I built TECS to explore a simple question: What if data routing was dynamic, cyclic, and resource-constrained instead of static? Most modern systems (including attention mechanisms) assume soft, continuous routing. TECS takes a different approach — elements physically “move” across slots over time, creating a discrete dynamic system: [math]p_e(t+1) = (p_e(t) + v_e) \bmod N[/math] and aggregation happens locally: [math]Y_s(t) = F_s({\varphi_e(t) \mid p_e(t)=s})[/math] This leads to interesting properties: natural resource competition emergent patterns and periodicity hybrid behavior between simulation and learning systems Originally, this started as a theoretical model of discrete dynamics, but it evolved into a working engine combining: simulation (agent-like movement) neural routing (Gumbel-Softmax) and system-level constraints I’m especially interested in feedback on: potential applications (AI / distributed systems / simulations) theoretical connections (dynamics, computation) and ideas for extending it further Happy to answer any questions 👇

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p/tecs-engine

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5mo ago

Introducing TECS Engine – a discrete dynamic system for Element-Slot interaction.

TECS (Time Element Cycle Slot Engine) lets you simulate and train complex routing of elements into slots, bridging simulation modeling with neuro-symbolic learning. Perfect for testing resource allocation, agent-based models, or hybrid AI systems.

Why it s exciting:

Structured data routing in dynamic environments
Models competition and priorities naturally
Easily extensible for simulation, training, or research

Check it out: GitHub repo

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TECS Engine

Discrete dynamic routing system for element-slot interaction

Discrete dynamic routing system for element-slot interaction

Forum Threads

Introducing TECS Engine – a discrete dynamic system for Element-Slot interaction.

Forum Threads

Introducing TECS Engine – a discrete dynamic system for Element-Slot interaction.