diff --git a/inference_engine/bridge/manifest.py b/inference_engine/bridge/manifest.py
index a93ef592..60df4479 100644
--- a/inference_engine/bridge/manifest.py
+++ b/inference_engine/bridge/manifest.py
@@ -29,7 +29,7 @@
 # bridge is for evidence runs and debugging, not for monopolizing the
 # single Mac with open-ended workloads.
 MAX_N_SAMPLES = 50
-MAX_NEW_TOKENS = 512
+MAX_NEW_TOKENS = 2048  # backstop for chat; natural EOS stops well before this
 MAX_BLOCK_SIZE = 16
 
 _ENV_PLACEHOLDER = re.compile(r"^\$\{ENV:([A-Z][A-Z0-9_]*)\}$")
@@ -650,6 +650,34 @@ def _harness_preset(
             },
             validate_reports=False,
         ),
+        Preset(
+            name="mlx-kakeya-chat-smoke",
+            description="Run gemma-4 on the Kakeya-for-Mac (MLX) engine via the "
+                        "interactive chat CLI in NON-interactive --scripted mode: "
+                        "single-stream generation over the Kakeya S5 bounded "
+                        "sink+window cache (sliding layers bounded; full-attn "
+                        "layers full). Writes a transcript JSON so we can verify "
+                        "gemma-4 responds coherently on the engine; the operator "
+                        "runs the same script without --scripted for a real "
+                        "interactive REPL on the Mac.",
+            command_templates=(
+                (
+                    "python3", "scripts/chat_mlx_kakeya.py",
+                    "--verifier-path", "${ENV:KAKEYA_MAC_VERIFIER_PATH}",
+                    "--sink", "4", "--window", "64",
+                    "--max-new-tokens", "{max_new_tokens}",
+                    "--scripted",
+                    "What is the capital of France? Answer in one short sentence."
+                    "||Explain how proof-of-work works, step by step."
+                    "||Name three primary colors.",
+                    "--output",
+                    "results/research/k3_mac_bridge_mlx_kakeya_chat.json",
+                ),
+            ),
+            timeout_minutes=45,
+            params={"max_new_tokens": ("int:max_new_tokens", "64")},
+            validate_reports=False,
+        ),
     )
 }
 
diff --git a/scripts/chat_mlx_kakeya.py b/scripts/chat_mlx_kakeya.py
new file mode 100644
index 00000000..e7adb1a8
--- /dev/null
+++ b/scripts/chat_mlx_kakeya.py
@@ -0,0 +1,276 @@
+#!/usr/bin/env python3
+"""Interactive CLI chat with gemma-4 on the Kakeya-for-Mac engine (MLX).
+
+Runs the gemma-4 MLX verifier with **Kakeya Attention's bounded sink+window KV
+cache (S5)**: the model's sliding-attention layers keep only ``sink + window``
+tokens resident, while gemma-4's native full-attention layers keep full context
+(the "S5 free lunch" — recall is carried by the full layers, so no f_θ/proposer
+restoration is needed on gemma-4). This is single-stream (B=1) generation, which
+sidesteps the MLX ``B>1, L=1`` batched-decode kernel bug entirely.
+
+Usage (on the Mac, in the repo checkout):
+
+    # interactive REPL — type a message, get gemma-4's reply, blank line/Ctrl-D quits
+    PYTHONPATH=. python3 scripts/chat_mlx_kakeya.py \
+        --verifier-path /Users/fluffy314/kakeya-models/gemma-4-26B-A4B-it-mlx-4bit
+
+    # non-interactive smoke (used by the Mac-bridge preset): fixed turns -> JSON transcript
+    PYTHONPATH=. python3 scripts/chat_mlx_kakeya.py --verifier-path <dir> \
+        --scripted "What is the capital of France?||Now multiply 6 by 7." \
+        --output results/research/mac_gemma4_kakeya_chat.json
+
+mlx_lm / mlx are imported lazily inside ``main`` so ``--help`` works off-Mac.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+
+def _log(msg: str) -> None:
+    print(f"[kakeya-chat] {msg}", file=sys.stderr, flush=True)
+
+
+def _resolve_eos(tok) -> set:
+    """All token ids that end gemma's turn — so generation stops NATURALLY (the
+    user must never tune output length). gemma ends a turn on ``<end_of_turn>``
+    (and ``<eos>``); the previous code used ``encode()`` + a single-token check
+    which silently DROPPED ``<end_of_turn>`` (it's a special token), so the model
+    ran past its turn (verbose echoes) until the max-tokens cap — the real bug."""
+    eos: set = set()
+    # mlx_lm's TokenizerWrapper exposes the authoritative stop set when present.
+    ids = getattr(tok, "eos_token_ids", None)
+    if ids:
+        eos |= {int(i) for i in ids}
+    if getattr(tok, "eos_token_id", None) is not None:
+        eos.add(int(tok.eos_token_id))
+    # convert_tokens_to_ids is the reliable lookup for a KNOWN special token
+    # (unlike encode(), which may split it or add specials).
+    unk = getattr(tok, "unk_token_id", None)
+    for marker in ("<end_of_turn>", "<eos>"):
+        try:
+            tid = tok.convert_tokens_to_ids(marker)
+        except Exception:
+            tid = None
+        if isinstance(tid, int) and tid >= 0 and tid != unk:
+            eos.add(int(tid))
+    return eos
+
+
+def _is_degenerate_loop(s: str, unit: int = 16) -> bool:
+    """True only on a TRUE consecutive loop: the same ``unit``-char block
+    repeated 3x back-to-back at the tail. (Deliberately strict so an answer that
+    merely echoes itself once — e.g. text + a json wrapper — is NOT cut.)"""
+    if len(s) < unit * 3:
+        return False
+    a, b, c = s[-unit:], s[-2 * unit:-unit], s[-3 * unit:-2 * unit]
+    return a == b == c and a.strip() != ""
+
+
+def _apply_template(tok, history, *, thinking: bool) -> List[int]:
+    """Encode the chat history. gemma-4 has a reasoning ("thought") channel; the
+    clean way to get direct answers is the template's ``enable_thinking`` flag
+    (NOT injecting a raw channel marker, which leaks 'thought' text and loops).
+    Falls back gracefully if the template doesn't accept the kwarg."""
+    try:
+        ids = tok.apply_chat_template(
+            history, add_generation_prompt=True, enable_thinking=thinking)
+    except TypeError:
+        ids = tok.apply_chat_template(history, add_generation_prompt=True)
+    return ids.tolist() if hasattr(ids, "tolist") else list(ids)
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="gemma-4 chat on the Kakeya-for-Mac (MLX) engine")
+    ap.add_argument("--verifier-path", required=True,
+                    help="Local MLX gemma-4 model dir.")
+    ap.add_argument("--sink", type=int, default=4, help="Kakeya sink tokens.")
+    ap.add_argument("--window", type=int, default=64,
+                    help="Kakeya sliding-window tokens (S5; sliding layers).")
+    ap.add_argument("--full-window", type=int, default=8192,
+                    help="Resident window for the full-attention (exact) layers "
+                         "— large = effectively full context (S5 recall carrier).")
+    ap.add_argument("--max-new-tokens", type=int, default=2048,
+                    help="Backstop cap ONLY — generation stops naturally at gemma's "
+                         "<end_of_turn>, so you should not need to tune this.")
+    ap.add_argument("--repetition-penalty", type=float, default=1.3,
+                    help="Penalize repeated tokens to stop greedy loops (1.0 = off).")
+    ap.add_argument("--thinking", action="store_true",
+                    help="Allow gemma-4's reasoning channel (default: direct answers).")
+    ap.add_argument("--system", default=None, help="Optional system prompt.")
+    ap.add_argument("--scripted", default=None,
+                    help="Non-interactive: '||'-separated user turns; writes a transcript.")
+    ap.add_argument("--output", default=None, help="Transcript JSON (scripted mode).")
+    args = ap.parse_args()
+
+    import mlx.core as mx  # type: ignore
+    import mlx_lm  # type: ignore
+    from mlx_lm.generate import generate_step  # type: ignore
+    from inference_engine.backends.mlx.cache import (
+        SinkWindowKVCache, total_kv_bytes, cache_seq_length,
+    )
+    from inference_engine.backends.mlx.cross_model_dlm_verifier import (
+        resolve_mlx_text_model, mlx_full_attention_layer_indices,
+    )
+
+    _log(f"loading MLX model: {args.verifier_path}")
+    t_load = time.time()
+    model, tok = mlx_lm.load(args.verifier_path)
+    text_model = resolve_mlx_text_model(model)
+    n_layers = len(text_model.layers)
+    full_idx = set(mlx_full_attention_layer_indices(text_model))
+    eos = _resolve_eos(tok)
+    _log(f"loaded in {time.time()-t_load:.1f}s | layers={n_layers} "
+         f"exact(full-attn)={sorted(full_idx)} sink={args.sink} window={args.window} "
+         f"eos={sorted(eos)}")
+    _log("Kakeya Attention: sliding layers bounded to sink+window; "
+         "exact layers keep full context (S5).")
+
+    logits_processors = None
+    if args.repetition_penalty and args.repetition_penalty != 1.0:
+        try:
+            from mlx_lm.sample_utils import make_logits_processors  # type: ignore
+            logits_processors = make_logits_processors(
+                repetition_penalty=args.repetition_penalty)
+            _log(f"repetition_penalty={args.repetition_penalty} enabled")
+        except Exception as exc:  # noqa: BLE001
+            _log(f"repetition penalty unavailable ({exc}); greedy")
+
+    def new_cache() -> list:
+        # S5 hybrid: exact (full-attn) layers get a large window (≈full context,
+        # the recall carrier); sliding layers get the tight Kakeya window.
+        return [
+            SinkWindowKVCache(
+                sink_size=args.sink,
+                window_size=(args.full_window if li in full_idx else args.window),
+            )
+            for li in range(n_layers)
+        ]
+
+    def build_prompt_ids(history: List[Dict[str, str]]) -> List[int]:
+        return _apply_template(tok, history, thinking=args.thinking)
+
+    def generate_turn(prompt_ids: List[int], on_delta=None) -> Dict[str, Any]:
+        """Single-stream greedy decode over a FRESH Kakeya bounded cache."""
+        cache = new_cache()
+        toks: List[int] = []
+        shown = ""
+        t0 = time.time()
+        gkw: Dict[str, Any] = dict(prompt_cache=cache, max_tokens=args.max_new_tokens)
+        if logits_processors is not None:
+            gkw["logits_processors"] = logits_processors
+        try:
+            stream = generate_step(mx.array(prompt_ids), model, **gkw)
+            first = next(stream)
+        except TypeError:  # older mlx_lm without logits_processors kwarg
+            gkw.pop("logits_processors", None)
+            stream = generate_step(mx.array(prompt_ids), model, **gkw)
+            first = next(stream)
+
+        def _iter():
+            yield first
+            yield from stream
+
+        stop_reason = "max"  # generator exhausts at max_tokens unless we break
+        for tok_id, _ in _iter():
+            t = int(tok_id)
+            if t in eos:
+                stop_reason = "eos"
+                break
+            toks.append(t)
+            full = tok.decode(toks, skip_special_tokens=True)
+            delta = full[len(shown):]
+            if delta and on_delta is not None:
+                on_delta(delta)
+            shown = full
+            if _is_degenerate_loop(full):  # true back-to-back repeat → stop
+                stop_reason = "loop"
+                break
+        dt = max(time.time() - t0, 1e-9)
+        return {
+            "text": tok.decode(toks, skip_special_tokens=True),
+            "n_tokens": len(toks),
+            "stop_reason": stop_reason,
+            "decode_tps": round(len(toks) / dt, 2),
+            "resident_kv_bytes": int(total_kv_bytes(cache)),
+            "resident_kv_seq_len_first_layer": int(cache_seq_length(cache)),
+            "prompt_tokens": len(prompt_ids),
+        }
+
+    history: List[Dict[str, str]] = []
+    if args.system:
+        history.append({"role": "system", "content": args.system})
+
+    # ---- scripted (non-interactive) mode: for Mac-bridge verification ----
+    if args.scripted is not None:
+        turns = [t for t in args.scripted.split("||") if t.strip()]
+        transcript: List[Dict[str, Any]] = []
+        for user in turns:
+            history.append({"role": "user", "content": user})
+            info = generate_turn(build_prompt_ids(history))
+            history.append({"role": "assistant", "content": info["text"]})
+            transcript.append({"user": user, **info})
+            _log(f"USER: {user!r}")
+            _log(f"GEMMA-4: {info['text'][:160]!r}  "
+                 f"({info['n_tokens']} tok, stop={info['stop_reason']}, "
+                 f"{info['decode_tps']} tok/s, resident_kv={info['resident_kv_bytes']/1e6:.1f}MB)")
+        report = {
+            "kind": "mac_gemma4_kakeya_chat", "schema_version": 1,
+            "model_path": args.verifier_path,
+            "engine": "Kakeya-for-Mac (MLX, S5 bounded sink+window, single-stream)",
+            "sink": args.sink, "window": args.window, "full_window": args.full_window,
+            "exact_layers": sorted(full_idx), "n_layers": n_layers,
+            "eos_token_ids": sorted(eos),
+            "max_new_tokens_cap": args.max_new_tokens,
+            "turns": transcript,
+        }
+        if args.output:
+            outp = Path(args.output)
+            outp.parent.mkdir(parents=True, exist_ok=True)
+            outp.write_text(json.dumps(report, indent=2), encoding="utf-8")
+            _log(f"wrote transcript -> {outp}")
+        else:
+            print(json.dumps(report, indent=2))
+        return 0
+
+    # ---- interactive REPL ----
+    _log("ready. Type a message and press Enter. Blank line or Ctrl-D to quit.")
+    while True:
+        try:
+            if sys.stdin.isatty():
+                sys.stderr.write("\nyou> ")
+                sys.stderr.flush()
+            line = sys.stdin.readline()
+        except KeyboardInterrupt:
+            _log("interrupted")
+            break
+        if not line:
+            break
+        user = line.strip()
+        if not user:
+            break
+        history.append({"role": "user", "content": user})
+        sys.stderr.write("gemma-4> ")
+        sys.stderr.flush()
+        info = generate_turn(
+            build_prompt_ids(history),
+            on_delta=lambda d: (sys.stdout.write(d), sys.stdout.flush()),
+        )
+        sys.stdout.write("\n")
+        sys.stdout.flush()
+        history.append({"role": "assistant", "content": info["text"]})
+        warn = ("  [WARN: hit --max-new-tokens; raise it for longer answers]"
+                if info["stop_reason"] == "max" else f"  [stopped: {info['stop_reason']}]")
+        _log(f"{info['n_tokens']} tok, {info['decode_tps']} tok/s, "
+             f"resident bounded-KV {info['resident_kv_bytes']/1e6:.1f} MB "
+             f"(sliding capped at sink+window={args.sink}+{args.window}){warn}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/tests/inference_engine/bridge/test_manifest.py b/tests/inference_engine/bridge/test_manifest.py
index a287d708..40745cd7 100644
--- a/tests/inference_engine/bridge/test_manifest.py
+++ b/tests/inference_engine/bridge/test_manifest.py
@@ -80,6 +80,7 @@ def test_allowlist_contains_exactly_the_documented_presets():
         "mlx-batched-multitenant",
         "mlx-batched-pad-decode",
         "mlx-env-probe",
+        "mlx-kakeya-chat-smoke",
         "mlx-multitenant-pressure",
         "mlx-upgrade",
         "mlx-upstream-batch-probe",
@@ -135,6 +136,18 @@ def test_pad_decode_preset_carries_flag_and_forces_trimmable_cache():
     assert HARNESS_ENV["KAKEYA_MAC_VERIFIER_PATH"] in argv
 
 
+def test_mlx_kakeya_chat_smoke_preset_resolves():
+    request = parse_manifest(_manifest(
+        preset="mlx-kakeya-chat-smoke", params={"max_new_tokens": "64"}))
+    (argv,) = build_commands(request, HARNESS_ENV)
+    assert argv[1].endswith("chat_mlx_kakeya.py")
+    assert HARNESS_ENV["KAKEYA_MAC_VERIFIER_PATH"] in argv
+    assert "--scripted" in argv
+    assert argv[argv.index("--max-new-tokens") + 1] == "64"
+    assert not [t for t in argv if t.startswith("${ENV:")]
+    assert not [t for t in argv if t.startswith("{") and t.endswith("}")]
+
+
 def test_drafter_parity_preset_resolves():
     request = parse_manifest(_manifest(
         preset="k3-drafter-parity", params={"block_size": "8"}))