diff --git a/tests/analytical_visualization/app.py b/tests/analytical_visualization/app.py
index 7146600..9ab82df 100644
--- a/tests/analytical_visualization/app.py
+++ b/tests/analytical_visualization/app.py
@@ -432,6 +432,85 @@ tab_auto, tab_layout, tab_memory, tab_stages, tab_compare, tab_hw = st.tabs([
 
 # ── TAB 1: Physical layout ──────────────────────────────────────
 with tab_layout:
+    # Quick shortcut: three buttons that pick the latency-optimal Pareto
+    # config for a chosen scope and load it into the sidebar. Layout
+    # re-renders immediately on the next Streamlit run with the new config.
+    from tests.analytical_visualization.auto_explore import (
+        run_auto_explore as _run_auto_explore_layout,
+    )
+
+    st.markdown(
+        "**Quick auto-suggest layout** — pick a scope; the latency-optimal "
+        "Pareto config for that scope is loaded into the sidebar and this "
+        "layout redraws around it."
+    )
+    _pl1, _pl2, _pl3, _pl4 = st.columns([1, 1, 1, 1])
+    with _pl1:
+        _pl_run_attn = st.button("Run sweep — Attention",
+                                   type="primary", width='stretch',
+                                   key="_pl_run_attn")
+    with _pl2:
+        _pl_run_ffn = st.button("Run sweep — FFN/MoE",
+                                  type="primary", width='stretch',
+                                  key="_pl_run_ffn")
+    with _pl3:
+        _pl_run_full = st.button("Run sweep — Attn + FFN/MoE",
+                                   type="primary", width='stretch',
+                                   key="_pl_run_full")
+
+    _pl_scope_meta = {
+        "attn": (True,  False, "Attention only"),
+        "ffn":  (False, True,  "FFN / MoE only"),
+        "full": (True,  True,  "Attn + FFN/MoE"),
+    }
+    _pl_button_click = {
+        "attn": _pl_run_attn, "ffn": _pl_run_ffn, "full": _pl_run_full,
+    }
+    for _pl_scope, _pl_clicked in _pl_button_click.items():
+        if not _pl_clicked:
+            continue
+        _pl_ia, _pl_ff, _pl_lbl = _pl_scope_meta[_pl_scope]
+        with st.spinner(f"Finding latency-optimal parallelism ({_pl_lbl})..."):
+            _pl_res = _run_auto_explore_layout(
+                model, machine, s_kv=s_kv, mode=mode,
+                include_attention=_pl_ia, include_ffn=_pl_ff,
+            )
+        if not _pl_res.pareto_scores:
+            st.error(
+                f"No feasible parallelism for {_pl_lbl} — every config "
+                "exceeds per-PE HBM. Try raising `pe_hbm_gb` or reducing "
+                "`s_kv`."
+            )
+        else:
+            _pl_best = min(_pl_res.pareto_scores,
+                            key=lambda s: s.total_latency_ns)
+            # Load the winning config into the sidebar's session state.
+            st.session_state["cp"] = _pl_best.cp
+            st.session_state["tp"] = _pl_best.tp
+            st.session_state["pp"] = _pl_best.pp
+            st.session_state["dp"] = _pl_best.dp
+            st.session_state["tp_placement"] = _pl_best.tp_placement
+            st.session_state["cp_placement"] = _pl_best.cp_placement
+            st.session_state["cp_ring_variant"] = _pl_best.cp_ring_variant
+            st.session_state["kv_mode"] = _pl_best.kv_shard_mode
+            _pl_tp, _pl_cp, _pl_dp = _pl_best.tp, _pl_best.cp, _pl_best.dp
+            _pl_ffn_map = {
+                "TP":       f"TP only (div={max(1, _pl_tp)})",
+                "TP+CP":    f"TP*CP (div={max(1, _pl_tp * _pl_cp)})",
+                "TP+CP+DP": f"TP*CP*DP (div={max(1, _pl_tp * _pl_cp * _pl_dp)})",
+            }
+            st.session_state["ffn_scope_label"] = \
+                _pl_ffn_map[_pl_best.ffn_shard_scope]
+            st.success(
+                f"Loaded latency-optimal {_pl_lbl} config: CP={_pl_best.cp} "
+                f"TP={_pl_best.tp} PP={_pl_best.pp} DP={_pl_best.dp} → "
+                f"{_pl_best.latency_ms:.2f} ms, {_pl_best.pes_used} PEs. "
+                "Redrawing layout..."
+            )
+            st.rerun()
+
+    st.divider()
+
     # Row A: pipeline diagram (color-coded) + per-stage table (formulas).
     st.markdown("**Per-layer pipeline (color = dominant bound)**")
     fig_pipe = draw_pipeline(cfg)