AI systems today generate language with extraordinary fluency-yet they also fabricate facts, invent citations, and produce confident falsehoods. These failures are commonly labeled "hallucinations," and are typically explained as data limitations, probabilistic errors, or imperfect alignment.
This book challenges that entire framing.
In Why Do AI Systems Hallucinate? Chavan argues that hallucination is not an accidental flaw, but a structural outcome. When systems are designed to always produce output, maintain continuity, and avoid suspension under uncertainty, fabrication becomes a stable equilibrium-not an exception.
Moving beyond intelligence and probability, the book introduces coherence as the central requirement for reliable systems. Through a systems-theory lens grounded in the Chavanian Axioms, it demonstrates why scaling, fine-tuning, and alignment techniques succeed locally but fail to resolve the underlying instability.
Hallucination is reframed as a consequence of unresolved internal pressure-driven by delta propagation, residue accumulation, and failed dissipation. In such conditions, increasing capability does not eliminate error; it makes it more fluent and harder to detect.
This work does not propose new architectures or optimization tricks. Instead, it offers a diagnostic foundation. It identifies the structural conditions any non-hallucinating system must satisfy-and shows why current approaches fall short.
For researchers, engineers, policymakers, and critical thinkers, this is not a guide to incremental fixes. It is a redefinition of the problem itself.
Hallucination is not a bug to patch. It is a predictable expression of system design.
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