Make Agent Frameworks Trainable

Current AI agent frameworks are inflexible software that cannot generalize or adapt easily, making development complex despite the power of modern LLMs. The proposed solution is to create a trainable "neural agent" by burning the framework's logic directly into the language model itself using reinforcement learning. This model, called the Thread Inference Model (TIM), automatically manages its own context by recursively decomposing tasks and pruning unnecessary information. This approach results in a more adaptable and efficient agent that outperforms traditional frameworks on complex reasoning tasks by learning context engineering and adapting its reasoning policy.