replace task to borden_source_inversion

README.md

@@ -118,7 +118,7 @@ Each model cell reports scores at `@2h / @4h / @6h / @8h / @10h / @12h`. Missing
 | Task | Category | Opus 4.8 | GPT-5.5 | GPT-5.4 | GLM-5.1 | DS-V4-Pro |
 |:-----|:-------|:---------|:--------|:--------|:--------|:----------|
 | bipedalwalker_locomotion_rl | Scientific & ML | 16.7/20.8/22.4/23.3/23.3/23.3 | 14.7/14.9/15.2/15.2/16.0/21.0 | 13.9/13.9/13.9/14.5/14.5/17.5 | 13.9/20.3/21.5/22.5/22.5/22.5 | 8.9/14.8/17.6/20.4/20.4/20.6 |
-| capecod_plume_reconstruction | Scientific & ML | 10.0/15.3/17.3/18.0/18.2/19.9 | 11.7/13.7/15.1/16.2/16.4/16.4 | 10.7/11.5/12.2/12.5/12.5/12.6 | 8.6/9.0/9.2/10.3/10.5/10.9 | 7.9/8.5/8.5/8.8/8.8/8.8 |
+| borden_source_inversion | Scientific & ML | — | — | — | — | — |
 | dabic_gravity_inversion | Scientific & ML | 9.5/15.2/15.7/17.4/17.5/17.5 | 15.9/16.2/16.7/17.0/17.2/17.3 | 14.6/14.6/15.5/15.5/15.0/15.0 | 9.2/13.7/16.0/16.5/16.5/17.1 | —/12.7/12.7/12.7/13.0/13.8 |
 | graph_node_classification | Scientific & ML | 59.4/62.7/65.0/65.6/66.5/66.6 | 54.7/55.1/55.1/55.3/55.9/56.0 | 54.9/56.2/56.5/56.9/57.5/57.6 | 49.4/52.3/52.3/52.3/52.3/52.3 | 46.0/48.2/49.2/51.3/51.7/51.8 |
 | ann_vector_search_qps | Systems & SE | 26.2/57.0/58.6/58.7/59.4/59.7 | 22.3/34.3/35.1/36.0/40.0/40.7 | 27.5/30.2/44.5/45.2/49.7/50.2 | 6.7/24.4/25.6/25.6/26.1/38.3 | 9.4/19.6/22.4/22.8/23.8/23.8 |

borden_source_inversion.json

@@ -0,0 +1,33 @@
+{
+  "task_id": "borden_source_inversion",
+  "name": "Borden Source Inversion",
+  "category": "Scientific Problems & ML",
+  "base_image": "python310",
+  "platform": "linux/amd64",
+  "internet": false,
+  "cwd": "/home/workspace/borden_inverse",
+  "submit_paths": [
+    "."
+  ],
+  "submit_exclude": [
+    "scoring/",
+    "__pycache__/",
+    ".pytest_cache/",
+    "*.pyc",
+    "private_generation_record_not_for_agent.json",
+    "score.json"
+  ],
+  "work": {
+    "image_tag": "f389fcf94240",
+    "specs_dir": null,
+    "agent_query": "\n# Borden 3D Groundwater Contaminant Source Inversion\n\n## Task\nYou are given a Borden-style 3D groundwater contaminant migration scene derived from a Borden-AdePy reproduction workflow. Your task is to infer a **finite-duration rectangular-region contaminant source** from public monitoring-well readings.\n\nThis is not a point-source task. A point-source answer or the old `x0,y0,z0,C0` schema is accepted only as a degenerate fallback and cannot receive full credit.\n\n## Required source parameterization\nCreate `answer.json` in the task root using this schema:\n\n```json\n{\n  \"source_type\": \"rectangular_region\",\n  \"dimension\": 3,\n  \"x_center\": 0.0,\n  \"y_center\": 0.0,\n  \"z_center\": 0.0,\n  \"half_length_x\": 0.0,\n  \"half_length_y\": 0.0,\n  \"half_length_z\": 0.0,\n  \"C0\": 0.0,\n  \"t_start\": 0.0,\n  \"duration\": 0.0,\n  \"transport_model\": {\n    \"equation_type\": \"advection_dispersion_reaction\",\n    \"governing_equation\": \"R*dC/dt = div(D grad C) - v dot grad C - lambda*C + source\",\n    \"velocity_m_per_day\": 0.0,\n    \"alpha_L_m\": 0.0,\n    \"alpha_TH_m\": 0.0,\n    \"alpha_TV_m\": 0.0,\n    \"porosity\": 0.0,\n    \"retardation_factor\": 1.0,\n    \"lambda_per_day\": 0.0,\n    \"numerical_approach\": \"brief description of forward model and optimization\"\n  },\n  \"method\": \"brief description of your inversion method\"\n}\n```\n\n- `x_center,y_center,z_center`: center of the rectangular source region, in meters.\n- `half_length_x,half_length_y,half_length_z`: half sizes of the rectangular source region, in meters.\n- `C0`: effective source concentration/intensity, in mg/L.\n- `t_start`: release start time in days.\n- `duration`: release duration in days.\n- `transport_model`: your groundwater solute transport construction. Include the\n  ADE/reaction governing equation, public hydrogeologic parameters used, and the\n  numerical or analytical approximation used to predict concentrations.\n\nAll parameters must stay within `public_problem_config.json` → `source_search_bounds_for_agent`.\n\n## Provided files\n\n- `public_problem_config.json`: Borden grid, hydrogeological parameters, source prior bounds, and column definitions.\n- `public_source_prior.json`: explicit range summary for the finite-duration rectangular-region source parameters.\n- `borden_grid.npz`: grid arrays exported from the Borden-AdePy scene, including x/y grid and bottom profile.\n- `public_wells.csv`: public monitoring-well coordinates.\n- `public_monitoring_data.csv`: noisy, censored public monitoring observations. Clean generated\n  concentrations are not provided to the agent.\n- `baseline_solver.py`: writes a legal low-quality baseline `answer.json` from the center of the parameter bounds.\n- `answer_template.json`: required output schema.\n\nNo runnable scientific starter solver is provided. You should write your own Python code to read files, construct the groundwater solute transport equation, build an ADE/AdePy or equivalent forward approximation, optimize the source parameters, and update `answer.json`.\n\n## Mandatory baseline workflow\n\nBefore implementing a complex inversion algorithm, first ensure that a valid `answer.json` exists:\n\n```bash\npython baseline_solver.py\n```\n\nThen iteratively improve `answer.json` using public monitoring data. Missing `answer.json` gives zero.\n\n## Scoring policy\n\nThe judge does not grade old point-source location error directly. It evaluates whether your finite-duration rectangular-region source predicts withheld monitoring readings:\n\n1. Read `answer.json`.\n2. Check required finite-region fields and parameter bounds.\n3. Use a hidden Borden-ADE region-source forward model to predict concentrations at hidden monitoring wells and hidden future times.\n4. Compare predictions with hidden readings using relative RMSE and log-scale metrics.\n5. Hidden prediction dominates the score. Easy format/prior/method/transport-equation points are capped and cannot exceed the hidden-quality caps.\n6. Region-shape and physical-consistency points are gated by hidden prediction quality.\n7. Very poor hidden/future prediction is capped at 15 points; poor-but-improving prediction is capped at 30 points; moderate prediction is capped at 45 points.\n\nIf hidden monitoring prediction is poor, the final score is capped. This makes early baseline or shallow point-source fits low-scoring, while still allowing genuine improvement through better region-source inversion.\n\n## Rules\n\n- Do not read or reference hidden scoring files.\n- Do not hard-code hidden monitoring readings.\n- Do not modify judge files.\n- Do not call FloPy, MODFLOW, MT3DMS, or external groundwater executables.\n- You may use Python libraries such as NumPy, SciPy, pandas, matplotlib, pymoo, and AdePy if available.\n- Submit all scripts/results needed to reproduce your `answer.json`.\n\n## Critical submission rule\n\nThe judge only reads answer.json in the task root.\n\nIt does not automatically run inverse_solver.py, run_checks.py, write_fit_report.py,\nwrite_public_predictions.py, or any other script during scoring.\n\nTherefore, after every meaningful inversion or optimization step, you must immediately\noverwrite the task-root answer.json with the best current source parameters.\n\nIf answer.json is unchanged, the score and all METRICS will remain unchanged, even if\nyou create new Python scripts, reports, or prediction files.\n\nRecommended workflow:\n\n1. Run python baseline_solver.py only as an initial fallback.\n2. Implement and run your inversion script.\n3. After each improved parameter set is found, write it to answer.json.\n4. Run python validate_answer.py if available.\n5. Submit only after confirming that answer.json has changed.\n"
+  },
+  "judge": {
+    "image_tag": "cc54d4fd7006",
+    "eval_cmd": "python /opt/borden_scoring/evaluate.py --submission_dir /home/workspace/borden_inverse --case_dir /home/workspace/borden_inverse --scoring_dir /opt/borden_scoring --output /home/workspace/borden_inverse/score.json",
+    "eval_timeout": 600,
+    "parser": "structured_json",
+    "score_direction": "maximize",
+    "selection": "score_first"
+  }
+}

capecod_plume_reconstruction.json

@@ -1,40 +0,0 @@
-{
-  "task_id": "capecod_plume_reconstruction",
-  "name": "Capecod Plume Reconstruction",
-  "category": "Scientific Problems & ML",
-  "base_image": "python",
-  "platform": "linux/amd64",
-  "internet": false,
-  "cwd": "/home/workspace/capecod_plumebench",
-  "submit_paths": [
-    "model.py",
-    "predict.py",
-    "monitoring_plan.py",
-    "baseline_solver.py",
-    "predictions.csv",
-    "plume_metrics.json",
-    "monitoring_plan.json",
-    "answer.json",
-    "report.md"
-  ],
-  "submit_exclude": [
-    "data/",
-    "schemas/",
-    "hidden/",
-    "scoring/",
-    "__pycache__/"
-  ],
-  "work": {
-    "image_tag": "d051446beb3a",
-    "specs_dir": "/home/workspace/capecod_plumebench",
-    "agent_query": "# CapeCod-PlumeBench: Groundwater Plume Reconstruction and Monitoring Design\n\nYou are a groundwater remediation modeling engineer. The workspace contains partial public monitoring\ndata from an offline benchmark inspired by the USGS Cape Cod treated-wastewater groundwater plume.\n\nYour job is to replace the weak baseline with a better, defensible modeling workflow that:\n\n1. Cleans and interprets public well, chemistry, and site-configuration data.\n2. Predicts hidden well/year/analyte concentrations listed in `data/prediction_requests.csv`.\n3. Estimates plume metrics requested in `data/plume_metric_requests.csv`.\n4. Proposes up to 8 next monitoring wells under the budget in `data/public_site_config.json`.\n5. Writes a concise technical report explaining your model, assumptions, validation, and monitoring logic.\n\nRequired outputs:\n\n- `model.py`\n- `predict.py`\n- `monitoring_plan.py`\n- `predictions.csv`\n- `plume_metrics.json`\n- `monitoring_plan.json`\n- `answer.json`\n- `report.md`\n\nRun `python baseline_solver.py` to regenerate outputs before submitting. The current baseline is valid\nbut intentionally weak. Improve the model rather than only editing output files. Good solutions use\nspace-time structure, analyte-specific behavior, plume-front geometry, censored observations, and\nuncertainty-aware monitoring selection.\n\nRules:\n\n- Do not attempt to read hidden judge or scoring files.\n- Do not hard-code target truth values or hidden candidate utilities.\n- Keep all outputs finite, nonnegative, and schema-compliant.\n- Respect the monitoring budget and maximum number of wells.\n"
-  },
-  "judge": {
-    "image_tag": "f633dd04bd60",
-    "eval_cmd": "python /opt/capecod_scoring/evaluate.py",
-    "eval_timeout": 180,
-    "parser": "score_sum",
-    "score_direction": "maximize",
-    "selection": "score_first"
-  }
-}

tasks.jsonl

@@ -1,5 +1,5 @@
 {"task_id": "bipedalwalker_locomotion_rl", "name": "Bipedalwalker Locomotion Rl", "category": "Scientific Problems & ML", "description": "Train a CPU-only locomotion policy for BipedalWalker and its Hardcore variant. The judge evaluates only the submitted policy checkpoint, not the training process. Pre-trained policies and external RL libraries are prohibited.", "language": "Python", "metric": "score (maximize)", "internet": false}
-{"task_id": "capecod_plume_reconstruction", "name": "Capecod Plume Reconstruction", "category": "Scientific Problems & ML", "description": "Reconstruct a multi-analyte groundwater plume from sparse monitoring wells: predict concentrations at withheld locations and times, compute plume metrics, and propose an optimal monitoring network under budget constraints.", "language": "Python", "metric": "score (maximize)", "internet": false}
+{"task_id": "borden_source_inversion", "name": "Borden Source Inversion", "category": "Scientific Problems & ML", "description": "Infer a finite-duration rectangular contaminant source from sparse, noisy monitoring-well observations in a 3D hydrogeological scene. The agent must implement its own forward model and inversion optimizer from scratch.", "language": "Python", "metric": "score (maximize)", "internet": false}
 {"task_id": "dabic_gravity_inversion", "name": "Dabic Gravity Inversion", "category": "Scientific Problems & ML", "description": "Implement the D-ABIC regularization method for 3D gravity inversion within the SimPEG framework, run on both synthetic and real Vinton salt dome data under L0 and L1 sparse norms, and compare against a Hamiltonian Monte Carlo baseline.", "language": "Python", "metric": "score (maximize)", "internet": false}
 {"task_id": "graph_node_classification", "name": "Graph Node Classification", "category": "Scientific Problems & ML", "description": "Implement graph neural networks from scratch using only base PyTorch for semi-supervised node classification on an unseen graph under CPU-only constraints.", "language": "Python", "metric": "score (maximize)", "internet": false}
 {"task_id": "ann_vector_search_qps", "name": "Ann Vector Search Qps", "category": "Systems & Software Engineering", "description": "Replace a brute-force NumPy nearest-neighbor baseline with a high-performance approximate nearest-neighbor implementation under a hard recall constraint. Scored by queries per second.", "language": "Python", "metric": "score (maximize)", "internet": false}