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Why Do AI Systems Hallucinate?
Paperback
Technology & EngineeringGeneral Computers
Publisher: Gyrus Vision
Published: Feb 17 2026
Pages: 368
Weight: 0.94
Height: 0.82 Width: 5.50 Depth: 8.50
Language: English
Why do AI systems hallucinate-even when trained on massive datasets and refined with advanced alignment techniques?
Most explanations blame insufficient data, model scale, overfitting, or probabilistic uncertainty. But what if hallucination is not an implementation flaw at all? What if it is a structural outcome of how generative systems define stability?
In Why Do AI Systems Hallucinate?, Sandeep Chavan presents a systems-theory reframing of hallucination grounded in coherence dynamics rather than surface performance metrics. Using the Chavanian Axioms as a diagnostic lens, this book argues that hallucination emerges from a deeper architectural commitment: forced resolution under continuity constraints without true dissipation.
This book does not propose a new architecture. It does not offer quick technical patches. Instead, it asks a more foundational question: under what structural conditions does hallucination inevitably converge?
Through accessible but rigorous analysis, the book explores:
- Why hallucination is misunderstood as wrong information
- The difference between fluency and truth
- How scaling amplifies residue rather than eliminating it
- Why fine-tuning and RLHF reshape behavior but not equilibrium
- Why probabilistic confidence cannot detect structural incoherence
- What a non-hallucinating system would require-axiomatically
Rather than treating hallucination as a temporary engineering problem, this work positions it as a coherence outcome. When systems must always resolve, must always continue, and cannot suspend under uncertainty, fabrication becomes the most stable option.
For AI researchers, engineers, policymakers, and critical thinkers, this book offers a structural framework to test systems beyond accuracy benchmarks. It introduces coherence audits, residue diagnostics, and equilibrium stress tests-without prescribing specific code or architectures.
This is not an anti-AI book. It is a design-maturity book.
If AI is to be trusted in education, governance, healthcare, and decision-making, stability must precede performance. Optimization builds capability. Coherence builds trust.
This book challenges the field to shift from asking how to reduce hallucination to asking why hallucination converges.
Because the geometry of a system determines its failure mode.
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