October 31, 2025
Big Brain, Small Maze
Reasoning Models Reason Well, Until They Don't
AI aces easy riddles, then face-plants on hard ones — commenters roast, skeptics cheer
TLDR: DeepRD tests reveal AI chat models crumble on truly complex problems, despite acing simpler ones. Comments split between eye-rolls (“obvious”), human solidarity, and demands for formal, checkable reasoning—warning that rare, hard cases still matter.
A new study says the “smart” chatbots that wow us with step-by-step answers can crush simple puzzles but fall off a cliff when problems get truly complicated. Researchers built a fresh test set called DeepRD to crank difficulty higher and found large reasoning models (LRMs—basically chatbots trained to show their work) stop generalizing once the maze gets deep. Cue the comments: iLoveOncall shrugs, calling it the “obvious outcome,” while equinox_nl jokes, “I also fail catastrophically” when things get hard—human solidarity unlocked. The spicy crowd, like WesolyKubeczek, claims these bots only “generate a seeming of reasoning” and don’t actually think, complete with theatrical “slams the door” and “touches the grass” stage directions. Meanwhile, brap wants structure: can we force models to reason like math, with proofs you can check? And alyxya steps in as the explainer, summarizing that old tests like NLGraph were too easy, so scaling complexity exposes the crash. The drama: hype vs reality, with a twist—most real-world tasks sit in the models’ “safe zone,” but the long tail of rare, gnarly problems is where things break. TL;DR vibes: useful now, but the hard stuff still haunts.
Key Points
- •Existing reasoning benchmarks (e.g., NLGraph) have limited complexity, potentially overstating model capabilities.
- •A new dataset, DeepRD, provides a generative process to create reasoning tasks with scalable complexity.
- •Evaluations on graph connectivity and natural language proof planning show LRMs’ performance drops abruptly at higher complexity.
- •LRMs do not generalize beyond the complexity levels present in their training distributions.
- •Real-world graph and proof datasets mostly fall within LRMs’ success regime, but long-tail complexities reveal significant failure risks.