Most AI mistakes happen mid-reasoning, not at the end — so checking each step catches what final-answer grading misses.

This reads the question as: why does verifying a chain of reasoning line-by-line (rather than grading the final answer) fit how AI systems fail? The short version from the corpus is that AI errors are overwhelmingly *process* errors, and line-level checking is the only place those errors are visible. When researchers added intermediate-state checks to long reasoning traces, task success jumped from 32% to 87% — because most failures weren't wrong answers, they were wrong *steps* that a final-answer score never sees Where do reasoning agents actually fail during long traces?. A proof that's correct at the end can be rotten in the middle; checking each line is what exposes that.

Line-by-line also happens to be cheap, which is the second half of the fit. Because each step is a self-contained claim, a verifier can run *alongside* generation instead of after it — forking off to check state and only interrupting when a line violates something. On correct runs the latency penalty is near zero, so you get continuous policing without paying for it Can verifiers monitor reasoning without slowing generation down?. This is the structural advantage of a per-line unit: it's small enough to check independently and in parallel with everything else.

The deeper reason is that a line is a *checkable object* in a way a paragraph of prose isn't. Formal proof systems like Lean or z3 give you a mechanical yes/no on each step, and this is exactly why work on auto-synthesizing verifiers pushes AI output into that form — translating prose policy into provably-correct checkers so each inference can be machine-validated rather than eyeballed Can we automatically generate formal verifiers from policy text?. The same instinct shows up outside theorem-proving: binding every claim to its source turns fluent generation into something auditable line by line Can source traceability make AI writing trustworthy?, and structuring arguments as attack/defense graphs lets a user contest a specific premise instead of the whole output Can formal argumentation make AI decisions truly contestable?. Granularity is what makes contestability possible.

But here's the catch the corpus insists on: line-by-line checking is only as good as the translation *into* lines. LLMs reliably produce syntactically valid logic that is semantically wrong — errors cluster at quantifiers, scope, and predicate granularity — so a proof can pass every mechanical line-check while quietly meaning the wrong thing Can large language models translate natural language to logic faithfully?. Worse, faithful formalization usually needs a whole coherent theory of axioms and lemmas, not isolated statements, so a single line rarely stands alone Can autoformalization work on individual statements alone?. Line-by-line checking guarantees the steps are *valid*; it doesn't guarantee they're *faithful to the question*.

What you'd want to know that you didn't ask: the field is split on whether you even need this. Verifier-free approaches skip step-checking entirely, using the likelihood of a reference answer given the reasoning as the reward signal, and match verifier-based methods on general-domain benchmarks verifier-free-rl-extends-reasoning-reinforcement-to-general-domains-by-conditio. So line-by-line proof checking isn't universally best — it's the right tool precisely where steps are formally expressible and where a wrong intermediate step is invisible to outcome scoring. Outside that zone, cheaper signals compete.