Add three unified benchmark tasks

TASK_DETAILS.md

@@ -82,7 +82,11 @@ We welcome new engineering problem ideas — even without complete verification
       <td>C++ SHA3-256 throughput (OpenSSL verified)</td>
     </tr>
     <tr>
-      <td rowspan="3"><b>CommunicationEngineering</b></td>
+      <td rowspan="4"><b>CommunicationEngineering</b></td>
+      <td><code>AdaptiveLinkScheduling</code></td>
+      <td>Downlink resource-block scheduling with MCS and power control under queue, latency, fairness, and power-budget constraints</td>
+    </tr>
+    <tr>
       <td><code>LDPCErrorFloor</code></td>
       <td>LDPC code error floor estimation via importance sampling on trapping sets</td>
     </tr>
@@ -298,7 +302,11 @@ We welcome new engineering problem ideas — even without complete verification
       <td>pyMOTO-based 2D beam topology optimization (SIMP + OC/MMA) under a volume-fraction constraint</td>
     </tr>
     <tr>
-      <td rowspan="6"><b>Robotics</b></td>
+      <td rowspan="7"><b>Robotics</b></td>
+      <td><code>AGVWarehouseRouting</code></td>
+      <td>Warehouse AGV pick-sequence optimization with aisle obstacles, congestion, and turn costs</td>
+    </tr>
+    <tr>
       <td><code>DynamicObstacleAvoidanceNavigation</code></td>
       <td>Navigate a differential-drive robot from start to goal in a dynamic environment</td>
     </tr>
@@ -337,10 +345,14 @@ We welcome new engineering problem ideas — even without complete verification
       <td>Importance-sampling BER estimator for Hamming(127,120)</td>
     </tr>
     <tr>
-      <td><b>PowerSystems</b></td>
+      <td rowspan="2"><b>PowerSystems</b></td>
       <td><code>EV2GymSmartCharging</code></td>
       <td>Upstream-aligned EV smart charging scheduling</td>
     </tr>
+    <tr>
+      <td><code>MicrogridBatteryDispatch</code></td>
+      <td>Commercial microgrid battery dispatch under solar forecasts, tariffs, demand charges, and degradation cost</td>
+    </tr>
     <tr>
       <td><b>AdditiveManufacturing</b></td>
       <td><code>DiffSimThermalControl</code></td>

TASK_DETAILS_zh-CN.md

@@ -82,7 +82,11 @@ Frontier-Eng 目前已覆盖以下领域的任务。每个任务均配有可运
       <td>C++ SHA3-256 吞吐量（OpenSSL 验证）</td>
     </tr>
     <tr>
-      <td rowspan="3"><b>CommunicationEngineering</b></td>
+      <td rowspan="4"><b>CommunicationEngineering</b></td>
+      <td><code>AdaptiveLinkScheduling</code></td>
+      <td>在队列、时延、公平性与功率预算约束下进行下行资源块调度、MCS 与功率控制</td>
+    </tr>
+    <tr>
       <td><code>LDPCErrorFloor</code></td>
       <td>使用 importance sampling 针对 trapping sets 估计 LDPC 码 error floor</td>
     </tr>
@@ -298,7 +302,11 @@ Frontier-Eng 目前已覆盖以下领域的任务。每个任务均配有可运
       <td>基于 pyMOTO 的 2D 梁拓扑优化（SIMP + OC/MMA），体积分数约束</td>
     </tr>
     <tr>
-      <td rowspan="6"><b>Robotics</b></td>
+      <td rowspan="7"><b>Robotics</b></td>
+      <td><code>AGVWarehouseRouting</code></td>
+      <td>考虑巷道障碍、拥堵与转向成本的仓储 AGV 拣货顺序优化</td>
+    </tr>
+    <tr>
       <td><code>DynamicObstacleAvoidanceNavigation</code></td>
       <td>在动态环境中控制差分轮机器人从起点到终点</td>
     </tr>
@@ -337,10 +345,14 @@ Frontier-Eng 目前已覆盖以下领域的任务。每个任务均配有可运
       <td>使用 importance sampling 估计 Hamming(127,120) 的 BER</td>
     </tr>
     <tr>
-      <td><b>PowerSystems</b></td>
+      <td rowspan="2"><b>PowerSystems</b></td>
       <td><code>EV2GymSmartCharging</code></td>
       <td>上游对齐的电动车智能充电调度</td>
     </tr>
+    <tr>
+      <td><code>MicrogridBatteryDispatch</code></td>
+      <td>在太阳能预测、分时电价、需量电费与退化成本下优化商业微电网电池调度</td>
+    </tr>
     <tr>
       <td><b>AdditiveManufacturing</b></td>
       <td><code>DiffSimThermalControl</code></td>

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/README.md

@@ -0,0 +1,47 @@
+# AdaptiveLinkScheduling
+
+Optimize per-resource-block user scheduling, modulation/coding selection, and
+transmit power for a small wireless downlink under queue, channel, latency, and
+power-budget constraints.
+
+## Files
+
+- `Task.md`: task contract and scoring details.
+- `scripts/init.py`: editable seed scheduler.
+- `verification/evaluator.py`: deterministic link simulator and scorer.
+- `verification/requirements.txt`: evaluator dependencies.
+- `frontier_eval/`: unified-task metadata.
+
+## Candidate Interface
+
+Edit `scripts/init.py` only. The evaluator imports:
+
+```python
+schedule_frame(frame: dict) -> list[dict]
+```
+
+Return one decision per resource block. Each decision should contain:
+
+```python
+{"user": <int>, "mcs": <int>, "power_dbm": <float>}
+```
+
+## Quick Run
+
+From this directory:
+
+```bash
+python verification/evaluator.py scripts/init.py
+```
+
+From the repository root:
+
+```bash
+python -m frontier_eval \
+  task=unified \
+  task.benchmark=CommunicationEngineering/AdaptiveLinkScheduling \
+  algorithm=openevolve \
+  algorithm.iterations=0
+```
+
+No GPU, Docker, API key, or external assets are required.

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/Task.md

@@ -0,0 +1,57 @@
+# Adaptive Link Scheduling
+
+## Background
+
+Wireless base stations continually decide which users receive resource blocks,
+what modulation and coding scheme (MCS) to use, and how much power to transmit.
+Good schedulers exploit favorable channel states while protecting users with
+urgent queues and respecting power budgets. These decisions directly affect
+throughput, latency, energy use, and fairness.
+
+## Objective
+
+For each fixed frame snapshot, produce one scheduling decision per resource
+block. The verifier simulates packet delivery using deterministic SNR thresholds
+and scores weighted throughput minus outage, power, budget, and fairness costs.
+
+## Candidate API
+
+The evaluator imports `schedule_frame(frame)` from `scripts/init.py`.
+
+`frame` contains:
+
+- `frame_id`
+- `num_resource_blocks`
+- `mcs_table`: list of `{mcs, snr_threshold_db, bits_per_rb}`
+- `power_min_dbm`, `power_max_dbm`, `power_budget_mw`
+- `users`: each user has `id`, `queue_bits`, `latency_weight`,
+  `min_service_bits`, and `snr_estimate_db` per resource block
+
+Return a list with `num_resource_blocks` entries. Each entry should be a mapping
+with `user`, `mcs`, and `power_dbm`.
+
+## Constraints
+
+- Do not import external packages.
+- Do not read or write files.
+- Keep the public `schedule_frame(frame)` interface.
+- Keep all editable logic inside the EVOLVE block.
+- The scheduler must be deterministic for the same input frame.
+
+## Scoring
+
+For each resource block, the evaluator computes:
+
+```text
+effective_snr = snr_estimate_db + (power_dbm - 20)
+```
+
+The selected MCS succeeds if `effective_snr` reaches the MCS threshold plus a
+small implementation margin. Successful transmissions deliver `bits_per_rb`
+subject to the selected user's remaining queue. Failed transmissions consume
+power and incur outage penalty.
+
+The frame utility combines latency-weighted delivered bits, minimum-service
+satisfaction, Jain fairness, power cost, and budget violations.
+
+`combined_score = mean(frame_utility across frames)`, so higher is better.

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/frontier_eval/agent_files.txt

@@ -0,0 +1,5 @@
+README.md
+Task.md
+scripts/init.py
+verification/evaluator.py
+frontier_eval/constraints.txt

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/frontier_eval/artifact_files.txt

@@ -0,0 +1,2 @@
+metrics.json
+artifacts.json

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/frontier_eval/candidate_destination.txt

@@ -0,0 +1 @@
+scripts/init.py

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/frontier_eval/constraints.txt

@@ -0,0 +1,6 @@
+AdaptiveLinkScheduling constraints:
+1) Modify only scripts/init.py.
+2) Keep schedule_frame(frame) as the public entrypoint.
+3) Return one decision per resource block with user, mcs, and power_dbm fields.
+4) Do not use external packages, file I/O, network access, or randomness.
+5) Keep EVOLVE-BLOCK markers intact and place editable scheduler logic inside them.

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/frontier_eval/copy_files.txt

@@ -0,0 +1 @@
+.

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/frontier_eval/eval_command.txt

@@ -0,0 +1 @@
+{python} verification/evaluator.py {candidate} --metrics-out metrics.json --artifacts-out artifacts.json

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/frontier_eval/eval_cwd.txt

@@ -0,0 +1 @@
+.

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/frontier_eval/initial_program.txt

@@ -0,0 +1 @@
+scripts/init.py

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/frontier_eval/readonly_files.txt

@@ -0,0 +1,5 @@
+README.md
+Task.md
+verification/evaluator.py
+verification/requirements.txt
+frontier_eval/

benchmarks/CommunicationEngineering/AdaptiveLinkScheduling/scripts/init.py

@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+from typing import Any
+
+
+def _choose_mcs(snr_db: float, mcs_table: list[dict[str, Any]], margin_db: float = 2.0) -> int:
+    selected = int(mcs_table[0]["mcs"])
+    for item in mcs_table:
+        if snr_db >= float(item["snr_threshold_db"]) + margin_db:
+            selected = int(item["mcs"])
+    return selected
+
+
+def schedule_frame(frame: dict[str, Any]) -> list[dict[str, float | int]]:
+    """Return one scheduling decision per resource block."""
+
+    # EVOLVE-BLOCK-START
+    users = frame["users"]
+    mcs_table = frame["mcs_table"]
+    num_rbs = int(frame["num_resource_blocks"])
+    power_min = float(frame["power_min_dbm"])
+    power_max = float(frame["power_max_dbm"])
+    nominal_power = min(power_max, max(power_min, 20.0))
+
+    remaining = {int(user["id"]): float(user["queue_bits"]) for user in users}
+    served = {int(user["id"]): 0.0 for user in users}
+    decisions: list[dict[str, float | int]] = []
+
+    for rb in range(num_rbs):
+        best_user = None
+        best_score = None
+        for user in users:
+            user_id = int(user["id"])
+            if remaining[user_id] <= 0:
+                continue
+            snr = float(user["snr_estimate_db"][rb])
+            latency = float(user["latency_weight"])
+            deficit = max(0.0, float(user["min_service_bits"]) - served[user_id])
+            score = snr + 3.0 * latency + 0.002 * deficit + 0.0005 * remaining[user_id]
+            if best_score is None or score > best_score:
+                best_score = score
+                best_user = user
+
+        if best_user is None:
+            decisions.append({"user": int(users[0]["id"]), "mcs": 0, "power_dbm": power_min})
+            continue
+
+        user_id = int(best_user["id"])
+        snr_at_power = float(best_user["snr_estimate_db"][rb]) + (nominal_power - 20.0)
+        mcs = _choose_mcs(snr_at_power, mcs_table, margin_db=2.0)
+        bits = 0.0
+        for item in mcs_table:
+            if int(item["mcs"]) == int(mcs):
+                bits = float(item["bits_per_rb"])
+                break
+        delivered_estimate = min(bits, remaining[user_id])
+        remaining[user_id] -= delivered_estimate
+        served[user_id] += delivered_estimate
+        decisions.append({"user": user_id, "mcs": int(mcs), "power_dbm": nominal_power})
+
+    return decisions
+    # EVOLVE-BLOCK-END
+
+
+if __name__ == "__main__":
+    demo = {
+        "num_resource_blocks": 2,
+        "power_min_dbm": 5.0,
+        "power_max_dbm": 24.0,
+        "mcs_table": [
+            {"mcs": 0, "snr_threshold_db": -3.0, "bits_per_rb": 180},
+            {"mcs": 1, "snr_threshold_db": 1.0, "bits_per_rb": 300},
+        ],
+        "users": [
+            {"id": 0, "queue_bits": 1000, "latency_weight": 1.0, "min_service_bits": 200, "snr_estimate_db": [2.0, 1.0]},
+            {"id": 1, "queue_bits": 800, "latency_weight": 1.4, "min_service_bits": 200, "snr_estimate_db": [0.0, 4.0]},
+        ],
+    }
+    print(schedule_frame(demo))