ArXiv paper 2605.12460 proposes instruction-tuning language models for simultaneous multi-stream computation rather than sequential message exchange. This arXiv:2605.12460 work shows each forward pass reads from multiple input streams and emits tokens across multiple output streams with causal dependence on prior timesteps. The approach directly addresses single-stream bottlenecks where agents cannot act while reading or think while generating output. Compared to prior single-stream agent formats in papers such as arXiv:2210.03629 and arXiv:2303.17580, the multi-stream design enables concurrent reading, reasoning and acting. Parallel streams improve separation of concerns for security monitoring and allow production systems to maintain distinct I/O and reasoning channels. Data-driven training on parallel formats yields measurable gains in efficiency and monitorability without altering base model scale.