Why the Future of AI is Neuro-Symbolic
"Neurosymbolic" AI is not yet a household word, but it should be.
While geneative AI is currently everywhere, we believe another wave is coming.
Neuro-symbolic AI is a new generation of AI that combines the statistical learning capabilities of neural networks that powers current generation LLMs and other approaches with the precision of symbolic logic. Neuro-symbolic AI can do deductive reasoning natively and is orders of magnitude more efficient than the purely statistical neural AI ubiquitous today. We believe neuro-symbolic AI will finally address the reliability issues that plague purely statistical AI because neuro-symbolic models can apply formal reasoning for provable results and reliable inference.
LLMs are powerful but they have serious liability issues. The business models are unsustainable, the energy and compute costs are sky-high, and the current AI wave will not yield reliable, explainable AI.
That's not the end of the road, however. Far from it. Neuro-symbolic AI is seeing rapid progress, and the opportunity it promises is enormous.
This post gives you a comprehensive overview of neuro-symbolic AI and why it represents a fundamental advance at least as consequential as generative AI. If you are an enterprise or individual wishing for more reliable and factual AI, we hope this post gets you excited.
A Brief History of AI
To understand neuro-symbolic AI, we first need to look back at the history of AI.
Some of the first attempts at AI viewed intelligence as a logical language. The legendary programming language LISP was designed specifically to model the structures of logical thought for AI. It was then believed that we could build knowledge directly into AI systems this way. However, there was a big problem. This symbolic approach to AI was hard to adapt to new scenarios—it didn’t learn. Designing systems of rules and inference alone wasn’t enough if those rules couldn’t adapt to experience.
Some of the most brilliant computer scientists of their age, indeed of all time, worked on this symbolic AI paradigm. They weren’t all wrong; they were just missing an essential piece: machine learning had to be solved before we could arrive at machine knowing.
It took a while before deep learning, loosely inspired by how the brain functions, resolved the experiential brittleness of symbolic AI by creating systems that could progressively update from inputs and predict outputs statistically. Deep learning created a revolution that transformed the world, and has certainly yielded many fruits such as computer vision, prediction models, recommendation systems, and generative AI. For all its benefits, however,deep learning is not perfect.
The Limitations of Statistical AI
Deep learning has its own problems. Indeed, its weaknesses are the symbolic approach’s strengths, and vice versa! These weaknesses fall into several major categories:
- Sample inefficiency
- Lack of grounded internal representations
- Hallucination and error propagation
- Poor generalization to out-of-distribution data
- Fundamental unexplainability (black box)
- Compositionality
Sample Inefficiency and Lack of Grounded Internal Representations
Statistical AI is often incredibly sample inefficient, which is why it takes so much data to feed these models. They excel at pattern recognition and detection, but they have no internal concept of truth, so they have no sense of what those patterns truly mean. They can’t identify related patterns as all pointing to a single entity or “internal representation.” So all they can do is see what patterns correlate, but they lack any idea of what pattern is the “true” one, or which patterns belong to what identity.
This inability to connect statistical patterns to underlying representations is called the “symbol grounding problem,” and is closely related to the “binding problem”—the challenge of dynamically associating roles and values in structured contexts. (Some debate whether LLMs actually have internal representations, but because statistical models are black boxes, it’s near impossible to prove it.)
Because symbolic systems operate on abstract rules rather than memorized examples, they can generalize from far fewer data points—making them drastically more sample efficient in many contexts. They basically discover the formula for a problem and apply it to new data points, without memorizing every example.
Hallucination and Error Propagation
Because pure statistical neural models don’t bind patterns to underlying representational invariants, they also "hallucinate." They don’t actually have a conceptual model of the world, only pattern associations. Scaling helps but cannot eliminate hallucination, because the problem stems from the lack of grounded representations, not from model size.
If you think of a balloon with black dots painted on it, and you increase the size of the balloon, the black dots increase in size proportionally. That’s a good analogy for why scaling doesn’t fix these problems. Scaling them up just means more surface area for the blind spots to spread.
Poor Generalization to Out-of-Distribution Data
These models also struggle to generalize to “out-of-distribution data”—that is, data which is outside anything in their training data. Because they don’t learn the “concepts of math” but only learn examples of those rules being applied, it’s possible to show them a niche problem that throws them off completely. Indeed, numerous studies have shown how even slightly varying how problems are presented will throw these models off by wide margins.
Fundamental Unexplainability
Large-scale statistical neural AI is also unexplainable. It's what gave AI a reputation for being a black box. The best you can hope for is correlational studies of large blobs of numbers. For LLMs, perhaps you could begin to find meaningful correlations in the giant blobs of numbers these models generate as throughputs—if, that is, you had a computer 10×–100× bigger than the already huge one you’re deploying the model on to crunch all those extra numbers the model cooks off while adjusting its weights or running inference.
Such scale issues quickly become untenable when you consider that the largest models have billions of parameters and therefore produce many trillions of Floating Point Operations (FLOPs) per second, and require enormous data centers just to run! Collecting and analyzing all those operations in search of a whiff of meaning is a waste of time and resources.
While statistical models can be probed using XAI techniques, these are often post-hoc, fragile, and fail to yield robust, human-readable explanations. The nature of statistics itself is uncertainty and approximation. If we want certainty, we need different mathematical tools and new architectures.
Compositionality
Perhaps the greatest weakness of statistical AI and neurosymbolic's greatest strength is compositionality. The simplest way to think about compositionality is to picture a blue circle and a red square. It's easy for us to "detach" the concept of "red" from the square and "reattach" it to the circle, and vice versa for the blue of the square. Treating distinct things as separable and combinable concepts is a fundamental capability of intelligence, and classical deep learning is really bad at it. Remember the symbol grounding issue mentioned earlier? Without underlying representational invariants, there's nothing to compose. Compositionality is where neuro-symbolic AI, in contrast, excels.
How Neuro-Symbolic AI Works
At a basic level, neuro-symbolic AI combines statistical machine learning with more traditional deterministic logic and symbolic reasoning. How these fusions are achieved is a subject of ongoing research, but most models tend to be hybrid, dual systems, with a neural component that feeds into a symbolic engine. (We believe a deeper unification is possible but that’s a topic for another time.)
Since neuro-symbolic models have a formula connected to everything they do, sample efficiency, generalization, and explainability are baked into it at ground level. The system's "thoughts" are the formulae that it uses to reason about the world—and these can be printed out and inspected. These models compose from first principles rather than merely associate patterns in training data. This isn’t just an incremental improvement: it’s a paradigm shift toward AI systems that can explain their reasoning and provide mathematical guarantees about their reliability.
Another huge benefit of neuro-symbolic AI for developers is that it lets you treat machine learning models more like regular programs. Whereas traditionally neural models are “set it and forget it”—you just feed them data, tweak some hyperparameters, and hope they learn the patterns.Compositional neuro-symbolic models can be stacked and arrayed like building blocks. Each component might learn a single feature of the model, and can be inspected, debugged, or have structured knowledge injected directly into it. This gives us far more control over our models than ever before.
What's the Evidence?
Progress in neurosymbolic AI has remained largely theoreticaly until very recently. Papers are being published that show remarkable gains and improvements. This progress hints at a quiet revolution brewing under the surface. Here's a small selection of recent findings:
Hersche, Zeqiri et al. (2023) propose a Neuro-Vector-Symbolic Architecture (NVSA) that combines deep neural networks with vector symbolic architectures to address the binding problem in neural networks and the exhaustive search problem in symbolic AI. On Raven’s Progressive Matrices tasks, NVSA achieves state-of-the-art accuracy (87.7% on RAVEN, 88.1% on I-RAVEN) without shortcut bias, offers real-time inference, and is up to 244× faster than prior symbolic reasoning methods while maintaining comparable or superior accuracy. source
Dhanraj and Eliasmith, (2025) present a neurosymbolic method for improving rule-based reasoning in LLMs by encoding hidden states into structured vector symbolic algebra (VSA) representations, applying symbolic algorithms in that space, and decoding results back into the model’s hidden state. Tested on a procedurally generated Symbolic-Math Dataset, their approach achieves an average of 88.6% lower cross-entropy loss and solves 15.4× more problems than chain-of-thought prompting or LoRA fine-tuning, while preserving performance on out-of-distribution and non-mathematical tasks through selective symbolic intervention. source
Ponzina and Rosing, (2024) introduce MicroHD, an accuracy-driven optimization framework for hyperdimensional computing (HDC) tailored to TinyML systems. Across multiple datasets and encoding methods, it achieves up to 266× compression and 267× workload reduction with ≤1% accuracy drop, outperforming state-of-the-art methods by up to 8× in efficiency, and providing significant benefits for in-memory computing accelerators and federated learning by lowering area, energy, and communication costs. source
These often explosive gains in efficiency and accuracy point to a fundamental paradigm shift that is simply beyond the reach of purely statistical models.
Looking Ahead
Today’s generative models are largely driven by token prediction. Based on their training data they try to construct the most likely continuation of the user input they received. Next-generation generative models might actively construct new outputs based on first principles. These would be truly knowledge-driven systems; their behavior would be a consequence of their understanding of the conceptual rules governing a domain. You’ll be able to see exactly why and how it came to its conclusions, just like how you could reconstruct a game of chess from each individual move the players make.
These rule-governed generative systems will change everything. Since it's much cheaper to learn a set of rules than countless examples of those rules, neuro-symbolic models will likely be orders of magnitude more sample, energy, and parameter efficient than current purely statistical models.
Neuro-symbolic models are not just a dream. Out-of-the-limelight research is showing how to combine the neural and symbolic parts of AI. For enterprises relying on AI for critical operations, these shortcomings in current models aren’t just technical, they’re operational risks. Models that hallucinate or fail to generalize can result in costly errors, failed projects, and damaged trust. Furthermore the costs of scaling deep learning infinitely are unsustainable, or at the very least, commiting to those costs is deeply unwise if better alternatives are right around the corner.
Neuro-symbolic AI offers a way forward.
Semantic Reach intends to be at the forefront of this next wave. We are excited to share our progress and findings in the coming months.