The agent performs well in simulation.When deployed in the real world, it makes strange decisions.The physics is slightly different.Small changes lead to big failures.
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My language model produces fluent responses.Even when it does not know the answer, it sounds confident.Users sometimes trust incorrect replies.There is no indication of uncertainty.
I trained a Keras model that gives good validation accuracy.After saving and loading it, the predictions become completely wrong.Even training samples are misclassified.Nothing crashes, but the outputs no longer make sense.
My model uses both image and text inputs.It works well when both are provided.If one modality is missing, outputs become random or broken.Real-world data is often incomplete.
My GAN generates faces.But many look distorted or unnatural.Eyes and mouths appear in wrong positions.The training seems stable, but outputs are flawed.
My speech-to-text model produces accurate transcripts when tested in a quiet office.However, when I try to use it in public places, accuracy drops sharply.Background noise causes words to be skipped or misheard.The model feels fragile outside controlled ...
I trained an object detection model on a mixed dataset containing people, vehicles, and small objects like phones and traffic signs.The model detects large objects such as cars and people very reliably.However, it almost completely ignores smaller objects, ...
I fine-tuned a pretrained Transformer on a small custom dataset.Training finishes without errors.But the generated outputs look random and off-topic.It feels like the model forgot everything.
My diagnostic CNN shows high accuracy on data from one hospital.When tested on scans from a different hospital, performance drops drastically.The disease patterns are the same.Only the scanners and imaging pipelines differ.
Short sequences work fine.Longer sequences cause GPU crashes.No code changes were made.Only input size increased.