LLMs and Neurosymbolic reasoning
When people discuss how LLMS "reason," you’ll often hear that they rely on transduction rather than abduction. It sounds technical, but the distinction matters - especially as we start wiring LLMs into systems that are supposed to think.
🔵 Transduction is case-to-case reasoning. It doesn’t build theories; it draws fuzzy connections based on resemblance. Think: “This metal conducts electricity, and that one looks similar - so maybe it does too.”
🔵 Abduction, by contrast, is about generating explanations. It’s what scientists (and detectives) do: “This metal is conducting - maybe it contains free electrons. That would explain it.”
The claim is that LLMs operate more like transducers - navigating high-dimensional spaces of statistical similarity, rather than forming crisp generalisations. But this isn’t the whole picture. In practice, it seems to me that LLMs also perform a kind of induction - abstracting general patterns from oceans of text. They learn the shape of ideas and apply them in novel ways. That’s closer to “All metals of this type have conducted in the past, so this one probably will.”
Now add tools to the mix - code execution, web search, Elon Musk's tweet history 😉 - and LLMs start doing something even more interesting: program search and synthesis. It's messy, probabilistic, and not at all principled or rigorous. But it’s inching toward a form of abductive reasoning.
Which brings us to a more principled approach for reasoning within an enterprise domain: the neuro-symbolic loop - a collaboration between large language models and knowledge graphs. The graph provides structure: formal semantics, ontologies, logic, and depth. The LLM brings intuition: flexible inference, linguistic creativity, and breadth. One grounds. The other leaps.
💡 The real breakthrough could come when the grounding isn’t just factual, but conceptual - when the ontology encodes clean, meaningful generalisations. That’s when the LLM’s leaps wouldn’t just reach further - they’d rise higher, landing on novel ideas that hold up under formal scrutiny. 💡
So where do metals fit into this new framing?
🔵 Transduction: “This metal conducts. That one looks the same - it probably does too.”
🔵 Induction: “I’ve tested ten of these. All conducted. It’s probably a rule.”
🔵 Abduction: “This metal is conducting. It shares properties with the ‘conductive alloy’ class - especially composition and crystal structure. The best explanation is a sea of free electrons.”
LLMs, in isolation, are limited in their ability to perform structured abduction. But when embedded in a system that includes a formal ontology, logical reasoning, and external tools, they can begin to participate in richer forms of reasoning. These hybrid systems are still far from principled scientific reasoners - but they hint at a path forward: a more integrated and disciplined neuro-symbolic architecture that moves beyond mere pattern completion.
