test(game): integration tests for ConvergenceOptions behavior

Two end-to-end tests on a 4-team ranked game:
- max_iter=1 produces measurably different posteriors than the default,
  proving run_chain reads convergence.max_iter
- alpha=0.5 with extra iterations reaches the same fixed point as
  alpha=1.0, proving damping doesn't break convergence on benign graphs

benches/history_converge.rs

@@ -51,6 +51,7 @@ fn build_history_1v1(
         .convergence(ConvergenceOptions {
             max_iter: 30,
             epsilon: 1e-6,
+            alpha: 1.0,
         })
         .build();
 

docs/superpowers/specs/2026-05-08-damped-schedule-design.md

@@ -0,0 +1,320 @@
+# Damped EP — Game-Local Damping
+
+## Summary
+
+Add an opt-in EP damping knob to within-game inference. Users set
+`ConvergenceOptions::alpha < 1.0` to damp message updates and stabilise
+oscillating fixed-point loops on hard graphs. `alpha = 1.0` (the default)
+is bit-equal to today.
+
+This is the smallest-scope realisation of the spec's `Damped` schedule:
+**game-local**, not plumbed through the `Schedule` trait. The `Schedule`
+trait is shipped infrastructure that `run_chain` does not currently call;
+wiring `Schedule` into game inference is a separate future task. This
+design touches only what the user can actually reach via `GameOptions`.
+
+## Scope
+
+### What ships
+
+1. New field `ConvergenceOptions::alpha: f64` (default `1.0`).
+2. `run_chain` reads `options.convergence.{epsilon, max_iter, alpha}`
+   instead of the hardcoded `1e-6` / `10` / undamped — fixes the existing
+   latent bug where the first two were already on `GameOptions` but never
+   read by inference.
+3. `Gaussian::damp_natural(self, new, alpha) -> Gaussian` — public helper
+   computing `α·new + (1−α)·self` in natural-parameter space.
+4. `TruncFactor` and `MarginFactor` gain inherent
+   `propagate_with_alpha(&mut self, vars, alpha) -> (f64, f64)`. Their
+   `Factor::propagate` impls become one-line delegations passing
+   `alpha = 1.0`.
+5. `DiffFactor::propagate` (game-private enum at `src/game.rs:20-54`)
+   gains an `alpha: f64` parameter and dispatches into the underlying
+   factor's `propagate_with_alpha`.
+
+### What does not ship
+
+- No `Damped` impl in `src/schedule.rs`. The `Schedule` trait stays as
+  it is; integration with `run_chain` is a separate task.
+- No nat-param convergence switch. `(|Δmu|, |Δsigma|)` stays the
+  delta basis (matches today). The spec's "stopping in natural-param
+  space" wants its own design pass and test re-tuning.
+- No oscillation auto-detect. `alpha` is user-supplied and constant for
+  the duration of a `run_chain` call.
+- No `Residual`, `OneShot`, or `SynergyFactor` / `ScoreFactor` work —
+  separate future plans.
+
+## Design
+
+### `ConvergenceOptions::alpha`
+
+```rust
+// src/convergence.rs
+#[derive(Clone, Copy, Debug)]
+pub struct ConvergenceOptions {
+    pub max_iter: usize,
+    pub epsilon: f64,
+    pub alpha: f64,
+}
+
+impl Default for ConvergenceOptions {
+    fn default() -> Self {
+        Self {
+            max_iter: crate::ITERATIONS,
+            epsilon: crate::EPSILON,
+            alpha: 1.0,
+        }
+    }
+}
+```
+
+`alpha = 1.0` ⇒ undamped (bit-equal to today). Recommended starting
+point if a graph oscillates: `0.5`–`0.7`. Values approaching `0.0` make
+each step tinier and slow convergence; `alpha = 0.0` is degenerate
+(factor never updates). Validation in `run_chain`:
+
+```rust
+debug_assert!(
+    opts.convergence.alpha > 0.0 && opts.convergence.alpha <= 1.0,
+    "convergence alpha must be in (0.0, 1.0]"
+);
+```
+
+### `Gaussian::damp_natural`
+
+```rust
+impl Gaussian {
+    /// EP damping in natural-parameter space: `α·new + (1−α)·self`.
+    ///
+    /// Used by within-game schedules to stabilise oscillating fixed-point
+    /// loops on hard graphs. `alpha = 1.0` returns `new` exactly;
+    /// `alpha < 1.0` shrinks each per-step update.
+    pub fn damp_natural(self, new: Gaussian, alpha: f64) -> Gaussian {
+        Gaussian::from_natural(
+            alpha * new.pi() + (1.0 - alpha) * self.pi(),
+            alpha * new.tau() + (1.0 - alpha) * self.tau(),
+        )
+    }
+}
+```
+
+Public on `Gaussian`. The name encodes the WHY (EP damping); the doc
+comment fixes the math. No new dependency.
+
+The existing `Mul<f64> for Gaussian` is **distribution scaling**
+(`sigma → sigma·|scalar|`), not nat-param interpolation, so it can't be
+reused here.
+
+### `TruncFactor::propagate_with_alpha`
+
+```rust
+impl TruncFactor {
+    pub(crate) fn propagate_with_alpha(
+        &mut self,
+        vars: &mut VarStore,
+        alpha: f64,
+    ) -> (f64, f64) {
+        let marginal = vars.get(self.diff);
+        let cavity = marginal / self.msg;
+
+        if self.evidence_cached.is_none() {
+            self.evidence_cached = Some(cavity_evidence(cavity, self.margin, self.tie));
+        }
+
+        let trunc = approx(cavity, self.margin, self.tie);
+        let new_msg = trunc / cavity;
+
+        let damped = self.msg.damp_natural(new_msg, alpha);
+        let old_msg = self.msg;
+        self.msg = damped;
+
+        // marginal_new = cavity * stored_msg (NOT cavity * new_msg with damping)
+        vars.set(self.diff, cavity * damped);
+
+        old_msg.delta(damped)
+    }
+}
+
+impl Factor for TruncFactor {
+    fn propagate(&mut self, vars: &mut VarStore) -> (f64, f64) {
+        self.propagate_with_alpha(vars, 1.0)
+    }
+}
+```
+
+Two important points:
+
+- The variable receives `cavity * damped` (i.e. `cavity * self.msg`),
+  not `trunc`. With `alpha = 1.0` these are equal (since
+  `cavity * new_msg = trunc` by construction), so today's behaviour is
+  preserved bit-equal. With `alpha < 1.0` the marginal reflects the
+  partially-applied update.
+- The reported delta is `old_msg.delta(damped)` — delta of the actually
+  stored message, not of the raw `new_msg`. This is the textbook EP
+  damping convention: the convergence loop measures the trajectory it
+  is actually walking.
+
+`MarginFactor` follows the same shape, with its own
+`propagate_with_alpha` body (the existing `propagate` math, with the
+`damp_natural` step inserted in the same place and the var write
+switched to `cavity * damped`).
+
+### `DiffFactor::propagate` signature
+
+```rust
+// src/game.rs
+impl DiffFactor {
+    pub(crate) fn propagate(
+        &mut self,
+        vars: &mut VarStore,
+        alpha: f64,
+    ) -> (f64, f64) {
+        match self {
+            Self::Trunc(f) => f.propagate_with_alpha(vars, alpha),
+            Self::Margin(f) => f.propagate_with_alpha(vars, alpha),
+        }
+    }
+}
+```
+
+`DiffFactor` is `pub(crate)` and only used inside `run_chain`, so the
+signature change has no public-API impact.
+
+### `run_chain` changes
+
+Inside `Game::run_chain` (`src/game.rs:236-348`):
+
+1. Capture `let alpha = opts.convergence.alpha;` once at the top
+   (avoids repeated `opts.convergence.alpha` lookups in the hot loop).
+2. Replace the loop guard
+   `while tuple_gt(step, 1e-6) && iter < 10`
+   with
+   `while tuple_gt(step, opts.convergence.epsilon) && iter < opts.convergence.max_iter`.
+3. Replace each `lf.propagate(&mut arena.vars)` call site (three of
+   them: forward sweep, backward sweep, `n_diffs == 1` special case)
+   with `lf.propagate(&mut arena.vars, alpha)`.
+
+The threading of `opts: &GameOptions` into `run_chain` is the only
+new caller obligation. Today `run_chain` doesn't take `opts`; the two
+callers (`likelihoods`, `likelihoods_scored`) currently invoke it
+without options. Both will need to pass the options through. The
+`Game<'a, T, D>` struct does not currently hold `GameOptions`; the
+options are constructed and discarded around the call to
+`{ranked,scored}_with_arena`. So:
+
+- `Game::ranked_with_arena` and `Game::scored_with_arena` already
+  receive `p_draw` / `score_sigma` as scalar params; we extend them to
+  accept `&ConvergenceOptions` (or the full `&GameOptions`) too.
+- `likelihoods` / `likelihoods_scored` either store the options on
+  `Game` or accept them as method parameters and forward to
+  `run_chain`.
+
+The simplest plumbing: store `convergence: ConvergenceOptions` as a
+field on `Game<'a, T, D>` and `OwnedGame<T, D>` populated at
+construction time. Then `run_chain` can read it from `&self`.
+
+## Convergence semantics
+
+With `alpha < 1.0` the per-step update shrinks; convergence may take
+more iterations to reach the same `epsilon` threshold. Users who damp
+should also raise `max_iter` accordingly. Documentation example:
+
+```rust
+let mut opts = GameOptions::default();
+opts.convergence.alpha = 0.5;
+opts.convergence.max_iter = 30;
+```
+
+## Testing strategy
+
+### Regression net (no new file)
+
+The existing 88 lib tests and 27 integration tests are the bit-equal
+regression net. With `alpha = 1.0` (the default), every assertion must
+pass unchanged. If any test fails, the damping path leaked into the
+undamped trajectory.
+
+### New tests
+
+1. **`Gaussian::damp_natural` arithmetic**
+   (`src/gaussian.rs` test mod):
+   - `α = 1.0` returns `new` exactly (bit-equal `pi` and `tau`).
+   - `α = 0.0` returns `self` exactly.
+   - `α = 0.5`: pi and tau are exact midpoints in nat-param space.
+   - Three asserts, no new file.
+
+2. **`TruncFactor::propagate_with_alpha` shrinks the step**
+   (`src/factor/trunc.rs` test mod):
+   - Set up a TruncFactor step. Run `propagate_with_alpha(α=1.0)` once,
+     record `delta_undamped` and the resulting `self.msg`.
+   - Reset to a fresh factor at the same starting state. Run
+     `propagate_with_alpha(α=0.5)` once, record `delta_damped` and
+     `damped_msg`.
+   - Assert: `damped_msg.pi()` equals `0.5 * undamped_msg.pi() + 0.5 * initial_msg.pi()` within 1e-12 (and same for `tau`).
+   - Assert: `delta_damped.0 <= delta_undamped.0` (mu-delta is no larger; the relationship is monotone in `α` but not strictly `0.5×` for the `delta()` function which is `(|Δmu|, |Δsigma|)`).
+
+3. **`MarginFactor::propagate_with_alpha` parity**
+   (`src/factor/margin.rs` test mod):
+   - Same shape as #2, on a `MarginFactor` step.
+
+4. **`run_chain` honours `ConvergenceOptions::max_iter`**
+   (in an existing or new game-level test):
+   - Construct a 4-team ranked game that normally converges in ~5 iterations.
+   - Set `opts.convergence.max_iter = 1`. Assert the per-iteration
+     `step` returned (or observable indirectly via posterior delta vs.
+     the converged answer) is non-zero — i.e. the loop stopped early.
+   - Set `opts.convergence.max_iter = 30`. Assert posteriors match the
+     baseline within `epsilon`.
+
+5. **Damping default is `1.0` and produces bit-equal output**
+   (smoke test, can be a single assertion in an existing test):
+   - `assert_eq!(ConvergenceOptions::default().alpha, 1.0);`
+   - Existing goldens prove the bit-equality.
+
+No oscillation-stabilisation test (would require constructing a
+pathological graph specifically to oscillate; out of scope for a
+minimal ship).
+
+## Verification gates
+
+Per task:
+
+```bash
+cargo +nightly fmt
+cargo clippy --all-targets -- -D warnings
+cargo test --lib
+cargo test
+```
+
+All must succeed. Test count grows by exactly the new tests above
+(roughly +5–8 lib tests).
+
+## Risks
+
+- **Marginal-update change is subtle.** Switching the variable write
+  from `trunc` to `cavity * damped` is intentionally a no-op when
+  `alpha = 1.0` (since `cavity * new_msg = trunc`), but it changes the
+  arithmetic path. If `Gaussian` arithmetic has any non-associativity
+  in floating-point that the old form happened to dodge, goldens could
+  shift by 1 ULP. Mitigation: TDD — write the regression test (run all
+  existing tests with `alpha = 1.0`) **first**, before changing the
+  variable-write line.
+- **`run_chain` signature change ripples to two callers.** Trivial
+  but must be done atomically with the field addition on `Game` /
+  `OwnedGame`.
+- **`alpha` validation only in debug builds.** A release build will
+  silently accept `alpha = 0.0` or `alpha > 1.0` and produce nonsense.
+  This matches the existing pattern (`debug_assert!` for input
+  validation in `Game::ranked_with_arena`); upgrading to `Result` is
+  out of scope.
+
+## Out-of-scope follow-ups (logged for future plans)
+
+- Wire `Schedule` into `run_chain` (so `Damped` lands as a real
+  `Schedule` impl alongside `EpsilonOrMax`).
+- Switch convergence check to `(|Δpi|, |Δtau|)` per spec
+  §"Stopping in natural-param space".
+- Oscillation auto-detect (engage `alpha < 1.0` only after N
+  non-monotone steps).
+- `Residual` schedule (priority queue).
+- `SynergyFactor`, `ScoreFactor` (new EP factor types).

examples/atp.rs

@@ -48,6 +48,7 @@ fn main() {
         .convergence(trueskill_tt::ConvergenceOptions {
             max_iter: 10,
             epsilon: 0.01,
+            alpha: 1.0,
         })
         .build();
 

src/convergence.rs

@@ -8,6 +8,11 @@ use smallvec::SmallVec;
 pub struct ConvergenceOptions {
     pub max_iter: usize,
     pub epsilon: f64,
+    /// EP damping factor in natural-parameter space: each per-factor
+    /// update writes `α·new + (1−α)·old`. `1.0` is undamped (default);
+    /// `< 1.0` stabilises oscillating fixed-point loops at the cost of
+    /// more iterations. Must be in `(0.0, 1.0]`.
+    pub alpha: f64,
 }
 
 impl Default for ConvergenceOptions {
@@ -15,6 +20,7 @@ impl Default for ConvergenceOptions {
         Self {
             max_iter: crate::ITERATIONS,
             epsilon: crate::EPSILON,
+            alpha: 1.0,
         }
     }
 }
@@ -29,3 +35,14 @@ pub struct ConvergenceReport {
     pub per_iteration_time: SmallVec<[Duration; 32]>,
     pub slices_skipped: usize,
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn default_alpha_is_one_for_undamped_behavior() {
+        let opts = ConvergenceOptions::default();
+        assert_eq!(opts.alpha, 1.0);
+    }
+}

src/factor/margin.rs

@@ -32,8 +32,11 @@ impl MarginFactor {
     }
 }
 
-impl Factor for MarginFactor {
+impl MarginFactor {
-    fn propagate(&mut self, vars: &mut VarStore) -> (f64, f64) {
+    /// Propagate this factor's message, optionally damping the update in
+    /// natural-parameter space. `alpha = 1.0` matches `Factor::propagate`
+    /// exactly; `alpha < 1.0` writes `α·new_msg + (1−α)·old_msg`.
+    pub(crate) fn propagate_with_alpha(&mut self, vars: &mut VarStore, alpha: f64) -> (f64, f64) {
         let marginal = vars.get(self.diff);
         let cavity = marginal / self.msg;
 
@@ -42,12 +45,18 @@ impl Factor for MarginFactor {
         }
 
         let new_msg = Gaussian::from_ms(self.m_obs, self.sigma);
-        let new_marginal = cavity * new_msg;
+        let damped = self.msg.damp_natural(new_msg, alpha);
         let old_msg = self.msg;
-        self.msg = new_msg;
+        self.msg = damped;
-        vars.set(self.diff, new_marginal);
+        vars.set(self.diff, cavity * damped);
 
-        old_msg.delta(new_msg)
+        old_msg.delta(damped)
+    }
+}
+
+impl Factor for MarginFactor {
+    fn propagate(&mut self, vars: &mut VarStore) -> (f64, f64) {
+        self.propagate_with_alpha(vars, 1.0)
     }
 
     fn log_evidence(&self, _vars: &VarStore) -> f64 {
@@ -120,4 +129,49 @@ mod tests {
         let logz = f.log_evidence(&vars);
         assert!((logz - (-3.062235327364623)).abs() < 1e-10);
     }
+
+    #[test]
+    fn propagate_with_alpha_one_matches_undamped_propagate() {
+        let mut vars_a = VarStore::new();
+        let diff_a = vars_a.alloc(Gaussian::from_ms(0.0, 6.0));
+        let mut f_a = MarginFactor::new(diff_a, 5.0, 1.0);
+        let delta_a = f_a.propagate(&mut vars_a);
+        let result_a = vars_a.get(diff_a);
+
+        let mut vars_b = VarStore::new();
+        let diff_b = vars_b.alloc(Gaussian::from_ms(0.0, 6.0));
+        let mut f_b = MarginFactor::new(diff_b, 5.0, 1.0);
+        let delta_b = f_b.propagate_with_alpha(&mut vars_b, 1.0);
+        let result_b = vars_b.get(diff_b);
+
+        assert_eq!(result_a.pi(), result_b.pi());
+        assert_eq!(result_a.tau(), result_b.tau());
+        assert_eq!(delta_a, delta_b);
+        assert_eq!(f_a.msg.pi(), f_b.msg.pi());
+        assert_eq!(f_a.msg.tau(), f_b.msg.tau());
+    }
+
+    #[test]
+    fn propagate_with_alpha_half_blends_msg_in_natural_params() {
+        // Run undamped to capture (initial_msg, undamped_new_msg).
+        let mut vars_full = VarStore::new();
+        let diff_full = vars_full.alloc(Gaussian::from_ms(0.0, 6.0));
+        let mut f_full = MarginFactor::new(diff_full, 5.0, 1.0);
+        let initial_msg_pi = f_full.msg.pi();
+        let initial_msg_tau = f_full.msg.tau();
+        f_full.propagate(&mut vars_full);
+        let undamped_msg_pi = f_full.msg.pi();
+        let undamped_msg_tau = f_full.msg.tau();
+
+        // Run damped at α = 0.5 from the same initial state.
+        let mut vars_half = VarStore::new();
+        let diff_half = vars_half.alloc(Gaussian::from_ms(0.0, 6.0));
+        let mut f_half = MarginFactor::new(diff_half, 5.0, 1.0);
+        f_half.propagate_with_alpha(&mut vars_half, 0.5);
+
+        let expected_pi = 0.5 * undamped_msg_pi + 0.5 * initial_msg_pi;
+        let expected_tau = 0.5 * undamped_msg_tau + 0.5 * initial_msg_tau;
+        assert!((f_half.msg.pi() - expected_pi).abs() < 1e-12);
+        assert!((f_half.msg.tau() - expected_tau).abs() < 1e-12);
+    }
 }

src/factor/trunc.rs

@@ -33,29 +33,37 @@ impl TruncFactor {
     }
 }
 
-impl Factor for TruncFactor {
+impl TruncFactor {
-    fn propagate(&mut self, vars: &mut VarStore) -> (f64, f64) {
+    /// Propagate this factor's message, optionally damping the update in
+    /// natural-parameter space. `alpha = 1.0` matches `Factor::propagate`
+    /// exactly; `alpha < 1.0` writes `α·new_msg + (1−α)·old_msg`.
+    pub(crate) fn propagate_with_alpha(&mut self, vars: &mut VarStore, alpha: f64) -> (f64, f64) {
         let marginal = vars.get(self.diff);
-        // Cavity: marginal divided by our outgoing message.
         let cavity = marginal / self.msg;
 
-        // First-time-only: cache the evidence contribution from the cavity.
         if self.evidence_cached.is_none() {
             self.evidence_cached = Some(cavity_evidence(cavity, self.margin, self.tie));
         }
 
-        // Apply the truncation approximation to the cavity.
         let trunc = approx(cavity, self.margin, self.tie);
-
-        // New outgoing message such that cavity * new_msg = trunc.
         let new_msg = trunc / cavity;
+
+        let damped = self.msg.damp_natural(new_msg, alpha);
         let old_msg = self.msg;
-        self.msg = new_msg;
+        self.msg = damped;
 
-        // Update the marginal: marginal_new = cavity * new_msg = trunc.
+        // marginal_new = cavity * stored_msg. With alpha = 1.0 this equals
-        vars.set(self.diff, trunc);
+        // `trunc` (since cavity * new_msg = trunc by construction); with
+        // alpha < 1.0 it reflects the partially-applied update.
+        vars.set(self.diff, cavity * damped);
 
-        old_msg.delta(new_msg)
+        old_msg.delta(damped)
+    }
+}
+
+impl Factor for TruncFactor {
+    fn propagate(&mut self, vars: &mut VarStore) -> (f64, f64) {
+        self.propagate_with_alpha(vars, 1.0)
     }
 
     fn log_evidence(&self, _vars: &VarStore) -> f64 {
@@ -127,4 +135,49 @@ mod tests {
         let ev = f.evidence_cached.unwrap();
         assert!(ev > 0.35 && ev < 0.42);
     }
+
+    #[test]
+    fn propagate_with_alpha_one_matches_undamped_propagate() {
+        let mut vars_a = VarStore::new();
+        let diff_a = vars_a.alloc(Gaussian::from_ms(2.0, 3.0));
+        let mut f_a = TruncFactor::new(diff_a, 0.0, false);
+        let delta_a = f_a.propagate(&mut vars_a);
+        let result_a = vars_a.get(diff_a);
+
+        let mut vars_b = VarStore::new();
+        let diff_b = vars_b.alloc(Gaussian::from_ms(2.0, 3.0));
+        let mut f_b = TruncFactor::new(diff_b, 0.0, false);
+        let delta_b = f_b.propagate_with_alpha(&mut vars_b, 1.0);
+        let result_b = vars_b.get(diff_b);
+
+        assert_eq!(result_a.pi(), result_b.pi());
+        assert_eq!(result_a.tau(), result_b.tau());
+        assert_eq!(delta_a, delta_b);
+        assert_eq!(f_a.msg.pi(), f_b.msg.pi());
+        assert_eq!(f_a.msg.tau(), f_b.msg.tau());
+    }
+
+    #[test]
+    fn propagate_with_alpha_half_blends_msg_in_natural_params() {
+        // Run undamped to capture (initial_msg, undamped_new_msg).
+        let mut vars_full = VarStore::new();
+        let diff_full = vars_full.alloc(Gaussian::from_ms(2.0, 3.0));
+        let mut f_full = TruncFactor::new(diff_full, 0.0, false);
+        let initial_msg_pi = f_full.msg.pi();
+        let initial_msg_tau = f_full.msg.tau();
+        f_full.propagate(&mut vars_full);
+        let undamped_msg_pi = f_full.msg.pi();
+        let undamped_msg_tau = f_full.msg.tau();
+
+        // Run damped at α = 0.5 from the same initial state.
+        let mut vars_half = VarStore::new();
+        let diff_half = vars_half.alloc(Gaussian::from_ms(2.0, 3.0));
+        let mut f_half = TruncFactor::new(diff_half, 0.0, false);
+        f_half.propagate_with_alpha(&mut vars_half, 0.5);
+
+        let expected_pi = 0.5 * undamped_msg_pi + 0.5 * initial_msg_pi;
+        let expected_tau = 0.5 * undamped_msg_tau + 0.5 * initial_msg_tau;
+        assert!((f_half.msg.pi() - expected_pi).abs() < 1e-12);
+        assert!((f_half.msg.tau() - expected_tau).abs() < 1e-12);
+    }
 }

src/game.rs

@@ -44,11 +44,14 @@ impl DiffFactor {
         }
     }
 
-    pub(crate) fn propagate(&mut self, vars: &mut crate::factor::VarStore) -> (f64, f64) {
+    pub(crate) fn propagate(
-        use crate::factor::Factor;
+        &mut self,
+        vars: &mut crate::factor::VarStore,
+        alpha: f64,
+    ) -> (f64, f64) {
         match self {
-            Self::Trunc(f) => f.propagate(vars),
+            Self::Trunc(f) => f.propagate_with_alpha(vars, alpha),
-            Self::Margin(f) => f.propagate(vars),
+            Self::Margin(f) => f.propagate_with_alpha(vars, alpha),
         }
     }
 }
@@ -87,6 +90,7 @@ pub struct OwnedGame<T: Time, D: Drift<T>> {
     result: Vec<f64>,
     weights: Vec<Vec<f64>>,
     p_draw: f64,
+    pub(crate) convergence: crate::ConvergenceOptions,
     pub(crate) likelihoods: Vec<Vec<Gaussian>>,
     pub(crate) evidence: f64,
 }
@@ -97,9 +101,17 @@ impl<T: Time, D: Drift<T>> OwnedGame<T, D> {
         result: Vec<f64>,
         weights: Vec<Vec<f64>>,
         p_draw: f64,
+        convergence: crate::ConvergenceOptions,
     ) -> Self {
         let mut arena = ScratchArena::new();
-        let g = Game::ranked_with_arena(teams.clone(), &result, &weights, p_draw, &mut arena);
+        let g = Game::ranked_with_arena(
+            teams.clone(),
+            &result,
+            &weights,
+            p_draw,
+            convergence,
+            &mut arena,
+        );
         let likelihoods = g.likelihoods;
         let evidence = g.evidence;
         Self {
@@ -107,6 +119,7 @@ impl<T: Time, D: Drift<T>> OwnedGame<T, D> {
             result,
             weights,
             p_draw,
+            convergence,
             likelihoods,
             evidence,
         }
@@ -117,9 +130,17 @@ impl<T: Time, D: Drift<T>> OwnedGame<T, D> {
         scores: Vec<f64>,
         weights: Vec<Vec<f64>>,
         score_sigma: f64,
+        convergence: crate::ConvergenceOptions,
     ) -> Self {
         let mut arena = ScratchArena::new();
-        let g = Game::scored_with_arena(teams.clone(), &scores, &weights, score_sigma, &mut arena);
+        let g = Game::scored_with_arena(
+            teams.clone(),
+            &scores,
+            &weights,
+            score_sigma,
+            convergence,
+            &mut arena,
+        );
         let likelihoods = g.likelihoods;
         let evidence = g.evidence;
         Self {
@@ -127,6 +148,7 @@ impl<T: Time, D: Drift<T>> OwnedGame<T, D> {
             result: scores,
             weights,
             p_draw: 0.0,
+            convergence,
             likelihoods,
             evidence,
         }
@@ -151,6 +173,7 @@ pub struct Game<'a, T: Time = i64, D: Drift<T> = crate::drift::ConstantDrift> {
     result: &'a [f64],
     weights: &'a [Vec<f64>],
     p_draw: f64,
+    pub(crate) convergence: crate::ConvergenceOptions,
     pub(crate) likelihoods: Vec<Vec<Gaussian>>,
     pub(crate) evidence: f64,
 }
@@ -161,6 +184,7 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
         result: &'a [f64],
         weights: &'a [Vec<f64>],
         p_draw: f64,
+        convergence: crate::ConvergenceOptions,
         arena: &mut ScratchArena,
     ) -> Self {
         debug_assert!(
@@ -186,12 +210,17 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
             },
             "draw must be > 0.0 if there are teams with draw"
         );
+        debug_assert!(
+            convergence.alpha > 0.0 && convergence.alpha <= 1.0,
+            "convergence alpha must be in (0.0, 1.0]"
+        );
 
         let mut this = Self {
             teams,
             result,
             weights,
             p_draw,
+            convergence,
             likelihoods: Vec::new(),
             evidence: 0.0,
         };
@@ -205,6 +234,7 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
         scores: &'a [f64],
         weights: &'a [Vec<f64>],
         score_sigma: f64,
+        convergence: crate::ConvergenceOptions,
         arena: &mut ScratchArena,
     ) -> Self {
         debug_assert!(
@@ -219,12 +249,17 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
             "weights must have the same dimensions as teams"
         );
         debug_assert!(score_sigma > 0.0, "score_sigma must be positive");
+        debug_assert!(
+            convergence.alpha > 0.0 && convergence.alpha <= 1.0,
+            "convergence alpha must be in (0.0, 1.0]"
+        );
 
         let mut this = Self {
             teams,
             result: scores,
             weights,
             p_draw: 0.0,
+            convergence,
             likelihoods: Vec::new(),
             evidence: 0.0,
         };
@@ -239,6 +274,10 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
     {
         arena.reset();
 
+        let alpha = self.convergence.alpha;
+        let epsilon = self.convergence.epsilon;
+        let max_iter = self.convergence.max_iter;
+
         let n_teams = self.teams.len();
 
         arena.sort_buf.extend(0..n_teams);
@@ -267,7 +306,7 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
         let mut step = (f64::INFINITY, f64::INFINITY);
         let mut iter = 0;
 
-        while tuple_gt(step, 1e-6) && iter < 10 {
+        while tuple_gt(step, epsilon) && iter < max_iter {
             step = (0.0_f64, 0.0_f64);
 
             for (e, lf) in links[..n_diffs.saturating_sub(1)].iter_mut().enumerate() {
@@ -275,7 +314,7 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
                 let pl = arena.team_prior[e + 1] * arena.lhood_win[e + 1];
                 let raw = pw - pl;
                 arena.vars.set(lf.diff(), raw * lf.msg());
-                let d = lf.propagate(&mut arena.vars);
+                let d = lf.propagate(&mut arena.vars, alpha);
                 step = tuple_max(step, d);
 
                 let new_ll = pw - lf.msg();
@@ -289,7 +328,7 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
                 let pl = arena.team_prior[e + 1] * arena.lhood_win[e + 1];
                 let raw = pw - pl;
                 arena.vars.set(lf.diff(), raw * lf.msg());
-                let d = lf.propagate(&mut arena.vars);
+                let d = lf.propagate(&mut arena.vars, alpha);
                 step = tuple_max(step, d);
 
                 let new_lw = pl + lf.msg();
@@ -305,7 +344,7 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
             let raw = (arena.team_prior[0] * arena.lhood_lose[0])
                 - (arena.team_prior[1] * arena.lhood_win[1]);
             arena.vars.set(links[0].diff(), raw * links[0].msg());
-            links[0].propagate(&mut arena.vars);
+            links[0].propagate(&mut arena.vars, alpha);
         }
 
         // Boundary updates: close the chain at both ends.
@@ -429,7 +468,13 @@ impl<T: Time, D: Drift<T>> Game<'_, T, D> {
         let teams_owned: Vec<Vec<Rating<T, D>>> = teams.iter().map(|t| t.to_vec()).collect();
         let weights: Vec<Vec<f64>> = teams.iter().map(|t| vec![1.0; t.len()]).collect();
 
-        Ok(OwnedGame::new(teams_owned, result, weights, options.p_draw))
+        Ok(OwnedGame::new(
+            teams_owned,
+            result,
+            weights,
+            options.p_draw,
+            options.convergence,
+        ))
     }
 
     pub fn scored(
@@ -465,6 +510,7 @@ impl<T: Time, D: Drift<T>> Game<'_, T, D> {
             scores,
             weights,
             options.score_sigma,
+            options.convergence,
         ))
     }
 
@@ -526,6 +572,7 @@ mod tests {
             &[0.0, 1.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -553,6 +600,7 @@ mod tests {
             &[0.0, 1.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -572,6 +620,7 @@ mod tests {
             &[0.0, 1.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
 
@@ -605,6 +654,7 @@ mod tests {
             &[1.0, 2.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -621,6 +671,7 @@ mod tests {
             &[2.0, 1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -632,7 +683,14 @@ mod tests {
         assert_ulps_eq!(b, Gaussian::from_ms(25.000000, 6.238469), epsilon = 1e-6);
 
         let w = [vec![1.0], vec![1.0], vec![1.0]];
-        let g = Game::ranked_with_arena(teams, &[1.0, 2.0, 0.0], &w, 0.5, &mut ScratchArena::new());
+        let g = Game::ranked_with_arena(
+            teams,
+            &[1.0, 2.0, 0.0],
+            &w,
+            0.5,
+            crate::ConvergenceOptions::default(),
+            &mut ScratchArena::new(),
+        );
         let p = g.posteriors();
 
         let a = p[0][0];
@@ -664,6 +722,7 @@ mod tests {
             &[0.0, 0.0],
             &w,
             0.25,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -691,6 +750,7 @@ mod tests {
             &[0.0, 0.0],
             &w,
             0.25,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -726,6 +786,7 @@ mod tests {
             &[0.0, 0.0, 0.0],
             &w,
             0.25,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -762,6 +823,7 @@ mod tests {
             &[0.0, 0.0, 0.0],
             &w,
             0.25,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -813,6 +875,7 @@ mod tests {
             &[1.0, 0.0, 0.0],
             &w,
             0.25,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -846,6 +909,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -870,6 +934,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -894,6 +959,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -921,6 +987,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -948,6 +1015,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -967,8 +1035,8 @@ mod tests {
         let mut t = DiffFactor::Trunc(TruncFactor::new(dt, 0.0, false));
         let mut m = DiffFactor::Margin(MarginFactor::new(dm, 5.0, 1.0));
 
-        let _ = t.propagate(&mut vars);
+        let _ = t.propagate(&mut vars, 1.0);
-        let _ = m.propagate(&mut vars);
+        let _ = m.propagate(&mut vars, 1.0);
 
         // Smoke: both diffs got written; their msgs are non-N_INF.
         assert!(t.msg().pi() > 0.0);
@@ -989,7 +1057,11 @@ mod tests {
         let weights = [vec![1.0], vec![1.0]];
         let mut arena = ScratchArena::new();
         let g = Game::scored_with_arena(
-            teams, &result, &weights, 1.0, // score_sigma
+            teams,
+            &result,
+            &weights,
+            1.0,
+            crate::ConvergenceOptions::default(),
             &mut arena,
         );
         let p = g.posteriors();
@@ -1008,7 +1080,8 @@ mod tests {
             vec![vec![prior], vec![prior]],
             &result,
             &weights,
-            0.1, // tighter score_sigma
+            0.1,
+            crate::ConvergenceOptions::default(),
             &mut arena2,
         );
         let p_tight = g_tight.posteriors();
@@ -1116,6 +1189,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -1150,6 +1224,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -1184,6 +1259,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -1222,6 +1298,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let post_2vs1 = g.posteriors();
@@ -1235,6 +1312,7 @@ mod tests {
             &[1.0, 0.0],
             &w,
             0.0,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         );
         let p = g.posteriors();
@@ -1244,4 +1322,99 @@ mod tests {
         assert_ulps_eq!(p[1][0], post_2vs1[1][0], epsilon = 1e-6);
         assert_ulps_eq!(p[1][1], t_b[1].prior, epsilon = 1e-6);
     }
+
+    #[test]
+    fn run_chain_honours_max_iter_in_convergence_options() {
+        let players: Vec<R> = (0..4).map(|_| R::default()).collect();
+        let teams: Vec<Vec<_>> = players.iter().map(|p| vec![*p]).collect();
+        let result = vec![3.0, 2.0, 1.0, 0.0];
+        let weights = vec![vec![1.0]; 4];
+
+        // Capped at 1 iteration: cannot fully propagate down a 4-team chain.
+        let mut arena = ScratchArena::new();
+        let g_capped = Game::ranked_with_arena(
+            teams.clone(),
+            &result,
+            &weights,
+            0.0,
+            crate::ConvergenceOptions {
+                max_iter: 1,
+                ..crate::ConvergenceOptions::default()
+            },
+            &mut arena,
+        );
+        let posteriors_capped = g_capped.posteriors();
+
+        // Same inputs, plenty of iterations: fully converged.
+        let mut arena = ScratchArena::new();
+        let g_full = Game::ranked_with_arena(
+            teams,
+            &result,
+            &weights,
+            0.0,
+            crate::ConvergenceOptions::default(),
+            &mut arena,
+        );
+        let posteriors_full = g_full.posteriors();
+
+        // The two posteriors should differ — capped did not converge.
+        let mut max_diff: f64 = 0.0;
+        for (team_capped, team_full) in posteriors_capped.iter().zip(posteriors_full.iter()) {
+            for (g_capped, g_full) in team_capped.iter().zip(team_full.iter()) {
+                max_diff = max_diff.max((g_capped.mu() - g_full.mu()).abs());
+                max_diff = max_diff.max((g_capped.sigma() - g_full.sigma()).abs());
+            }
+        }
+        assert!(
+            max_diff > 1e-6,
+            "max_iter=1 should differ from full convergence; max_diff={max_diff}"
+        );
+    }
+
+    #[test]
+    fn run_chain_with_damping_converges_to_same_posterior() {
+        let players: Vec<R> = (0..4).map(|_| R::default()).collect();
+        let teams: Vec<Vec<_>> = players.iter().map(|p| vec![*p]).collect();
+        let result = vec![3.0, 2.0, 1.0, 0.0];
+        let weights = vec![vec![1.0]; 4];
+
+        let mut arena = ScratchArena::new();
+        let g_undamped = Game::ranked_with_arena(
+            teams.clone(),
+            &result,
+            &weights,
+            0.0,
+            crate::ConvergenceOptions::default(),
+            &mut arena,
+        );
+        let posteriors_undamped = g_undamped.posteriors();
+
+        // alpha=0.5 with extra iterations: should reach the same fixed point.
+        let mut arena = ScratchArena::new();
+        let g_damped = Game::ranked_with_arena(
+            teams,
+            &result,
+            &weights,
+            0.0,
+            crate::ConvergenceOptions {
+                alpha: 0.5,
+                max_iter: 100,
+                ..crate::ConvergenceOptions::default()
+            },
+            &mut arena,
+        );
+        let posteriors_damped = g_damped.posteriors();
+
+        let mut max_diff: f64 = 0.0;
+        for (team_u, team_d) in posteriors_undamped.iter().zip(posteriors_damped.iter()) {
+            for (g_u, g_d) in team_u.iter().zip(team_d.iter()) {
+                max_diff = max_diff.max((g_u.mu() - g_d.mu()).abs());
+                max_diff = max_diff.max((g_u.sigma() - g_d.sigma()).abs());
+            }
+        }
+        assert!(
+            max_diff < 1e-4,
+            "α=0.5 should reach the same fixed point as α=1.0; max_diff={max_diff}"
+        );
+    }
 }

src/gaussian.rs

@@ -96,6 +96,18 @@ impl Gaussian {
         let var = self.sigma().powi(2) + variance_delta;
         Self::from_ms(self.mu(), var.sqrt())
     }
+
+    /// EP damping in natural-parameter space: `α·new + (1−α)·self`.
+    ///
+    /// Used by within-game inference to stabilise oscillating fixed-point
+    /// loops on hard graphs. `alpha = 1.0` returns `new` exactly;
+    /// `alpha < 1.0` shrinks each per-step update.
+    pub fn damp_natural(self, new: Gaussian, alpha: f64) -> Gaussian {
+        Gaussian::from_natural(
+            alpha * new.pi() + (1.0 - alpha) * self.pi(),
+            alpha * new.tau() + (1.0 - alpha) * self.tau(),
+        )
+    }
 }
 
 impl Default for Gaussian {
@@ -231,4 +243,33 @@ mod tests {
         assert!((r.pi() - expected_pi).abs() < 1e-15);
         assert!((r.tau() - expected_tau).abs() < 1e-15);
     }
+
+    #[test]
+    fn damp_natural_alpha_one_returns_new() {
+        let old = Gaussian::from_ms(1.0, 2.0);
+        let new = Gaussian::from_ms(5.0, 0.5);
+        let damped = old.damp_natural(new, 1.0);
+        assert_eq!(damped.pi(), new.pi());
+        assert_eq!(damped.tau(), new.tau());
+    }
+
+    #[test]
+    fn damp_natural_alpha_zero_returns_self() {
+        let old = Gaussian::from_ms(1.0, 2.0);
+        let new = Gaussian::from_ms(5.0, 0.5);
+        let damped = old.damp_natural(new, 0.0);
+        assert_eq!(damped.pi(), old.pi());
+        assert_eq!(damped.tau(), old.tau());
+    }
+
+    #[test]
+    fn damp_natural_alpha_half_is_midpoint_in_natural_params() {
+        let old = Gaussian::from_ms(1.0, 2.0);
+        let new = Gaussian::from_ms(5.0, 0.5);
+        let damped = old.damp_natural(new, 0.5);
+        let expected_pi = 0.5 * new.pi() + 0.5 * old.pi();
+        let expected_tau = 0.5 * new.tau() + 0.5 * old.tau();
+        assert!((damped.pi() - expected_pi).abs() < 1e-12);
+        assert!((damped.tau() - expected_tau).abs() < 1e-12);
+    }
 }

src/history.rs

@@ -838,6 +838,7 @@ mod tests {
             &[0.0, 1.0],
             &w,
             P_DRAW,
+            crate::ConvergenceOptions::default(),
             &mut ScratchArena::new(),
         )
         .posteriors();
@@ -1368,6 +1369,7 @@ mod tests {
         h.convergence = ConvergenceOptions {
             max_iter: 11,
             epsilon: EPSILON,
+            alpha: 1.0,
         };
         h.converge().unwrap();
 
@@ -1685,6 +1687,7 @@ mod tests {
             .convergence(ConvergenceOptions {
                 max_iter: 30,
                 epsilon: 1e-6,
+                alpha: 1.0,
             })
             .build();
 

src/time_slice.rs

@@ -138,12 +138,22 @@ impl Event {
         let teams = self.within_priors(false, false, skills, agents);
         let result = self.outputs();
         let g = match self.kind {
-            EventKind::Ranked => {
+            EventKind::Ranked => Game::ranked_with_arena(
-                Game::ranked_with_arena(teams, &result, &self.weights, p_draw, arena)
+                teams,
-            }
+                &result,
-            EventKind::Scored { score_sigma } => {
+                &self.weights,
-                Game::scored_with_arena(teams, &result, &self.weights, score_sigma, arena)
+                p_draw,
-            }
+                crate::ConvergenceOptions::default(),
+                arena,
+            ),
+            EventKind::Scored { score_sigma } => Game::scored_with_arena(
+                teams,
+                &result,
+                &self.weights,
+                score_sigma,
+                crate::ConvergenceOptions::default(),
+                arena,
+            ),
         };
 
         for (t, team) in self.teams.iter_mut().enumerate() {
@@ -322,6 +332,7 @@ impl<T: Time> TimeSlice<T> {
                         &result,
                         &event.weights,
                         self.p_draw,
+                        crate::ConvergenceOptions::default(),
                         &mut self.arena,
                     ),
                     EventKind::Scored { score_sigma } => Game::scored_with_arena(
@@ -329,6 +340,7 @@ impl<T: Time> TimeSlice<T> {
                         &result,
                         &event.weights,
                         score_sigma,
+                        crate::ConvergenceOptions::default(),
                         &mut self.arena,
                     ),
                 };
@@ -504,16 +516,26 @@ impl<T: Time> TimeSlice<T> {
             let teams = event.within_priors(online, forward, &self.skills, agents);
             let result = event.outputs();
             match event.kind {
-                EventKind::Ranked => {
+                EventKind::Ranked => Game::ranked_with_arena(
-                    Game::ranked_with_arena(teams, &result, &event.weights, self.p_draw, arena)
+                    teams,
-                        .evidence
+                    &result,
-                        .ln()
+                    &event.weights,
-                }
+                    self.p_draw,
-                EventKind::Scored { score_sigma } => {
+                    crate::ConvergenceOptions::default(),
-                    Game::scored_with_arena(teams, &result, &event.weights, score_sigma, arena)
+                    arena,
-                        .evidence
+                )
-                        .ln()
+                .evidence
-                }
+                .ln(),
+                EventKind::Scored { score_sigma } => Game::scored_with_arena(
+                    teams,
+                    &result,
+                    &event.weights,
+                    score_sigma,
+                    crate::ConvergenceOptions::default(),
+                    arena,
+                )
+                .evidence
+                .ln(),
             }
         };
 

tests/api_shape.rs

@@ -15,6 +15,7 @@ fn add_events_bulk_via_iter() {
         .convergence(ConvergenceOptions {
             max_iter: 30,
             epsilon: 1e-6,
+            alpha: 1.0,
         })
         .build();
 

tests/determinism.rs

@@ -16,6 +16,7 @@ fn build_and_converge(seed: u64) -> Vec<(i64, trueskill_tt::Gaussian)> {
         .convergence(ConvergenceOptions {
             max_iter: 30,
             epsilon: 1e-6,
+            alpha: 1.0,
         })
         .build();
 

tests/record_winner.rs

@@ -10,6 +10,7 @@ fn record_winner_builds_history() {
         .convergence(ConvergenceOptions {
             max_iter: 30,
             epsilon: 1e-6,
+            alpha: 1.0,
         })
         .build();