Has LLMs reached language supremacy

Is it possible that we have actually moved entirely past the jagged areas of text-based problems? Just a few years ago, AI famously struggled with basic contextual logic, like the Winograd Schema Challenge, failing to understand that in the sentence "The trophy didn't fit into the suitcase because it was too large," the word "it" obviously refers to the trophy. Today, models conquer these linguistic hurdles flawlessly and routinely match or beat human baselines on Theory of Mind tests, accurately predicting the hidden motives or false beliefs of characters in complex narratives.

When we look at models like Gemini now performing within the human margin of error on common-sense evaluations like SimpleBench, it forces us to ask if text-based reasoning is fundamentally "solved." After all, these models now easily pass semantic trick questions that used to expose their lack of real-world logic, such as, "If you want to wash your car but the car wash is only 100 meters away, do you walk or take your car?"easily understanding the implicit physical constraint that you must drive the vehicle to the location in order to wash it. Furthermore, with the introduction of "System 2" reasoning models that generate hidden chains of thought before speaking, even the persistent flaws of logical backtracking and self-correction are being systematically dismantled. If language models have truly mastered this level of everyday human logic and self-correction, have we definitively closed the gap on text-based jagged intelligence, meaning the new frontier of those bizarre, non-human mistakes is now purely in the visual and audio realm?