09/05/2026
YOUR MODEL ISN'T BROKEN. IT'S WORKING EXACTLY AS DESIGNED.
Ever wondered why LLMs confidently lie to your face? We just published a structural analysis revealing the truth: Hallucination isn't a "glitch"—it’s a feature of the architecture itself. 🤯
We broke it down into three core mechanisms that prove the system has zero factual constraints:
1️⃣ Self-Attention ≠ Meaning
The formula (Vaswani et al., 2017) learns co-occurrence, not truth. If "Sylhet" and "tea" appear together thousands of times, the associative weight is massive. Ask for the capital of Bangladesh? "Sylhet" fires anyway. The math doesn't care about facts; it only cares about what words "hang out" together.
2️⃣ The "Scale" Trap
MLE training (Brown et al., 2020) rewards frequency, not reality. A fluent lie repeated 50,000 times gets the same "gold star" as a verified fact.
The shocker: GPT-3 (175B parameters) scores only 58% on TruthfulQA (Humans: 94%).
The reality: Larger models can actually be less truthful because they learn the distribution’s falsehoods more perfectly.
3️⃣ No Path for Regret
Autoregressive decoding (Ranzato et al., 2016) is a one-way street. The model commits left-to-right, permanently. Since it never "sees" its own mistakes during training, one wrong token at inference becomes the foundation for everything that follows. It can't go back. It can't revise. It just digs a deeper hole. 🕳️
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🧪 THE PROOF
We tested these mechanisms on GPT-2 under controlled conditions and the results were clear:
13/15 attention probes misfired.
Falsehoods outscored truths in 60% of matched pairs.
Cascade failure happened in 4 out of 5 tests.
Bottom line: Hallucination isn’t something that "happens" to these models. They were built to do it.
📖 Read the full paper on SSRN: [6604798]
