diff --git a/Cargo.lock b/Cargo.lock
index 2e81285..0baa02a 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -5898,6 +5898,7 @@ dependencies = [
  "anyhow",
  "burn",
  "rand 0.9.2",
+ "rand_distr",
  "trictrac-store",
 ]
 
diff --git a/spiel_bot/Cargo.toml b/spiel_bot/Cargo.toml
index fba2aab..323c953 100644
--- a/spiel_bot/Cargo.toml
+++ b/spiel_bot/Cargo.toml
@@ -7,4 +7,5 @@ edition = "2021"
 trictrac-store = { path = "../store" }
 anyhow         = "1"
 rand           = "0.9"
+rand_distr     = "0.5"
 burn           = { version = "0.20", features = ["ndarray", "autodiff"] }
diff --git a/spiel_bot/src/lib.rs b/spiel_bot/src/lib.rs
index 6e71016..5beb37c 100644
--- a/spiel_bot/src/lib.rs
+++ b/spiel_bot/src/lib.rs
@@ -1,2 +1,3 @@
 pub mod env;
+pub mod mcts;
 pub mod network;
diff --git a/spiel_bot/src/mcts/mod.rs b/spiel_bot/src/mcts/mod.rs
new file mode 100644
index 0000000..e92bd09
--- /dev/null
+++ b/spiel_bot/src/mcts/mod.rs
@@ -0,0 +1,408 @@
+//! Monte Carlo Tree Search with PUCT selection and policy-value network guidance.
+//!
+//! # Algorithm
+//!
+//! The implementation follows AlphaZero's MCTS:
+//!
+//! 1. **Expand root** — run the network once to get priors and a value
+//!    estimate; optionally add Dirichlet noise for training-time exploration.
+//! 2. **Simulate** `n_simulations` times:
+//!    - *Selection* — traverse the tree with PUCT until an unvisited leaf.
+//!    - *Chance bypass* — call [`GameEnv::apply_chance`] at chance nodes;
+//!      chance nodes are **not** stored in the tree (outcome sampling).
+//!    - *Expansion* — evaluate the network at the leaf; populate children.
+//!    - *Backup* — propagate the value upward; negate at each player boundary.
+//! 3. **Policy** — normalized visit counts at the root ([`mcts_policy`]).
+//! 4. **Action** — greedy (temperature = 0) or sampled ([`select_action`]).
+//!
+//! # Perspective convention
+//!
+//! Every [`MctsNode::w`] is stored **from the perspective of the player who
+//! acts at that node**.  The backup negates the child value whenever the
+//! acting player differs between parent and child.
+//!
+//! # Stochastic games
+//!
+//! When [`GameEnv::current_player`] returns [`Player::Chance`], the
+//! simulation calls [`GameEnv::apply_chance`] to sample a random outcome and
+//! continues.  Chance nodes are skipped transparently; Q-values converge to
+//! their expectation over many simulations (outcome sampling).
+
+pub mod node;
+mod search;
+
+pub use node::MctsNode;
+
+use rand::Rng;
+
+use crate::env::GameEnv;
+
+// ── Evaluator trait ────────────────────────────────────────────────────────
+
+/// Evaluates a game position for use in MCTS.
+///
+/// Implementations typically wrap a [`PolicyValueNet`](crate::network::PolicyValueNet)
+/// but the `mcts` module itself does **not** depend on Burn.
+pub trait Evaluator: Send + Sync {
+    /// Evaluate `obs` (flat observation vector of length `obs_size`).
+    ///
+    /// Returns:
+    /// - `policy_logits`: one raw logit per action (`action_space` entries).
+    ///   Illegal action entries are masked inside the search — no need to
+    ///   zero them here.
+    /// - `value`: scalar in `(-1, 1)` from **the current player's** perspective.
+    fn evaluate(&self, obs: &[f32]) -> (Vec<f32>, f32);
+}
+
+// ── Configuration ─────────────────────────────────────────────────────────
+
+/// Hyperparameters for [`run_mcts`].
+#[derive(Debug, Clone)]
+pub struct MctsConfig {
+    /// Number of MCTS simulations per move.  Typical: 50–800.
+    pub n_simulations: usize,
+    /// PUCT exploration constant `c_puct`.  Typical: 1.0–2.0.
+    pub c_puct: f32,
+    /// Dirichlet noise concentration α.  Set to `0.0` to disable.
+    /// Typical: `0.3` for Chess, `0.1` for large action spaces.
+    pub dirichlet_alpha: f32,
+    /// Weight of Dirichlet noise mixed into root priors.  Typical: `0.25`.
+    pub dirichlet_eps: f32,
+    /// Action sampling temperature.  `> 0` = proportional sample, `0` = argmax.
+    pub temperature: f32,
+}
+
+impl Default for MctsConfig {
+    fn default() -> Self {
+        Self {
+            n_simulations: 200,
+            c_puct: 1.5,
+            dirichlet_alpha: 0.3,
+            dirichlet_eps: 0.25,
+            temperature: 1.0,
+        }
+    }
+}
+
+// ── Public interface ───────────────────────────────────────────────────────
+
+/// Run MCTS from `state` and return the populated root [`MctsNode`].
+///
+/// `state` must be a player-decision node (`P1` or `P2`).
+/// Use [`mcts_policy`] and [`select_action`] on the returned root.
+///
+/// # Panics
+///
+/// Panics if `env.current_player(state)` is not `P1` or `P2`.
+pub fn run_mcts<E: GameEnv>(
+    env: &E,
+    state: &E::State,
+    evaluator: &dyn Evaluator,
+    config: &MctsConfig,
+    rng: &mut impl Rng,
+) -> MctsNode {
+    let player_idx = env
+        .current_player(state)
+        .index()
+        .expect("run_mcts called at a non-decision node");
+
+    // ── Expand root (network called once here, not inside the loop) ────────
+    let mut root = MctsNode::new(1.0);
+    search::expand::<E>(&mut root, state, env, evaluator, player_idx);
+
+    // ── Optional Dirichlet noise for training exploration ──────────────────
+    if config.dirichlet_alpha > 0.0 && config.dirichlet_eps > 0.0 {
+        search::add_dirichlet_noise(&mut root, config.dirichlet_alpha, config.dirichlet_eps, rng);
+    }
+
+    // ── Simulations ────────────────────────────────────────────────────────
+    for _ in 0..config.n_simulations {
+        search::simulate::<E>(
+            &mut root,
+            state.clone(),
+            env,
+            evaluator,
+            config,
+            rng,
+            player_idx,
+        );
+    }
+
+    root
+}
+
+/// Compute the MCTS policy: normalized visit counts at the root.
+///
+/// Returns a vector of length `action_space` where `policy[a]` is the
+/// fraction of simulations that visited action `a`.
+pub fn mcts_policy(root: &MctsNode, action_space: usize) -> Vec<f32> {
+    let total: f32 = root.children.iter().map(|(_, c)| c.n as f32).sum();
+    let mut policy = vec![0.0f32; action_space];
+    if total > 0.0 {
+        for (a, child) in &root.children {
+            policy[*a] = child.n as f32 / total;
+        }
+    } else if !root.children.is_empty() {
+        // n_simulations = 0: uniform over legal actions.
+        let uniform = 1.0 / root.children.len() as f32;
+        for (a, _) in &root.children {
+            policy[*a] = uniform;
+        }
+    }
+    policy
+}
+
+/// Select an action index from the root after MCTS.
+///
+/// * `temperature = 0` — greedy argmax of visit counts.
+/// * `temperature > 0` — sample proportionally to `N^(1 / temperature)`.
+///
+/// # Panics
+///
+/// Panics if the root has no children.
+pub fn select_action(root: &MctsNode, temperature: f32, rng: &mut impl Rng) -> usize {
+    assert!(!root.children.is_empty(), "select_action called on a root with no children");
+    if temperature <= 0.0 {
+        root.children
+            .iter()
+            .max_by_key(|(_, c)| c.n)
+            .map(|(a, _)| *a)
+            .unwrap()
+    } else {
+        let weights: Vec<f32> = root
+            .children
+            .iter()
+            .map(|(_, c)| (c.n as f32).powf(1.0 / temperature))
+            .collect();
+        let total: f32 = weights.iter().sum();
+        let mut r: f32 = rng.random::<f32>() * total;
+        for (i, (a, _)) in root.children.iter().enumerate() {
+            r -= weights[i];
+            if r <= 0.0 {
+                return *a;
+            }
+        }
+        root.children.last().map(|(a, _)| *a).unwrap()
+    }
+}
+
+// ── Tests ──────────────────────────────────────────────────────────────────
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use rand::{SeedableRng, rngs::SmallRng};
+    use crate::env::Player;
+
+    // ── Minimal deterministic test game ───────────────────────────────────
+    //
+    // "Countdown" — two players alternate subtracting 1 or 2 from a counter.
+    // The player who brings the counter to 0 wins.
+    // No chance nodes, two legal actions (0 = -1, 1 = -2).
+
+    #[derive(Clone, Debug)]
+    struct CState {
+        remaining: u8,
+        to_move: usize, // at terminal: last mover (winner)
+    }
+
+    #[derive(Clone)]
+    struct CountdownEnv;
+
+    impl crate::env::GameEnv for CountdownEnv {
+        type State = CState;
+
+        fn new_game(&self) -> CState {
+            CState { remaining: 6, to_move: 0 }
+        }
+
+        fn current_player(&self, s: &CState) -> Player {
+            if s.remaining == 0 {
+                Player::Terminal
+            } else if s.to_move == 0 {
+                Player::P1
+            } else {
+                Player::P2
+            }
+        }
+
+        fn legal_actions(&self, s: &CState) -> Vec<usize> {
+            if s.remaining >= 2 { vec![0, 1] } else { vec![0] }
+        }
+
+        fn apply(&self, s: &mut CState, action: usize) {
+            let sub = (action as u8) + 1;
+            if s.remaining <= sub {
+                s.remaining = 0;
+                // to_move stays as winner
+            } else {
+                s.remaining -= sub;
+                s.to_move = 1 - s.to_move;
+            }
+        }
+
+        fn apply_chance<R: rand::Rng>(&self, _s: &mut CState, _rng: &mut R) {}
+
+        fn observation(&self, s: &CState, _pov: usize) -> Vec<f32> {
+            vec![s.remaining as f32 / 6.0, s.to_move as f32]
+        }
+
+        fn obs_size(&self) -> usize { 2 }
+        fn action_space(&self) -> usize { 2 }
+
+        fn returns(&self, s: &CState) -> Option<[f32; 2]> {
+            if s.remaining != 0 { return None; }
+            let mut r = [-1.0f32; 2];
+            r[s.to_move] = 1.0;
+            Some(r)
+        }
+    }
+
+    // Uniform evaluator: all logits = 0, value = 0.
+    // `action_space` must match the environment's `action_space()`.
+    struct ZeroEval(usize);
+    impl Evaluator for ZeroEval {
+        fn evaluate(&self, _obs: &[f32]) -> (Vec<f32>, f32) {
+            (vec![0.0f32; self.0], 0.0)
+        }
+    }
+
+    fn rng() -> SmallRng {
+        SmallRng::seed_from_u64(42)
+    }
+
+    fn config_n(n: usize) -> MctsConfig {
+        MctsConfig {
+            n_simulations: n,
+            c_puct: 1.5,
+            dirichlet_alpha: 0.0, // off for reproducibility
+            dirichlet_eps: 0.0,
+            temperature: 1.0,
+        }
+    }
+
+    // ── Visit count tests ─────────────────────────────────────────────────
+
+    #[test]
+    fn visit_counts_sum_to_n_simulations() {
+        let env = CountdownEnv;
+        let state = env.new_game();
+        let root = run_mcts(&env, &state, &ZeroEval(2), &config_n(50), &mut rng());
+        let total: u32 = root.children.iter().map(|(_, c)| c.n).sum();
+        assert_eq!(total, 50, "visit counts must sum to n_simulations");
+    }
+
+    #[test]
+    fn all_root_children_are_legal() {
+        let env = CountdownEnv;
+        let state = env.new_game();
+        let legal = env.legal_actions(&state);
+        let root = run_mcts(&env, &state, &ZeroEval(2), &config_n(30), &mut rng());
+        for (a, _) in &root.children {
+            assert!(legal.contains(a), "child action {a} is not legal");
+        }
+    }
+
+    // ── Policy tests ─────────────────────────────────────────────────────
+
+    #[test]
+    fn policy_sums_to_one() {
+        let env = CountdownEnv;
+        let state = env.new_game();
+        let root = run_mcts(&env, &state, &ZeroEval(2), &config_n(20), &mut rng());
+        let policy = mcts_policy(&root, env.action_space());
+        let sum: f32 = policy.iter().sum();
+        assert!((sum - 1.0).abs() < 1e-5, "policy sums to {sum}, expected 1.0");
+    }
+
+    #[test]
+    fn policy_zero_for_illegal_actions() {
+        let env = CountdownEnv;
+        // remaining = 1 → only action 0 is legal
+        let state = CState { remaining: 1, to_move: 0 };
+        let root = run_mcts(&env, &state, &ZeroEval(2), &config_n(10), &mut rng());
+        let policy = mcts_policy(&root, env.action_space());
+        assert_eq!(policy[1], 0.0, "illegal action must have zero policy mass");
+    }
+
+    // ── Action selection tests ────────────────────────────────────────────
+
+    #[test]
+    fn greedy_selects_most_visited() {
+        let env = CountdownEnv;
+        let state = env.new_game();
+        let root = run_mcts(&env, &state, &ZeroEval(2), &config_n(60), &mut rng());
+        let greedy = select_action(&root, 0.0, &mut rng());
+        let most_visited = root.children.iter().max_by_key(|(_, c)| c.n).map(|(a, _)| *a).unwrap();
+        assert_eq!(greedy, most_visited);
+    }
+
+    #[test]
+    fn temperature_sampling_stays_legal() {
+        let env = CountdownEnv;
+        let state = env.new_game();
+        let legal = env.legal_actions(&state);
+        let mut r = rng();
+        let root = run_mcts(&env, &state, &ZeroEval(2), &config_n(30), &mut r);
+        for _ in 0..20 {
+            let a = select_action(&root, 1.0, &mut r);
+            assert!(legal.contains(&a), "sampled action {a} is not legal");
+        }
+    }
+
+    // ── Zero-simulation edge case ─────────────────────────────────────────
+
+    #[test]
+    fn zero_simulations_uniform_policy() {
+        let env = CountdownEnv;
+        let state = env.new_game();
+        let root = run_mcts(&env, &state, &ZeroEval(2), &config_n(0), &mut rng());
+        let policy = mcts_policy(&root, env.action_space());
+        // With 0 simulations, fallback is uniform over the 2 legal actions.
+        let sum: f32 = policy.iter().sum();
+        assert!((sum - 1.0).abs() < 1e-5);
+    }
+
+    // ── Root value ────────────────────────────────────────────────────────
+
+    #[test]
+    fn root_q_in_valid_range() {
+        let env = CountdownEnv;
+        let state = env.new_game();
+        let root = run_mcts(&env, &state, &ZeroEval(2), &config_n(40), &mut rng());
+        let q = root.q();
+        assert!(q >= -1.0 && q <= 1.0, "root Q={q} outside [-1, 1]");
+    }
+
+    // ── Integration: run on a real Trictrac game ──────────────────────────
+
+    #[test]
+    fn no_panic_on_trictrac_state() {
+        use crate::env::TrictracEnv;
+
+        let env = TrictracEnv;
+        let mut state = env.new_game();
+        let mut r = rng();
+
+        // Advance past the initial chance node to reach a decision node.
+        while env.current_player(&state).is_chance() {
+            env.apply_chance(&mut state, &mut r);
+        }
+
+        if env.current_player(&state).is_terminal() {
+            return; // unlikely but safe
+        }
+
+        let config = MctsConfig {
+            n_simulations: 5, // tiny for speed
+            dirichlet_alpha: 0.0,
+            dirichlet_eps: 0.0,
+            ..MctsConfig::default()
+        };
+
+        let root = run_mcts(&env, &state, &ZeroEval(514), &config, &mut r);
+        assert!(root.n > 0);
+        let total: u32 = root.children.iter().map(|(_, c)| c.n).sum();
+        assert_eq!(total, 5);
+    }
+}
diff --git a/spiel_bot/src/mcts/node.rs b/spiel_bot/src/mcts/node.rs
new file mode 100644
index 0000000..aff7735
--- /dev/null
+++ b/spiel_bot/src/mcts/node.rs
@@ -0,0 +1,91 @@
+//! MCTS tree node.
+//!
+//! [`MctsNode`] holds the visit statistics for one player-decision position in
+//! the search tree.  A node is *expanded* the first time the policy-value
+//! network is evaluated there; before that it is a leaf.
+
+/// One node in the MCTS tree, representing a player-decision position.
+///
+/// `w` stores the sum of values backed up into this node, always from the
+/// perspective of **the player who acts here**.  `q()` therefore also returns
+/// a value in `(-1, 1)` from that same perspective.
+#[derive(Debug)]
+pub struct MctsNode {
+    /// Visit count `N(s, a)`.
+    pub n: u32,
+    /// Sum of backed-up values `W(s, a)` — from **this node's player's** perspective.
+    pub w: f32,
+    /// Prior probability `P(s, a)` assigned by the policy head (after masked softmax).
+    pub p: f32,
+    /// Children: `(action_index, child_node)`, populated on first expansion.
+    pub children: Vec<(usize, MctsNode)>,
+    /// `true` after the network has been evaluated and children have been set up.
+    pub expanded: bool,
+}
+
+impl MctsNode {
+    /// Create a fresh, unexpanded leaf with the given prior probability.
+    pub fn new(prior: f32) -> Self {
+        Self {
+            n: 0,
+            w: 0.0,
+            p: prior,
+            children: Vec::new(),
+            expanded: false,
+        }
+    }
+
+    /// `Q(s, a) = W / N`, or `0.0` if this node has never been visited.
+    #[inline]
+    pub fn q(&self) -> f32 {
+        if self.n == 0 { 0.0 } else { self.w / self.n as f32 }
+    }
+
+    /// PUCT selection score:
+    ///
+    /// ```text
+    /// Q(s,a) + c_puct · P(s,a) · √N_parent / (1 + N(s,a))
+    /// ```
+    #[inline]
+    pub fn puct(&self, parent_n: u32, c_puct: f32) -> f32 {
+        self.q() + c_puct * self.p * (parent_n as f32).sqrt() / (1.0 + self.n as f32)
+    }
+}
+
+// ── Tests ──────────────────────────────────────────────────────────────────
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn q_zero_when_unvisited() {
+        let node = MctsNode::new(0.5);
+        assert_eq!(node.q(), 0.0);
+    }
+
+    #[test]
+    fn q_reflects_w_over_n() {
+        let mut node = MctsNode::new(0.5);
+        node.n = 4;
+        node.w = 2.0;
+        assert!((node.q() - 0.5).abs() < 1e-6);
+    }
+
+    #[test]
+    fn puct_exploration_dominates_unvisited() {
+        // Unvisited child should outscore a visited child with negative Q.
+        let mut visited = MctsNode::new(0.5);
+        visited.n = 10;
+        visited.w = -5.0; // Q = -0.5
+
+        let unvisited = MctsNode::new(0.5);
+
+        let parent_n = 10;
+        let c = 1.5;
+        assert!(
+            unvisited.puct(parent_n, c) > visited.puct(parent_n, c),
+            "unvisited child should have higher PUCT than a negatively-valued visited child"
+        );
+    }
+}
diff --git a/spiel_bot/src/mcts/search.rs b/spiel_bot/src/mcts/search.rs
new file mode 100644
index 0000000..c4960c7
--- /dev/null
+++ b/spiel_bot/src/mcts/search.rs
@@ -0,0 +1,170 @@
+//! Simulation, expansion, backup, and noise helpers.
+//!
+//! These are internal to the `mcts` module; the public entry points are
+//! [`super::run_mcts`], [`super::mcts_policy`], and [`super::select_action`].
+
+use rand::Rng;
+use rand_distr::{Gamma, Distribution};
+
+use crate::env::GameEnv;
+use super::{Evaluator, MctsConfig};
+use super::node::MctsNode;
+
+// ── Masked softmax ─────────────────────────────────────────────────────────
+
+/// Numerically stable softmax over `legal` actions only.
+///
+/// Illegal logits are treated as `-∞` and receive probability `0.0`.
+/// Returns a probability vector of length `action_space`.
+pub(super) fn masked_softmax(logits: &[f32], legal: &[usize], action_space: usize) -> Vec<f32> {
+    let mut probs = vec![0.0f32; action_space];
+    if legal.is_empty() {
+        return probs;
+    }
+    let max_logit = legal
+        .iter()
+        .map(|&a| logits[a])
+        .fold(f32::NEG_INFINITY, f32::max);
+    let mut sum = 0.0f32;
+    for &a in legal {
+        let e = (logits[a] - max_logit).exp();
+        probs[a] = e;
+        sum += e;
+    }
+    if sum > 0.0 {
+        for &a in legal {
+            probs[a] /= sum;
+        }
+    } else {
+        let uniform = 1.0 / legal.len() as f32;
+        for &a in legal {
+            probs[a] = uniform;
+        }
+    }
+    probs
+}
+
+// ── Dirichlet noise ────────────────────────────────────────────────────────
+
+/// Mix Dirichlet(α, …, α) noise into the root's children priors for exploration.
+///
+/// Standard AlphaZero parameters: `alpha = 0.3`, `eps = 0.25`.
+/// Uses the Gamma-distribution trick: Dir(α,…,α) = Gamma(α,1)^n / sum.
+pub(super) fn add_dirichlet_noise(
+    node: &mut MctsNode,
+    alpha: f32,
+    eps: f32,
+    rng: &mut impl Rng,
+) {
+    let n = node.children.len();
+    if n == 0 {
+        return;
+    }
+    let Ok(gamma) = Gamma::new(alpha as f64, 1.0_f64) else {
+        return;
+    };
+    let samples: Vec<f32> = (0..n).map(|_| gamma.sample(rng) as f32).collect();
+    let sum: f32 = samples.iter().sum();
+    if sum <= 0.0 {
+        return;
+    }
+    for (i, (_, child)) in node.children.iter_mut().enumerate() {
+        let noise = samples[i] / sum;
+        child.p = (1.0 - eps) * child.p + eps * noise;
+    }
+}
+
+// ── Expansion ──────────────────────────────────────────────────────────────
+
+/// Evaluate the network at `state` and populate `node` with children.
+///
+/// Sets `node.n = 1`, `node.w = value`, `node.expanded = true`.
+/// Returns the network value estimate from `player_idx`'s perspective.
+pub(super) fn expand<E: GameEnv>(
+    node: &mut MctsNode,
+    state: &E::State,
+    env: &E,
+    evaluator: &dyn Evaluator,
+    player_idx: usize,
+) -> f32 {
+    let obs = env.observation(state, player_idx);
+    let legal = env.legal_actions(state);
+    let (logits, value) = evaluator.evaluate(&obs);
+    let priors = masked_softmax(&logits, &legal, env.action_space());
+    node.children = legal.iter().map(|&a| (a, MctsNode::new(priors[a]))).collect();
+    node.expanded = true;
+    node.n = 1;
+    node.w = value;
+    value
+}
+
+// ── Simulation ─────────────────────────────────────────────────────────────
+
+/// One MCTS simulation from an **already-expanded** decision node.
+///
+/// Traverses the tree with PUCT selection, expands the first unvisited leaf,
+/// and backs up the result.
+///
+/// * `player_idx` — the player (0 or 1) who acts at `state`.
+/// * Returns the backed-up value **from `player_idx`'s perspective**.
+pub(super) fn simulate<E: GameEnv>(
+    node: &mut MctsNode,
+    state: E::State,
+    env: &E,
+    evaluator: &dyn Evaluator,
+    config: &MctsConfig,
+    rng: &mut impl Rng,
+    player_idx: usize,
+) -> f32 {
+    debug_assert!(node.expanded, "simulate called on unexpanded node");
+
+    // ── Selection: child with highest PUCT ────────────────────────────────
+    let parent_n = node.n;
+    let best = node
+        .children
+        .iter()
+        .enumerate()
+        .max_by(|(_, (_, a)), (_, (_, b))| {
+            a.puct(parent_n, config.c_puct)
+                .partial_cmp(&b.puct(parent_n, config.c_puct))
+                .unwrap_or(std::cmp::Ordering::Equal)
+        })
+        .map(|(i, _)| i)
+        .expect("expanded node must have at least one child");
+
+    let (action, child) = &mut node.children[best];
+    let action = *action;
+
+    // ── Apply action + advance through any chance nodes ───────────────────
+    let mut next_state = state;
+    env.apply(&mut next_state, action);
+    while env.current_player(&next_state).is_chance() {
+        env.apply_chance(&mut next_state, rng);
+    }
+
+    let next_cp = env.current_player(&next_state);
+
+    // ── Evaluate leaf or terminal ──────────────────────────────────────────
+    // All values are converted to `player_idx`'s perspective before backup.
+    let child_value = if next_cp.is_terminal() {
+        let returns = env
+            .returns(&next_state)
+            .expect("terminal node must have returns");
+        returns[player_idx]
+    } else {
+        let child_player = next_cp.index().unwrap();
+        let v = if child.expanded {
+            simulate(child, next_state, env, evaluator, config, rng, child_player)
+        } else {
+            expand::<E>(child, &next_state, env, evaluator, child_player)
+        };
+        // Negate when the child belongs to the opponent.
+        if child_player == player_idx { v } else { -v }
+    };
+
+    // ── Backup ────────────────────────────────────────────────────────────
+    node.n += 1;
+    node.w += child_value;
+
+    child_value
+}