Rizwan Qureshi and a coalition of researchers from dozens of institutions offer a sweeping, cross-disciplinary synthesis on the path to AGI in Thinking Beyond Tokens:

We argue that true intelligence arises not from scale alone but from the integration of memory and reasoning: an orchestration of modular, interactive, and self-improving components where compression enables adaptive behavior. Drawing on advances in neurosymbolic systems, reinforcement learning, and cognitive scaffolding, we explore how recent architectures begin to bridge the gap between statistical learning and goal-directed cognition.

It is easy to become mesmerised by the sheer fluency of today's generative models. Their ability to predict the next token in a sequence has unlocked remarkable capabilities. Yet, this paper argues that this very foundation, token-level prediction, is also a fundamental constraint, a ceiling that separates statistical pattern matching from genuine, goal-directed cognition. The current paradigm, for all its power, lacks grounded agency and the kind of robust, flexible reasoning that defines general intelligence.

The path forward, this paper suggests, is not simply to build bigger models but to build different ones. The proposal is to look towards the one working example of general intelligence we have: the human brain. This means moving away from monolithic, end-to-end trained systems and towards what the authors call an "orchestration of modular, interactive, and self-improving components." I suspect this points to a future of more complex, hybrid systems that integrate memory, reasoning, and perception in a structured way, much like cognitive architectures have long proposed. The core idea is that intelligence is not just about scale, but about the effective compression of information into adaptive, abstract representations.

What does it mean for those of us designing and building products with this technology? It suggests a significant shift in focus. The work may move from prompt engineering, which coaxes behaviour from a black box, towards a discipline more akin to cognitive architecture. The challenge becomes one of designing the interactions between specialised agents, structuring memory, and defining the rules of reasoning.