feat: continuous concurrent gate orchestrator (algo #6)

- validate_skills_concurrent: 多题型全部臂共享题槽并发编排,发射序 =
  题型 round-robin × 阶梯序(base 先 cand 后),终态统一组装 outcome,
  verdict None(全 INFRA)保留 RuntimeError 语义
- gate_evidence 列 block_idx → ladder_rank(阶梯序号,0-based);旧块路径
  _build_evidence_rows 仅键名同步(值仍为块号)保持落库兼容
- 新增 3 项编排测试:乱序到达前缀有序性/双题型隔离/全 INFRA raise

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

tests/unit/test_gate_concurrent.py

@@ -0,0 +1,167 @@
+"""连续并发 gate 编排测试:乱序到达/多题型隔离/终态组装/全 INFRA。"""
+
+from __future__ import annotations
+
+import asyncio
+from typing import TYPE_CHECKING
+
+import pytest
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+from app.harness.gate_ladder import BaselineCache
+from app.harness.validate import GateSpec, validate_skills_concurrent
+from tests.unit.test_gate_prefix import _PARAMS, _mk_unit
+from tests.unit.test_gate_unit_arm import _FakeLog
+
+
+def _mk_spec(task_type: str, slug: str, n: int) -> GateSpec:
+    """构造 n 个 single 单元的 gate 规格(unit_id 形如 <slug>-q<i>)。"""
+    return GateSpec(
+        task_type=task_type,
+        target_file=f"{slug}.md",
+        candidate_content=f"cand-{slug}",
+        base_skill_content=f"base-{slug}",
+        units=tuple(_mk_unit(f"{slug}-q{i}", task_type) for i in range(n)),
+        gate_run_prefix=f"r_e1_s0_gate_{slug}",
+    )
+
+
+def _scripted_inference(log: _FakeLog, script: dict[str, tuple[bool, float]]):
+    """脚本化假推理:按 question_id+臂 决定 (对错, 延迟秒),制造乱序到达。"""
+
+    class _R:
+        def __init__(self, run_id: str, total: int) -> None:
+            self.run_id = run_id
+            self.total = total
+
+    async def _run(questions, *, run_id: str, skills_dir: Path):
+        arm = "cand" if run_id.endswith("_cand") else "base"
+        correct, delay = script[f"{questions[0].question_id}|{arm}"]
+        await asyncio.sleep(delay)
+        for q in questions:
+            log.rows.append(
+                {
+                    "run_id": run_id,
+                    "question_id": q.question_id,
+                    "prediction": "A" if correct else "B",
+                    "answer": "A",
+                    "stop_reason": "finished",
+                    "steps_json": "[]",
+                }
+            )
+        return _R(run_id, len(questions))
+
+    return _run
+
+
+@pytest.mark.asyncio
+async def test_out_of_order_arrival_still_ladder_order(tmp_path, monkeypatch) -> None:
+    """尾部先到、头部后到:判定结果与顺序到达完全相同(前缀有序性端到端)。"""
+    spec = _mk_spec("Action Reasoning", "action-reasoning", 4)
+    log = _FakeLog()
+    script = {}
+    for i in range(4):  # 头部 q0 最慢;全部翻转为 W(base 错 cand 对)
+        script[f"action-reasoning-q{i}|base"] = (False, 0.05 if i == 0 else 0.0)
+        script[f"action-reasoning-q{i}|cand"] = (True, 0.05 if i == 0 else 0.0)
+    monkeypatch.setattr(
+        "app.harness.validate.materialize_candidate_skill",
+        lambda *a, **k: tmp_path / "cand",
+    )
+    outcomes = await validate_skills_concurrent(
+        workspace_dir=tmp_path,
+        base_skills_version="v1",
+        specs=[spec],
+        gate_params=_PARAMS,
+        gate_guard_err=0.10,
+        baseline_cache=BaselineCache(tmp_path / "bc.json"),
+        prompts_version="v1",
+        run_inference=_scripted_inference(log, script),
+        log=log,
+        concurrency=8,
+    )
+    o = outcomes["Action Reasoning"]
+    assert o.w == 4 and o.l == 0
+    assert [r["ladder_rank"] for r in o.evidence_rows] == [0, 1, 2, 3]
+
+
+@pytest.mark.asyncio
+async def test_two_types_isolated(tmp_path, monkeypatch) -> None:
+    """两题型并行:计数互不污染,各自独立判定。
+
+    A 型 4 单元全 W(题尽 accept_provisional);B 型 2 单元全平
+    (futility 早停,W=L=0)——两型结果都不受对方污染。
+    """
+    spec_a = _mk_spec("Action Reasoning", "action-reasoning", 4)
+    spec_b = _mk_spec("Counting Problem", "counting-problem", 2)
+    log = _FakeLog()
+    script = {}
+    for i in range(4):
+        script[f"action-reasoning-q{i}|base"] = (False, 0.0)
+        script[f"action-reasoning-q{i}|cand"] = (True, 0.0)
+    for i in range(2):
+        script[f"counting-problem-q{i}|base"] = (True, 0.0)
+        script[f"counting-problem-q{i}|cand"] = (True, 0.0)
+    monkeypatch.setattr(
+        "app.harness.validate.materialize_candidate_skill",
+        lambda *a, **k: tmp_path / "cand",
+    )
+    outcomes = await validate_skills_concurrent(
+        workspace_dir=tmp_path,
+        base_skills_version="v1",
+        specs=[spec_a, spec_b],
+        gate_params=_PARAMS,
+        gate_guard_err=0.10,
+        baseline_cache=BaselineCache(tmp_path / "bc.json"),
+        prompts_version="v1",
+        run_inference=_scripted_inference(log, script),
+        log=log,
+        concurrency=8,
+    )
+    assert outcomes["Action Reasoning"].w == 4
+    assert outcomes["Counting Problem"].w == 0
+    assert outcomes["Counting Problem"].l == 0
+
+
+@pytest.mark.asyncio
+async def test_all_infra_raises(tmp_path, monkeypatch) -> None:
+    """全单元 INFRA:保留现行 RuntimeError 语义(检查推理基础设施)。"""
+    spec = _mk_spec("Action Reasoning", "action-reasoning", 2)
+    log = _FakeLog()
+
+    class _R:
+        def __init__(self, run_id, total):
+            self.run_id, self.total = run_id, total
+
+    async def _infra_run(questions, *, run_id, skills_dir):
+        for q in questions:
+            log.rows.append(
+                {
+                    "run_id": run_id,
+                    "question_id": q.question_id,
+                    "prediction": "",
+                    "answer": "A",
+                    "stop_reason": "error",
+                    "steps_json": "[]",
+                }
+            )
+        return _R(run_id, len(questions))
+
+    monkeypatch.setattr(
+        "app.harness.validate.materialize_candidate_skill",
+        lambda *a, **k: tmp_path / "cand",
+    )
+    with pytest.raises(RuntimeError):
+        await validate_skills_concurrent(
+            workspace_dir=tmp_path,
+            base_skills_version="v1",
+            specs=[spec],
+            gate_params=_PARAMS,
+            gate_guard_err=0.99,  # 护栏放宽,逼出全 INFRA 分支
+            baseline_cache=BaselineCache(tmp_path / "bc.json"),
+            prompts_version="v1",
+            run_inference=_infra_run,
+            log=log,
+            concurrency=8,
+        )