The biggest predictor of which AI models solve hard multi-step tasks isn't intelligence — it's whether they give up too soon.

This explores why frontier models give up or stop short on long-horizon tasks — and the corpus's sharpest answer is that the deciding factor isn't raw intelligence but persistence. Across 17 frontier models on 36 expert optimization tasks, the dominant predictor of success was simply staying in the benchmark-edit-incorporate loop within the time budget; most models either terminated early or burned their budget unproductively, while Claude Opus 4.6 stood out for sticking with it What predicts success in ultra-long-horizon agent tasks?. So 'why terminate early' partly reframes to: what keeps a model in the loop, and what makes the loop worth staying in?

One deep reason is that the loop itself gets unreliable without an external anchor. Pure self-improvement stalls because of the generation-verification gap, diversity collapse, and reward hacking — a model can keep generating but can't reliably tell whether its next step is better, so continuing feels pointless Can models reliably improve themselves without external feedback?. The methods that do sustain progress smuggle in external signals: tests, proofs, type checks, judges, or tool feedback. A committee of weak model calls only matches strong models when a local soundness check converts good-but-unselected proposals into chosen ones When can weak models match strong model performance?. Without that verification scaffold, a long horizon is just a long walk in the dark, and early termination is the rational response.

There's also an architectural floor. Autoregressive generation can't retract a token it has already emitted, while many long tasks (constraint satisfaction especially) depend on discarding invalid partial work and backtracking — so the model hits a wall not because it ran out of ideas but because it has no primitive for undoing a bad commitment Why does autoregressive generation fail at constraint satisfaction?. Relatedly, the more instructions or constraints a task piles on, the more performance degrades in predictable patterns; reasoning models hold steady to around 150 instructions and then fail steeply How does instruction density affect model performance?. Long horizons accumulate exactly this kind of density, so 'terminate early' can be the visible symptom of a model quietly losing the thread.

The most uncomfortable possibility is that the benchmarks themselves manufacture the failure. Automated benchmarks privilege precisely-specified, auto-gradable tasks, which both overstate and understate real capability — open-world evaluation of messy long-horizon work, read through qualitative logs with cost reported, tells a different story Do automated benchmarks hide what frontier AI systems can really do?. The benchmark-to-GDP gap makes this concrete: agents clear abstract contests but fail real occupational workflows, because the field optimizes what it measures and has been measuring contests rather than work Why do agent benchmarks not predict real economic value?. Early termination on a long-horizon benchmark may say as much about how the horizon was constructed as about the model walking it.

The thing you might not have expected to want to know: the fixes here are less about bigger models and more about restructuring the loop. Returns from better memory architecture now exceed returns from adding parameters Has memory architecture replaced parameter count as the scaling frontier?, routing the right query to the right specialist beats scaling a single model Can routing beat building one better model?, and iterative latent depth lets a model spend more computation on harder steps instead of more parameters everywhere Can looped computation replace parameter count in world models?. Persistence, in other words, can be engineered into the harness — it doesn't have to be hoped for from the model.