diff --git a/README.md b/README.md
index 36a8729..87ff9ae 100644
--- a/README.md
+++ b/README.md
@@ -18,6 +18,7 @@ Text mode models:
 | claude-sonnet-4-5         | 300/300   | 300/300     | 300/300   | 100.0%    | 100.0%      | 2234ms   | 3062ms   | 5438ms   |
 | gpt-4.1 †                 | 283/300   | 273/300     | 298/300   | 94.9%     | 97.8%       | 683ms    | 1052ms   | 3860ms   |
 | gemini-2.5-flash †        | 275/300   | 268/300     | 300/300   | 93.7%     | 94.4%       |  594ms   | 1349ms   | 2104ms   |
+| kimi-k2-instruct-0905 ‡   | 28/30     | 29/30       | 30/30     | 93.3%     | 93.3%       | 286ms    | 1774ms   | 2465ms   |
 | nova-2-pro-preview        | 288/300   | 278/300     | 289/300   | 92.7%     | 93.3%       |  686ms   |  750ms   | 1459ms   |
 | gpt-5-mini                | 271/300   | 272/300     | 289/300   | 92.4%     | 95.6%       | 6339ms   | 17845ms  | 27028ms  |
 | gpt-4o-mini               | 271/300   | 262/300     | 293/300   | 91.8%     | 92.2%       |  760ms   | 1322ms   | 3256ms   |
@@ -30,10 +31,12 @@ Text mode models:
 
 > - [ † ] - gpt-4.1 and gemini-2.5-flash are used for most production voice agents because they currently offer the best balance of overall intelligence and low TTFB.
 > - [ * ] - Nemotron 3 Nano running in-cluster on NVIDIA Blackwell hardware
+> - [ ‡ ] - Single-run result from `runs/aiwf_medium_context/20260217T124339_moonshotai_kimi-k2-instruct-0905_d7392630` (not yet a 10-run aggregate). Claude summary for this run: Tool Use 28/30, Instruction 29/30, KB Grounding 30/30, Turn-Taking 30/30.
 
-Each conversation in this benchmark is 30 turns. The scores above are aggregated across 10 runs for each model. **Pass Rate** means the percentage of total turns across all runs that the judge model scored as successful. Each run is also scored independently. **Median Rate** is the median individual run pass rate. Think of pass rate as the model's average performance, and the median rate as a way to measure the model's consistency. See gpt-5.2, for example, which has a pass rate of 78.0% but a median rate of 92.2%, indicating that while it performs well on average, it can have very poor runs that drag down the median conversation score.
+Each conversation in this benchmark is 30 turns. Unless marked otherwise (for example `‡`), the scores above are aggregated across 10 runs for each model. **Pass Rate** means the percentage of total turns across all runs that the judge model scored as successful. Each run is also scored independently. **Median Rate** is the median individual run pass rate. Think of pass rate as the model's average performance, and the median rate as a way to measure the model's consistency. See gpt-5.2, for example, which has a pass rate of 78.0% but a median rate of 92.2%, indicating that while it performs well on average, it can have very poor runs that drag down the median conversation score.
 
 TTFB is the number reported by the Pipecat service for each model. It is the time from the request to generate inference to the first byte of the response. An optimized speech-to-speech pipeline with typical network latencies should be able to achieve a total voice-to-voice latency of approximately LLM TTFB + 500ms. In general, a model with a TTFB above ~700ms is too slow for most voice agent use cases.
+For the same `kimi-k2-instruct-0905` run above, end-to-end per-turn latency (`latency_ms` in `transcript.jsonl`) was: median 899ms, p95 3954ms, max 5249ms.
 
 Speech-to-speech models:
 
@@ -97,6 +100,8 @@ cd multi-turn-eval
 uv sync
 ```
 
+On Intel macOS (`x86_64`), `torch`/`torchaudio` wheels for the latest versions are not published. The project skips those dependencies on that platform so `uv sync` can complete normally.
+
 ## Environment Variables
 
 Set the appropriate API keys for the services you want to use:
@@ -156,8 +161,13 @@ uv run multi-turn-eval run aiwf_medium_context --model gpt-4o --service openai -
 
 # Verbose logging
 uv run multi-turn-eval run aiwf_medium_context --model gpt-4o --service openai --verbose
+
+# Run multiple full 30-turn evaluations sequentially (e.g., 10 runs => 300 turns aggregate)
+uv run multi-turn-eval run aiwf_medium_context --model moonshotai/kimi-k2-instruct-0905 --service openrouter --runs 10
 ```
 
+Each run now also writes `native_summary.json` in the run directory (computed directly from `transcript.jsonl`, no judge required). When `--runs` is greater than 1, a single parent run directory is created with per-iteration outputs under `iterations/run_XX/`, plus `native_aggregate.json` in the parent directory.
+
 ### Judging Runs
 
 After a benchmark run completes, judge the results using Claude:
@@ -166,6 +176,13 @@ After a benchmark run completes, judge the results using Claude:
 # Judge a specific run
 uv run multi-turn-eval judge runs/aiwf_medium_context/20251213T123456_claude-sonnet-4-5
 
+# If the run was executed with --runs > 1, judge the parent run directory.
+# The CLI judges each iterations/run_XX/ folder and writes an aggregate claude_summary.json at the parent.
+uv run multi-turn-eval judge runs/aiwf_medium_context/20251213T123456_claude-sonnet-4-5
+
+# Judge only specific iterations from a parent run (and still write aggregate summary at parent)
+uv run multi-turn-eval judge runs/aiwf_medium_context/20251213T123456_claude-sonnet-4-5 --only-runs 1,3,9
+
 # Judge with specific turns
 uv run multi-turn-eval judge runs/aiwf_medium_context/20251213T123456_claude-sonnet-4-5 --only-turns 0,1,2
 
diff --git a/pyproject.toml b/pyproject.toml
index a538751..4df9334 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,8 +13,8 @@ dependencies = [
   "soundfile>=0.12.1",
   "numpy>=1.26",
   "claude-agent-sdk>=0.1.8",
-  "torch>=2.9.1",
-  "torchaudio>=2.9.1",
+  "torch>=2.9.1; sys_platform != \"darwin\" or platform_machine != \"x86_64\"",
+  "torchaudio>=2.9.1; sys_platform != \"darwin\" or platform_machine != \"x86_64\"",
 ]
 
 [project.scripts]
diff --git a/scripts/aggregate_transcript_native.py b/scripts/aggregate_transcript_native.py
new file mode 100644
index 0000000..b3357b9
--- /dev/null
+++ b/scripts/aggregate_transcript_native.py
@@ -0,0 +1,354 @@
+#!/usr/bin/env python3
+"""Aggregate native performance and latency stats from transcript.jsonl files.
+
+This script reads run directories under runs/{benchmark}/... and computes:
+- Native transcript metrics (turn count, tool call usage, token totals)
+- Latency metrics from transcript fields (ttfb_ms, latency_ms)
+- Optional judged metrics from claude_summary.json when available
+
+Usage:
+  uv run python scripts/aggregate_transcript_native.py --benchmark aiwf_medium_context
+  uv run python scripts/aggregate_transcript_native.py --model moonshotai/kimi-k2-instruct-0905
+  uv run python scripts/aggregate_transcript_native.py --pattern "runs/aiwf_medium_context/*kimi*" --jsonl-out out.jsonl
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import statistics
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Aggregate native transcript stats across benchmark runs"
+    )
+    parser.add_argument(
+        "--benchmark",
+        default="aiwf_medium_context",
+        help="Benchmark folder under runs/ (default: aiwf_medium_context)",
+    )
+    parser.add_argument(
+        "--model",
+        help="Model name filter (unsanitized, e.g. moonshotai/kimi-k2-instruct-0905)",
+    )
+    parser.add_argument(
+        "--pattern",
+        action="append",
+        default=[],
+        help="Glob pattern for run directories (can be used multiple times)",
+    )
+    parser.add_argument(
+        "--output",
+        default="runs/transcript_native_summary.json",
+        help="Output JSON file path",
+    )
+    parser.add_argument(
+        "--jsonl-out",
+        help="Optional output JSONL path with one line per run summary",
+    )
+    parser.add_argument(
+        "--include-judge",
+        action="store_true",
+        help="Include claude_summary.json fields when present",
+    )
+    return parser.parse_args()
+
+
+def p95(values: list[float]) -> float | None:
+    if not values:
+        return None
+    if len(values) == 1:
+        return values[0]
+    # Keep behavior aligned with existing eval scripts.
+    if len(values) >= 20:
+        return statistics.quantiles(values, n=20)[18]
+    return max(values)
+
+
+def summarize_numeric(values: list[float]) -> dict[str, float | int | None]:
+    if not values:
+        return {
+            "count": 0,
+            "mean": None,
+            "median": None,
+            "p95": None,
+            "min": None,
+            "max": None,
+        }
+    return {
+        "count": len(values),
+        "mean": statistics.mean(values),
+        "median": statistics.median(values),
+        "p95": p95(values),
+        "min": min(values),
+        "max": max(values),
+    }
+
+
+def infer_model_from_dir(run_dir: Path) -> str:
+    # Format: YYYYMMDDTHHmmss_model_name_hash
+    parts = run_dir.name.split("_", 1)
+    if len(parts) < 2:
+        return run_dir.name
+    tail = parts[1]
+    maybe_model, maybe_hash = tail.rsplit("_", 1)
+    if len(maybe_hash) == 8:
+        return maybe_model.replace("_", "/")
+    return tail.replace("_", "/")
+
+
+def load_transcript(run_dir: Path) -> list[dict[str, Any]]:
+    path = run_dir / "transcript.jsonl"
+    if not path.exists():
+        return []
+    rows: list[dict[str, Any]] = []
+    with path.open() as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                rows.append(json.loads(line))
+            except json.JSONDecodeError:
+                continue
+    return rows
+
+
+def summarize_run(run_dir: Path, include_judge: bool) -> dict[str, Any] | None:
+    rows = load_transcript(run_dir)
+    if not rows:
+        return None
+
+    model_name = rows[0].get("model_name") or infer_model_from_dir(run_dir)
+    ttfb_values = [
+        float(r["ttfb_ms"])
+        for r in rows
+        if r.get("ttfb_ms") is not None and isinstance(r.get("ttfb_ms"), (int, float))
+    ]
+    latency_values = [
+        float(r["latency_ms"])
+        for r in rows
+        if r.get("latency_ms") is not None
+        and isinstance(r.get("latency_ms"), (int, float))
+    ]
+
+    prompt_tokens = 0
+    completion_tokens = 0
+    total_tokens = 0
+    tool_call_turns = 0
+    tool_calls_total = 0
+    empty_assistant_turns = 0
+
+    for r in rows:
+        tool_calls = r.get("tool_calls") or []
+        if tool_calls:
+            tool_call_turns += 1
+            tool_calls_total += len(tool_calls)
+
+        assistant_text = r.get("assistant_text")
+        if not isinstance(assistant_text, str) or not assistant_text.strip():
+            empty_assistant_turns += 1
+
+        tok = r.get("tokens") or {}
+        prompt_tokens += int(tok.get("prompt_tokens") or 0)
+        completion_tokens += int(tok.get("completion_tokens") or 0)
+        total_tokens += int(tok.get("total_tokens") or 0)
+
+    run_summary: dict[str, Any] = {
+        "run_dir": str(run_dir),
+        "model_name": model_name,
+        "turns": len(rows),
+        "native_performance": {
+            "tool_call_turns": tool_call_turns,
+            "tool_calls_total": tool_calls_total,
+            "empty_assistant_turns": empty_assistant_turns,
+            "assistant_response_rate": (
+                (len(rows) - empty_assistant_turns) / len(rows) if rows else None
+            ),
+        },
+        "latency_ms": summarize_numeric(latency_values),
+        "ttfb_ms": summarize_numeric(ttfb_values),
+        "tokens": {
+            "prompt_total": prompt_tokens,
+            "completion_total": completion_tokens,
+            "total": total_tokens,
+            "avg_total_per_turn": (total_tokens / len(rows)) if rows else None,
+        },
+    }
+
+    if include_judge:
+        summary_path = run_dir / "claude_summary.json"
+        if summary_path.exists():
+            try:
+                run_summary["judge_summary"] = json.loads(summary_path.read_text())
+            except json.JSONDecodeError:
+                run_summary["judge_summary"] = None
+
+    return run_summary
+
+
+def find_runs(args: argparse.Namespace) -> list[Path]:
+    run_dirs: list[Path] = []
+
+    if args.pattern:
+        for pattern in args.pattern:
+            run_dirs.extend([p for p in Path().glob(pattern) if p.is_dir()])
+    else:
+        base = Path("runs") / args.benchmark
+        if base.exists():
+            run_dirs.extend([p for p in base.iterdir() if p.is_dir()])
+
+    if args.model:
+        safe = args.model.replace("/", "_").replace(":", "_")
+        run_dirs = [d for d in run_dirs if safe in d.name]
+
+    run_dirs = sorted(set(run_dirs), key=lambda d: d.stat().st_mtime)
+    return run_dirs
+
+
+def aggregate_by_model(run_summaries: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    grouped: dict[str, dict[str, Any]] = {}
+
+    for r in run_summaries:
+        model = r["model_name"]
+        g = grouped.setdefault(
+            model,
+            {
+                "model_name": model,
+                "runs": 0,
+                "turns": 0,
+                "ttfb_values": [],
+                "latency_values": [],
+                "tool_call_turns": 0,
+                "tool_calls_total": 0,
+                "empty_assistant_turns": 0,
+                "prompt_tokens": 0,
+                "completion_tokens": 0,
+                "total_tokens": 0,
+            },
+        )
+        g["runs"] += 1
+        g["turns"] += r["turns"]
+        g["tool_call_turns"] += r["native_performance"]["tool_call_turns"]
+        g["tool_calls_total"] += r["native_performance"]["tool_calls_total"]
+        g["empty_assistant_turns"] += r["native_performance"]["empty_assistant_turns"]
+        g["prompt_tokens"] += r["tokens"]["prompt_total"]
+        g["completion_tokens"] += r["tokens"]["completion_total"]
+        g["total_tokens"] += r["tokens"]["total"]
+
+        # Rehydrate approximate distributions from run-level values only if present.
+        # Prefer exact per-turn data from runs instead of medians.
+        # We collect values by re-reading from already summarized fields is not possible,
+        # so these are filled in a second pass below.
+
+    # second pass to accumulate exact per-turn arrays
+    for r in run_summaries:
+        model = r["model_name"]
+        run_dir = Path(r["run_dir"])
+        rows = load_transcript(run_dir)
+        grouped[model]["ttfb_values"].extend(
+            [
+                float(x.get("ttfb_ms"))
+                for x in rows
+                if isinstance(x.get("ttfb_ms"), (int, float))
+            ]
+        )
+        grouped[model]["latency_values"].extend(
+            [
+                float(x.get("latency_ms"))
+                for x in rows
+                if isinstance(x.get("latency_ms"), (int, float))
+            ]
+        )
+
+    out: list[dict[str, Any]] = []
+    for model in sorted(grouped.keys()):
+        g = grouped[model]
+        turns = g["turns"]
+        out.append(
+            {
+                "model_name": model,
+                "runs": g["runs"],
+                "turns": turns,
+                "native_performance": {
+                    "tool_call_turns": g["tool_call_turns"],
+                    "tool_calls_total": g["tool_calls_total"],
+                    "empty_assistant_turns": g["empty_assistant_turns"],
+                    "assistant_response_rate": (
+                        (turns - g["empty_assistant_turns"]) / turns if turns else None
+                    ),
+                },
+                "ttfb_ms": summarize_numeric(g["ttfb_values"]),
+                "latency_ms": summarize_numeric(g["latency_values"]),
+                "tokens": {
+                    "prompt_total": g["prompt_tokens"],
+                    "completion_total": g["completion_tokens"],
+                    "total": g["total_tokens"],
+                    "avg_total_per_turn": (g["total_tokens"] / turns) if turns else None,
+                },
+            }
+        )
+    return out
+
+
+def main() -> int:
+    args = parse_args()
+    run_dirs = find_runs(args)
+    if not run_dirs:
+        print("No matching run directories found.")
+        return 1
+
+    run_summaries: list[dict[str, Any]] = []
+    skipped: list[str] = []
+    for run_dir in run_dirs:
+        s = summarize_run(run_dir, include_judge=args.include_judge)
+        if s is None:
+            skipped.append(str(run_dir))
+            continue
+        run_summaries.append(s)
+
+    if not run_summaries:
+        print("No usable transcript.jsonl files found in matching runs.")
+        return 1
+
+    output = {
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "inputs": {
+            "benchmark": args.benchmark,
+            "model": args.model,
+            "patterns": args.pattern,
+        },
+        "totals": {
+            "matching_run_dirs": len(run_dirs),
+            "processed_runs": len(run_summaries),
+            "skipped_runs": len(skipped),
+        },
+        "skipped_runs": skipped,
+        "models": aggregate_by_model(run_summaries),
+        "runs": run_summaries,
+    }
+
+    out_path = Path(args.output)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text(json.dumps(output, indent=2) + "\n")
+
+    if args.jsonl_out:
+        jsonl_path = Path(args.jsonl_out)
+        jsonl_path.parent.mkdir(parents=True, exist_ok=True)
+        with jsonl_path.open("w") as f:
+            for row in run_summaries:
+                f.write(json.dumps(row) + "\n")
+
+    print(f"Wrote JSON summary: {out_path}")
+    if args.jsonl_out:
+        print(f"Wrote run-level JSONL: {args.jsonl_out}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+
diff --git a/src/multi_turn_eval/cli.py b/src/multi_turn_eval/cli.py
index 24b7739..ae184d1 100644
--- a/src/multi_turn_eval/cli.py
+++ b/src/multi_turn_eval/cli.py
@@ -11,6 +11,7 @@
 import json
 import logging
 import os
+import statistics
 import sys
 from datetime import datetime
 from pathlib import Path
@@ -157,6 +158,196 @@ def setup_logging(run_dir: Path, verbose: bool = False):
     )
 
 
+def _p95(values: list[float]) -> float | None:
+    if not values:
+        return None
+    if len(values) == 1:
+        return values[0]
+    if len(values) >= 20:
+        return statistics.quantiles(values, n=20)[18]
+    return max(values)
+
+
+def _summarize_numeric(values: list[float]) -> dict:
+    if not values:
+        return {
+            "count": 0,
+            "mean": None,
+            "median": None,
+            "p95": None,
+            "min": None,
+            "max": None,
+        }
+    return {
+        "count": len(values),
+        "mean": statistics.mean(values),
+        "median": statistics.median(values),
+        "p95": _p95(values),
+        "min": min(values),
+        "max": max(values),
+    }
+
+
+def write_native_summary(run_dir: Path, model: str) -> dict | None:
+    """Write native summary from transcript.jsonl to native_summary.json."""
+    transcript_path = run_dir / "transcript.jsonl"
+    if not transcript_path.exists():
+        return None
+
+    rows = []
+    with transcript_path.open() as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                rows.append(json.loads(line))
+            except json.JSONDecodeError:
+                continue
+
+    if not rows:
+        return None
+
+    ttfb_values = []
+    latency_values = []
+    tool_call_turns = 0
+    tool_calls_total = 0
+    empty_assistant_turns = 0
+    prompt_tokens = 0
+    completion_tokens = 0
+    total_tokens = 0
+
+    for r in rows:
+        ttfb = r.get("ttfb_ms")
+        if isinstance(ttfb, (int, float)):
+            ttfb_values.append(float(ttfb))
+
+        latency = r.get("latency_ms")
+        if isinstance(latency, (int, float)):
+            latency_values.append(float(latency))
+
+        tool_calls = r.get("tool_calls") or []
+        if tool_calls:
+            tool_call_turns += 1
+            tool_calls_total += len(tool_calls)
+
+        assistant_text = r.get("assistant_text")
+        if not isinstance(assistant_text, str) or not assistant_text.strip():
+            empty_assistant_turns += 1
+
+        tokens = r.get("tokens") or {}
+        prompt_tokens += int(tokens.get("prompt_tokens") or 0)
+        completion_tokens += int(tokens.get("completion_tokens") or 0)
+        total_tokens += int(tokens.get("total_tokens") or 0)
+
+    turns = len(rows)
+    summary = {
+        "model_name": model,
+        "run_dir": str(run_dir),
+        "turns": turns,
+        "native_performance": {
+            "tool_call_turns": tool_call_turns,
+            "tool_calls_total": tool_calls_total,
+            "empty_assistant_turns": empty_assistant_turns,
+            "assistant_response_rate": (
+                (turns - empty_assistant_turns) / turns if turns else None
+            ),
+        },
+        "ttfb_ms": _summarize_numeric(ttfb_values),
+        "latency_ms": _summarize_numeric(latency_values),
+        "tokens": {
+            "prompt_total": prompt_tokens,
+            "completion_total": completion_tokens,
+            "total": total_tokens,
+            "avg_total_per_turn": (total_tokens / turns) if turns else None,
+        },
+    }
+
+    out_path = run_dir / "native_summary.json"
+    out_path.write_text(json.dumps(summary, indent=2) + "\n")
+    return summary
+
+
+def write_native_aggregate(
+    output_dir: Path,
+    benchmark_name: str,
+    model: str,
+    run_dirs: list[Path],
+    summaries: list[dict],
+) -> Path | None:
+    """Write aggregate native summary for a multi-run invocation."""
+    if not run_dirs or not summaries:
+        return None
+
+    ttfb_values = []
+    latency_values = []
+    total_turns = 0
+    tool_call_turns = 0
+    tool_calls_total = 0
+    empty_assistant_turns = 0
+    prompt_tokens = 0
+    completion_tokens = 0
+    total_tokens = 0
+
+    for s in summaries:
+        total_turns += int(s.get("turns", 0))
+        native_perf = s.get("native_performance", {})
+        tool_call_turns += int(native_perf.get("tool_call_turns", 0))
+        tool_calls_total += int(native_perf.get("tool_calls_total", 0))
+        empty_assistant_turns += int(native_perf.get("empty_assistant_turns", 0))
+
+        tokens = s.get("tokens", {})
+        prompt_tokens += int(tokens.get("prompt_total", 0))
+        completion_tokens += int(tokens.get("completion_total", 0))
+        total_tokens += int(tokens.get("total", 0))
+
+        transcript_path = Path(s["run_dir"]) / "transcript.jsonl"
+        if not transcript_path.exists():
+            continue
+        with transcript_path.open() as f:
+            for line in f:
+                try:
+                    row = json.loads(line)
+                except json.JSONDecodeError:
+                    continue
+                ttfb = row.get("ttfb_ms")
+                if isinstance(ttfb, (int, float)):
+                    ttfb_values.append(float(ttfb))
+                latency = row.get("latency_ms")
+                if isinstance(latency, (int, float)):
+                    latency_values.append(float(latency))
+
+    aggregate = {
+        "model_name": model,
+        "benchmark": benchmark_name,
+        "runs": len(run_dirs),
+        "run_dirs": [str(d) for d in run_dirs],
+        "turns": total_turns,
+        "native_performance": {
+            "tool_call_turns": tool_call_turns,
+            "tool_calls_total": tool_calls_total,
+            "empty_assistant_turns": empty_assistant_turns,
+            "assistant_response_rate": (
+                (total_turns - empty_assistant_turns) / total_turns
+                if total_turns
+                else None
+            ),
+        },
+        "ttfb_ms": _summarize_numeric(ttfb_values),
+        "latency_ms": _summarize_numeric(latency_values),
+        "tokens": {
+            "prompt_total": prompt_tokens,
+            "completion_total": completion_tokens,
+            "total": total_tokens,
+            "avg_total_per_turn": (total_tokens / total_turns) if total_turns else None,
+        },
+    }
+
+    out_path = output_dir / "native_aggregate.json"
+    out_path.write_text(json.dumps(aggregate, indent=2) + "\n")
+    return out_path
+
+
 # ============================================================================
 # CLI Commands
 # ============================================================================
@@ -177,6 +368,13 @@ def cli():
     help="Pipeline type (text, realtime, nova-sonic). Auto-detected if not specified.",
 )
 @click.option("--only-turns", help="Comma-separated turn indices to run (e.g., 0,1,2)")
+@click.option(
+    "--runs",
+    type=int,
+    default=1,
+    show_default=True,
+    help="Number of full benchmark runs to execute sequentially",
+)
 @click.option("--verbose", "-v", is_flag=True, help="Enable verbose logging")
 def run(
     benchmark_name: str,
@@ -184,6 +382,7 @@ def run(
     service: Optional[str],
     pipeline: Optional[str],
     only_turns: Optional[str],
+    runs: int,
     verbose: bool,
 ):
     """Run a benchmark against an LLM.
@@ -193,7 +392,7 @@ def run(
         uv run multi-turn-eval run aiwf_medium_context --model gpt-4o --service openai
         uv run multi-turn-eval run aiwf_medium_context --model gpt-realtime --service openai-realtime --pipeline realtime
     """
-    asyncio.run(_run(benchmark_name, model, service, pipeline, only_turns, verbose))
+    asyncio.run(_run(benchmark_name, model, service, pipeline, only_turns, runs, verbose))
 
 
 async def _run(
@@ -202,12 +401,15 @@ async def _run(
     service: Optional[str],
     pipeline_type: Optional[str],
     only_turns: Optional[str],
-    verbose: bool,
+    runs: int = 1,
+    verbose: bool = False,
 ):
     """Async implementation of the run command."""
+    if runs < 1:
+        raise click.UsageError("--runs must be >= 1")
+
     # Load benchmark
     BenchmarkConfig = load_benchmark(benchmark_name)
-    benchmark = BenchmarkConfig()
 
     # Infer pipeline if not specified
     if not pipeline_type:
@@ -224,51 +426,93 @@ async def _run(
     # Load service class if provided
     service_class = load_service_class(service) if service else None
 
-    # Create output directory
-    run_dir = create_run_directory(benchmark_name, model)
-    click.echo(f"Output directory: {run_dir}")
-
-    # Setup logging
-    setup_logging(run_dir, verbose)
-
-    # Create recorder
-    from multi_turn_eval.recording.transcript_recorder import TranscriptRecorder
-
-    recorder = TranscriptRecorder(run_dir, model)
-
     # Parse turn indices if provided
     turn_indices = None
     if only_turns:
         turn_indices = [int(i.strip()) for i in only_turns.split(",")]
         click.echo(f"Running only turns: {turn_indices}")
 
-    # Run the pipeline
-    try:
-        pipeline_instance = pipeline_cls(benchmark)
-        await pipeline_instance.run(
-            recorder=recorder,
+    base_run_dir = create_run_directory(benchmark_name, model)
+    click.echo(f"Output directory: {base_run_dir}")
+    if runs > 1:
+        iterations_base = base_run_dir / "iterations"
+        iterations_base.mkdir(parents=True, exist_ok=True)
+    else:
+        iterations_base = None
+
+    completed_runs: list[Path] = []
+    native_summaries: list[dict] = []
+    for run_index in range(1, runs + 1):
+        if runs > 1:
+            click.echo(f"\n=== Run {run_index}/{runs} ===")
+
+        if runs > 1:
+            run_dir = iterations_base / f"run_{run_index:02d}"
+            run_dir.mkdir(parents=True, exist_ok=True)
+            click.echo(f"Iteration directory: {run_dir}")
+        else:
+            run_dir = base_run_dir
+
+        # Setup logging
+        setup_logging(run_dir, verbose)
+
+        # Create recorder
+        from multi_turn_eval.recording.transcript_recorder import TranscriptRecorder
+
+        recorder = TranscriptRecorder(run_dir, model)
+
+        # Run the pipeline
+        try:
+            benchmark = BenchmarkConfig()
+            pipeline_instance = pipeline_cls(benchmark)
+            await pipeline_instance.run(
+                recorder=recorder,
+                model=model,
+                service_class=service_class,
+                service_name=service,
+                turn_indices=turn_indices,
+            )
+            completed_runs.append(run_dir)
+            click.echo("Completed benchmark run")
+            click.echo(f"  Transcript: {run_dir / 'transcript.jsonl'}")
+            native_summary = write_native_summary(run_dir, model)
+            if native_summary is not None:
+                native_summaries.append(native_summary)
+                click.echo(f"  Native summary: {run_dir / 'native_summary.json'}")
+        except Exception as e:
+            logger.exception(f"Pipeline failed: {e}")
+            raise click.ClickException(str(e))
+        finally:
+            recorder.close()
+
+    if runs > 1:
+        aggregate_path = write_native_aggregate(
+            output_dir=base_run_dir,
+            benchmark_name=benchmark_name,
             model=model,
-            service_class=service_class,
-            service_name=service,
-            turn_indices=turn_indices,
+            run_dirs=completed_runs,
+            summaries=native_summaries,
         )
-        click.echo(f"Completed benchmark run")
-        click.echo(f"  Transcript: {run_dir / 'transcript.jsonl'}")
-    except Exception as e:
-        logger.exception(f"Pipeline failed: {e}")
-        raise click.ClickException(str(e))
-    finally:
-        recorder.close()
+        click.echo("\nCompleted iteration directories:")
+        for run_dir in completed_runs:
+            click.echo(f"  - {run_dir}")
+        if aggregate_path is not None:
+            click.echo(f"Native aggregate: {aggregate_path}")
 
 
 @cli.command()
 @click.argument("run_dir", type=click.Path(exists=True))
 @click.option("--only-turns", help="Comma-separated turn indices to judge (e.g., 0,1,2)")
+@click.option(
+    "--only-runs",
+    help="For parent run dirs: comma-separated iteration selectors (e.g., 1,3,9 or run_01,run_03)",
+)
 @click.option("--judge-model", default="claude-opus-4-5", help="Model for judging")
 @click.option("--debug", is_flag=True, help="Enable debug logging")
 def judge(
     run_dir: str,
     only_turns: Optional[str],
+    only_runs: Optional[str],
     judge_model: str,
     debug: bool,
 ):
@@ -280,19 +524,14 @@ def judge(
     """
     run_path = Path(run_dir)
 
-    # Infer benchmark from path: runs/{benchmark}/{timestamp}_{model}/
-    benchmark_name = run_path.parent.name
-
-    # Load transcript
-    transcript_path = run_path / "transcript.jsonl"
-    if not transcript_path.exists():
-        raise click.UsageError(f"No transcript found at {transcript_path}")
-
     # Parse turn indices
     turn_indices_set: Optional[set[int]] = None
     if only_turns:
         turn_indices_set = {int(i.strip()) for i in only_turns.split(",")}
 
+    # Infer benchmark from path: runs/{benchmark}/{timestamp}_{model}/
+    benchmark_name = run_path.parent.name
+
     # Load benchmark for expected turns
     try:
         BenchmarkConfig = load_benchmark(benchmark_name)
@@ -306,50 +545,234 @@ def judge(
     # Run judge
     from multi_turn_eval.judging.claude_judge import judge_with_claude, load_transcript, write_outputs
 
-    records = load_transcript(run_path)
-    if turn_indices_set is not None:
-        records = [r for r in records if r["turn"] in turn_indices_set]
+    def judge_one(run_path_single: Path) -> dict:
+        transcript_path_single = run_path_single / "transcript.jsonl"
+        if not transcript_path_single.exists():
+            raise click.UsageError(f"No transcript found at {transcript_path_single}")
 
-    try:
-        result = asyncio.run(
-            judge_with_claude(
-                run_path,
-                only_turns=turn_indices_set,
-                debug=debug,
-                expected_turns=expected_turns,
+        records = load_transcript(run_path_single)
+        if turn_indices_set is not None:
+            records = [r for r in records if r["turn"] in turn_indices_set]
+
+        try:
+            result = asyncio.run(
+                judge_with_claude(
+                    run_path_single,
+                    only_turns=turn_indices_set,
+                    debug=debug,
+                    expected_turns=expected_turns,
+                )
             )
+        except Exception as e:
+            raise click.ClickException(f"Judgment failed for {run_path_single}: {e}")
+
+        write_outputs(
+            run_path_single,
+            records,
+            result["judgments"],
+            result["summary"],
+            result["model_name"],
+            result.get("realignment_notes", ""),
+            result.get("function_tracking", {}),
+            result.get("turn_taking_analysis"),
         )
-    except Exception as e:
-        raise click.ClickException(f"Judgment failed: {e}")
-
-    # Write outputs
-    write_outputs(
-        run_path,
-        records,
-        result["judgments"],
-        result["summary"],
-        result["model_name"],
-        result.get("realignment_notes", ""),
-        result.get("function_tracking", {}),
-        result.get("turn_taking_analysis"),
-    )
 
-    # Print summary
-    summary_path = run_path / "claude_summary.json"
-    summary = json.loads(summary_path.read_text())
-    passes = summary.get("claude_passes", {})
-    total = summary.get("turns_scored", 0)
-
-    click.echo(f"Judged {total} turns (with turn-taking analysis)")
-    click.echo(f"  Turn-taking: {passes.get('turn_taking', total)}/{total}")
-    click.echo(f"  Tool use: {passes.get('tool_use_correct', 0)}/{total}")
-    click.echo(f"  Instruction following: {passes.get('instruction_following', 0)}/{total}")
-    click.echo(f"  KB grounding: {passes.get('kb_grounding', 0)}/{total}")
-
-    # Report turn-taking failures if any
-    turn_taking_failures = summary.get("turn_taking_failures", [])
-    if turn_taking_failures:
-        click.echo(f"\nTurn-taking failures: {turn_taking_failures}")
+        summary_path_single = run_path_single / "claude_summary.json"
+        return json.loads(summary_path_single.read_text())
+
+    transcript_path = run_path / "transcript.jsonl"
+    if transcript_path.exists():
+        # Single-run directory (legacy and runs=1 behavior)
+        summary = judge_one(run_path)
+        passes = summary.get("claude_passes", {})
+        total = summary.get("turns_scored", 0)
+
+        click.echo(f"Judged {total} turns (with turn-taking analysis)")
+        click.echo(f"  Turn-taking: {passes.get('turn_taking', total)}/{total}")
+        click.echo(f"  Tool use: {passes.get('tool_use_correct', 0)}/{total}")
+        click.echo(f"  Instruction following: {passes.get('instruction_following', 0)}/{total}")
+        click.echo(f"  KB grounding: {passes.get('kb_grounding', 0)}/{total}")
+
+        turn_taking_failures = summary.get("turn_taking_failures", [])
+        if turn_taking_failures:
+            click.echo(f"\nTurn-taking failures: {turn_taking_failures}")
+        return
+
+    # Parent directory with iterations/run_XX
+    iterations_dir = run_path / "iterations"
+    iteration_dirs = []
+    if iterations_dir.exists():
+        iteration_dirs = sorted(
+            [
+                d
+                for d in iterations_dir.iterdir()
+                if d.is_dir() and (d / "transcript.jsonl").exists()
+            ]
+        )
+
+    if not iteration_dirs:
+        raise click.UsageError(
+            f"No transcript found at {transcript_path} and no iteration transcripts under {iterations_dir}"
+        )
+
+    all_iteration_dirs = list(iteration_dirs)
+
+    if only_runs:
+        selected: set[str] = set()
+        for raw in only_runs.split(","):
+            token = raw.strip()
+            if not token:
+                continue
+            if token.isdigit():
+                selected.add(f"run_{int(token):02d}")
+            else:
+                selected.add(token)
+        filtered = [d for d in iteration_dirs if d.name in selected]
+        if not filtered:
+            raise click.UsageError(
+                f"--only-runs matched no iteration dirs. Available: {', '.join(d.name for d in iteration_dirs)}"
+            )
+        iteration_dirs = filtered
+
+    if only_runs:
+        click.echo(
+            f"Found {len(all_iteration_dirs)} iterations total. Judging {len(iteration_dirs)} selected runs..."
+        )
+    else:
+        click.echo(f"Found {len(iteration_dirs)} iterations. Judging each run...")
+    iteration_summaries: list[dict] = []
+    successful_iteration_dirs: list[Path] = []
+    failed_iterations: list[dict] = []
+    for idx, iteration_dir in enumerate(iteration_dirs, start=1):
+        click.echo(f"\n[{idx}/{len(iteration_dirs)}] {iteration_dir.name}")
+        try:
+            summary = judge_one(iteration_dir)
+            iteration_summaries.append(summary)
+            successful_iteration_dirs.append(iteration_dir)
+        except Exception as e:
+            failed_iterations.append({"run": iteration_dir.name, "error": str(e)})
+            click.echo(f"  Failed: {e}")
+
+    if not iteration_summaries and only_runs:
+        click.echo("No selected iterations were successfully judged in this invocation.")
+
+    # Aggregate judged output at parent run dir using all iteration summaries available.
+    aggregate_iteration_dirs: list[Path] = []
+    aggregate_iteration_summaries: list[dict] = []
+    unreadable_existing_summaries: list[dict] = []
+    for d in all_iteration_dirs:
+        summary_path = d / "claude_summary.json"
+        if not summary_path.exists():
+            continue
+        try:
+            aggregate_iteration_summaries.append(json.loads(summary_path.read_text()))
+            aggregate_iteration_dirs.append(d)
+        except Exception as e:
+            unreadable_existing_summaries.append({"run": d.name, "error": str(e)})
+
+    if not aggregate_iteration_summaries:
+        raise click.ClickException(
+            "No iteration claude_summary.json files available to aggregate. "
+            "Run judge on at least one iteration first."
+        )
+
+    pass_keys = ["turn_taking", "tool_use_correct", "instruction_following", "kb_grounding"]
+    total_turns = sum(int(s.get("turns_scored", 0)) for s in aggregate_iteration_summaries)
+    aggregate_passes = {
+        key: sum(int(s.get("claude_passes", {}).get(key, 0)) for s in aggregate_iteration_summaries)
+        for key in pass_keys
+    }
+    aggregate_failures = []
+    turn_taking_affected_instruction = 0
+    for s, d in zip(aggregate_iteration_summaries, aggregate_iteration_dirs):
+        for t in s.get("turn_taking_failures", []):
+            aggregate_failures.append({"run": d.name, "turn": t})
+        turn_taking_affected_instruction += int(s.get("turn_taking_affected_instruction", 0))
+
+    aggregate_summary = {
+        "model_name": aggregate_iteration_summaries[0].get("model_name"),
+        "claude_passes": aggregate_passes,
+        "turns_scored": total_turns,
+        "judge_version": "claude-agent-sdk-v4-turn-taking-aggregate",
+        "judge_model": judge_model,
+        "judged_at": datetime.utcnow().isoformat() + "Z",
+        "is_aggregate": True,
+        "iteration_count": len(aggregate_iteration_dirs),
+        "iterations": [d.name for d in aggregate_iteration_dirs],
+        "total_iterations_in_folder": len(all_iteration_dirs),
+        "missing_summary_iterations": [
+            d.name for d in all_iteration_dirs if not (d / "claude_summary.json").exists()
+        ],
+        "unreadable_summary_iterations": unreadable_existing_summaries,
+        "failed_iteration_count": len(failed_iterations),
+        "failed_iterations": failed_iterations,
+        "turn_taking_failures": aggregate_failures,
+        "turn_taking_affected_instruction": turn_taking_affected_instruction,
+    }
+
+    (run_path / "claude_summary.json").write_text(json.dumps(aggregate_summary, indent=2) + "\n")
+
+    analysis_lines = [
+        "# Claude Aggregate Evaluation",
+        "",
+        f"**Parent Run**: `{run_path}`",
+        f"**Iterations In Folder**: {len(all_iteration_dirs)}",
+        f"**Iterations Requested This Run**: {len(iteration_dirs)}",
+        f"**Iterations Judged This Run**: {len(successful_iteration_dirs)}",
+        f"**Iterations Failed**: {len(failed_iterations)}",
+        f"**Iterations Included In Aggregate**: {len(aggregate_iteration_dirs)}",
+        f"**Turns Scored**: {total_turns}",
+        "",
+        "## Aggregate Metrics",
+        "",
+        f"- **Turn-Taking**: {aggregate_passes['turn_taking']}/{total_turns}",
+        f"- **Tool Use Correct**: {aggregate_passes['tool_use_correct']}/{total_turns}",
+        f"- **Instruction Following**: {aggregate_passes['instruction_following']}/{total_turns}",
+        f"- **KB Grounding**: {aggregate_passes['kb_grounding']}/{total_turns}",
+        "",
+        "## Failed Iterations",
+        "",
+    ]
+    if failed_iterations:
+        analysis_lines.extend([f"- `{f['run']}`: {f['error']}" for f in failed_iterations])
+    else:
+        analysis_lines.append("- None")
+    analysis_lines.extend([
+        "",
+        "## Missing/Unreadable Existing Summaries",
+        "",
+    ])
+    missing_summary_iterations = [
+        d.name for d in all_iteration_dirs if not (d / "claude_summary.json").exists()
+    ]
+    if missing_summary_iterations:
+        for name in missing_summary_iterations:
+            analysis_lines.append(f"- Missing `claude_summary.json`: `{name}`")
+    if unreadable_existing_summaries:
+        for item in unreadable_existing_summaries:
+            analysis_lines.append(f"- Unreadable summary `{item['run']}`: {item['error']}")
+    if not missing_summary_iterations and not unreadable_existing_summaries:
+        analysis_lines.append("- None")
+    analysis_lines.extend([
+        "",
+        "Per-iteration outputs remain in `iterations/run_XX/`.",
+    ])
+    (run_path / "claude_analysis.md").write_text("\n".join(analysis_lines) + "\n")
+
+    click.echo(f"\nJudged {len(successful_iteration_dirs)}/{len(iteration_dirs)} requested iterations at parent path")
+    click.echo(f"  Summary: {run_path / 'claude_summary.json'}")
+    click.echo(f"  Analysis: {run_path / 'claude_analysis.md'}")
+    if failed_iterations:
+        click.echo(f"  Failed iterations: {len(failed_iterations)}")
+    click.echo(
+        f"  Aggregate includes: {len(aggregate_iteration_dirs)}/{len(all_iteration_dirs)} iterations in folder"
+    )
+    click.echo(f"  Turn-taking: {aggregate_passes.get('turn_taking', total_turns)}/{total_turns}")
+    click.echo(f"  Tool use: {aggregate_passes.get('tool_use_correct', 0)}/{total_turns}")
+    click.echo(
+        f"  Instruction following: {aggregate_passes.get('instruction_following', 0)}/{total_turns}"
+    )
+    click.echo(f"  KB grounding: {aggregate_passes.get('kb_grounding', 0)}/{total_turns}")
 
 
 @cli.command("list-benchmarks")
diff --git a/uv.lock b/uv.lock
index e4a7955..8c57613 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 1
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.14'",
@@ -15,9 +15,9 @@ dependencies = [
     { name = "aiobotocore", extra = ["boto3"] },
     { name = "aiofiles" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a2/01/92e9ab00f36e2899315f49eefcd5b4685fbb19016c7f19a9edf06da80bb0/aioboto3-15.5.0.tar.gz", hash = "sha256:ea8d8787d315594842fbfcf2c4dce3bac2ad61be275bc8584b2ce9a3402a6979", size = 255069, upload-time = "2025-10-30T13:37:16.122Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a2/01/92e9ab00f36e2899315f49eefcd5b4685fbb19016c7f19a9edf06da80bb0/aioboto3-15.5.0.tar.gz", hash = "sha256:ea8d8787d315594842fbfcf2c4dce3bac2ad61be275bc8584b2ce9a3402a6979", size = 255069 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/3e/e8f5b665bca646d43b916763c901e00a07e40f7746c9128bdc912a089424/aioboto3-15.5.0-py3-none-any.whl", hash = "sha256:cc880c4d6a8481dd7e05da89f41c384dbd841454fc1998ae25ca9c39201437a6", size = 35913, upload-time = "2025-10-30T13:37:14.549Z" },
+    { url = "https://files.pythonhosted.org/packages/e5/3e/e8f5b665bca646d43b916763c901e00a07e40f7746c9128bdc912a089424/aioboto3-15.5.0-py3-none-any.whl", hash = "sha256:cc880c4d6a8481dd7e05da89f41c384dbd841454fc1998ae25ca9c39201437a6", size = 35913 },
 ]
 
 [[package]]
@@ -33,9 +33,9 @@ dependencies = [
     { name = "python-dateutil" },
     { name = "wrapt" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/62/94/2e4ec48cf1abb89971cb2612d86f979a6240520f0a659b53a43116d344dc/aiobotocore-2.25.1.tar.gz", hash = "sha256:ea9be739bfd7ece8864f072ec99bb9ed5c7e78ebb2b0b15f29781fbe02daedbc", size = 120560, upload-time = "2025-10-28T22:33:21.787Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/62/94/2e4ec48cf1abb89971cb2612d86f979a6240520f0a659b53a43116d344dc/aiobotocore-2.25.1.tar.gz", hash = "sha256:ea9be739bfd7ece8864f072ec99bb9ed5c7e78ebb2b0b15f29781fbe02daedbc", size = 120560 }
 wheels = [
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