The Illusion of Thinking, and the argument about what reasoning is

Apple researchers published "The Illusion of Thinking," evaluating reasoning models on controllable puzzle environments — Tower of Hanoi, river crossing, blocks world — where difficulty can be scaled precisely.

The headline finding: past a certain complexity, accuracy collapses to zero, and counterintuitively the models reduce their reasoning effort as problems get harder, despite having budget remaining.

The paper is good, the reaction was overheated in both directions, and the rebuttals are worth reading alongside it.

what the paper found#

Three regimes:

1. Low complexity — standard models match or beat reasoning models. The extra thinking is wasted and sometimes harmful, because the model overthinks its way past a correct early answer.
2. Medium complexity — reasoning models win clearly. This is the regime the benchmarks live in.
3. High complexity — both collapse to zero accuracy.

The reduced-effort finding at high complexity is the genuinely interesting one. The models emit fewer reasoning tokens on harder problems, which is exactly backwards, and suggests something like learned giving-up rather than a compute limit.

the rebuttals#

Several, and they land differently.

The output length objection. Tower of Hanoi with N disks requires 2^N − 1 moves. At N=15 that is 32,767 moves. If the model must enumerate every move in its output, it hits the token limit before it hits a reasoning limit. Several researchers showed models explicitly stating they would not enumerate all moves due to length — and being scored as failures. That is measuring output capacity, not reasoning.

Ask instead for a program that generates the solution and the models do fine. That is a meaningful distinction: knowing the algorithm versus executing it by hand.

The unsolvable instances objection. Some river-crossing configurations in the evaluation set have no solution. Models were penalized for failing to solve them, which is not a reasoning failure, it is a benchmark bug.

The framing objection. "Reasoning models cannot reason" was the headline everywhere. The paper does not claim that. It claims a specific scaling limitation on a specific class of problem.

what survives#

After the corrections, the real finding is narrower and still important:

Current reasoning models do not reliably execute long deterministic procedures. They can identify the right algorithm and fail to carry it out over many steps. Error rates compound; there is no self-correction mechanism strong enough to catch accumulated drift over hundreds of steps.

That is a genuine limitation with direct practical consequences. If your task requires exact multi-step execution — a data migration, a complex refactor across many files, a financial calculation — the model should be writing code that does it, not doing it token by token.

the practical rule#

Use the model to produce the procedure. Use a computer to execute it.

This is not a workaround, it is the correct architecture. Deterministic execution is what computers are for. A model that writes a correct script and runs it is strictly better than a model that simulates the script in its head, and it is verifiable, repeatable, and debuggable.

Every production agent architecture I have seen work well converges on this. Every one that tries to do arithmetic in the reasoning trace eventually produces a number that is wrong in a way nobody catches.

the meta-lesson#

A paper from a large company with a competitive interest, on a contested topic, with a provocative title, will be read as a position statement regardless of its contents. The authors probably knew that.

Read the methodology section. The methodology section is where papers are true or false.