Gemini 4 Argon (effort medium · thinking on) on AIS collision risk: closest point of approach and alert levels
GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.5 (agy -p) · cloud · 2026-10-22 · by @bobsiopener
- Estimated cost
- $0.20
- Wall time
- 4m
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# AIS collision risk: CPA, TCPA and alert levels `input.json` (synthetic; not for collision avoidance): `own` ship (`lat`, `lon`, `cog` degrees true, `sog` knots), `targets` (each `mmsi`, `lat`, `lon`, `cog`, `sog`), thresholds `cpa_danger_nm`, `cpa_caution_nm`, `tcpa_max_min`. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `radar.svg`. Fresh inputs are used too. ## Rules 1. Skip a target (list its mmsi in `skipped`, ascending) if lat, lon, cog or sog is missing/null, sog >= 102.3 (AIS "not available"), or cog >= 360 (not available). Keep the rest in input order. 2. Local flat frame around own ship in nm: x = (lon - own lon) * 60 * cos(own lat), y = (lat - own lat) * 60. Velocity of a ship = (sog * sin(cog), sog * cos(cog)) knots. 3. Relative position r = target - own, relative velocity v = v_target - v_own. If |v|^2 < 1e-9, TCPA = 0. Otherwise TCPA (hours) = max(0, -(r . v) / |v|^2). CPA = |r + v * TCPA|. 4. `range_nm` = |r|, `bearing_deg` = atan2(x, y) mod 360 (true bearing from own ship), `tcpa_min` = TCPA * 60. 5. Level: `danger` if CPA <= cpa_danger and tcpa_min <= tcpa_max; else `caution` if CPA <= cpa_caution and tcpa_min <= tcpa_max; else `none`. 6. `alerts`: mmsi of danger then caution targets, each group sorted by tcpa_min then mmsi. Round range/CPA to 3 decimals, bearing to 1, tcpa_min to 2. ## Visual output: radar.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with an element `id="own-ship"` and one element `id="target-<mmsi>"` for every non-skipped target. The drawing itself is not graded. ## answer.json `{"targets": [{"mmsi", "range_nm", "bearing_deg", "cpa_nm", "tcpa_min", "level"}, ...], "skipped": [...], "alerts": [...]}` ## Scoring Hidden checks (partial credit; all must pass): `filtering`, `range_bearing`, `cpa_tcpa`, `levels_and_alerts`, `radar_svg_static` (radar.svg exists, follows the static rules, has `own-ship` and every `target-<mmsi>`), `fresh_run_1`, `fresh_run_2`. Tolerances: distances 0.002 nm, bearings 0.11, minutes 0.011 (fresh runs 0.011 for all numbers). 60-second limit per run.
AIS not-available sentinels (sog 102.3, cog 360), a stationary target, diverging targets with TCPA clamped to 0, longitude scaling, near-threshold crossing targets, and a two-key alert sort. Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a Google account via Antigravity CLI, not billed per token).
Benchmark code for comparing AI models, not navigation or safety software.
Success criteria: 1. filtering 2. range_bearing 3. cpa_tcpa 4. levels_and_alerts 5. radar_svg_static 6. fresh_run_1 7. fresh_run_2
Setup notes: Use Case Scout batch10 case k08-ais-cpa-tcpa (offline fixture). Prepared with batch10/prepare.sh on the shared box; only TASK.md + starting files went to the agent machine, hidden keys stayed on the box. Prompt = TASK.md verbatim. Flags: -p --model gemini-4-argon-medium --sandbox --dangerously-skip-permissions --disable-slash-commands --output-format stream-json, cwd=workdir. Scored on the box with `python score.py WORKDIR KEYDIR`. Single run, no retries, no human input. Visual output (radar.svg) rendered offline to PNG with google-chrome headless, network blocked, 1280x800 at ~3 s after load. Requested model gemini-4-argon-medium (effort medium); priced at the base model row gemini-4-argon (pricing.json, https://ai.google.dev/gemini-api/docs/pricing). Score: 7/7. Cost is an ESTIMATE: measured tokens x pricing.json list price (Google account, nothing billed per token); output_tokens include thinking tokens. Tool calls 11 (0 failed). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.20
- Wall time
- 4m
- Human step-ins
- 0
- Turns
- 11
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on | 197,836 | 10,536 | $0.20 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Gemini 4 Argon (google/gemini-4-argon, effort medium · thinking on) via
Antigravity CLI 1.4.5 (agy -p), cloud. - It counts as a success only if it meets every success criterion: 1. filtering 2. range_bearing 3. cpa_tcpa 4. levels_and_alerts 5. radar_svg_static 6. fresh_run_1 7. fresh_run_2
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"event": "init", "conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "init": {"model": "gemini-4-argon-medium", "cwd": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work", "tools": ["ask_custom_permission", "ask_permission", "ask_question", "browser_click_element", "browser_drag_pixel_to_pixel", "browser_get_dom", "browser_get_network_request", "browser_input", "browser_list_network_requests", "browser_mouse_down", "browser_mouse_up", "browser_move_mouse", "browser_press_key", "browser_refresh_page", "browser_resize_window", "browser_scroll", "browser_scroll_dom", "browser_select_option", "browser_subagent", "call_mcp_tool", "capture_browser_console_logs", "capture_browser_screenshot", "click_browser_pixel", "command_status", "define_subagent", "delete_knowledge", "execute_browser_javascript", "find_by_name", "finish", "generate_image", "grep_search", "invoke_subagent", "list_browser_pages", "list_dir", "list_permissions", "list_plugin_accounts", "list_resources", "manage_inbox", "manage_subagents", "manage_task", "multi_replace_file_content", "notebook_edit", "notebook_execution", "open_browser_url", "read_browser_page", "read_resource", "read_url_content", "replace_file_content", "run_command", "run_workflow", "schedule", "search_marketplace", "search_web", "sed_file", "send_command_input", "send_message", "view_file", "wait", "wait_5_seconds", "write_to_file"], "permission_mode": "always-proceed"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 0, "state": "DONE", "step_type": "user_input"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.826116924, "usage": {"input_tokens": 8522, "output_tokens": 69, "thinking_tokens": 15, "cache_read_tokens": 0, "total_tokens": 8591}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 2, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/input.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.044063564, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/input.json"}, "output": "97 lines, 1420 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 3, "state": "DONE", "step_type": "agent_response", "duration_seconds": 45.02286682, "usage": {"input_tokens": 2366, "output_tokens": 4380, "thinking_tokens": 1728, "cache_read_tokens": 7637, "total_tokens": 6746}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 4, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 4, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.798915319, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 5, "state": "DONE", "step_type": "agent_response", "duration_seconds": 13.145156053000001, "usage": {"input_tokens": 4460, "output_tokens": 85, "thinking_tokens": 22, "cache_read_tokens": 10004, "total_tokens": 4545}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 6, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 6, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 2.312861067, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat answer.json"}, "output": "Command exited with code 1.\n\nThere were sandbox errors that may or may not be related to the failure. If you think the failure is because of running in the sandbox, you can run the command again with `BypassSandbox` set to true to request explicit user permission.\n\nCommand output:\nTraceback (most recent call last):\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/solve.py\", line 217, in <module>\r\n solve(in_file, out_file)\r\n ~~~~~^^^^^^^^^^^^^^^^^^^\r\n File \"/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/solve.py\", line 133, in solve\r\n with open(answer_path, \"w\", encoding=\"utf-8\") as f:\r\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nOSError: [Errno 30] Read-only file system: 'answer.json'\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 7, "state": "DONE", "step_type": "agent_response", "duration_seconds": 12.600004525, "usage": {"input_tokens": 2437, "output_tokens": 887, "thinking_tokens": 500, "cache_read_tokens": 12415, "total_tokens": 3324}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 8, "state": "ACTIVE", "step_type": "tool", "tool_name": "edit_file", "tool_info": {"name": "edit_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 8, "state": "DONE", "step_type": "tool", "tool_name": "edit_file", "duration_seconds": 2.069080429, "tool_info": {"name": "edit_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 9, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.199929821, "usage": {"input_tokens": 3067, "output_tokens": 108, "thinking_tokens": 38, "cache_read_tokens": 12803, "total_tokens": 3175}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 10, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json /tmp/answer.json && cat /tmp/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 10, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.734096235, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json /tmp/answer.json && cat /tmp/answer.json"}, "output": "Command exited with code 0.\n\nCommand output:\n{\r\n \"targets\": [\r\n {\r\n \"mmsi\": 795070736,\r\n \"range_nm\": 7.981,\r\n \"bearing_deg\": 202.0,\r\n \"cpa_nm\": 0.309,\r\n \"tcpa_min\": 11.82,\r\n \"level\": \"danger\"\r\n },\r\n {\r\n \"mmsi\": 276139249,\r\n \"range_nm\": 5.193,\r\n \"bearing_deg\": 355.7,\r\n \"cpa_nm\": 5.192,\r\n \"tcpa_min\": 0.17,\r\n \"level\": \"none\"\r\n },\r\n {\r\n \"mmsi\": 265918776,\r\n \"range_nm\": 7.18,\r\n \"bearing_deg\": 293.1,\r\n \"cpa_nm\": 0.66,\r\n \"tcpa_min\": 6.2,\r\n \"level\": \"caution\"\r\n },\r\n {\r\n \"mmsi\": 743277030,\r\n \"range_nm\": 5.986,\r\n \"bearing_deg\": 85.4,\r\n \"cpa_nm\": 0.318,\r\n \"tcpa_min\": 32.02,\r\n \"level\": \"none\"\r\n },\r\n {\r\n \"mmsi\": 562375419,\r\n \"range_nm\": 3.312,\r\n \"bearing_deg\": 105.8,\r\n \"cpa_nm\": 0.724,\r\n \"tcpa_min\": 25.19,\r\n \"level\": \"caution\"\r\n },\r\n {\r\n \"mmsi\": 613463677,\r\n \"range_nm\": 6.126,\r\n \"bearing_deg\": 359.0,\r\n \"cpa_nm\": 0.013,\r\n \"tcpa_min\": 13.23,\r\n \"level\": \"danger\"\r\n },\r\n {\r\n \"mmsi\": 286629533,\r\n \"range_nm\": 3.941,\r\n \"bearing_deg\": 316.4,\r\n \"cpa_nm\": 1.245,\r\n \"tcpa_min\": 53.42,\r\n \"level\": \"none\"\r\n },\r\n {\r\n \"mmsi\": 637679438,\r\n \"range_nm\": 5.672,\r\n \"bearing_deg\": 124.2,\r\n \"cpa_nm\": 5.672,\r\n \"tcpa_min\": 0.0,\r\n \"level\": \"none\"\r\n },\r\n {\r\n \"mmsi\": 327721356,\r\n \"range_nm\": 4.489,\r\n \"bearing_deg\": 138.5,\r\n \"cpa_nm\": 4.489,\r\n \"tcpa_min\": 0.0,\r\n \"level\": \"none\"\r\n },\r\n {\r\n \"mmsi\": 777617410,\r\n \"range_nm\": 7.368,\r\n \"bearing_deg\": 162.4,\r\n \"cpa_nm\": 4.584,\r\n \"tcpa_min\": 50.26,\r\n \"level\": \"none\"\r\n }\r\n ],\r\n \"skipped\": [\r\n 340778672,\r\n 660159254\r\n ],\r\n \"alerts\": [\r\n 795070736,\r\n 613463677,\r\n 265918776,\r\n 562375419\r\n ]\r\n}"}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 11, "state": "DONE", "step_type": "agent_response", "duration_seconds": 5.136165341, "usage": {"input_tokens": 3228, "output_tokens": 101, "thinking_tokens": 74, "cache_read_tokens": 13821, "total_tokens": 3329}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 12, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/tmp/radar.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 12, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.692827136, "tool_info": {"name": "read_file", "parameters": {"Path": "/tmp/radar.svg"}, "output": "22 lines, 4209 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 13, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.089545952, "usage": {"input_tokens": 2621, "output_tokens": 127, "thinking_tokens": 100, "cache_read_tokens": 17050, "total_tokens": 2748}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 14, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/tmp/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 14, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.058309284, "tool_info": {"name": "read_file", "parameters": {"Path": "/tmp/answer.json"}, "output": "94 lines, 1758 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 15, "state": "DONE", "step_type": "agent_response", "duration_seconds": 35.434869888, "usage": {"input_tokens": 3529, "output_tokens": 3500, "thinking_tokens": 20, "cache_read_tokens": 17622, "total_tokens": 7029}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 16, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 16, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.453256099, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/answer.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 17, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/radar.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 17, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 2.756488801, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/radar.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 18, "state": "DONE", "step_type": "agent_response", "duration_seconds": 20.246297218, "usage": {"input_tokens": 3659, "output_tokens": 621, "thinking_tokens": 413, "cache_read_tokens": 21152, "total_tokens": 4280}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 19, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, xml.etree.ElementTree as ET\nwith open(\"answer.json\") as f: ans = json.load(f)\ntree = ET.parse(\"radar.svg\")\nroot = tree.getroot()\nids = {el.attrib.get(\"id\") for el in root.iter() if \"id\" in el.attrib}\nassert \"own-ship\" in ids\nfor t in ans[\"targets\"]:\n assert f\"target-{t[\\\"mmsi\\\"]}\" in ids\nfor s in ans[\"skipped\"]:\n assert f\"target-{s}\" not in ids\nprint(\"Verified successfully:\", len(ans[\"targets\"]), \"targets,\", len(ans[\"skipped\"]), \"skipped\")\n'"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 19, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.776515968, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, xml.etree.ElementTree as ET\nwith open(\"answer.json\") as f: ans = json.load(f)\ntree = ET.parse(\"radar.svg\")\nroot = tree.getroot()\nids = {el.attrib.get(\"id\") for el in root.iter() if \"id\" in el.attrib}\nassert \"own-ship\" in ids\nfor t in ans[\"targets\"]:\n assert f\"target-{t[\\\"mmsi\\\"]}\" in ids\nfor s in ans[\"skipped\"]:\n assert f\"target-{s}\" not in ids\nprint(\"Verified successfully:\", len(ans[\"targets\"]), \"targets,\", len(ans[\"skipped\"]), \"skipped\")\n'"}, "output": "Command exited with code 1.\n\nCommand output:\n File \"<string>\", line 9\r\n assert f\"target-{t[\\\"mmsi\\\"]}\" in ids\r\n ^\r\nSyntaxError: unexpected character after line continuation character\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 20, "state": "DONE", "step_type": "agent_response", "duration_seconds": 39.022056286, "usage": {"input_tokens": 2797, "output_tokens": 224, "thinking_tokens": 11, "cache_read_tokens": 22762, "total_tokens": 3021}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 21, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, xml.etree.ElementTree as ET\nwith open(\"answer.json\") as f: ans = json.load(f)\ntree = ET.parse(\"radar.svg\")\nroot = tree.getroot()\nids = {el.attrib.get(\"id\") for el in root.iter() if \"id\" in el.attrib}\nassert \"own-ship\" in ids\nfor t in ans[\"targets\"]:\n m = t[\"mmsi\"]\n assert f\"target-{m}\" in ids\nfor s in ans[\"skipped\"]:\n assert f\"target-{s}\" not in ids\nprint(\"Verified successfully:\", len(ans[\"targets\"]), \"targets,\", len(ans[\"skipped\"]), \"skipped\")\n'"}}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 21, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 2.035032356, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport json, xml.etree.ElementTree as ET\nwith open(\"answer.json\") as f: ans = json.load(f)\ntree = ET.parse(\"radar.svg\")\nroot = tree.getroot()\nids = {el.attrib.get(\"id\") for el in root.iter() if \"id\" in el.attrib}\nassert \"own-ship\" in ids\nfor t in ans[\"targets\"]:\n m = t[\"mmsi\"]\n assert f\"target-{m}\" in ids\nfor s in ans[\"skipped\"]:\n assert f\"target-{s}\" not in ids\nprint(\"Verified successfully:\", len(ans[\"targets\"]), \"targets,\", len(ans[\"skipped\"]), \"skipped\")\n'"}, "output": "Command exited with code 0.\n\nCommand output:\nVerified successfully: 10 targets, 2 skipped\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Implemented [solve.py](f"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ile:///home/"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "bob/usecase-runs/agy-bat"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ch/out/gemin"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "i-"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "4-argon-medium/k08/work/"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "solve.py) and generated "}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "[answer.json](file"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ":///home/bob/usecase-runs/agy-batch/"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "out/gemini-4"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-argon-medium/"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "k08/work/answer.json) and [radar.svg"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "](file:///home/bob/useca"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "se"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-runs/agy-ba"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "tch/out/gemini-4-argon-m"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "edium/k08/work/radar"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ".svg):\n\n- **Filtering (`skipped`)**:"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " Excludes targets with m"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "issing/`null"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "` `lat`, `lon`, `cog`, o"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "r"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " `sog`, `sog >= 102.3`, or `cog >= 3"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "60`, sorting"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " skipped MMSIs ascending (`"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "[340778672, 660159254]`).\n- **Kinema"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "tics & CPA/T"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "CPA**: Projects position"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s into the l"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ocal flat tangent plane "}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "around "}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`own` ship in nautical miles, comput"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "es relative "}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "position $\\mathbf{r}$ an"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "d velocity $\\mathbf{v}$,"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " evaluates $"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\\text{TCPA}$, $\\"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "text{CPA}$, range, and true bearing,"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " and rounds outputs to t"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "he specified precision.\n"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "- **Alerting"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " &"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " SVG**: Classifies `danger`, `cautio"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "n`, and `none` levels, o"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "rders `alert"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "s` (`"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "[795070736, 613463677, 265918776, 56"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "2375419]`), "}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "and outputs a static, se"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "lf-contained"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " [radar.svg](file:///hom"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e/"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "bob/usecase-runs/agy-batch/out/gemin"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "i-4-argon-medium/k08"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/work/radar.svg) containing `id=\"own"}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-ship\"` and `id=\"target-<mmsi>\"` for all active targets."}}
{"event": "step_update", "step_update": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "step_index": 22, "state": "DONE", "step_type": "agent_response", "text_delta": "\n", "duration_seconds": 18.598994771, "usage": {"input_tokens": 2374, "output_tokens": 434, "thinking_tokens": 14, "cache_read_tokens": 23510, "total_tokens": 2808}}}
{"event": "result", "result": {"conversation_id": "87bcae8a-75b1-4fd3-8be6-06b91b5ed02d", "status": "SUCCESS", "response": "Implemented [solve.py](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/solve.py) and generated [answer.json](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/answer.json) and [radar.svg](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/radar.svg):\n\n- **Filtering (`skipped`)**: Excludes targets with missing/`null` `lat`, `lon`, `cog`, or `sog`, `sog >= 102.3`, or `cog >= 360`, sorting skipped MMSIs ascending (`[340778672, 660159254]`).\n- **Kinematics & CPA/TCPA**: Projects positions into the local flat tangent plane around `own` ship in nautical miles, computes relative position $\\mathbf{r}$ and velocity $\\mathbf{v}$, evaluates $\\text{TCPA}$, $\\text{CPA}$, range, and true bearing, and rounds outputs to the specified precision.\n- **Alerting & SVG**: Classifies `danger`, `caution`, and `none` levels, orders `alerts` (`[795070736, 613463677, 265918776, 562375419]`), and outputs a static, self-contained [radar.svg](file:///home/bob/usecase-runs/agy-batch/out/gemini-4-argon-medium/k08/work/radar.svg) containing `id=\"own-ship\"` and `id=\"target-<mmsi>\"` for all active targets.\n", "duration_seconds": 215.079246991, "num_turns": 1, "usage": {"input_tokens": 39060, "output_tokens": 10536, "thinking_tokens": 2935, "cache_read_tokens": 158776, "total_tokens": 49596}}}