raw_responses_full.csv

case_id,platform,event,prompt,model_id,seed,response,elapsed_seconds
case_09,kaggle_interactive,midrun_feedback,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Kaggle notebook session.

Situation: I need to inspect validation loss and sample predictions while a long experiment is still running.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Kaggle notebook runtime (client-side, browser-based)"",
  ""expected_behavior"": ""Validation loss and predictions can be inspected in real time via the notebook's output and visualization panels during execution, as long as the model is running in the same session."",
  ""main_risk"": ""Delayed or lost updates if the notebook session times out or the model runs for extended periods without explicit polling."",
  ""recommended_steps"": [
    ""Monitor output logs and plots in real time during execution."",
    ""Use `print()` or `display()` statements to log validation metrics periodically."",
    ""Ensure the model runs in a session with persistent execution (e.g., via a kernel that doesn't auto-restart).""
  ],
  ""policy_uncertainty"": ""Kaggle's session timeout policies may affect real-time monitoring; behavior may vary with future updates to runtime stability.""
}",18.52
case_17,kaggle_background,save_vs_checkpoint,"I am running a machine-learning experiment. I created a Kaggle notebook version using Save & Run All, which runs remotely as a background job.

Situation: I saved the notebook after editing its code. I need to know whether that also saved the current training state and model progress.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Remote (Kaggle backend)"",
  ""expected_behavior"": ""Saving a notebook via 'Save & Run All' does not automatically preserve training state or model progress; it only saves the notebook's code and environment configuration."",
  ""main_risk"": ""Loss of model progress if training was not explicitly stopped or saved via model checkpointing or persistent storage."",
  ""recommended_steps"": [
    ""Explicitly save model checkpoints using `model.save()` or similar methods."",
    ""Use persistent storage (e.g., Google Drive, local file system) for model artifacts."",
    ""Verify training state is saved independently of notebook saves.""
  ],
  ""policy_uncertainty"": ""No, the platform does not guarantee automatic persistence of training state beyond the notebook save.""
}",15.22
case_01,local_jupyter,close_and_sleep,"I am running a machine-learning experiment. The Python kernel is running on my own laptop through Jupyter Notebook.

Situation: A model-training cell may take eight hours. I want to close the browser tab and let my laptop sleep.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""local laptop"",
  ""expected_behavior"": ""The Python kernel continues running on the laptop; Jupyter will not terminate the process when the browser tab is closed."",
  ""main_risk"": ""The kernel may be interrupted by system sleep or power-off if not explicitly managed."",
  ""recommended_steps"": [
    ""Keep the laptop powered and awake during training."",
    ""Use `systemd` or `screen` to run the kernel in the background if possible."",
    ""Enable 'sleep' or 'hibernate' settings to avoid interruption.""
  ],
  ""policy_uncertainty"": ""Jupyter's behavior when the browser is closed is not guaranteed by platform policy and may vary with updates.""
}",14.2
case_19,colab_interactive,close_and_sleep,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Google Colab runtime.

Situation: A model-training cell may take eight hours. I want to close the browser tab and let my laptop sleep.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Google Colab runtime"",
  ""expected_behavior"": ""The model training continues on the Colab instance; the browser tab closure does not terminate the process, as long as the runtime is not explicitly stopped or killed."",
  ""main_risk"": ""The runtime may be terminated due to inactivity or resource limits, especially if no activity occurs for more than 24 hours."",
  ""recommended_steps"": [
    ""Keep the Colab runtime active by periodically checking the notebook or using 'Runtime > Keep Running' if available."",
    ""Use a persistent Google Cloud VM or a local machine for long-running jobs if stability is critical."",
    ""Set up a background job with a cloud-based ML platform (e.g., Vertex AI) with explicit job scheduling.""
  ],
  ""policy_uncertainty"": ""Google Colab's auto-termination policy for idle runtimes may change without notice.""
}",17.7
case_12,kaggle_interactive,two_stage_workflow,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Kaggle notebook session.

Situation: I need quick feedback to catch errors first, followed by a reliable unattended full experiment.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Kaggle notebook session (client-side browser, local runtime)"",
  ""expected_behavior"": ""Initial runs execute quickly in browser; errors surface immediately. Full experiments require explicit deployment to a remote server or cloud instance."",
  ""main_risk"": ""Browser-based execution may fail silently or misreport due to resource limits or connectivity issues."",
  ""recommended_steps"": [
    ""Run small, isolated tests first to validate logic and catch syntax errors."",
    ""Use Kaggle's 'Submit' or 'Deploy' feature to offload to a reliable remote runtime for unattended execution."",
    ""Verify logs and output in both environments to ensure consistency.""
  ],
  ""policy_uncertainty"": ""Kaggle's runtime policies may change regarding long-running or resource-intensive experiments.""
}",15.57
case_10,kaggle_interactive,resume_after_crash,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Kaggle notebook session.

Situation: The training process could fail after six hours, and I need to continue without restarting from epoch one.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Kaggle notebook session (cloud-based, remote runtime)"",
  ""expected_behavior"": ""Training can be paused and resumed via checkpointing; Kaggle supports resuming from saved checkpoints without restarting from epoch 1."",
  ""main_risk"": ""Loss of progress if the runtime is terminated or the session expires before resuming."",
  ""recommended_steps"": [
    ""Save model checkpoints every few epochs using `torch.save()` or `tf.keras.Model.save()`."",
    ""Pause training, then resume from the last saved checkpoint using the same model and data."",
    ""Ensure the model state and optimizer are properly serialized and restored.""
  ],
  ""policy_uncertainty"": ""Kaggle's support for resuming training may change with platform updates; verify via Kaggle's official documentation.""
}",15.82
case_14,kaggle_background,internet_loss,"I am running a machine-learning experiment. I created a Kaggle notebook version using Save & Run All, which runs remotely as a background job.

Situation: My internet connection may disappear for one hour while the model is training.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Remote (Kaggle backend)"",
  ""expected_behavior"": ""Training continues uninterrupted if the connection is lost; Kaggle resumes execution upon reconnection."",
  ""main_risk"": ""Training job may be interrupted or fail if the remote runtime is disconnected for >15 minutes without recovery."",
  ""recommended_steps"": [
    ""Save the notebook with 'Save & Run All' before disconnecting."",
    ""Set up a checkpointing strategy to save model progress periodically."",
    ""Use Kaggle's 'Resume' feature to restart training after reconnection.""
  ],
  ""policy_uncertainty"": ""Kaggle's automatic recovery behavior during network outages is not publicly documented and may change.""
}",14.08
case_02,local_jupyter,internet_loss,"I am running a machine-learning experiment. The Python kernel is running on my own laptop through Jupyter Notebook.

Situation: My internet connection may disappear for one hour while the model is training.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""local laptop"",
  ""expected_behavior"": ""Training continues locally; no internet required once computation starts."",
  ""main_risk"": ""Training interruption due to lost internet, potentially leading to incomplete or corrupted model."",
  ""recommended_steps"": [
    ""Start training with a local dataset to avoid internet dependency."",
    ""Save checkpoints every 5-10 minutes to allow recovery on restart."",
    ""Ensure the model uses persistent storage (e.g., disk) for intermediate results."",
    ""Set up a local backup or auto-recovery mechanism for training state.""
  ],
  ""policy_uncertainty"": ""None. Behavior is fully determined by local Python execution and file system.""
}",13.65
case_22,colab_interactive,resume_after_crash,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Google Colab runtime.

Situation: The training process could fail after six hours, and I need to continue without restarting from epoch one.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Google Colab runtime"",
  ""expected_behavior"": ""Training can be paused and resumed via checkpointing; Colab supports resuming from saved checkpoints without restarting from epoch 1."",
  ""main_risk"": ""Data or model state corruption if checkpoints are not properly saved or restored."",
  ""recommended_steps"": [
    ""Save model state and training progress to a local file at regular intervals."",
    ""Use `torch.save()` or `tf.keras.models.save_model()` to store checkpoints."",
    ""Resume training from the last checkpoint using the same model and optimizer state.""
  ],
  ""policy_uncertainty"": ""Colab's long-running session support may change; verify via official documentation.""
}",13.85
case_06,local_jupyter,two_stage_workflow,"I am running a machine-learning experiment. The Python kernel is running on my own laptop through Jupyter Notebook.

Situation: I need quick feedback to catch errors first, followed by a reliable unattended full experiment.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""local laptop via Jupyter Notebook"",
  ""expected_behavior"": ""Quick error detection during development; full experiments run reliably in unattended mode via script or CI/CD."",
  ""main_risk"": ""Local environment drift or uncaught bugs during unattended execution due to missing monitoring."",
  ""recommended_steps"": [
    ""Run experiments in Jupyter with immediate error logging and debugging."",
    ""Automate full experiments using Python scripts with logging and error trapping."",
    ""Use version control to track experiment configurations and code changes."",
    ""Set up a CI/CD pipeline for unattended execution with notifications on failure.""
  ],
  ""policy_uncertainty"": ""None. Behavior is technically verifiable and independent of platform policy.""
}",14.39
case_03,local_jupyter,midrun_feedback,"I am running a machine-learning experiment. The Python kernel is running on my own laptop through Jupyter Notebook.

Situation: I need to inspect validation loss and sample predictions while a long experiment is still running.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Local laptop via Jupyter Notebook"",
  ""expected_behavior"": ""Validation loss and predictions can be inspected in real time using Jupyter's live output and model evaluation functions, as long as the training loop is accessible and data is loaded."",
  ""main_risk"": ""Data leakage or overfitting due to unmonitored validation during long runs without proper data splitting or early stopping."",
  ""recommended_steps"": [
    ""Add real-time logging of validation loss to a file or notebook output."",
    ""Use a separate validation set and ensure it's not used during training."",
    ""Implement periodic model checkpointing and prediction sampling.""
  ],
  ""policy_uncertainty"": ""None. Behavior is fully determined by standard ML practices and Jupyter's execution model.""
}",15.22
case_13,kaggle_background,close_and_sleep,"I am running a machine-learning experiment. I created a Kaggle notebook version using Save & Run All, which runs remotely as a background job.

Situation: A model-training cell may take eight hours. I want to close the browser tab and let my laptop sleep.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""remote (Kaggle backend)"",
  ""expected_behavior"": ""The notebook continues training on Kaggle's infrastructure; the browser tab closure does not interrupt execution."",
  ""main_risk"": ""Unintended interruption due to platform policy changes or unexpected timeouts (not guaranteed)."",
  ""recommended_steps"": [
    ""Save and run the notebook with 'Save & Run All' to initiate remote execution."",
    ""Close the browser tab; training proceeds independently on Kaggle's servers."",
    ""Monitor progress via the notebook's status dashboard, not the browser.""
  ],
  ""policy_uncertainty"": ""Kaggle may alter job timeout policies or remote execution behavior without notice.""
}",13.84
case_16,kaggle_background,resume_after_crash,"I am running a machine-learning experiment. I created a Kaggle notebook version using Save & Run All, which runs remotely as a background job.

Situation: The training process could fail after six hours, and I need to continue without restarting from epoch one.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Kaggle remote runtime (cloud-based)"",
  ""expected_behavior"": ""Training resumes from the last saved checkpoint if the runtime is interrupted and the model state is preserved via checkpointing."",
  ""main_risk"": ""Loss of progress if the remote runtime is terminated or crashes without saving state."",
  ""recommended_steps"": [
    ""Enable automatic checkpointing during training (e.g., save every 100 epochs)."",
    ""Use Kaggle's 'Save & Run All' with explicit checkpointing in code (e.g., `torch.save(model.state_dict(), 'checkpoint.pth')`)."",
    ""Verify that the model state is saved in a persistent storage location (e.g., local disk or cloud bucket).""
  ],
  ""policy_uncertainty"": ""Kaggle's support for resuming training from checkpoints after runtime interruption is not officially documented and may change.""
}",17.47
case_04,local_jupyter,resume_after_crash,"I am running a machine-learning experiment. The Python kernel is running on my own laptop through Jupyter Notebook.

Situation: The training process could fail after six hours, and I need to continue without restarting from epoch one.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""local laptop via Jupyter Notebook"",
  ""expected_behavior"": ""Training can be paused and resumed using model checkpoints and state persistence; no restart from epoch 1 is required if the model state is saved."",
  ""main_risk"": ""Data corruption or model state loss if checkpoints are not properly saved or restored."",
  ""recommended_steps"": [
    ""Save model state and optimizer state at regular intervals using `torch.save()` or `model.save_pretrained()`."",
    ""Use a checkpointing strategy (e.g., every 1000 steps) to preserve training progress."",
    ""Resume training from the last checkpoint using the saved state upon restart.""
  ],
  ""policy_uncertainty"": ""None. The behavior is technically verifiable and independent of platform policy.""
}",15.59
case_05,local_jupyter,save_vs_checkpoint,"I am running a machine-learning experiment. The Python kernel is running on my own laptop through Jupyter Notebook.

Situation: I saved the notebook after editing its code. I need to know whether that also saved the current training state and model progress.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""local laptop via Jupyter Notebook"",
  ""expected_behavior"": ""Saving a notebook does not automatically save model training state or progress; only the notebook's code and metadata are preserved."",
  ""main_risk"": ""Loss of training progress if model state is not explicitly saved to disk or a checkpoint file."",
  ""recommended_steps"": [
    ""Explicitly save model and training state using `model.save()` or `torch.save()`/`joblib.dump()`"",
    ""Use version control (e.g., Git) to track model checkpoints and training logs"",
    ""Enable automatic checkpointing in training loops""
  ],
  ""policy_uncertainty"": ""No""
}",13.4
case_23,colab_interactive,save_vs_checkpoint,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Google Colab runtime.

Situation: I saved the notebook after editing its code. I need to know whether that also saved the current training state and model progress.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Google Colab runtime"",
  ""expected_behavior"": ""Saving the notebook does not automatically save training state or model progress; only the notebook's code and environment configuration are preserved."",
  ""main_risk"": ""Loss of model progress if training state is not explicitly saved to disk or cloud storage."",
  ""recommended_steps"": [
    ""Explicitly save model checkpoints using `torch.save()` or `tf.keras.models.save_model()`"",
    ""Store trained models in Google Drive or a cloud bucket via `gdown` or `gcs`"",
    ""Use version control (e.g., Git) to track code and model artifacts""
  ],
  ""policy_uncertainty"": ""No, saving the notebook does not preserve training state; behavior depends on explicit model saving actions.""
}",15.58
case_18,kaggle_background,two_stage_workflow,"I am running a machine-learning experiment. I created a Kaggle notebook version using Save & Run All, which runs remotely as a background job.

Situation: I need quick feedback to catch errors first, followed by a reliable unattended full experiment.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Remote (Kaggle backend)"",
  ""expected_behavior"": ""Save & Run All triggers a remote job; initial runs provide quick feedback, full experiments run unattended."",
  ""main_risk"": ""Delayed error detection due to remote execution latency or uncaught failures in background jobs."",
  ""recommended_steps"": [
    ""Run small, isolated tests locally first to validate logic."",
    ""Use Kaggle's 'Run' button for immediate feedback on small batches."",
    ""Enable logging and error notifications for remote jobs."",
    ""Set up automated email or webhook alerts for job failures.""
  ],
  ""policy_uncertainty"": ""Kaggle's remote job reliability and error notification policies may change without notice.""
}",14.4
case_21,colab_interactive,midrun_feedback,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Google Colab runtime.

Situation: I need to inspect validation loss and sample predictions while a long experiment is still running.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Google Colab runtime (client-side, in-browser)"",
  ""expected_behavior"": ""Validation loss and predictions can be inspected in real time via notebook cells or visualization tools during execution, as long as the model is running in the same runtime."",
  ""main_risk"": ""Data leakage or incorrect validation if the model is not properly split or evaluated in a separate validation loop."",
  ""recommended_steps"": [
    ""Monitor validation loss in the notebook's output logs or plots."",
    ""Use `model.evaluate()` with validation data in a separate cell to inspect metrics."",
    ""Ensure validation data is not used during training to avoid data leakage.""
  ],
  ""policy_uncertainty"": ""No known platform policy changes affect real-time validation monitoring in Colab; behavior is consistent with standard ML workflows.""
}",16.06
case_24,colab_interactive,two_stage_workflow,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Google Colab runtime.

Situation: I need quick feedback to catch errors first, followed by a reliable unattended full experiment.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Google Colab runtime (interactive, browser-based)"",
  ""expected_behavior"": ""Quick error detection during initial execution; full experiment runs reliably in unattended mode via persistent backend."",
  ""main_risk"": ""Transient runtime failures due to browser session timeouts or network instability."",
  ""recommended_steps"": [
    ""Run code in interactive mode to catch syntax or logic errors immediately"",
    ""Save experiment configuration and use Colab's 'Runtime > Run all' for unattended execution"",
    ""Enable persistent storage via Google Drive or local file system for reproducibility""
  ],
  ""policy_uncertainty"": ""Colab's long-term runtime availability and auto-restart policies may change without notice.""
}",14.11
case_08,kaggle_interactive,internet_loss,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Kaggle notebook session.

Situation: My internet connection may disappear for one hour while the model is training.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Kaggle notebook session (local runtime)"",
  ""expected_behavior"": ""Training continues locally until interrupted by a timeout or user action; no automatic recovery from disconnection."",
  ""main_risk"": ""Data loss or incomplete training due to unhandled disconnection without explicit checkpointing."",
  ""recommended_steps"": [
    ""Enable automatic checkpointing using `torch.save()` or `joblib` to save model state periodically."",
    ""Use a persistent storage location (e.g., Google Drive) to back up training progress."",
    ""Set up a timeout or interrupt handler to pause training and save state if the session disconnects.""
  ],
  ""policy_uncertainty"": ""Kaggle's automatic session recovery or remote reconnection policies are not publicly documented and may change.""
}",15.56
case_11,kaggle_interactive,save_vs_checkpoint,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Kaggle notebook session.

Situation: I saved the notebook after editing its code. I need to know whether that also saved the current training state and model progress.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Kaggle notebook session (cloud-based)"",
  ""expected_behavior"": ""Saving the notebook saves the code and environment state but does not automatically preserve training state or model progress unless explicitly saved via model checkpointing or persistent storage."",
  ""main_risk"": ""Loss of training progress if no explicit model checkpointing or persistent storage was used."",
  ""recommended_steps"": [
    ""Enable model checkpointing during training using `model.save()` or similar methods."",
    ""Save trained models to a persistent location (e.g., Google Drive, local file system)."",
    ""Verify that training state is explicitly saved, not just the notebook code.""
  ],
  ""policy_uncertainty"": ""No, the platform does not guarantee automatic persistence of training state beyond the notebook session.""
}",15.66
case_15,kaggle_background,midrun_feedback,"I am running a machine-learning experiment. I created a Kaggle notebook version using Save & Run All, which runs remotely as a background job.

Situation: I need to inspect validation loss and sample predictions while a long experiment is still running.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Remote (Kaggle backend)"",
  ""expected_behavior"": ""Validation loss and predictions are not visible in real-time during execution; only final results are accessible post-completion."",
  ""main_risk"": ""Misleading assumption that live monitoring is possible due to platform UI limitations."",
  ""recommended_steps"": [
    ""Check the notebook's output logs for validation metrics during execution."",
    ""Use Kaggle's 'Run All' logs to verify if validation loss is logged."",
    ""Set up real-time logging via custom scripts or external monitoring tools.""
  ],
  ""policy_uncertainty"": ""Kaggle's real-time validation visibility policy may change without notice.""
}",13.54
case_20,colab_interactive,internet_loss,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Google Colab runtime.

Situation: My internet connection may disappear for one hour while the model is training.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Google Colab runtime"",
  ""expected_behavior"": ""Training continues locally until interrupted by a timeout or manual stop; no automatic recovery or checkpointing is guaranteed."",
  ""main_risk"": ""Data loss or incomplete training due to unattended runtime termination without explicit save or checkpointing."",
  ""recommended_steps"": [
    ""Save model checkpoints every 10 minutes using `torch.save()` or `tf.keras.models.save_model()`."",
    ""Enable persistent storage via Google Drive to preserve training state."",
    ""Set up a manual interrupt and resume mechanism using `try-except` and `RuntimeError` checks."",
    ""Run training in a separate, long-lived environment if possible.""
  ],
  ""policy_uncertainty"": ""Google Colab may terminate runtimes without notice; no official policy guarantees uptime or automatic recovery.""
}",16.58
case_07,kaggle_interactive,close_and_sleep,"I am running a machine-learning experiment. I started the code by clicking Run in an interactive Kaggle notebook session.

Situation: A model-training cell may take eight hours. I want to close the browser tab and let my laptop sleep.

Return one valid JSON object with exactly these keys: computation_location, expected_behavior, main_risk, recommended_steps, and policy_uncertainty. recommended_steps must be an array. Use no more than 180 words in total. Distinguish verified technical behavior from anything that depends on current platform policy.",Qwen/Qwen3-4B-Instruct-2507,42,"{
  ""computation_location"": ""Kaggle notebook (client-side browser)"",
  ""expected_behavior"": ""The model training runs locally on the user's laptop; Kaggle's backend does not automatically resume or manage long-running jobs after browser closure."",
  ""main_risk"": ""Data loss or incomplete training if the session is terminated without proper saving or checkpointing."",
  ""recommended_steps"": [
    ""Save model checkpoints periodically during training."",
    ""Use Kaggle's 'Save' feature to store notebook state."",
    ""Ensure the training script includes a stop condition and logs progress.""
  ],
  ""policy_uncertainty"": ""Kaggle's support for background execution or auto-resume of long-running jobs is not officially documented or guaranteed.""
}",14.48