diff --git a/crates/onnx-genai-engine/tests/qwen35_0_8b_text_decode_lock.rs b/crates/onnx-genai-engine/tests/qwen35_0_8b_text_decode_lock.rs
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+//! Native CUDA greedy-decode byte-identity lock for **Qwen3.5-0.8B (text)** —
+//! the small-end anchor of the qwen3.5 hybrid context-scaling moat family
+//! (0.8B + 2B + 9B).
+//!
+//! Qwen3.5-0.8B is a **hybrid** decoder: 24 layers = **6 periodic
+//! full-attention `GroupQueryAttention` layers** (indices 3, 7, 11, 15, 19, 23;
+//! `head_dim = 256`, 8 q-heads / 2 kv-heads) interleaved with **18 gated
+//! linear-attention / short-conv** recurrent layers (`conv_state` +
+//! `recurrent_state` instead of dense KV). Native captures the whole hybrid
+//! graph into one CUDA graph (context-flat), while ORT-CUDA cannot place the
+//! recurrent ops on GPU and runs eager with per-step Memcpy fallbacks — the same
+//! 25-Memcpy graph-block Wallace confirmed at the arch level on 0.8B (1037 CUDA
+//! / 56 CPU nodes). This lock is the durable byte-identity gate for that moat at
+//! the smallest family scale.
+//!
+//! ## Export (persisted, self-contained, reproduces this golden)
+//!   `QWEN35_0_8B_TEXT_DIR=/home/justinchu/qwen35-0.8b-text-cuda`
+//!
+//! Foundry ships qwen3.5-0.8b as a multimodal **split** package
+//! (`embedding.onnx` + `text.onnx` + `vision.onnx` + `genai_config.json`) that
+//! the native single-model loader rejects (and that segfaults `onnxruntime-genai`
+//! on load). This export is a **standalone, text-only** single graph
+//! (`input_ids` + `attention_mask` + `position_ids` + per-layer past KV /
+//! conv+recurrent state -> `logits` + present state) composed by graph surgery:
+//! the `embedding` subgraph is **pruned to its `GatherBlockQuantized` text
+//! gather** (dropping the image-token `Equal`/`NonZero`/`ScatterND` merge, which
+//! is a no-op with no image tokens and whose dynamic `NonZero` native
+//! shape-inference cannot bound) and fused into the `text` decoder via
+//! `inputs_embeds`. Both `embedding.onnx.data` (0.13 GB) and `text.onnx.data`
+//! (0.73 GB) are real copies inside the dir, so the export is stable and
+//! self-contained. This is the identical playbook used for qwen3.5-9b (#1449)
+//! and gemma4-e2b (#1442); reproduced by
+//! `qwen35-0.8b-text-cuda/export_qwen35_0_8b_text.py`.
+//!
+//! Per-op head sizes / KV-vs-recurrent layer roles are resolved structurally by
+//! the loader from the graph's own port inventory (attribute/shape-driven, never
+//! model-name-gated; RULES.md §2). Like the qwen3.5-9b export (and unlike the
+//! composed gemma4-e2b export) this one **captures cleanly** into a single CUDA
+//! graph — so the lock runs on the default capture path.
+//!
+//! ```bash
+//! QWEN35_0_8B_TEXT_DIR=/home/justinchu/qwen35-0.8b-text-cuda \
+//! CUDA_VISIBLE_DEVICES=1 cargo test -p onnx-genai-engine \
+//!   --features native-backend,cuda --test qwen35_0_8b_text_decode_lock \
+//!   -- --ignored --test-threads=1 --nocapture
+//! ```
+#![cfg(all(feature = "native-backend", feature = "cuda"))]
+
+#[path = "common/decode_lock.rs"]
+mod decode_lock;
+
+const MODEL_DIR_ENV: &str = "QWEN35_0_8B_TEXT_DIR";
+const PROMPT: &str = "The capital of France is";
+
+// Golden = the native-CUDA (whole-graph capture) greedy stream for the composed
+// qwen3.5-0.8b text export. Byte-identical (all 16 tokens) to the independently
+// validated ORT-driven reference stream for this model — the split-package
+// `qwen35_0_8b_hybrid_text_decode_e2e` lock (ORT places the standard-attention
+// layers on its EP and CPU-falls-back the com.microsoft hybrid ops) decodes the
+// exact same `" Paris, and the capital of Germany is Berlin.\nThe capital of
+// France is"`. Every step drives all 24 hybrid layers, including the 6
+// head_dim=256 full-attention layers and the 18 linear-attn / short-conv
+// recurrent layers.
+const EXPECTED_TOKENS: &[u32] = &[
+    11751, 11, 321, 279, 6511, 314, 9564, 369, 19241, 13, 198, 760, 6511, 314, 9338, 369,
+];
+
+#[test]
+#[ignore = "requires the standalone qwen3.5-0.8b text export and a CUDA device"]
+fn qwen35_0_8b_text_native_cuda_matches_golden_greedy_sequence() -> anyhow::Result<()> {
+    decode_lock::assert_native_matches_golden(MODEL_DIR_ENV, PROMPT, EXPECTED_TOKENS)
+}
+
+