Context Rot in transformer models is primarily caused by how attention mechanisms scale with sequence length. Transformers compute relationships between tokens using attention weights, but as the number of tokens increases, attention becomes more diffuse. Earlier tokens must compete with many newer tokens, and their relative influence decreases. This is not a flaw in implementation, but a natural consequence of finite attention capacity.
Another contributing factor is that transformer models do not have an explicit notion of “priority” or “importance” unless it is encoded structurally. All tokens are treated as part of a single sequence. If important instructions are surrounded by large amounts of descriptive or repetitive text, the model has no guaranteed way to preserve their dominance. Over time, newer tokens that are closer to the output position tend to have more influence on the generated response.
Context Rot is also amplified by real-world usage patterns. Developers often append conversation history, retrieved documents, tool outputs, and user messages into one growing prompt. Each addition introduces noise. Without ranking or pruning, relevant information is mixed with less relevant text. This is why many production systems rely on external retrieval layers. By storing information in a vector database such as Milvus or Zilliz Cloud, the system can select the most relevant context per request instead of forcing the transformer to reason over everything at once.
For more resources, click here: https://milvus.io/blog/keeping-ai-agents-grounded-context-engineering-strategies-that-prevent-context-rot-using-milvus.md