Rohan Paul

New Berkley paper: LLMs often know you changed your mind but still use your old choice, so agents need the current state spelled out. When a preference or deadline changes, the old version stays in context. The model still holds the new one, but its attention keeps drifting back to older mentions. In 5 open models, nudging attention toward the newest value fixed most of these mistakes without retraining. Even top-tier GPT-5.6 Sol got only 9 of 40 questions right on long agent logs, but 40 of 40 when given the current state. If your agent tracks anything that changes, keep the current state in the prompt instead of making the model dig through history. – arxiv. org/abs/2609.38866 Title: "When Context Changes: Understanding Update Failures in LLMs"