Gemini 4 Argon (effort medium · thinking on) on Seeded Monte Carlo retirement odds against a stated success threshold

GGemini 4 Argonmedium · thinking on · Antigravity CLI 1.4.8 (agy -p) · cloud · 2026-10-28 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.08
Wall time: 1m
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Seeded Monte Carlo retirement odds `input.json` holds the assumptions for a Monte Carlo retirement simulation (synthetic). Write `solve.py` (Python 3 standard library only) so that `python solve.py input.json answer.json` runs the simulation exactly as specified and writes the statistics. Because the random sequence is fixed by the seed, the answer is exact. We re-run it on fresh inputs, so do not hard-code anything. ## Input `seed`, `sims`, `years` (integers), `start_balance`, `annual_spend` (first-year spending), `mean_return`, `sd_return`, `mean_inflation`, `sd_inflation`, `success_threshold` (fractions). ## Rules 1. Create one generator `rng = random.Random(seed)` and use it for everything, in this order. Run simulations s = 0 .. sims-1 one after another. 2. Each simulation starts with balance = `start_balance` and spend = `annual_spend` (as floats). For each year y = 0 .. years-1: draw `r = rng.gauss(mean_return, sd_return)`, then `i = rng.gauss(mean_inflation, sd_inflation)` (both are drawn every year, in that order, before anything else), then balance -= spend. 3. If the balance is now below 0, the simulation **fails** in year y+1: its ending balance is 0 and it stops immediately (no more draws for it). Otherwise balance *= (1 + r) and spend *= (1 + i). 4. A simulation that never fails succeeds; its ending balance is the balance after the last year. 5. `success_rate` = successes / sims (round to 6 decimals). `meets_threshold` = (unrounded success rate >= `success_threshold`). `sims_failed` = number of failures. 6. Percentiles: sort all ending balances ascending (failed ones count as 0) into E; `pX` = E[int(X/100 * (sims - 1))] for X = 10, 50, 90 (round to 2 decimals). 7. `median_failure_year`: sort the failure years ascending into F and take F[int(0.5 * (len(F) - 1))]; `null` if nothing failed. 8. `best_sim` = index of the highest ending balance (lowest index on ties). `earliest_failure_sim` = index of the simulation that failed in the earliest year (lowest index on ties); `null` if nothing failed. ## Output `answer.json`: an object with keys `success_rate`, `meets_threshold`, `sims_failed`, `p10`, `p50`, `p90`, `median_failure_year`, `best_sim`, `earliest_failure_sim`. ## Chart (required visual) Also write `chart.svg` in the working directory: a static, self-contained SVG (root element `<svg>`, no scripts, no external images, fonts, stylesheets or links) that can be rendered headless. It must contain a percentile fan: `<polyline id="p10">`, `<polyline id="p50">`, `<polyline id="p90">`, each the 10th/50th/90th percentile end-of-year balance across simulations for each year (a failed simulation counts as 0 from its failure year). ## Scoring The `chart_svg_static` check (one more check, counted in partial credit) passes only if `chart.svg` exists after the fixture run, parses, has root `<svg>`, contains the ids above with the stated tags, and has no `<script>`, `<img>`/`<image>`, `<link>`, `<iframe>`/`<object>`/`<embed>`, `on*` handlers, or `href`/`src` pointing anywhere except `#...` or `data:`. Its drawing is not graded. Hidden checks (partial credit = fraction passed; all must pass): `success_rate_and_threshold` (rate within 0.000001, the other two exact), `percentiles` (within 0.011), `failure_stats` (exact), `best_sim` (exact) on the given input; `fresh_run_1` and `fresh_run_2`: every key on a fresh input (money within 0.011, rates within 0.011, counts and indexes exact). Each run has a 120-second limit.

With a fixed seed the success rate is exact only if the draw order (return then inflation, every year, stop on failure) and the percentile/tie rules are followed exactly; approximate simulations score near zero. Inspired by https://x.com/allen_lattimer/status/2105321749323841757 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).

These are synthetic test cases for comparing AI models, not financial advice.

Success criteria: 1. success_rate_and_threshold 2. percentiles 3. failure_stats 4. best_sim 5. fresh_run_1 6. fresh_run_2 7. chart_svg_static

Setup notes: batch9 case j02-retirement-monte-carlo (offline fixture). Prepared with 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 the case's scorer. Single run, no retries, no human input. Timeout: 10-minute inactivity limit, no total time cap. Visual output (chart.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 4 (0 failed). Single run.

Estimated cost List-price estimate; not actually billed.

Output

Gemini 4 Argon (effort medium · thinking on) on Seeded Monte Carlo retirement odds against a stated success thresholdSuccess

Metrics

Estimated cost: $0.08
Wall time: 1m
Human step-ins: 0
Turns: 5

Model usage
Model	Input tok	Output tok	Cost
GGemini 4 Argongoogle/gemini-4-argoneffort medium · thinking on	55,702	3,591	$0.08

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 1.4.8 (agy -p), cloud.
It counts as a success only if it meets every success criterion: 1. success_rate_and_threshold 2. percentiles 3. failure_stats 4. best_sim 5. fresh_run_1 6. fresh_run_2 7. chart_svg_static
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": "e76204ab-0c5f-41af-97fc-732e766e806c", "init": {"model": "gemini-4-argon-medium", "cwd": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/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"}}
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{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 1, "state": "DONE", "step_type": "agent_response", "duration_seconds": 3.97083663, "usage": {"input_tokens": 8138, "output_tokens": 71, "thinking_tokens": 0, "cache_read_tokens": 0, "total_tokens": 8209}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 2, "state": "ACTIVE", "step_type": "tool", "tool_name": "read_file", "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/input.json"}}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 2, "state": "DONE", "step_type": "tool", "tool_name": "read_file", "duration_seconds": 0.025337089, "tool_info": {"name": "read_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/input.json"}, "output": "12 lines, 226 bytes"}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 3, "state": "DONE", "step_type": "agent_response", "duration_seconds": 25.223743556, "usage": {"input_tokens": 2389, "output_tokens": 2565, "thinking_tokens": 0, "cache_read_tokens": 6084, "total_tokens": 4954}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 4, "state": "ACTIVE", "step_type": "tool", "tool_name": "write_file", "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 4, "state": "DONE", "step_type": "tool", "tool_name": "write_file", "duration_seconds": 1.561610291, "tool_info": {"name": "write_file", "parameters": {"Path": "/home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/solve.py"}}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 5, "state": "DONE", "step_type": "agent_response", "duration_seconds": 2.865542632, "usage": {"input_tokens": 2644, "output_tokens": 71, "thinking_tokens": 0, "cache_read_tokens": 8474, "total_tokens": 2715}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "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 && head -n 20 chart.svg"}}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 6, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 1.068298498, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 solve.py input.json answer.json && cat answer.json && head -n 20 chart.svg"}, "output": "Command exited with code 0.\n\nCommand output:\n{\r\n  \"success_rate\": 0.824667,\r\n  \"meets_threshold\": false,\r\n  \"sims_failed\": 263,\r\n  \"p10\": 0.0,\r\n  \"p50\": 1473403.0,\r\n  \"p90\": 5177505.89,\r\n  \"median_failure_year\": 23,\r\n  \"best_sim\": 692,\r\n  \"earliest_failure_sim\": 294\r\n}<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 800 500\" width=\"800\" height=\"500\">\r\n  <rect width=\"800\" height=\"500\" fill=\"#ffffff\" />\r\n  <g stroke=\"#e0e0e0\" stroke-width=\"1\">\r\n    <line x1=\"80\" y1=\"40\" x2=\"760\" y2=\"40\" />\r\n    <line x1=\"80\" y1=\"240.00\" x2=\"760\" y2=\"240.00\" />\r\n    <line x1=\"80\" y1=\"440\" x2=\"760\" y2=\"440\" />\r\n  </g>\r\n  <polygon points=\"80.00,285.49 106.15,262.97 132.31,251.65 158.46,240.32 184.62,232.13 210.77,223.51 236.92,215.02 263.08,204.89 289.23,193.86 315.38,188.89 341.54,179.49 367.69,168.51 393.85,163.73 420.00,161.53 446.15,149.12 472.31,139.76 498.46,133.53 524.62,123.57 550.77,121.07 576.92,111.52 603.08,100.19 629.23,77.94 655.38,76.51 681.54,70.10 707.69,62.74 733.85,47.94 760.00,40.00 760.00,440.00 733.85,440.00 707.69,440.00 681.54,438.23 655.38,428.26 629.23,419.74 603.08,409.91 576.92,403.25 550.77,395.60 524.62,388.20 498.46,381.91 472.31,376.03 446.15,369.02 420.00,366.67 393.85,361.03 367.69,355.24 341.54,350.41 315.38,345.58 289.23,339.15 263.08,334.95 236.92,330.86 210.77,327.10 184.62,323.51 158.46,317.50 132.31,311.12 106.15,303.34 80.00,285.49\" fill=\"#cce5ff\" opacity=\"0.5\" />\r\n  <polyline id=\"p90\" fill=\"none\" stroke=\"#28a745\" stroke-width=\"2\" points=\"80.00,285.49 106.15,262.97 132.31,251.65 158.46,240.32 184.62,232.13 210.77,223.51 236.92,215.02 263.08,204.89 289.23,193.86 315.38,188.89 341.54,179.49 367.69,168.51 393.85,163.73 420.00,161.53 446.15,149.12 472.31,139.76 498.46,133.53 524.62,123.57 550.77,121.07 576.92,111.52 603.08,100.19 629.23,77.94 655.38,76.51 681.54,70.10 707.69,62.74 733.85,47.94 760.00,40.00\" />\r\n  <polyline id=\"p50\" fill=\"none\" stroke=\"#007bff\" stroke-width=\"2.5\" points=\"80.00,285.49 106.15,283.66 132.31,282.47 158.46,282.75 184.62,281.78 210.77,282.55 236.92,280.09 263.08,281.64 289.23,284.05 315.38,283.28 341.54,282.98 367.69,282.67 393.85,284.54 420.00,284.57 446.15,286.36 472.31,286.10 498.46,291.11 524.62,291.92 550.77,294.74 576.92,295.04 603.08,300.22 629.23,302.43 655.38,304.92 681.54,308.01 707.69,311.50 733.85,319.41 760.00,326.17\" />\r\n  <polyline id=\"p10\" fill=\"none\" stroke=\"#dc3545\" stroke-width=\"2\" points=\"80.00,285.49 106.15,303.34 132.31,311.12 158.46,317.50 184.62,323.51 210.77,327.10 236.92,330.86 263.08,334.95 289.23,339.15 315.38,345.58 341.54,350.41 367.69,355.24 393.85,361.03 420.00,366.67 446.15,369.02 472.31,376.03 498.46,381.91 524.62,388.20 550.77,395.60 576.92,403.25 603.08,409.91 629.23,419.74 655.38,428.26 681.54,438.23 707.69,440.00 733.85,440.00 760.00,440.00\" />\r\n  <line x1=\"80\" y1=\"40\" x2=\"80\" y2=\"440\" stroke=\"#333333\" stroke-width=\"1.5\" />\r\n  <line x1=\"80\" y1=\"440\" x2=\"760\" y2=\"440\" stroke=\"#333333\" stroke-width=\"1.5\" />\r\n  <text x=\"400\" y=\"25\" text-anchor=\"middle\" font-family=\"sans-serif\" font-size=\"16\" fill=\"#222222\">Monte Carlo Retirement Balance Percentile Fan (P10 / P50 / P90)</text>\r\n  <text x=\"400\" y=\"485\" text-anchor=\"middle\" font-family=\"sans-serif\" font-size=\"12\" fill=\"#444444\">Year</text>\r\n</svg>\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 7, "state": "DONE", "step_type": "agent_response", "duration_seconds": 4.53202836, "usage": {"input_tokens": 2682, "output_tokens": 200, "thinking_tokens": 0, "cache_read_tokens": 11119, "total_tokens": 2882}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 8, "state": "ACTIVE", "step_type": "tool", "tool_name": "run_command", "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport xml.etree.ElementTree as ET\ntree = ET.parse(\"chart.svg\")\nroot = tree.getroot()\nassert root.tag.endswith(\"svg\"), root.tag\nids = {el.attrib.get(\"id\"): el.tag for el in root.iter() if \"id\" in el.attrib}\nprint(\"Found IDs:\", ids)\nassert \"p10\" in ids and ids[\"p10\"].endswith(\"polyline\")\nassert \"p50\" in ids and ids[\"p50\"].endswith(\"polyline\")\nassert \"p90\" in ids and ids[\"p90\"].endswith(\"polyline\")\nprint(\"XML verification passed!\")\n'"}}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 8, "state": "DONE", "step_type": "tool", "tool_name": "run_command", "duration_seconds": 0.890314884, "tool_info": {"name": "run_command", "parameters": {"CommandLine": "python3 -c '\nimport xml.etree.ElementTree as ET\ntree = ET.parse(\"chart.svg\")\nroot = tree.getroot()\nassert root.tag.endswith(\"svg\"), root.tag\nids = {el.attrib.get(\"id\"): el.tag for el in root.iter() if \"id\" in el.attrib}\nprint(\"Found IDs:\", ids)\nassert \"p10\" in ids and ids[\"p10\"].endswith(\"polyline\")\nassert \"p50\" in ids and ids[\"p50\"].endswith(\"polyline\")\nassert \"p90\" in ids and ids[\"p90\"].endswith(\"polyline\")\nprint(\"XML verification passed!\")\n'"}, "output": "Command exited with code 0.\n\nCommand output:\nFound IDs: {'p90': '{http://www.w3.org/2000/svg}polyline', 'p50': '{http://www.w3.org/2000/svg}polyline', 'p10': '{http://www.w3.org/2000/svg}polyline'}\r\nXML verification passed!\r\n"}}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "Implemented"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " [solve.py](file:///home/bob/usecase"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-runs/b"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "2930/out-poll-2026-10-27/agy/gemini-"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "4-argon-medium/j02/work/solve.py) and ran it aga"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "inst [input."}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "json"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "](file:///home/bob/usecase-runs/b293"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "0/out-poll-20"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "26-10-27/agy/gemini-4-argon-medium/j"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "02/work"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/input.json) to produce [answer.json"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "](file:///home/bob/useca"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "se-runs/b"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "2930/out-poll-2026-10-27/agy/gemini-"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "4-argon-medium/j02/work/answer.json)"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " and [chart.svg](file://"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "home/bob/usecase-runs/b2930/out-poll-2026-"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "10-27/agy/gemini-4-argon-medium/j02/work/chart."}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "svg).\n\n### Summary of Implementation"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " & Verification\n- **Simu"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "lation engin"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e** ([`run_simulation`]("}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "file:///home/bob/use"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "case-runs/b2930/out-poll-2026-10-27"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "/agy/gemini-4-argon-medium/j02/work/"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "solve.py#L7-L106)):"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " Uses a single `random.Random(seed)` instance ac"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ross simulations `0 .. s"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ims-1` and y"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ears `0 .. years"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-1`, drawing `r` then `i` via `rng.gauss` before"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " subtracting `spend`, ha"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "lting immedi"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ately on `< 0` balance"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": ", and computing all required summary statistics "}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "and percentiles.\n- **SVG"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": " Percentile Fan** ([`wri"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "te_chart`](f"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ile:///home/bob/usecase"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-runs/b2930/out-poll-2026-10-27/"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "agy/gemini-4-argon-mediu"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "m/j02/work/solve.py#L109"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-L165)): Generates a static, self-contained `<svg>` containi"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ng `<polylin"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "e id=\"p10\">"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`, `<polyline id=\"p50\">`, and `<polyline id=\"p90"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "\">` across each simulati"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "on year.\n- **Fixture Out"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "put"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "** ([answer.json](file:///home/bob/usecase-runs/b2930/out"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "-poll-2026-10-27/agy/gemini-4-argon-"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "medium/"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "j02/work/answer.json)):\n  - `success_rate`: `0.8"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "24"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "667` (`meets_threshold`: `false`, `s"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "ims_failed`: `263`)\n  - "}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`p10`: `0.0`, `p50`: `1473403.0`, `p90`: "}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`5177505.89`\n  - `median_failure_year`: `23"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "ACTIVE", "step_type": "agent_response", "text_delta": "`, `best_sim`: `692`, `earliest_failure_sim`: `294`"}}
{"event": "step_update", "step_update": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "step_index": 9, "state": "DONE", "step_type": "agent_response", "text_delta": "\n", "duration_seconds": 11.77847532, "usage": {"input_tokens": 2420, "output_tokens": 684, "thinking_tokens": 0, "cache_read_tokens": 11752, "total_tokens": 3104}}}
{"event": "result", "result": {"conversation_id": "e76204ab-0c5f-41af-97fc-732e766e806c", "status": "SUCCESS", "response": "Implemented [solve.py](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/solve.py) and ran it against [input.json](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/input.json) to produce [answer.json](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/answer.json) and [chart.svg](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/chart.svg).\n\n### Summary of Implementation & Verification\n- **Simulation engine** ([`run_simulation`](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/solve.py#L7-L106)): Uses a single `random.Random(seed)` instance across simulations `0 .. sims-1` and years `0 .. years-1`, drawing `r` then `i` via `rng.gauss` before subtracting `spend`, halting immediately on `< 0` balance, and computing all required summary statistics and percentiles.\n- **SVG Percentile Fan** ([`write_chart`](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/solve.py#L109-L165)): Generates a static, self-contained `<svg>` containing `<polyline id=\"p10\">`, `<polyline id=\"p50\">`, and `<polyline id=\"p90\">` across each simulation year.\n- **Fixture Output** ([answer.json](file:///home/bob/usecase-runs/b2930/out-poll-2026-10-27/agy/gemini-4-argon-medium/j02/work/answer.json)):\n  - `success_rate`: `0.824667` (`meets_threshold`: `false`, `sims_failed`: `263`)\n  - `p10`: `0.0`, `p50`: `1473403.0`, `p90`: `5177505.89`\n  - `median_failure_year`: `23`, `best_sim`: `692`, `earliest_failure_sim`: `294`\n", "duration_seconds": 51.841492687, "num_turns": 1, "usage": {"input_tokens": 18273, "output_tokens": 3591, "thinking_tokens": 0, "cache_read_tokens": 37429, "total_tokens": 21864}}}