hermes · spark · shootout

← qwen3-next-80b

bandit-delayed-feedback

machine-learning fail 10 turns 0s 9 tool calls
grader output from /tests/test.sh in the task sandbox 
grader verdict: FAIL (exit 0)
hold
ERROR ../tests/test_outputs.py::TestDelayModel::test_conversion_rate_error_threshold
ERROR ../tests/test_outputs.py::TestArmStatistics::test_conversion_rate_rmse_threshold
FAILED ../tests/test_outputs.py::TestOutputFormat::test_predictions_file_exists
FAILED ../tests/test_outputs.py::TestOutputFormat::test_delay_model_file_exists
FAILED ../tests/test_outputs.py::TestOutputFormat::test_arm_statistics_file_exists
========================= 3 failed, 17 errors in 1.33s =========================
system · msg 0 
You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands.
user · msg 1 
# Multi-Armed Bandit with Delayed Feedback

You are a machine learning engineer at AdTech Corp, an online advertising platform. Your team 
manages a real-time ad selection system that chooses which advertisement to show to each user.
The challenge: **conversions (clicks, purchases) arrive with significant delays** - users may 
convert hours or even days after seeing an ad.

## BUSINESS CONTEXT

The platform serves 10 different ad campaigns (arms). Each impression generates revenue only 
if the user converts. The key challenge is that conversion feedback is **delayed**:
- Some users convert within minutes
- Most conversions happen within 1-24 hours  
- Some conversions take up to 72 hours
- After 72 hours, we assume no conversion will occur

This delay creates a critical problem: **recently shown ads appear to perform worse** because 
their conversions haven't arrived yet. Naive algorithms will under-explore promising new ads 
and over-exploit older ads with complete feedback.

Additionally, ad performance is **non-stationary** - conversion rates vary by:
- Hour of day (higher during business hours)
- Day of week (weekends differ from weekdays)
- User context features

## INPUT DATA

All data files are located in `/app/data/`.

### A. Historical Impressions: `impressions.csv`
Records of past ad impressions with 50,000 rows.

| Column | Type | Description |
|--------|------|-------------|
| `impression_id` | string | Unique identifier (IMP_XXXXXX) |
| `arm_id` | int | Ad campaign ID (0-9) |
| `timestamp` | int | Unix timestamp of impression |
| `hour_of_day` | int | Hour (0-23) |
| `day_of_week` | int | Day (0=Monday, 6=Sunday) |
| `user_segment` | string | User segment ("young", "middle", "senior") |
| `device_type` | string | Device ("mobile", "desktop", "tablet") |
| `context_feature_1` | float | Contextual signal 1 (normalized 0-1) |
| `context_feature_2` | float | Contextual signal 2 (normalized 0-1) |

### B. Conversion Events: `conversions.csv`
Records of conversions linked to impressions.

| Column | Type | Description |
|--------|------|-------------|
| `impression_id` | string | Links to impressions.csv |
| `conversion_timestamp` | int | Unix timestamp when conversion occurred |
| `delay_hours` | float | Hours between impression and conversion |
| `conversion_value` | float | Revenue from conversion ($0.5 - $10.0) |

**Note**: Not all impressions have conversions. An impression without a matching conversion 
record means either: (1) the user didn't convert, or (2) the conversion hasn't arrived yet 
(if the impression is recent).

### C. Arm Metadata: `arms_metadata.json`
Information about each ad campaign.

```json
{
  "arms": [
    {
      "arm_id": 0,
      "name": "Campaign A",
      "category": "electronics",
      "base_bid": 0.5
    },
    ...
  ]
}
```

### D. Test Contexts: `test_contexts.csv`
New contexts where you must select the best arm. Contains 2,000 decision points.

| Column | Type | Description |
|--------|------|-------------|
| `context_id` | string | Unique identifier (CTX_XXXXXX) |
| `timestamp` | int | Decision timestamp |
| `hour_of_day` | int | Hour (0-23) |
| `day_of_week` | int | Day (0=Monday, 6=Sunday) |
| `user_segment` | string | User segment |
| `device_type` | string | Device type |
| `context_feature_1` | float | Contextual signal 1 |
| `context_feature_2` | float | Contextual signal 2 |

## PROBLEM DEFINITION

### Task 1: Delay Distribution Modeling
Model the delay distribution for conversions. This is critical for:
- Estimating "pending" conversions for recent impressions
- Adjusting reward estimates to account for incomplete feedback

### Task 2: Arm Selection Policy
For each context in `test_contexts.csv`, select the best arm (ad campaign) to maximize 
expected conversion value. Your policy should:
- Use historical data to estimate arm performance
- Account for delayed feedback in reward estimation
- Consider contextual features for personalized selection
- Balance exploration vs ex
… (3618 more chars)
assistant · msg 2 1 tool call 
I'll solve this multi-armed bandit problem with delayed feedback step by step.

First, let's explore the data to understand the problem better.


search_files({"pattern": "/app/data/", "target": "files"})
tool · msg 3 
{"total_count": 0}
assistant · msg 4 1 tool call 
I need to check the correct path for the data files. Let me look in the root directory to find the data.



search_files({"pattern": "*.csv", "target": "files"})
tool · msg 5 
{"total_count": 3, "files": ["./data/test_contexts.csv", "./data/impressions.csv", "./data/conversions.csv"]}
assistant · msg 6 1 tool call 
Great! I found the data files. Now let's examine the contents of these files to understand the data structure better.



read_file({"path": "/app/data/impressions.csv", "offset": 1, "limit": 5})
tool · msg 7 
{"content": "     1|impression_id,arm_id,timestamp,hour_of_day,day_of_week,user_segment,device_type,context_feature_1,context_feature_2\n     2|IMP_000000,0,1709340758,9,5,young,mobile,0.7797,0.5969\n     3|IMP_000001,6,1710354874,3,3,young,desktop,0.7081,0.0206\n     4|IMP_000002,9,1710010543,3,6,young,desktop,0.6117,0.0071\n     5|IMP_000003,3,1710416879,20,3,senior,mobile,0.7852,0.1997\n     6|", "total_lines": 50001, "file_size": 2818804, "truncated": true, "hint": "Use offset=6 to continue reading (showing 1-5 of 50001 lines)", "is_binary": false, "is_image": false}
assistant · msg 8 1 tool call 
Now let's check the conversions file:




read_file({"path": "/app/data/conversions.csv", "offset": 1, "limit": 5})
tool · msg 9 
{"content": "     1|impression_id,conversion_timestamp,delay_hours,conversion_value\n     2|IMP_000010,1709305348,10.43,3.58\n     3|IMP_000013,1709320719,21.49,1.07\n     4|IMP_000022,1709843815,9.32,3.47\n     5|IMP_000069,1710312110,4.57,2.48\n     6|", "total_lines": 4175, "file_size": 135100, "truncated": true, "hint": "Use offset=6 to continue reading (showing 1-5 of 4175 lines)", "is_binary": false, "is_image": false}
assistant · msg 10 1 tool call 
Now let's check the test contexts file:





read_file({"path": "/app/data/test_contexts.csv", "offset": 1, "limit": 5})
tool · msg 11 
{"content": "     1|context_id,timestamp,hour_of_day,day_of_week,user_segment,device_type,context_feature_1,context_feature_2\n     2|CTX_000000,1710307872,14,2,middle,mobile,0.747,0.0933\n     3|CTX_000001,1710319811,17,2,senior,mobile,0.9059,0.1018\n     4|CTX_000002,1710341834,23,2,senior,mobile,0.0783,0.8936\n     5|CTX_000003,1710405538,17,3,middle,mobile,0.4505,0.7765\n     6|", "total_lines": 2001, "file_size": 108862, "truncated": true, "hint": "Use offset=6 to continue reading (showing 1-5 of 2001 lines)", "is_binary": false, "is_image": false}
assistant · msg 12 1 tool call 
Let's check the arms metadata file:





search_files({"pattern": "arms_metadata.json", "target": "files"})
tool · msg 13 
{"total_count": 1, "files": ["./data/arms_metadata.json"]}
assistant · msg 14 1 tool call 
Now let's read the arms metadata:





read_file({"path": "/app/data/arms_metadata.json"})
tool · msg 15 
{"content": "     1|{\n     2|  \"arms\": [\n     3|    {\n     4|      \"arm_id\": 0,\n     5|      \"name\": \"Campaign A\",\n     6|      \"category\": \"electronics\",\n     7|      \"base_bid\": 0.3\n     8|    },\n     9|    {\n    10|      \"arm_id\": 1,\n    11|      \"name\": \"Campaign B\",\n    12|      \"category\": \"fashion\",\n    13|      \"base_bid\": 0.4\n    14|    },\n    15|    {\n    16|      \"arm_id\": 2,\n    17|      \"name\": \"Campaign C\",\n    18|      \"category\": \"home\",\n    19|      \"base_bid\": 0.5\n    20|    },\n    21|    {\n    22|      \"arm_id\": 3,\n    23|      \"name\": \"Campaign D\",\n    24|      \"category\": \"sports\",\n    25|      \"base_bid\": 0.6\n    26|    },\n    27|    {\n    28|      \"arm_id\": 4,\n    29|      \"name\": \"Campaign E\",\n    30|      \"category\": \"beauty\",\n    31|      \"base_bid\": 0.7\n    32|    },\n    33|    {\n    34|      \"arm_id\": 5,\n    35|      \"name\": \"Campaign F\",\n    36|      \"category\": \"food\",\n    37|      \"base_bid\": 0.8\n    38|    },\n    39|    {\n    40|      \"arm_id\": 6,\n    41|      \"name\": \"Campaign G\",\n    42|      \"category\": \"travel\",\n    43|      \"base_bid\": 0.9\n    44|    },\n    45|    {\n    46|      \"arm_id\": 7,\n    47|      \"name\": \"Campaign H\",\n    48|      \"category\": \"finance\",\n    49|      \"base_bid\": 1.0\n    50|    },\n    51|    {\n    52|      \"arm_id\": 8,\n    53|      \"name\": \"Campaign I\",\n    54|      \"category\": \"entertainment\",\n    55|      \"base_bid\": 1.1\n    56|    },\n    57|    {\n    58|      \"arm_id\": 9,\n    59|      \"name\": \"Campaign J\",\n    60|      \"category\": \"health\",\n    61|      \"base_bid\": 1.2\n    62|    }\n    63|  ]\n    64|}", "total_lines": 63, "file_size": 1128, "truncated": false, "is_binary": false, "is_image": false}
assistant · msg 16 1 tool call 
Now I have a good understanding of the data. Let's analyze the delay distribution first, as it's critical for modeling the delayed feedback.

I'll calculate the log-normal parameters for the delay distribution using the conversion data.




read_file({"path": "/app/data/conversions.csv", "offset": 1, "limit": 4175})
tool · msg 17 
{"content": "     1|impression_id,conversion_timestamp,delay_hours,conversion_value\n     2|IMP_000010,1709305348,10.43,3.58\n     3|IMP_000013,1709320719,21.49,1.07\n     4|IMP_000022,1709843815,9.32,3.47\n     5|IMP_000069,1710312110,4.57,2.48\n     6|IMP_000071,1710446648,25.86,1.37\n     7|IMP_000090,1709973192,28.94,1.46\n     8|IMP_000097,1710100917,68.48,1.67\n     9|IMP_000109,1709851031,16.83,1.67\n    10|IMP_000119,1709561787,17.17,3.28\n    11|IMP_000124,1709437238,6.31,1.1\n    12|IMP_000126,1709398071,12.93,2.35\n    13|IMP_000127,1709550349,3.65,3.02\n    14|IMP_000130,1709845772,67.73,1.5\n    15|IMP_000131,1709832027,11.88,0.96\n    16|IMP_000138,1709742702,27.46,2.38\n    17|IMP_000152,1710203669,9.83,1.31\n    18|IMP_000188,1710048427,4.77,1.79\n    19|IMP_000201,1709914355,4.67,2.85\n    20|IMP_000202,1710336058,72.0,1.63\n    21|IMP_000213,1710348149,29.0,3.11\n    22|IMP_000218,1709681051,25.35,1.82\n    23|IMP_000220,1710010244,12.4,1.14\n    24|IMP_000224,1710015389,41.47,1.45\n    25|IMP_000231,1710325201,8.36,1.11\n    26|IMP_000246,1709806072,12.57,2.36\n    27|IMP_000255,1710275599,11.5,2.59\n    28|IMP_000258,1709250382,3.75,1.87\n    29|IMP_000270,1709913684,72.0,1.63\n    30|IMP_000271,1710186715,11.24,2.48\n    31|IMP_000272,1709488486,18.14,3.52\n    32|IMP_000276,1709656966,3.9,3.21\n    33|IMP_000291,1709828196,11.24,1.57\n    34|IMP_000306,1709282610,2.98,2.76\n    35|IMP_000311,1709819596,31.67,1.45\n    36|IMP_000318,1709827532,19.47,2.27\n    37|IMP_000321,1710536428,35.28,1.53\n    38|IMP_000329,1709833191,9.92,5.81\n    39|IMP_000331,1709767987,7.8,3.36\n    40|IMP_000374,1709635656,15.55,7.22\n    41|IMP_000401,1710260706,12.08,1.77\n    42|IMP_000405,1710322398,40.13,1.59\n    43|IMP_000408,1709940036,14.92,1.8\n    44|IMP_000415,1709304759,6.51,2.13\n    45|IMP_000420,1710344679,6.1,1.96\n    46|IMP_000422,1709833485,17.29,1.28\n    47|IMP_000426,1709632080,9.08,1.88\n    48|IMP_000431,1709991062,6.52,2.81\n    49|IMP_000437,1709527940,7.58,1.35\n    50|IMP_000444,1710423768,13.18,1.42\n    51|IMP_000461,1709464663,48.77,1.46\n    52|IMP_000468,1709812993,53.65,3.36\n    53|IMP_000503,1709546111,26.66,1.62\n    54|IMP_000519,1709929302,4.16,1.06\n    55|IMP_000525,1710130448,2.41,3.14\n    56|IMP_000550,1709951032,45.11,3.15\n    57|IMP_000555,1709630723,24.16,3.22\n    58|IMP_000556,1709394703,18.48,1.71\n    59|IMP_000569,1709982311,10.78,1.47\n    60|IMP_000576,1709801042,5.51,2.0\n    61|IMP_000586,1710104400,8.57,1.27\n    62|IMP_000600,1709449320,14.31,1.37\n    63|IMP_000629,1710331945,14.61,1.46\n    64|IMP_000645,1710332072,6.71,0.72\n    65|IMP_000649,1709553524,28.39,2.01\n    66|IMP_000654,1709794632,13.91,3.99\n    67|IMP_000657,1709745547,15.01,1.53\n    68|IMP_000662,1710066365,4.36,1.56\n    69|IMP_000668,1709805503,51.93,4.57\n    70|IMP_000673,1710294449,2.07,0.91\n    71|IMP_000708,1709413869,8.73,5.55\n    72|IMP_000712,1709328344,11.27,2.7\n    73|IMP_000718,1709333720,17.21,2.05\n    74|IMP_000725,1709468577,8.66,3.0\n    75|IMP_000729,1709963912,22.4,2.43\n    76|IMP_000735,1709935084,13.22,1.17\n    77|IMP_000739,1709733876,12.26,3.03\n    78|IMP_000748,1710353603,4.25,3.31\n    79|IMP_000751,1709660140,18.6,2.05\n    80|IMP_000752,1709326582,16.12,3.01\n    81|IMP_000755,1710197766,28.99,3.95\n    82|IMP_000760,1710327027,7.31,0.98\n    83|IMP_000771,1709425285,20.59,2.82\n    84|IMP_000784,1709759129,9.21,1.76\n    85|IMP_000825,1709339856,4.76,0.78\n    86|IMP_000827,1710470193,17.44,2.01\n    87|IMP_000849,1709327403,22.81,1.44\n    88|IMP_000867,1710215035,24.2,1.75\n    89|IMP_000870,1710057142,7.2,3.39\n    90|IMP_000885,1710000989,8.39,1.19\n    91|IMP_000899,1710485787,72.0,1.77\n    92|IMP_000900,1709619466,6.96,1.96\n    93|IMP_000927,1709653274,12.93,2.44\n    94|IMP_000938,1709670030,27.6,1.45\n    95|IMP_000952,1710367452,27.72,2.08\n    96|IMP_000954,1709761184,17.14,2.67\n    97|IMP_000962,1709644573,7.83,1.18\n    98|IMP_000974,1709588424,11.16
… (76923 more chars)
assistant · msg 18 1 tool call 
Now let's get the full conversion data to analyze the delay distribution:




read_file({"path": "/app/data/conversions.csv", "offset": 1, "limit": 4175})
tool · msg 19 
{"content": "     1|impression_id,conversion_timestamp,delay_hours,conversion_value\n     2|IMP_000010,1709305348,10.43,3.58\n     3|IMP_000013,1709320719,21.49,1.07\n     4|IMP_000022,1709843815,9.32,3.47\n     5|IMP_000069,1710312110,4.57,2.48\n     6|IMP_000071,1710446648,25.86,1.37\n     7|IMP_000090,1709973192,28.94,1.46\n     8|IMP_000097,1710100917,68.48,1.67\n     9|IMP_000109,1709851031,16.83,1.67\n    10|IMP_000119,1709561787,17.17,3.28\n    11|IMP_000124,1709437238,6.31,1.1\n    12|IMP_000126,1709398071,12.93,2.35\n    13|IMP_000127,1709550349,3.65,3.02\n    14|IMP_000130,1709845772,67.73,1.5\n    15|IMP_000131,1709832027,11.88,0.96\n    16|IMP_000138,1709742702,27.46,2.38\n    17|IMP_000152,1710203669,9.83,1.31\n    18|IMP_000188,1710048427,4.77,1.79\n    19|IMP_000201,1709914355,4.67,2.85\n    20|IMP_000202,1710336058,72.0,1.63\n    21|IMP_000213,1710348149,29.0,3.11\n    22|IMP_000218,1709681051,25.35,1.82\n    23|IMP_000220,1710010244,12.4,1.14\n    24|IMP_000224,1710015389,41.47,1.45\n    25|IMP_000231,1710325201,8.36,1.11\n    26|IMP_000246,1709806072,12.57,2.36\n    27|IMP_000255,1710275599,11.5,2.59\n    28|IMP_000258,1709250382,3.75,1.87\n    29|IMP_000270,1709913684,72.0,1.63\n    30|IMP_000271,1710186715,11.24,2.48\n    31|IMP_000272,1709488486,18.14,3.52\n    32|IMP_000276,1709656966,3.9,3.21\n    33|IMP_000291,1709828196,11.24,1.57\n    34|IMP_000306,1709282610,2.98,2.76\n    35|IMP_000311,1709819596,31.67,1.45\n    36|IMP_000318,1709827532,19.47,2.27\n    37|IMP_000321,1710536428,35.28,1.53\n    38|IMP_000329,1709833191,9.92,5.81\n    39|IMP_000331,1709767987,7.8,3.36\n    40|IMP_000374,1709635656,15.55,7.22\n    41|IMP_000401,1710260706,12.08,1.77\n    42|IMP_000405,1710322398,40.13,1.59\n    43|IMP_000408,1709940036,14.92,1.8\n    44|IMP_000415,1709304759,6.51,2.13\n    45|IMP_000420,1710344679,6.1,1.96\n    46|IMP_000422,1709833485,17.29,1.28\n    47|IMP_000426,1709632080,9.08,1.88\n    48|IMP_000431,1709991062,6.52,2.81\n    49|IMP_000437,1709527940,7.58,1.35\n    50|IMP_000444,1710423768,13.18,1.42\n    51|IMP_000461,1709464663,48.77,1.46\n    52|IMP_000468,1709812993,53.65,3.36\n    53|IMP_000503,1709546111,26.66,1.62\n    54|IMP_000519,1709929302,4.16,1.06\n    55|IMP_000525,1710130448,2.41,3.14\n    56|IMP_000550,1709951032,45.11,3.15\n    57|IMP_000555,1709630723,24.16,3.22\n    58|IMP_000556,1709394703,18.48,1.71\n    59|IMP_000569,1709982311,10.78,1.47\n    60|IMP_000576,1709801042,5.51,2.0\n    61|IMP_000586,1710104400,8.57,1.27\n    62|IMP_000600,1709449320,14.31,1.37\n    63|IMP_000629,1710331945,14.61,1.46\n    64|IMP_000645,1710332072,6.71,0.72\n    65|IMP_000649,1709553524,28.39,2.01\n    66|IMP_000654,1709794632,13.91,3.99\n    67|IMP_000657,1709745547,15.01,1.53\n    68|IMP_000662,1710066365,4.36,1.56\n    69|IMP_000668,1709805503,51.93,4.57\n    70|IMP_000673,1710294449,2.07,0.91\n    71|IMP_000708,1709413869,8.73,5.55\n    72|IMP_000712,1709328344,11.27,2.7\n    73|IMP_000718,1709333720,17.21,2.05\n    74|IMP_000725,1709468577,8.66,3.0\n    75|IMP_000729,1709963912,22.4,2.43\n    76|IMP_000735,1709935084,13.22,1.17\n    77|IMP_000739,1709733876,12.26,3.03\n    78|IMP_000748,1710353603,4.25,3.31\n    79|IMP_000751,1709660140,18.6,2.05\n    80|IMP_000752,1709326582,16.12,3.01\n    81|IMP_000755,1710197766,28.99,3.95\n    82|IMP_000760,1710327027,7.31,0.98\n    83|IMP_000771,1709425285,20.59,2.82\n    84|IMP_000784,1709759129,9.21,1.76\n    85|IMP_000825,1709339856,4.76,0.78\n    86|IMP_000827,1710470193,17.44,2.01\n    87|IMP_000849,1709327403,22.81,1.44\n    88|IMP_000867,1710215035,24.2,1.75\n    89|IMP_000870,1710057142,7.2,3.39\n    90|IMP_000885,1710000989,8.39,1.19\n    91|IMP_000899,1710485787,72.0,1.77\n    92|IMP_000900,1709619466,6.96,1.96\n    93|IMP_000927,1709653274,12.93,2.44\n    94|IMP_000938,1709670030,27.6,1.45\n    95|IMP_000952,1710367452,27.72,2.08\n    96|IMP_000954,1709761184,17.14,2.67\n    97|IMP_000962,1709644573,7.83,1.18\n    98|IMP_000974,1709588424,11.16
… (76923 more chars)