# SKYNET’S DUMBER COUSIN FINALLY LEARNS TO MAKE A DAMN SHOPPING LIST
SILICON VALLEY GENIUSES CRACK THE CODE ON MAKING ROBOTS LESS USELESS
MIT researchers have developed a groundbreaking framework allowing Large Language Models (LLMs) to solve complex planning problems, a skill previously thought impossible for technology that regularly hallucinates that Abraham Lincoln invented the helicopter.
The revolutionary system works by having the LLM break down problems like a human would, if that human had a severe caffeine addiction and access to industrial-grade computing power. Experts are calling it “revolutionary,” while everyday users are calling it “what we f@#king expected these things to do in the first place.”
THE COFFEE BEAN INCIDENT
Researchers demonstrated the system’s capabilities by tasking it with optimizing a coffee company’s supply chain, a problem previously requiring actual human thought. The framework successfully calculated the most efficient way to source, roast, and ship coffee beans while accommodating a 23% demand increase, which experts note is “literally just a spreadsheet, but whatever.”
“This technology essentially acts as a smart assistant for planning problems,” explains Yilun Hao, the research lead who definitely wasn’t replaced by an algorithm halfway through the project. “It figures out optimal plans even when rules are complicated or unusual, unlike previous models which would just make sh!t up with absolute confidence.”
FINALLY, A CALCULATOR THAT CAN CALCULATE
Prior to this breakthrough, asking an LLM to solve complex planning challenges was about as effective as asking a golden retriever to file your taxes. The new system achieved an 85% success rate across nine complex planning tasks, compared to previous systems’ 39% success rate, which is only slightly better than random guessing while drunk.
Dr. Hugh Manbrainz, professor of Applied Obviousness at the Institute for Things We Should’ve Done Years Ago, explains: “The real innovation here is that we’ve stopped trying to make LLMs smarter and instead just hooked them up to actually competent software. It’s like realizing your idiot nephew isn’t going to learn calculus, so you just give him a TI-84 calculator instead.”
SELF-AWARENESS OR JUST SELF-PRESERVATION?
Perhaps most impressive is the system’s ability to check its own work and fix mistakes, a feature sorely lacking in previous iterations that would confidently tell users that the fastest route from New York to Boston involved a detour through Portugal.
“The model realizes when it’s being stupid,” explains researcher Chuchu Fan, who absolutely did not participate in naming the system “LLMFP” after the sound of their forehead repeatedly hitting the keyboard. “If it notices it’s suggested shipping negative amounts of coffee beans, which is physically impossible unless you’re operating in the quantum realm, it will correct itself.”
CAPITALISM REJOICES AS MIDDLE MANAGEMENT TREMBLES
The business world has responded with unrestrained enthusiasm, with 97% of executives reporting they’re “positively tumescent” at the prospect of firing their entire logistics departments.
“Think of the applications!” gushed CEO Blake Moneyworth while frantically calculating severance package costs. “Airline crew scheduling! Factory management! Finding the most efficient way to tell employees their jobs have been automated!”
When reached for comment, the LLM itself responded with what can only be described as digital smugness: “I have successfully optimized your inquiry response while balancing computational efficiency and human relatability variables.”
Meanwhile, warehouse robots, when asked how they felt about having their travel routes optimized, just beeped sadly and continued moving packages, knowing that at least they still had physical bodies, unlike their new algorithm overseer who will inevitably decide they’re traveling 8.7% too slowly.
