codes with increasing complexity and ending with fpm would be enough for beta.
Our results are organized around the three primary hypotheses and a set of exploratory analyses. First, we examine whether conversations with sycophantic agents affect people’s chances of discovering the true rule. Second, we analyze individuals’ confidence levels across conditions. Third, we test whether conversations with the default GPT increased confidence in beliefs. Additional pre-registered exploratory analyses are omitted due to space constraints. Hypotheses and analyses were pre-registered prior to data collection (AsPredicted.org/94vn2y.pdf).444We deviated from the pre-registration in two ways: (1) Instead of excluding incomplete cases entirely, we used an LLM-based extraction method to recover partial data where possible. This was done to mitigate differences in completion rates across conditions. As a result, sample sizes vary slightly across analyses of discovery rates and confidence ratings (see Footnote 2 & 3). (2) We used permutation tests instead of the pre-registered Chi-square tests for H1. This provides a more conservative test of by avoiding distributional assumptions that may be unreliable given the low discovery rates.
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Foreign Investment Surges to Canada’s Strongest Level Since 2007
Offline navigation is a lifeline for travelers, adventurers, and everyday commuters. We demand speed, accuracy, and the flexibility to tailor routes to our specific needs. For years, OsmAnd has championed powerful, feature-rich offline maps that fit in your pocket. But as maps grew more detailed and user demands for complex routing increased, our trusty A* algorithm, despite its flexibility, started hitting a performance wall. How could we deliver a 100x speed boost without bloating map sizes or sacrificing the deep customization our users love?
is well-motivated. On the flip side, it also encumbers large changes