February 21, 2026
Counting is hard, apparently
Large Language Model Reasoning Failures
Chatbots still flub “easy” thinking, and the comments are ruthless
TLDR: A major survey says chatbots still stumble on basic logic, math, and moral judgment, sorting failures into clear categories to guide fixes. The crowd split between “stop anthropomorphizing,” “told you so,” and “future = hybrid systems,” with extra snark for bots that can chat beautifully but can’t count reliably.
A new survey drops a cold splash of reality: today’s chatbots can talk a big game but still mess up basic thinking. The authors map failures across simple logic, moral judgment, and even counting—splitting “reasoning” into physical-world stuff vs. mind-only stuff, then flagging core flaws, app-specific limits, and shaky consistency. Translation: your AI pal can write poems, but it may fumble kindergarten math and social norms. The paper promises fixes and even a big GitHub list, but the internet had other plans.
The comments? Absolutely on fire. One camp rolled its eyes—“humans expected too much” was the vibe, with a chorus of “stop treating chatbots like tiny people.” Another camp cheered the wake-up call: They’re not human, folks reminded, saying this is the bucket of ice water the hype needs. Then came the big swing: “an llm will never reason,” declared one poster, predicting a future where symbolic logic and chatbots merge like a superhero team-up. There was snark for days, too—mocking the idea of machines doing moral reasoning (“LOL. Finally the Techbr…”) and roasting arithmetic fails with “my toaster counts better.” Skeptics pushed back on the paper’s math claims, asking if tests were fair or just trick questions. Drama score: 10/10.
Key Points
- •The survey focuses on persistent reasoning failures in Large Language Models despite strong overall capabilities.
- •It proposes a reasoning taxonomy separating embodied from non-embodied reasoning, with non-embodied split into informal (intuitive) and formal (logical).
- •It classifies failures into fundamental (architecture-intrinsic), application-specific, and robustness issues (sensitivity to minor variations).
- •For each failure type, the survey offers definitions, reviews existing studies, analyzes root causes, and suggests mitigation strategies.
- •A curated GitHub repository of research on LLM reasoning failures is provided to support further study and development.