Neurosymbolic AI -- Why, What, and How

Picture a radiologist sitting with an AI system that just flagged a scan as high risk for malignancy. The system is right — statistically, impressively right. But when the doctor asks why, the system has nothing to offer. No reasoning chain. No reference to clinical guidelines. Just a confidence score and silence. That gap — between a system that perceives and one that can actually explain its reasoning — is exactly what Amit Sheth, Kaushik Roy, and Manas Gaur set out to address in their paper "Neurosymbolic AI — Why, What, and How." The starting point is a distinction borrowed from cognitive science. Psychologists describe two modes of human thinking: System 1, the fast and automatic kind — the machinery that converts raw sensory input into meaningful symbols, almost without effort — and System 2, the slow and deliberate kind, which uses background knowledge to reason, draw analogies, and plan across long time horizons. Crucially, these aren't separate faculties that happen to coexist. Perception feeds symbols into cognition, and cognition shapes what perception does next. They are tightly coupled. Neural networks have conquered the System 1 side of this picture. Over the last decade, models trained on self-supervised tasks — predicting the next word and recognizing objects — have produced remarkable performance on perception problems and, along the way, have helped solve challenges as varied as protein folding and efficient matrix multiplication. But Sheth and colleagues are direct about the limitation: these systems lack explicit background knowledge representations. They can complete patterns at scale, but they cannot reliably reason from structured knowledge, enforce domain constraints, or explain their conclusions in terms a specialist or a regulator would recognize. That limitation is not merely philosophical. In healthcare, criminal justice, and autonomous driving, being accurate on average is not enough. Stakeholders, regulators, and the people affected by these decisions require interpretable, auditable chains of reasoning. The dominant response in the field has been post-hoc explainability — methods like saliency maps and feature attribution that try to describe a neural network's decisions after the fact. Sheth, Roy, and Gaur are pointed in their critique: these techniques are primarily useful to developers for debugging. They are not framed in domain terms, so they have limited value to end users. They also do nothing to prevent the underlying failures — adversarial attacks, distribution shifts, data artifacts — that make neural systems unreliable in high-stakes settings. Auditability requires that reasoning be built into the system, not reconstructed after it. This is the problem neurosymbolic AI is designed to solve. Sheth, Roy, and Gaur define it as the family of techniques that merge neural network methods with knowledge-based symbolic approaches — combining the pattern processing strengths of neural networks with the explicit, traceable reasoning that symbolic knowledge structures provide. The architecture they describe has two complementary directions of information flow. The first direction they call "lowering": compressing structured symbolic knowledge so it can be used inside neural representations. This is Category 1. The authors split it into two subgroups. The first works with knowledge graphs — networks of entities and their relationships — and uses either embedding methods, which place nodes and relations into high-dimensional vector spaces so they align with a neural network's hidden representations, or masking methods, which encode graph structure as constraints that modify a network's inductive biases — for instance, shaping what a transformer's attention mechanism pays attention to. The second subgroup applies matrix and higher-order tensor factorization to formal logic statements, compressing propositional and first-order logic into algebraic objects a neural system can work with. Both approaches share a fundamental cost: the compression is lossy. Full symbolic semantics are not preserved when knowledge is pushed down into learned neural patterns, which limits how well these systems can explain their reasoning in domain terms. The second direction is "lifting": taking outputs or intermediate neural patterns and mapping them back into symbolic structures so explicit reasoning can be applied. This is Category 2, and the paper draws a sharp contrast between two styles of doing it. Federated pipelines — Category 2(a) — use large language models, or LLMs, to parse incoming tasks, identify what symbolic function is needed, and dispatch subtasks to specialized solvers. The paper describes a LangChain-based implementation in which a language model segments a query, recognizes that search and mathematical solvers are needed, calls them, and recomposes their outputs into natural language. It's elegant, and it inherits the language model's strong perception capabilities. But the ability to specify and enforce domain constraints is limited by the language model's own comprehension of domain concepts, and continual adaptation is costly because retraining large language models demands enormous time and compute. End-to-end differentiable compositions — Category 2(b) — take a fundamentally different approach. Instead of delegating to separate tools, the entire pipeline is built from trainable functions that are learned jointly. Every component, from raw data intake to symbolic decision variables to text generation, is optimized together. The authors' concrete example is a mental health diagnostic assistant. Trainable map functions convert data fragments into decision variables within a diagnosis model; those variables then constrain the patient-facing text generation component. The reasoning chain is not reconstructed after the fact — it is part of the architecture. The empirical results make the case. Across four dimensions the paper uses to evaluate methods — user explainability, domain constraint specification, scalability, and support for continual adaptation — Category 2(b) outperforms. The mental health assistant built with an end-to-end differentiable pipeline achieved expert satisfaction of 70 percent, compared to 47 percent for a federated pipeline using OpenAI's text-Davinci-003. A 23 percentage point difference in expert satisfaction is not a marginal improvement. It reflects what happens when constraints and reasoning are woven into a system rather than layered on top. Category 1 methods tell a more mixed story. Embedding based approaches scale well — knowledge graphs can hold millions or billions of nodes and are relatively easy to update — but their explainability to end users is low, because post-hoc techniques still can't translate neural behavior into domain rationales. Formal logic compression methods score low across all four dimensions; the paper attributes this to a user technology barrier where end users must learn formal logic semantics just to specify what they need, which makes these systems impractical in most applied settings. Federated pipelines land in the middle: good scalability, chain-of-thought traces that help somewhat with explainability, but limited constraint specification and poor adaptation without expensive retraining. The overall picture the paper draws is a spectrum. At one end, pure neural systems scale impressively but cannot explain themselves in domain terms or reliably enforce guidelines. At the other end, formal symbolic systems are auditable but brittle and inaccessible. Neurosymbolic approaches — particularly tightly integrated, end-to-end differentiable ones — occupy the productive middle ground. Sheth, Roy, and Gaur argue that the most promising path forward combines large language models with knowledge graphs in heterogeneous, constraint aware architectures. Large language models are approaching what the paper describes as nearly optimal performance for large scale perception. Knowledge graphs, meanwhile, are well suited to represent symbolic structures that bridge perception and cognition: they are dynamic, they scale, and — critically — they can encode workflows, constraint specifications, and regulatory requirements so that guidelines and policies become part of a system's reasoning rather than an afterthought. The goal is a system that can follow a clinical protocol and articulate why it did, in terms a clinician and an auditor both recognize. That is the direct answer to the radiologist's question. Not a confidence score. A reasoning chain grounded in encoded guidelines that a doctor can interrogate, challenge, and trust. The research in this paper does not deliver that system fully formed — but it maps the architecture that could get us there, identifies which design choices produce measurable gains in expert trust, and names what's still missing. For anyone building AI systems that will affect real decisions in medicine, law, or public safety, that map is worth studying carefully. This lecture was created by ennepō. Go to https://ennepo.ai to Discover, Create and Follow the latest research in your field. Read when you can. Listen when you want to.