Vikram

### DissentAI: Learning from Model Disagreement to Build More Reliable AI Large AI models perform well on common examples but often fail on edge cases, ambiguous problems, and difficult reasoning tasks. Traditional training pipelines usually treat all training examples equally, even though some examples contain much more information about a model’s weaknesses than others. Our problem is to investigate whether AI models can improve more effectively by identifying examples where models disagree, fail, or show uncertainty, and then using these high-value examples to create an adaptive training dataset. We aim to build a self-improving training pipeline that: 1. Identifies model disagreements and failure patterns. 2. Scores examples based on difficulty and information value. 3. Creates and validates targeted training data. 4. Uses Adaption’s Adaptive Data and AutoScientist to optimize the training process. 5. Evaluates the resulting model against the official baseline. The goal is not simply to train a larger model, but to discover whether better selection and adaptation of training data can produce measurable performance improvements with a more efficient training process.