Early-Stage Idea: A Cognitive Architecture Built on Attention and Graph Traversal

Hellucigen
Thu, 04 Dec 2025 18:24:54 +0000

Hello everyone,

I’m a undergraduate student who has been independently exploring AGI and cognitive architectures. For the past eight months I’ve been working on a theoretical framework that I’m calling an “attention-driven graph-based cognitive architecture.” Because I don’t have access to large compute or teams, the project is fully conceptual rather than experimental. It focuses on the logic of the architecture, the internal flow of computation, and a plausible path toward implementation.

I’m posting here because HuggingFace has always felt like a community where researchers, engineers, and students can exchange ideas openly. I would be genuinely grateful for any thoughts—critical or encouraging—on whether this framework has any research value, or whether I’m missing something fundamental.

Very briefly, the core idea is to treat the entire cognitive system as a dynamic, multi-modal knowledge graph. Concepts, events, actions, rules, emotions, self-states, and even decision strategies are represented as nodes, linked by directed weighted edges that encode semantic, causal, or procedural relations. There is no central controller; the system “thinks” by propagating activation through the graph and by shifting its attention focus toward the currently most active region.

Memory is organized into semantic structures, episodic sequences, and a self-model that represents internal variables like emotional tone, task load, arousal, uncertainty, and system confidence. These internal states act as a global modulation field, shaping how activation diffuses across the graph. The flow of attention is not a pointer but a momentary cluster of highly active nodes; its movement emerges from activation competition, task relevance, and internal state.

One part I spent a lot of time designing is a continuous mode parameter that controls whether the system behaves more like a divergent, free-associative “DMN-like” mode or a convergent, goal-driven “CEN-like” mode. The same underlying graph traversal behaves very differently depending on this parameter, producing both exploratory and focused reasoning patterns without switching architectures. The framework also integrates simple decision-theory nodes, prospective-intention nodes for delayed intentions, and action templates that can be instantiated into executable logic depending on context.

My main question is simple: from your perspective, does this kind of architecture seem to have potential research value, or is it mostly reinventing ideas that already exist? I’m fully aware that without experiments it is just a theoretical construct, so I’m hoping to learn whether it is worth developing further, or whether I should rethink the approach entirely.

If anyone is interested in taking a look, I’ve uploaded the full paper draft to GitHub. It’s completely open:

github.com

GitHub - Hellucigen/AGI-thoughts-of-Hellucigen: safety

safety

Any form of feedback—critique, suggestions, reading recommendations, or even just general impressions—would mean a lot to me. Thank you for taking the time to read this.