Reflexion Co-Author Says Instinct’s Edge May Be Its Handling of Long-Term User State

Former MIT professor and Reflexion co-author Ashwin Gopinath thinks the real edge of Instinct, the personal agent that has been getting attention in Silicon Valley, may not be execution power or even ordinary long-term memory. Instead, he said, the difference could lie in how the system keeps track of a user’s “long-term state.” Gopinath said his view is based on hands-on use, not access to Instinct’s internal design. He also noted that he was once the teacher of founder Noah Shinn, and that the two worked together on Reflexion research in 2023. On task execution alone, he said Instinct does not appear dramatically more capable than agents such as Hermes or OpenClaw. Many agents now already review their own work, store memories and even generate skills automatically. What stood out to him was something else: the longer he used Instinct, the more it seemed to understand the user, without getting weighed down by a growing pile of old history. Gopinath said the likely difference is that Instinct may not simply save every conversation and action log, then retrieve them later with vector search or a knowledge graph. Instead, it appears to maintain an ongoing picture of the user’s current state — which preferences are still valid, which facts have changed, which tasks are unfinished, and whether the next step should be to wait, keep following up or act now. He called that process “memory compilation”: taking noisy life records, extracting what matters and compressing them into a state that is updated continuously. The point is not to remember more, he said, but to know what should be kept, what should expire and what kind of change should trigger action. That, in his view, is closely connected to Reflexion. Three years ago, Reflexion taught agents to turn failures into text memories for the next attempt. With long-term personal agents, the problem becomes broader: how to keep understanding a person and a changing real-world environment over time. As large language models converge in capability and tool-use frameworks become more common, Gopinath said the hardest part to copy may be the ability to remember the right things for a long time and act at the right moment.