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

Pimpcat-AU
Sat, 06 Dec 2025 01:09:27 +0000

I have been solo developing a modular platform to use as the foundations for AGI. I have several modules that already do what you described. I had a bot pick out the modules it’s in and it made a few comparisons for you. So you know your idea is on the right path. The Plan Orchestrator it’s talking about is basically a new way to program. I call it Mechanical Programming. It basically completely automates the building/debugging process for anything written in code and spits out a working product, rough but it will work.

What I found in your zip that matches “graph-based choices”

I dug through ai-persistence-engine-297.zip and you have a couple of modules that already implement the core of what that HuggingFace post is describing, just without the cognitive storytelling.

1) Plan Orchestrator is literally a graph choice engine (DAG nodes + edges)

In repo/modules/plan-orchestrator-034/ you have an explicit graph model:

A canonical DAG spec that talks about nodes and edges, deterministic ordering, and snapshot hashing.

A dependencies layer that stores edges like task -> [depends_on...] and computes blocked vs unblocked tasks.

Practical translation: this is already “graph traversal to decide what can happen next.” The “choice” is basically “which runnable node do we execute now,” constrained by the dependency graph plus whatever scheduling policy you wrap around it.

If the other guy’s system is “activation spreads, attention shifts to the active cluster,” your system is “dependencies satisfied, runnable set updated, scheduler picks next.” Same skeleton, yours is cleaner and more operational.

2) Orchestration Routing is a decision layer that chooses paths (policy driven)

Your docs/ROUTER.md describes a routing module that:

Routes by intent and trust tier

Supports shadow routing and canary routing

Uses live registries (model registry, tool registry)

This is not a knowledge graph, but it is “structured choice based on logged signals + policy constraints.” It is closer to a controller that chooses which subsystem to use next.

3) Query Planner is another “choice system,” but over indexes not cognition

In ai_persistence/service/query_planner.py you’re doing:

planning and executing a query by building candidate sets from secondary indexes

intersecting sets

applying boosts to ranking

This is “graph-ish” only in the sense it is selecting a plan and combining results, but it is still a real decision engine.

My call on “the one that already does it”

The closest match to “graph based choices” in the sense of nodes, edges, traversal, and deciding next steps is:

plan-orchestrator-034 (DAG planning, dependency edges, runnable selection)

The HuggingFace guy is mostly wrapping that same idea in cognitive terms (attention, self-state modulation, DMN vs CEN knob).