Atom2.7m, published on the Hugging Face blog by the UC Riverside team, is a causal language model with only 2.74M parameters, yet it scores 69.24% on the ArithMark2.0 benchmark, directly crushing GPT-2 XL (1.56B) — 568× its size — at 29.92%. Its core idea is to re-attribute arithmetic failure to the representation level, rather than to insufficient parameters. BPE-style natural language tokenizers, when facing numbers, will split 12345 into irregular chunks like 123+45, 12+345, 1+2+3+4+5, breaking place value and operand role; general positional encoding describes the position of a token in the sequence, but doesn't tell the model whether this 7 is the tens place or the ones place. Atom2.7m explicitly exposes the digit span, place value, and operand identity to the model, supplemented by Abacus-style positional embeddings — exactly the same line of thinking verified in 2024 papers like Position Coupling, which enabled addition to generalize from a training length of 30 digits to 200 digits. The article also points out that MobileLLM-R1-base at 140M also significantly outperforms GPT-2 XL on ArithMark2.0, further corroborating that small models can win big models by structure rather than memory. Its lesson for the present is direct: when evaluating LLM's arithmetic and logical-structural ability, parameter count is no longer the primary variable — tokenizer design, numeric representation, and positional encoding are the new battlegrounds.