looks like you've done some thorough testing. Have you found that prompting reliably reduces premature quitting?
And have you found that reducing premature quitting results in more accuracy?
From my reading, the official docs don’t support the strong
claim that frontier LLMs are explicitly RL-trained to “be lazy”
or conserve tokens as claimed in this thread. What they do document
is adaptive / hidden reasoning compute: OpenAI says reasoning
models allocate internal reasoning tokens and reasoning.effort
controls how many are used
(https://developers.openai.com/api/docs/guides/reasoning), and
Anthropic says adaptive thinking decides whether/how much to use
extended thinking based on request complexity, with effort as
soft guidance and max_tokens as the hard cap
(https://docs.anthropic.com/en/docs/build-with-claude/adaptiv...
hinking). So prompt wording may change how the same budget is
spent, but it can’t exceed the hard token cap.
Also, the “encouragement helps” anecdote seems real in the
AlphaEvolve workflow, but I can't see that forpublic
models. Gómez-Serrano says this in Quanta
(https://www.quantamagazine.org/the-ai-revolution-in-math-has...
rived-20260413/), and the released AlphaEvolve notebooks really
do contain prompts like “Good luck, I believe in you...”
(https://github.com/google-deepmind/alphaevolve_repository_of...
oblems, e.g.
https://github.com/google-deepmind/alphaevolve_repository_of...
blems/blob/main/experiments/finite_field_kakeya_problem/finite_f
ield_kakeya.ipynb). But those prompts also bundled strong
structural hints (“find a general solution”, “better
constructions are possible”), so from my reading the evidence
is: prompt phrasing matters, especially in an internal search
stack, but not “pep talks are a universal reasoning hack.”
The P≠NP conjecture in CS says checking a solution is easier than finding one. Verifying a Sudoku is fast; solving it
from scratch is hard. But Brandolini's Law says the opposite: refuting bullshit costs way more than producing it.
Not actually contradictory. Verification is cheap when there's a spec to check against. 'Valid Sudoku?' is mechanical.
But 'good paper?' has no spec. That's judgment, not verification.
Skill issue.
I'm far more interactive when reading with LLMs. I try things out instead of passively reading. I fact check actively. I ask dumb questions that I'd be embarrassed to ask otherwise.
There's a famous satirical study that "proved" parachutes don't work by having people jump from grounded planes. This study proves AI rots your brain by measuring people using it the dumbest way possible.