Deep Dive: What Comes After Large Language Models?
bigdata · x · 2026-08-07
Gradient Flow explores the limitations of current LLM architectures in achieving higher-level intelligence, such as scientific discovery.
Referencing a recent position paper, the article categorizes reasoning into three types:
- Induction: Finding general patterns from examples.
- Deduction: Working out consequences from assumptions.
- Abduction: Proposing new explanations when existing rules and data fall short.
While today's AI excels at the first two, it lacks the conceptual "jumps" required for abduction. For instance, an AI might derive the field equations of general relativity if given the equivalence principle, but it couldn't invent the principle via thought experiments like Einstein did. The author argues that merely scaling LLMs is insufficient; future breakthroughs may require interactive world models acting as synthetic laboratories where agents can run counterfactual experiments to form new hypotheses.
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