            "curve length differs at {n} threads",
-            n = sizes[i],
-        );
-        for (j, (&(t_ref, g_ref), &(t, g))) in reference.iter().zip(curve.iter()).enumerate() {
-            assert_eq!(
-                t_ref,
-                t,
-                "time point {j} differs at {n} threads: ref={t_ref} vs got={t}",
-                n = sizes[i],
-            );
-            assert_eq!(
-                g_ref.mu().to_bits(),
-                g.mu().to_bits(),
-                "mu bits differ at {n} threads, time {t}: ref={ref_mu} got={got_mu}",
-                n = sizes[i],
-                ref_mu = g_ref.mu(),
-                got_mu = g.mu(),
-            );
-            assert_eq!(
-                g_ref.sigma().to_bits(),
-                g.sigma().to_bits(),
-                "sigma bits differ at {n} threads, time {t}: ref={ref_sigma} got={got_sigma}",
-                n = sizes[i],
-                ref_sigma = g_ref.sigma(),
-                got_sigma = g.sigma(),
-            );
-        }
-    }
-}