The emergence of Large Language Models (LLMs) has demonstrated promising progress in solving logical reasoning tasks effectively. Several recent approaches have proposed to change the role of the LLM from the reasoner into a translator between natural language statements and symbolic representations which are then sent to external symbolic solvers to resolve. This paradigm has established the current state-of-the-art result in logical reasoning (i.e., deductive reasoning). However, it remains unclear whether the variance in performance of these approaches stems from the methodologies employed or the specific symbolic solvers utilized. There is a lack of consistent comparison between symbolic solvers and how they influence the overall reported performance. This is important, as each symbolic solver also has its own input symbolic language, presenting varying degrees of challenge in the translation process. To address this gap, we perform experiments on 3 deductive reasoning benchmarks with LLMs augmented with widely used symbolic solvers: Z3, Pyke, and Prover9. The tool-executable rates of symbolic translation generated by different LLMs exhibit a near 50% performance variation. This highlights a significant difference in performance rooted in very basic choices of tools. The almost linear correlation between the executable rate of translations and the accuracy of the outcomes from Prover9 highlight a strong alignment between LLMs ability to translate into Prover9 symbolic language, and the correctness of those translations.

About two years ago, I was in a car with my best friend and her toddler. She was driving, and I was sitting in the back next to her 10-month-old, who was tucked into his car seat. For a while, the ride was smooth--then the baby burst into tears. We tried every common trick to comfort him. I contorted my face into the silliest of poses, my friend burst into a catchy song, but our efforts were met with louder wails until finally--mercifully!--we pulled into my friend's driveway and she was able to scoop her son up in her arms.

There are lots of companies who make wearable tails for humans, but they're usually for cosplay or other entertainment pursuits. Researchers at Keio University in Japan have created a wearable animated tail that promises to genuinely augment the wearer's capabilities--not just appearance--by improving their balance and agility. I wrote a post about Disney's efforts to market Zootopia to furries last week, and I really pissed… The easiest way to understand what inspired this creation is to watch a video of monkeys effortlessly leaping from tree to tree. Their tails not only serve as an additional limb for grasping branches but also help them reposition their bodies mid-flight for a safe landing by shifting the monkey's center of balance as it moves. The Arque tail, as it's been named, does essentially the same thing for humans, although leaping from the highest branches of a tree isn't recommended just yet.