The first method is called the consensus game to address the issue of models giving different answers to the same question depending on how it’s phrased

Although humans can reason and, therefore, reply in a more coherent manner (according to one's own cosmos which contains personality traits, knowledge, mood, etc), this phenomenon kind of also happens with humans. Depending on how multifaceted is the question/statement, a slightly different phrasing can "induce" an answer. Actually, it's a fundamental principle behind mesmerism, gas-lighting and social engineering: inducing someone to a certain reply/action/behavior/thought, sometimes relying on repetition, sometimes relying on complexity.

Artificial automatons are particularly sensible to this because of how their underlying principles are purely algorithmic. We aren't exactly algorithmic, although we have physical components of "determinism" (e.g. muscles contracting when in contact with electricity, body always seeking homeostasis, etc).

However, I understood what you meant with it. It'd be akin to a human trying to think twice/thrice when faced by complex and potentially mischievous/misleading questions/statements. "Thinking" before "acting" through consensus game.

The second method is to use neurosymbolic systems that combine deep learning to identify patterns in data with reasoning based on knowledge using symbolic logic. It has the potential to outperform systems relying either solely on neural networks or symbolic logic while providing clear explanations for decisions. This involves encoding symbolic knowledge into a format compatible with neural networks, and mapping data from neural patterns back to symbolic representations.

Yeah. I see a great potential on it, too. "Signs and symbols rule the world, not words or laws" (unfortunately this Confucian quote is often misused by people, but it captures the essence of how symbols are a fundamental piece of the cosmos).