[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Rohan Paul [@rohanpaul_ai](/creator/twitter/rohanpaul_ai) on x 73.9K followers Created: 2025-07-12 14:14:00 UTC Frontier language models shine on Olympiad‑level benchmarks yet stumble on chores like counting letters. The paper samples “easy” reasoning tasks, dials up length or distractions, and watches accuracy crash. Tests cover word or character counting, logic trees, proof‑style math stories, and travel itineraries that only need basic bookkeeping. As paragraphs grow or extra names appear, small step errors snowball, models lose track of state, guess from phrase frequency, or copy memorised solutions instead of thinking. A hand‑built “Unpuzzles” set flips famous riddles into trivial variants, yet models often reuse the original, wrong reasoning, a pattern the authors call reasoning delirium. Even reinforced “thinking” versions like o1 and o3 still collapse once tasks demand several dozen sequential checks, confirming that big improvements on elite benchmarks do not guarantee basic robustness. The takeaway is simple, models must master short, boring problems before boasting about deep reasoning. ---- Paper – arxiv. org/abs/2507.07313 Paper Title: "Frontier LLMs Still Struggle with Simple Reasoning Tasks"  XXXXXXX engagements  [Post Link](https://x.com/rohanpaul_ai/status/1944037530728382874)
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Rohan Paul @rohanpaul_ai on x 73.9K followers
Created: 2025-07-12 14:14:00 UTC
Frontier language models shine on Olympiad‑level benchmarks yet stumble on chores like counting letters.
The paper samples “easy” reasoning tasks, dials up length or distractions, and watches accuracy crash.
Tests cover word or character counting, logic trees, proof‑style math stories, and travel itineraries that only need basic bookkeeping.
As paragraphs grow or extra names appear, small step errors snowball, models lose track of state, guess from phrase frequency, or copy memorised solutions instead of thinking.
A hand‑built “Unpuzzles” set flips famous riddles into trivial variants, yet models often reuse the original, wrong reasoning, a pattern the authors call reasoning delirium.
Even reinforced “thinking” versions like o1 and o3 still collapse once tasks demand several dozen sequential checks, confirming that big improvements on elite benchmarks do not guarantee basic robustness.
The takeaway is simple, models must master short, boring problems before boasting about deep reasoning.
Paper – arxiv. org/abs/2507.07313
Paper Title: "Frontier LLMs Still Struggle with Simple Reasoning Tasks"
XXXXXXX engagements
