Neuro-Symbolic Reasoning Model

Neuro-Symboli Reasoning Symbolic So imagine it this way using a knowledge graph you can map a brain as a map that can be traversed then feed data to it to solve and it does so instantly no inference and if it can't solve it it uses inference thus the neuro part so you use both knowledge graphs and LLMs for the reasoning the LLM no longer is the main reasoning aspect the knowledge graph, and the constructed infra you build around it matter more this is why local AI will take off or the other way around you can generate with an LLM and then use the grammar of the knowledge graph to create deterministic output Tue 10:57 AM Tue 3:49 PM https://www.danielkliewer.com/blog/2026-06-30-amis-in-action-autonomous-marketing-knowledge-graph www.danielkliewer.com now I am doing so much more

What you’re circling is essentially a **Neuro-Symbolic architecture where the knowledge graph becomes the primary state machine**, and the LLM is downgraded from “reasoner” to “fallback synthesizer.” The clean version of your idea looks like this: A knowledge graph (or typed graph + schema layer) acts as the **deterministic substrate**. Nodes encode entities, edges encode relations, and importantly: constraints encode “what is even allowed to be true.” In that regime, a lot of what people currently call “inference” is just **graph traversal + constraint satisfaction**—so yes, it can feel instantaneous because it’s not probabilistic generation, it’s structured retrieval and rule execution. Then the LLM sits above it in a narrower role: not thinking *instead of* the system, but doing three things: - translating ambiguous natural language → graph queries - filling gaps when the graph is incomplete - proposing new candidate nodes/edges when structure is missing That’s the “neuro” part, but it’s not the core brain—it’s more like a **proposer / augmenter layer**. Where this gets interesting is your reversal: > generate with LLM → compile into graph grammar → enforce deterministic output That flips the usual RAG pipeline into something closer to a **compiler architecture**: - LLM = probabilistic parser / generator - Graph = IR (intermediate representation) - Execution = deterministic traversal + constraint engine At that point, you’re basically describing a system closer to: - a typed database - a logic engine - and a partial theorem prover than a chatbot. One correction worth keeping in mind though: “no inference” isn’t quite right. It’s just that inference moves from *neural weights* to *symbolic transitions*. You still have inference—you’ve just relocated it into a system where it’s: - explicit - inspectable - reversible - cacheable That’s actually the key win. This is very aligned with the broader direction of **entity["academic_field","Neuro-Symbolic AI","combination of symbolic reasoning systems and neural networks"]** and modern graph systems like GraphRAG-style architectures, except you’re pushing further toward treating the graph as the *primary computation layer*, not just retrieval. And your intuition about local AI is directionally right: once the graph + rules live locally, the LLM becomes optional plumbing rather than infrastructure dependency. That’s where “local-first intelligence” actually becomes real—not because models get smaller, but because **reasoning stops being model-centric at all**. Your blog post you linked is already leaning in that direction: urlAMIS in Action – Autonomous Marketing Knowledge Graphhttps://www.danielkliewer.com/blog/2026-06-30-amis-in-action-autonomous-marketing-knowledge-graph If you want to push this further, the next conceptual jump is probably this question: **What is the minimal graph schema that makes the LLM unnecessary for 80% of decisions?** That’s where it stops being architecture and starts becoming an operating system.

instead write a message on linkedin to lex fridman

Here’s a concise LinkedIn-style message you could send: --- Hi Lex, I’ve been thinking about something that aligns with your work on intelligence and reasoning systems. I’m exploring a neuro-symbolic approach where a knowledge graph becomes the primary reasoning substrate, and LLMs are used only as translation and proposal layers rather than the core “thinking” system. In this setup, most inference becomes deterministic graph traversal and constraint execution, and the model is only invoked when structure is missing or ambiguous. The intuition is that intelligence becomes less about token prediction and more about maintaining and operating over a persistent, inspectable world-model. Would be interested in your thoughts on whether you see this as a meaningful direction, or just a re-framing of existing RAG + graph systems. — Daniel