diff --git a/crates/xserv-model/src/bin/bench-eagle3.rs b/crates/xserv-model/src/bin/bench-eagle3.rs
index 2ee2a3d..a07db72 100644
--- a/crates/xserv-model/src/bin/bench-eagle3.rs
+++ b/crates/xserv-model/src/bin/bench-eagle3.rs
@@ -93,6 +93,7 @@ fn main() {
     let prompt_count = arg_usize(&args, "--prompts", PROMPTS.len()).min(PROMPTS.len());
     let max_seq_len = arg_usize(&args, "--max-seq-len", DEFAULT_MAX_SEQ_LEN);
     let device = arg_usize(&args, "--device", 0) as u32;
+    let gamma = arg_usize(&args, "--gamma", 2).max(1);
 
     xserv_cuda::device::set_device(device).unwrap();
     let info = xserv_cuda::device::device_info(device).unwrap();
@@ -122,7 +123,9 @@ fn main() {
         let _ = run_baseline(&target, &mut cache, &tokenizer, &ids, 4);
         drop(cache);
     }
-    eprintln!("Warmup done. Running {prompt_count} prompts, gen_tokens={gen_tokens}\n");
+    eprintln!(
+        "Warmup done. Running {prompt_count} prompts, gen_tokens={gen_tokens}, gamma={gamma}\n"
+    );
 
     let mut baseline_total_s = 0.0f64;
     let mut baseline_tokens = 0usize;
@@ -147,17 +150,30 @@ fn main() {
         baseline_tokens += baseline.ids.len();
         drop(baseline_cache);
 
-        // Speculative with EAGLE γ=1.
+        // Speculative with EAGLE, γ from CLI.
         let mut target_cache = new_cache(&target_config, max_seq_len, device);
-        let spec = run_eagle_gamma1(
-            &target,
-            &mut eagle,
-            &embed_tokens,
-            &mut target_cache,
-            &tokenizer,
-            &ids,
-            gen_tokens,
-        );
+        let spec = if gamma == 1 {
+            run_eagle_gamma1(
+                &target,
+                &mut eagle,
+                &embed_tokens,
+                &mut target_cache,
+                &tokenizer,
+                &ids,
+                gen_tokens,
+            )
+        } else {
+            run_eagle_gamma_multi(
+                &target,
+                &mut eagle,
+                &embed_tokens,
+                &mut target_cache,
+                &tokenizer,
+                &ids,
+                gen_tokens,
+                gamma,
+            )
+        };
         spec_total_s += spec.total_s;
         spec_tokens += spec.ids.len();
         spec_accepted += spec.accepted;
@@ -336,6 +352,148 @@ fn run_eagle_gamma1(
     }
 }
 
+/// γ≥2 speculative with recursive EAGLE drafting + batched target verify.
+///
+/// Per round (state entering: prev_token committed at position pos-1, and
+/// prev_seed_hooks = target hidden states at position pos-1 from previous
+/// round's verify):
+///
+/// 1. EAGLE γ recursive drafts using step_with_aux + step_recursive.
+/// 2. Verify: target forward on [prev_token, d0..d_{γ-2}] to get γ logits.
+///    verify_argmax[i] = target's answer at position pos+i. Longest matching
+///    prefix accepts d[i] iff d[i] == verify_argmax[i].
+/// 3. Correction: verify_argmax[accepted] IS target's correction for the
+///    first rejected position (or verify_argmax[γ-1] if all γ accepted).
+///    Commit accepted tokens + correction.
+/// 4. Seed next round with hooks at the position of the last committed token.
+///    Take hooks from verify_hooks at index `accepted` (0-indexed).
+#[allow(clippy::too_many_arguments)]
+fn run_eagle_gamma_multi(
+    target: &Qwen3,
+    eagle: &mut Eagle3Head,
+    embed_tokens: &Tensor,
+    cache: &mut PagedKVCache,
+    tokenizer: &Tokenizer,
+    prompt_ids: &[u32],
+    gen_tokens: usize,
+    gamma: usize,
+) -> RunStats {
+    use xserv_model::eagle3::EAGLE_HOOK_LAYERS;
+    let slot = 0;
+    cache.register_sequence(slot).unwrap();
+    eagle.reset();
+    let t0 = Instant::now();
+
+    // Prefill. Advance one target decode step to seed hidden hooks.
+    let prefill_logits = target.forward_prefill_paged(prompt_ids, slot, cache);
+    let first_token = last_argmax(&prefill_logits);
+    let mut generated = vec![first_token];
+
+    // Seed: run one target decode with first_token to get hooks at that position.
+    let (logits, mut seed_hooks) = target_decode_with_hidden(target, first_token, cache, slot);
+    let mut prev_token = last_argmax(&logits);
+    generated.push(prev_token);
+    let mut target_steps = 2; // 1 prefill + 1 decode seed
+    let mut accepted_total = 0usize;
+    let mut proposed_total = 0usize;
+
+    while generated.len() < gen_tokens && !tokenizer.is_eos(prev_token) {
+        let round_pos = cache.seq_len(slot); // position of next token to predict
+        let remaining = gen_tokens - generated.len();
+        let round_gamma = gamma.min(remaining);
+
+        // Draft γ tokens recursively.
+        let mut drafts: Vec<u32> = Vec::with_capacity(round_gamma);
+        let (d0, _, aux0) = eagle.step_with_aux(&seed_hooks, embed_tokens, prev_token, round_pos);
+        drafts.push(d0);
+        let mut prev_aux = aux0;
+        let mut prev_draft = d0;
+        for k in 1..round_gamma {
+            let (dk, _, auxk) =
+                eagle.step_recursive(prev_aux, embed_tokens, prev_draft, round_pos + k);
+            drafts.push(dk);
+            prev_aux = auxk;
+            prev_draft = dk;
+        }
+        proposed_total += round_gamma;
+
+        // Verify: target forward on [prev_token, d0..d_{γ-1}] (length γ+1) to
+        // get γ+1 logits. verify_argmax[i] predicts position round_pos+i.
+        // Accept d[i] iff d[i] == verify_argmax[i]. The extra verify_argmax[γ]
+        // provides a free correction for the all-accepted case.
+        let mut verify_input: Vec<u32> = Vec::with_capacity(round_gamma + 1);
+        verify_input.push(prev_token);
+        for &d in drafts.iter() {
+            verify_input.push(d);
+        }
+        // Roll back the seed decode (we wrote prev_token at round_pos-1).
+        cache
+            .truncate_sequence(slot, round_pos - 1)
+            .expect("truncate before verify");
+        let (verify_logits, _verify_hooks) = target
+            .forward_verify_paged_decode_attention_with_hidden(
+                &verify_input,
+                slot,
+                cache,
+                &EAGLE_HOOK_LAYERS,
+            );
+        target_steps += 1;
+
+        // Longest matching prefix: d[i] accepted iff d[i] == argmax(verify_logits[i]).
+        let verify_argmax = argmax_rows(&verify_logits);
+        let mut accepted = 0usize;
+        while accepted < round_gamma && drafts[accepted] == verify_argmax[accepted] {
+            accepted += 1;
+        }
+        accepted_total += accepted;
+
+        // Commit accepted tokens (already in cache at positions round_pos..round_pos+accepted-1).
+        // Truncate cache to that length.
+        cache
+            .truncate_sequence(slot, round_pos + accepted)
+            .expect("truncate to accepted");
+        for i in 0..accepted {
+            generated.push(drafts[i]);
+            if generated.len() >= gen_tokens || tokenizer.is_eos(drafts[i]) {
+                break;
+            }
+        }
+        if generated.len() >= gen_tokens {
+            break;
+        }
+        if !generated.is_empty() && tokenizer.is_eos(*generated.last().unwrap()) {
+            break;
+        }
+
+        // Correction: verify_argmax[accepted] is target's answer for position
+        // round_pos+accepted. Write its K/V via one target decode. This also
+        // gives us seed_hooks for the next round.
+        let correction = verify_argmax[accepted];
+        let (_, new_hooks) = target_decode_with_hidden(target, correction, cache, slot);
+        target_steps += 1;
+        generated.push(correction);
+        prev_token = correction;
+        // Extract the position-`accepted` slice of verify_hooks as the seed for next round?
+        // Actually the correction just wrote K/V AT position round_pos+accepted, and
+        // its hidden state = new_hooks. We use new_hooks as the seed for the next round.
+        seed_hooks = new_hooks;
+    }
+    sync_device();
+    let total_s = t0.elapsed().as_secs_f64();
+    cache.free_sequence(slot);
+    RunStats {
+        ids: generated,
+        total_s,
+        target_steps,
+        accepted: accepted_total,
+        proposed: proposed_total,
+    }
+}
+
+fn argmax_rows(logits: &Tensor) -> Vec<u32> {
+    xserv_kernels::argmax_bf16_to_host(logits)
+}
+
 fn target_decode_with_hidden(
     target: &Qwen3,
     token: u32,
@@ -381,5 +539,5 @@ fn arg_usize(args: &[String], flag: &str, default: usize) -> usize {
 
 fn new_cache(config: &ModelConfig, max_seq_len: usize, device: u32) -> PagedKVCache {
     let num_blocks = (max_seq_len + BLOCK_SIZE - 1) / BLOCK_SIZE + 2;
-    PagedKVCache::new(config, num_blocks, 0, 1, num_blocks, DType::BF16, device)
+    PagedKVCache::new(config, num_blocks, 0, 16, num_blocks, DType::BF16, device)
 }
diff --git a/crates/xserv-model/src/eagle3.rs b/crates/xserv-model/src/eagle3.rs
index e00e57a..0addc71 100644
--- a/crates/xserv-model/src/eagle3.rs
+++ b/crates/xserv-model/src/eagle3.rs
@@ -165,6 +165,44 @@ impl Eagle3Head {
         prev_token: u32,
         position: usize,
     ) -> (u32, Tensor) {
+        let (id, logits, _) = self.step_with_aux(target_hidden, embed_table, prev_token, position);
+        (id, logits)
+    }
+
+    /// Like `step`, but also returns the final hidden state (aux) usable as
+    /// the fused_h for a subsequent recursive draft step via `step_recursive`.
+    pub fn step_with_aux(
+        &mut self,
+        target_hidden: &[Tensor; 3],
+        embed_table: &Tensor,
+        prev_token: u32,
+        position: usize,
+    ) -> (u32, Tensor, Tensor) {
+        // Fuse 3 target hidden states into fused_h via fc.
+        let h_cat = concat_hidden(target_hidden);
+        let fused_h = matmul_2d(&h_cat, &self.fc_wt);
+        self.forward_from_fused(fused_h, embed_table, prev_token, position)
+    }
+
+    /// Recursive draft step: reuses the previous EAGLE step's aux as fused_h,
+    /// bypassing the fc+3-hidden fusion. Used for γ≥2 chained drafts.
+    pub fn step_recursive(
+        &mut self,
+        fused_h: Tensor,
+        embed_table: &Tensor,
+        prev_token: u32,
+        position: usize,
+    ) -> (u32, Tensor, Tensor) {
+        self.forward_from_fused(fused_h, embed_table, prev_token, position)
+    }
+
+    fn forward_from_fused(
+        &mut self,
+        fused_h: Tensor,
+        embed_table: &Tensor,
+        prev_token: u32,
+        position: usize,
+    ) -> (u32, Tensor, Tensor) {
         let eps = 1e-6f32;
         assert!(
             self.current_len < self.max_seq_len,
@@ -173,20 +211,12 @@ impl Eagle3Head {
             self.max_seq_len
         );
 
-        // 1. Fuse target hidden states: concat [h_low, h_mid, h_high] → fc
-        let h_cat = concat_hidden(target_hidden);
-        let fused_h = matmul_2d(&h_cat, &self.fc_wt); // [1, hidden]
-
-        // 2. Embed previous token (shared with target)
-        let emb = embedding(embed_table, &[prev_token]); // [1, hidden]
-
-        // 3. Norm both, concat, remember residual = fused_h (pre-norm).
+        let emb = embedding(embed_table, &[prev_token]);
         let residual = fused_h.clone();
         let emb_normed = rmsnorm(&emb, &self.input_layernorm, eps);
         let h_normed = rmsnorm(&fused_h, &self.hidden_norm, eps);
-        let attn_in = concat_last_dim(&emb_normed, &h_normed); // [1, 2*hidden]
+        let attn_in = concat_last_dim(&emb_normed, &h_normed);
 
-        // 4. Q/K/V projection then RoPE (position from caller).
         let q = matmul_2d(&attn_in, &self.q_proj_wt);
         let k = matmul_2d(&attn_in, &self.k_proj_wt);
         let v = matmul_2d(&attn_in, &self.v_proj_wt);
@@ -197,8 +227,6 @@ impl Eagle3Head {
         rope_inplace(&q_3d, &self.rope_cache, &positions);
         rope_inplace(&k_3d, &self.rope_cache, &positions);
 
-        // 5. Append new K/V to the internal cache at slot `current_len`, then
-        // build a contiguous view [1, num_kv_heads, current_len+1, head_dim].
         let v_3d = v.reshape(&[1, self.num_kv_heads, self.head_dim]);
         self.append_to_kv_cache(&k_3d, &v_3d);
         self.current_len += 1;
@@ -206,31 +234,26 @@ impl Eagle3Head {
         let k_view = self.k_cache.narrow(2, 0, kv_len).contiguous();
         let v_view = self.v_cache.narrow(2, 0, kv_len).contiguous();
 
-        // 6. Attention: q [1, num_q_heads, 1, head_dim] × k/v [1, num_kv_heads, kv_len, head_dim]
         let q_4d = q_3d.reshape(&[1, self.num_heads, 1, self.head_dim]);
         let attn_out = decode_attention(&q_4d, &k_view, &v_view);
 
-        // 7. Merge heads and o_proj.
         let attn_merged = attn_out.reshape(&[1, self.num_heads * self.head_dim]);
         let attn_proj = matmul_2d(&attn_merged, &self.o_proj_wt);
 
-        // 8. Post-attn fused add_rmsnorm.
         let (mlp_in, residual) =
             add_rmsnorm(&attn_proj, &residual, &self.post_attention_layernorm, eps);
 
-        // 9. MLP.
         let gate = matmul_2d(&mlp_in, &self.gate_proj_wt);
         let up = matmul_2d(&mlp_in, &self.up_proj_wt);
         let hidden = silu_mul(&gate, &up);
         let down = matmul_2d(&hidden, &self.down_proj_wt);
 
-        // 10. Final fused add_rmsnorm → lm_head.
         let (x, _) = add_rmsnorm(&down, &residual, &self.norm, eps);
         let logits = matmul_2d(&x, &self.lm_head_wt);
 
         let draft_id = argmax_bf16_single(&logits);
         let target_id = (draft_id as i64 + self.d2t[draft_id as usize]) as u32;
-        (target_id, logits)
+        (target_id, logits, x)
     }
 
     /// Write new K/V rows (shape [1, num_kv_heads, head_dim]) at position
diff --git a/crates/xserv-model/src/qwen3.rs b/crates/xserv-model/src/qwen3.rs
index 1806cb0..4707801 100644
--- a/crates/xserv-model/src/qwen3.rs
+++ b/crates/xserv-model/src/qwen3.rs
@@ -1113,6 +1113,123 @@ impl Qwen3 {
         matmul_batched_gemv(&x, &self.lm_head_t)
     }
 
+    /// Like `forward_verify_paged_decode_attention`, but also captures hidden
+    /// states at 3 layer indices (per position). Returns
+    /// (logits [new_tokens, vocab], hooks [3][new_tokens, hidden]). Used by
+    /// EAGLE3 speculative γ≥2 verify path so we can seed the next round's
+    /// EAGLE draft with target's real hidden states at the accepted position.
+    pub fn forward_verify_paged_decode_attention_with_hidden(
+        &self,
+        token_ids: &[u32],
+        slot: usize,
+        paged_cache: &mut PagedKVCache,
+        hook_layers: &[usize; 3],
+    ) -> (Tensor, [Tensor; 3]) {
+        let new_tokens = token_ids.len();
+        let pos_offset = paged_cache.seq_len(slot);
+        let num_heads = self.local_num_heads;
+        let num_kv_heads = self.local_num_kv_heads;
+        let head_dim = self.config.head_dim();
+        let eps = self.config.rms_norm_eps.unwrap_or(1e-6) as f32;
+
+        paged_cache.ensure_capacity(slot, pos_offset + new_tokens);
+        paged_cache.advance_seq_len(slot, new_tokens);
+
+        let positions: Vec<u32> = (pos_offset..pos_offset + new_tokens)
+            .map(|p| p as u32)
+            .collect();
+        let kv_lens: Vec<i32> = (0..new_tokens)
+            .map(|i| (pos_offset + i + 1) as i32)
+            .collect();
+        let slots = vec![slot; new_tokens];
+        paged_cache.sync_active_batch_with_lens(&slots, &kv_lens);
+        let bt_ptr = paged_cache.block_table_gpu().as_ptr() as *const i32;
+        let cl_ptr = paged_cache.context_lens_gpu().as_ptr() as *const i32;
+        let max_blocks = paged_cache.max_blocks_per_seq();
+
+        let mut x = embedding(&self.embed_tokens, token_ids);
+        let mut hooks: [Option<Tensor>; 3] = [None, None, None];
+
+        for (layer_idx, layer) in self.layers.iter().enumerate() {
+            let residual = x.clone();
+            let normed = rmsnorm(&x, &layer.input_norm, eps);
+
+            let qkv = matmul_batched_gemv(&normed, &layer.qkv_proj_wt);
+            let q_dim = num_heads * head_dim;
+            let kv_dim = num_kv_heads * head_dim;
+            let q_all = qkv.narrow(1, 0, q_dim);
+            let k_all = qkv.narrow(1, q_dim, kv_dim);
+            let v_all = qkv.narrow(1, q_dim + kv_dim, kv_dim);
+
+            let q_flat = q_all
+                .contiguous()
+                .reshape(&[new_tokens * num_heads, head_dim]);
+            let k_flat = k_all
+                .contiguous()
+                .reshape(&[new_tokens * num_kv_heads, head_dim]);
+            let q_normed = rmsnorm(&q_flat, &layer.q_norm, eps);
+            let k_normed = rmsnorm(&k_flat, &layer.k_norm, eps);
+
+            let q_3d = q_normed.reshape(&[new_tokens, num_heads, head_dim]);
+            let k_3d = k_normed.reshape(&[new_tokens, num_kv_heads, head_dim]);
+            rope_inplace(&q_3d, &self.rope_cache, &positions);
+            rope_inplace(&k_3d, &self.rope_cache, &positions);
+
+            let v_3d = v_all
+                .contiguous()
+                .reshape(&[new_tokens, num_kv_heads, head_dim]);
+            paged_cache.append_tokens_batched(layer_idx, &k_3d, &v_3d, new_tokens);
+
+            let q_decode = q_3d.reshape(&[new_tokens, num_heads, 1, head_dim]);
+            let k_pool_ptr = paged_cache.k_pool(layer_idx).as_ptr() as *const std::ffi::c_void;
+            let v_pool_ptr = paged_cache.v_pool(layer_idx).as_ptr() as *const std::ffi::c_void;
+            let attn_out = xserv_kernels::paged_decode_attention(
+                &q_decode,
+                k_pool_ptr,
+                v_pool_ptr,
+                bt_ptr,
+                cl_ptr,
+                new_tokens,
+                num_heads,
+                num_kv_heads,
+                head_dim,
+                max_blocks,
+            );
+
+            let attn_merged = attn_out.reshape(&[new_tokens, num_heads * head_dim]);
+            let attn_proj = matmul_batched_gemv(&attn_merged, &layer.o_proj_wt);
+            self.all_reduce(&attn_proj);
+
+            let (normed, x_new) =
+                xserv_kernels::add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
+            let residual = x_new.clone();
+
+            let gate_up = matmul_batched_gemv(&normed, &layer.gate_up_proj_wt);
+            let ffn_dim = gate_up.shape()[1] / 2;
+            let gate = gate_up.narrow(1, 0, ffn_dim).contiguous();
+            let up = gate_up.narrow(1, ffn_dim, ffn_dim).contiguous();
+            let hidden_states = xserv_kernels::silu_mul(&gate, &up);
+            let down = matmul_batched_gemv(&hidden_states, &layer.down_proj_wt);
+            self.all_reduce(&down);
+            x = add_any(&residual, &down);
+
+            for (h_idx, &h_layer) in hook_layers.iter().enumerate() {
+                if layer_idx == h_layer {
+                    hooks[h_idx] = Some(x.clone());
+                }
+            }
+        }
+
+        let x = rmsnorm(&x, &self.norm, eps);
+        let logits = matmul_batched_gemv(&x, &self.lm_head_t);
+        let hidden_arr = [
+            hooks[0].take().expect("hook layer 0 not reached"),
+            hooks[1].take().expect("hook layer 1 not reached"),
+            hooks[2].take().expect("hook layer 2 not reached"),
+        ];
+        (logits, hidden_arr)
+    }
+
     /// Forward with GPU-resident KV cache and GPU transpose/reshape kernels.
     pub fn forward_gpu_cache(&self, token_ids: &[u32], cache: &mut GpuKVCache) -> Tensor {
         let new_tokens = token_ids.len();