Hallucination

When it comes to automating customer service, generative AI walks a tightrope: it can understand free-form text better than any tool before it—but with a dangerous twist. Sometimes, it just makes things up. These hallucinations, already infamous in legal and healthcare settings, can turn minor misunderstandings into costly liabilities. But what if instead of trusting one all-powerful AI model, we take a lesson from bees? A recent paper by Amer & Amer proposes just that: a multi-agent system inspired by collective intelligence in nature, combining LLMs, regex parsing, fuzzy logic, and tool-based validators to build a hallucination-resilient automation pipeline. Their case study—processing prescription renewal SMS requests—may seem narrow, but its implications are profound for any business relying on LLMs for critical operations. ...

What happens when we ask the smartest AI models to do something truly difficult—like solve a real math problem and prove their answer is correct? That’s the question tackled by a group of researchers in their paper “Mathematical Proof as a Litmus Test.” Instead of testing AI with casual tasks like summarizing news or answering trivia, they asked it to write formal mathematical proofs—the kind that leave no room for error. And the results? Surprisingly poor. ...

The Crossroads of Reason: When AI Hallucinates with Purpose On this day of reflection and sacrifice, we ask not what AI can do, but what it should become. Good Friday is not just a historical commemoration—it’s a paradox made holy: a moment when failure is reinterpreted as fulfillment, when death is the prelude to transformation. In today’s Cognaptus Insights, we draw inspiration from this theme to reimagine the way we evaluate, guide, and build large language models (LLMs). ...

When your AI believes too much, you pay the price. AI-driven quantitative trading is supposed to be smart—smarter than the market, even. But just like scientific AI systems that hallucinate new protein structures that don’t exist, trading models can conjure signals out of thin air. These errors aren’t just false positives—they’re corrosive hallucinations: misleading outputs that look plausible, alter real decisions, and resist detection until it’s too late. The Science of Hallucination Comes to Finance In a recent philosophical exploration of AI in science, Charles Rathkopf introduced the concept of corrosive hallucinations—a specific kind of model error that is both epistemically disruptive and resistant to anticipation1. These are not benign missteps. They’re illusions that change the course of reasoning, especially dangerous when embedded in high-stakes workflows. ...