Ben Karciauskas

You know those weeks when you glance at the AI headlines and think, “Okay, that’s neat, but it’ll be years before we feel it on the ground?” Then something drops that makes you double-take. That’s exactly how the Samsung Tiny Recursive Model (TRM) news hit me. And just as I was digesting that, the Thinking Machines Lab’s work on deterministic LLM inference showed up. Both are challenging what we thought AI needed to get better and more reliable.

Samsung’s Tiny But Mighty Recursive Model

Remember when we all just assumed that bigger was always better? Throw more parameters at a model, crank up the compute budget, and… hope for the best? Samsung’s Montreal AI lab just flipped that script. They built a recursive reasoning model with only 7 million parameters—that’s basically minuscule by today’s standards—yet it’s outpacing much larger models like DeepSeek R1 and Gemini 2.5 Pro on various reasoning benchmarks (Sudoku Extreme, Maze-Hard, and even the ARC-AGI challenge).​

How? Instead of stacking layers and hoping complexity equals intelligence, TRM loops over its own answers, refining and improving step by step. Think of it like a super-quick student checking and re-checking their math homework. Each cycle, the model queries itself: “Is this answer right or can I do better?” Rinse, repeat, and stop when the answer levels out. All this, with adaptive halting, deep supervision, and not an ounce of “chain-of-thought” hacks that have become popular lately.​

Key results? TRM nails 87% accuracy on Sudoku Extreme, 85% on Maze-Hard, and a breakthrough 44.6% on ARC-AGI-1. Most importantly, it achieves all this while being smaller, simpler, and—crucially for hardware or edge deployments—much lighter to run.​

The Determinism Problem: Thinking Machines’ Bold Fix

Now, swing to the other end: reliability and control. It’s no secret to anyone working in applied AI that getting the same output from a large language model—even with temp=0—can be a headache. Cue dozens of subtle bugs, flaky test suites, and reruns to “see what changed.” At best, it’s annoying. At worst, it makes regulated, production-grade AI feel like a gamble.

Enter Thinking Machines Lab’s latest: they’ve tracked down the core nondeterminism culprits, mostly lurking in the way our inference engines (like vLLM) handle GPU kernels—matrix multiplications, reductions, attention ops, all that jazz. By building batch-invariant versions of the problem operators, they squeezed out perfect reproducibility: 1,000 runs, 1,000 identical outputs. Greedy sampling is now really deterministic when you want it.​

The cost? There’s a performance penalty—deterministic runs can take 60% longer depending on config. But for fields like compliance, QA, or any research that needs “known in equals known out,” this is a price worth paying. Imagine never having to explain “well, the model just wanted to say something else this time…” to a project stakeholder again.​

Why This Actually Matters

For anyone architecting AI solutions—whether it’s edge deployments, agentic workflows, or just sturdy LLM pipelines—these breakthroughs are more than theoretical milestones. Smaller, recursive models could run in places we never thought practical. Deterministic inference might finally let us treat certain generative pipelines with the same confidence as SQL queries or unit-tested functions.​

If you’re betting your business, customer experience, or research integrity on AI, this is your roadmap: look for algorithmic leaps (like recursion and batch-invariance), not just scale; demand reproducibility, not just accuracy stats. The era of “throw hardware at it and hope” is being outclassed by well-designed, tight algorithms—and that’s worth getting excited about.