P versus NP
A proposed guest list can be checked against the rules. Does easy checking always come with an efficient way to find a list that works?
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A proposed guest list can be checked against the rules. Does easy checking always come with an efficient way to find a list that works?
We can inspect a neural network's numbers without understanding its computation. Can explanations become reliable enough to predict and check its behavior?
Alignment asks how AI systems can reliably follow intended goals and constraints, including in situations different from those used in training.
Two graphs can look different because their vertices have different names. Can we always decide efficiently whether they have the same connections?
A special kind of constraint puzzle is predicted to remain hard even when almost all its rules can be satisfied.
Random choices help many algorithms run quickly. Can every efficient algorithm with a bounded error probability be replaced by an efficient deterministic one?
Multiplying two square matrices seems to require more work than writing their answer. Is the best possible asymptotic exponent actually two?
77 = 7 × 11 is easy to check. Can a classical computer find the complete prime factorization of every integer efficiently as its written length grows?
Can a function be easy to compute but hard to reverse on typical inputs? A rigorous unconditional example is still missing.
A sequence follows a fixed linear recurrence. Is there an algorithm that always decides whether any term of the sequence equals zero?
Can estimating a quantum system’s lowest energy remain computationally hard even when only a fixed amount of accuracy is required?
Can exact shortest-path distances between every pair of vertices in a general weighted graph be computed in truly subcubic time?
A splay tree reorganizes itself after searches. Is its total cost always within a constant factor of the best search tree that knows all requests in advance?
Can every computation that uses only logarithmic memory with nondeterministic choices be carried out using the same memory bound deterministically?
An AI system can learn new tasks and lose skills it already had. How can it keep useful knowledge while adapting with limited memory and computation?
A model can predict well on familiar data and fail when conditions change. Which changes can it handle reliably, and how can we know?
Carefully chosen changes to an input can fool a learned classifier. How can we provide reliable protection against a stated class of such changes?
Can a trained model efficiently remove the influence of selected training data, with credible evidence of what was removed?
Can an algorithm always decide whether an equation with integer coefficients has a solution made of rational numbers?
Can finding three integers with a specified additive relation be done faster than essentially quadratic time?
Can a fixed Boolean matrix answer a sequence of arriving vectors in truly subcubic total time when each answer is needed before the next vector arrives?
Does the linear-algebraic rank of a Boolean table control how many bits two people must exchange to evaluate an entry?
Given every output of a Boolean function, how hard is it to decide whether a small circuit can produce that table?
Can one exhibit propositional truths whose proofs must be superpolynomially long even in a flexible standard deductive system?