add live bench

eval_protocol/benchmarks/registry.py

@@ -126,7 +126,7 @@ def _deep_update(base: Dict[str, Any], over: Dict[str, Any]) -> Dict[str, Any]:
             server_script_path = ep_config.get("server_script_path")
             steps = ep_config.get("steps")
             mode = ep_config.get("mode")
-            combine_datasets = ep_config.get("combine_datasets")
+            # combine_datasets captured but not used here
 
             # Choose the first rollout param set by default
             rollout_params = None
@@ -169,3 +169,162 @@ def _deep_update(base: Dict[str, Any], over: Dict[str, Any]) -> Dict[str, Any]:
         return test_wrapper
 
     return _decorator
+
+
+def register_composite_benchmark(name: str, children: List[str]) -> None:
+    """
+    Register a composite benchmark that runs multiple exported benchmarks and aggregates results.
+
+    The composite runner forwards common overrides to each child benchmark and aggregates
+    a combined score as a rows-weighted mean of each child's aggregated score.
+
+    Args:
+        name: Name of the composite benchmark to register.
+        children: List of child benchmark names previously registered via export_benchmark.
+    """
+
+    def _composite_runner(
+        *,
+        model: Optional[str] = None,
+        print_summary: bool = False,
+        out: Optional[str] = None,
+        reasoning_effort: Optional[str] = None,
+        max_rows: Optional[int | str] = None,
+        num_runs: Optional[int] = None,
+        input_params_override: Optional[Dict[str, Any]] = None,
+        max_concurrency: Optional[int] = None,
+    ) -> Dict[str, Any]:
+        # Resolve child runners at call-time to ensure all suites are imported
+        # Local import avoided to prevent circular import at module import time
+        _get_benchmark_runner = get_benchmark_runner
+        import pathlib as _pathlib
+        import time as _time
+        _json = json
+
+        child_summaries: List[Dict[str, Any]] = []
+        total_rows = 0
+        weighted_sum = 0.0
+        # For per-metric aggregation across children
+        metric_weighted_sums: Dict[str, float] = {}
+        metric_total_rows: Dict[str, int] = {}
+        combined_rows: List[Any] = []
+
+        # If 'out' is a file path, also compute a directory for child artifacts
+        child_out_dir: Optional[str] = None
+        if out:
+            p = _pathlib.Path(out)
+            if p.suffix.lower() == ".json" and not str(out).endswith("/"):
+                # Use parent directory for child artifacts
+                child_out_dir = str(p.parent)
+            else:
+                child_out_dir = out
+
+        for child_name in children:
+            runner = _get_benchmark_runner(child_name)
+            result = runner(
+                model=model,
+                print_summary=print_summary,
+                out=child_out_dir,
+                reasoning_effort=reasoning_effort,
+                max_rows=max_rows,
+                num_runs=num_runs,
+                input_params_override=input_params_override,
+                max_concurrency=max_concurrency,
+            )
+            summary = (result or {}).get("summary") if isinstance(result, dict) else None
+            if not summary:
+                continue
+            # Gather underlying rows to recompute CI across children
+            try:
+                rows_obj = result.get("results") if isinstance(result, dict) else None
+                if isinstance(rows_obj, list):
+                    combined_rows.extend(rows_obj)
+            except Exception:
+                pass
+            child_summaries.append(summary)
+            rows = int(summary.get("rows", 0) or 0)
+            agg = summary.get("agg_score")
+            if isinstance(agg, (int, float)) and rows > 0:
+                total_rows += rows
+                weighted_sum += float(agg) * rows
+            # Combine per-metric means if available
+            metrics_agg = summary.get("metrics_agg") or {}
+            if isinstance(metrics_agg, dict):
+                for m_name, m_vals in metrics_agg.items():
+                    m_mean = m_vals.get("mean")
+                    if isinstance(m_mean, (int, float)) and rows > 0:
+                        metric_weighted_sums[m_name] = metric_weighted_sums.get(m_name, 0.0) + float(m_mean) * rows
+                        metric_total_rows[m_name] = metric_total_rows.get(m_name, 0) + rows
+
+        combined_agg = (weighted_sum / total_rows) if total_rows > 0 else None
+        # Compute 95% CI for combined rows if available
+        ci_low: Optional[float] = None
+        ci_high: Optional[float] = None
+        if combined_rows:
+            try:
+                from eval_protocol.stats.confidence_intervals import compute_fixed_set_mu_ci as _compute_ci
+
+                r = _compute_ci(combined_rows)
+                if r and len(r) >= 3 and r[1] is not None and r[2] is not None:
+                    ci_low = float(r[1])
+                    ci_high = float(r[2])
+            except Exception:
+                ci_low = None
+                ci_high = None
+        combined_metrics: Dict[str, Dict[str, float]] = {}
+        for m_name, wsum in metric_weighted_sums.items():
+            denom = metric_total_rows.get(m_name, 0)
+            if denom > 0:
+                combined_metrics[m_name] = {"mean": float(wsum / denom)}
+        combined = {
+            "suite": name,
+            "model": model,
+            "agg_score": float(combined_agg) if combined_agg is not None else None,
+            "rows": total_rows,
+            "children": child_summaries,
+            "num_runs": num_runs,
+            **({"metrics_agg": combined_metrics} if combined_metrics else {}),
+            **({"agg_ci_low": ci_low, "agg_ci_high": ci_high} if (ci_low is not None and ci_high is not None) else {}),
+        }
+
+        # Optional print and persist
+        # Respect either function arg or EP_PRINT_SUMMARY env
+        _should_print = print_summary or (os.getenv("EP_PRINT_SUMMARY") == "1")
+        if _should_print:
+            try:
+                if combined_agg is not None:
+                    if ci_low is not None and ci_high is not None:
+                        print(
+                            f"EP Summary | suite={name} model={model} agg={combined['agg_score']:.3f} ci95=[{ci_low:.3f},{ci_high:.3f}] rows={total_rows}"
+                        )
+                    else:
+                        print(
+                            f"EP Summary | suite={name} model={model} agg={combined['agg_score']:.3f} rows={total_rows}"
+                        )
+                else:
+                    print(
+                        f"EP Summary | suite={name} model={model} agg=None rows={total_rows}"
+                    )
+            except Exception:
+                pass
+
+        if out:
+            out_path = _pathlib.Path(out)
+            if out_path.suffix.lower() == ".json" and not str(out).endswith("/"):
+                # Write to the specified file
+                out_path.parent.mkdir(parents=True, exist_ok=True)
+                with open(out_path, "w", encoding="utf-8") as f:
+                    _json.dump({**combined, "timestamp": int(_time.time())}, f)
+            else:
+                # Treat as directory
+                dir_path = out_path
+                dir_path.mkdir(parents=True, exist_ok=True)
+                safe_name = name.replace("/", "__")
+                file_path = dir_path / f"{safe_name}__composite.json"
+                with open(file_path, "w", encoding="utf-8") as f:
+                    _json.dump({**combined, "timestamp": int(_time.time())}, f)
+
+        return {"summary": combined}
+
+    # Register (overwrite if exists)
+    _BENCHMARK_REGISTRY[name] = _composite_runner

eval_protocol/benchmarks/suites/aime25.py

@@ -69,6 +69,7 @@ def aime2025_dataset_adapter(rows: List[Dict[str, Any]]) -> List[EvaluationRow]:
     rollout_input_params=[{"max_tokens": 131000, "extra_body": {"reasoning_effort": "low"}}],
     rollout_processor=default_single_turn_rollout_processor,
     aggregation_method="mean",
+    passed_threshold=None,
     num_runs=8,
     max_dataset_rows=2,
     max_concurrent_rollouts=4,

eval_protocol/benchmarks/suites/gpqa.py

@@ -39,8 +39,6 @@ def _load_gpqa_messages_from_csv() -> List[List[Message]]:
             [
                 Message(role="system", content=SYSTEM_PROMPT),
                 Message(role="user", content=user_content),
-                # Correct answer is always option A by construction
-                Message(role="system", content="__GT__:A"),
             ]
         )
     if not messages_list:
@@ -57,14 +55,31 @@ def _extract_abcd_letter(text: str) -> str | None:
 
 _GPQA_INPUT_MESSAGES = _load_gpqa_messages_from_csv()
 
+def _strip_gt_messages(msgs: List[Message]) -> List[Message]:
+    return [m for m in msgs if not (m.role == "system" and (m.content or "").startswith("__GT__:"))]
+
+
+async def gpqa_strip_gt_rollout_processor(rows: List[EvaluationRow], config) -> List[EvaluationRow]:
+    """Preprocess rows to set ground_truth and remove __GT__ messages, then delegate to default processor."""
+    processed: List[EvaluationRow] = []
+    for r in rows:
+        gt_tokens = [m.content for m in r.messages if m.role == "system" and (m.content or "").startswith("__GT__:")]
+        if gt_tokens:
+            gt_val = gt_tokens[-1].split(":", 1)[1].strip()
+            r.ground_truth = gt_val
+            r.messages = [m for m in r.messages if not (m.role == "system" and (m.content or "").startswith("__GT__:"))]
+        processed.append(r)
+    return await default_single_turn_rollout_processor(processed, config)
+
 
 @export_benchmark("gpqa")
 @evaluation_test(
     model=["fireworks_ai/accounts/fireworks/models/gpt-oss-120b"],
     input_messages=_GPQA_INPUT_MESSAGES,
     rollout_input_params=[{"extra_body": {"reasoning_effort": "low"}}],
-    rollout_processor=default_single_turn_rollout_processor,
+    rollout_processor=gpqa_strip_gt_rollout_processor,
     aggregation_method="mean",
+    passed_threshold=None,
     num_runs=8,
     mode="pointwise",
 )
@@ -73,9 +88,8 @@ def gpqa_pointwise(row: EvaluationRow) -> EvaluationRow:
     content = assistant_msgs[-1].content if assistant_msgs else ""
 
     pred = _extract_abcd_letter(content or "")
-    # Retrieve GT from the trailing system message we appended
-    gt_tokens = [m.content for m in row.messages if m.role == "system" and (m.content or "").startswith("__GT__:")]
-    gt = gt_tokens[-1].split(":", 1)[1].strip() if gt_tokens else None
+    # GPQA diamond CSV constructs options so that the correct answer is always A
+    gt = "A"
 
     is_valid = pred is not None and gt in {"A", "B", "C", "D"}
     score = 1.0 if (is_valid and pred == gt) else 0.0