In mathematics, there are many obstructions to solving problems, and computer scientists have been effective in ruling out certain approaches through no-go theorems, indicating that some problems may be undecidable.

The Riemann hypothesis, which concerns the distribution of prime numbers, is a fundamental problem in mathematics that remains unsolved, with significant implications for number theory.

In undergraduate education, you learn about really hard problems like the Riemann Hypothesis and the Twin Primes Conjecture, but what's interesting are the problems that exist on the boundary between solvable and unsolvable.

Turing's work on uncomputable numbers and Gödel's incompleteness theorems reveal that not all mathematical truths can be proven, challenging the belief that every mathematical problem is solvable.

If we solve the Riemann hypothesis, it could lead to new mathematics that millions of mathematicians would be eager to understand.

While AI is currently proving mathematical concepts, the future challenge is whether AI can propose interesting new hypotheses, which remains uncertain.

The Collatz conjecture, which involves iterating a simple process on natural numbers, is easy to state but remains unsolved, illustrating the complexity of seemingly simple mathematical problems.

The parity barrier in number theory limits the density of primes, making it a significant obstacle in proving conjectures like the twin prime conjecture.

Public evidence of RLVR's effectiveness in math and code is still lacking; we need to see if it generalizes to other domains.