The Multimedia Mingle

AlphaEvolve integrates large language models and evolutionary computation to improve or create algorithms. The prompt sampler provides context about past trials and ideas to the LLM ensemble. The LLM ensemble provides proposed improved algorithms to the evaluator pool. The evaluator pool provides algorithms with quality scores to the program database. The program database provides algorithms to the prompt sampler for improvement or inspiration.

Players with private data use secret shares and mathematical sorcery to perform arbitrary computations on their data without revealing anything about it beyond what the output of the computation itself reveals: not to each other, and not to any third party. Techniques differ in properties including which finite fields they work in, how many players they support, their efficiency, and against what kinds of adversaries they are secure.

Algorithm analysis models running time with mathematical expressions. It compares performance ignoring coefficients and constant terms. Greedy algorithms, divide and conquer, and dynamic programming sometimes outperform brute force searches. Does an algorithm exist to solve a problem in polynomial time? If not, can one verify a solution in polynomial time? Reduction techniques compare problems by relative difficulty, even without knowing the performance of the best algorithms.

Human intelligence relies heavily on memorization, interpolation, and reasoning. Large language models can memorize and interpolate, but the ARC benchmark persuasively demonstrates with its easily solvable tasks that they cannot reason in a meaningful way. Program synthesis and discrete program search may complement large language models to cover the deficit. There is a sense in which carefully guarded industry secrets do not count as progress in AI research.

Aliens have always come to people in dreams, secretly bestowing upon them humanity's greatest ideas. But when Chloe does nothing, the aliens decide to communicate with her more directly. Despite doubt, ambivalence, and life struggles, Chloe agrees to make it happen at the last possible moment. She hopes to find purpose, self respect, and approval from the people in her life. But sometimes ideas turn out to be just okay.

Performal models systems as networks with nodes augmented by timestamps. It requires empirical latency measurements within nodes, and uses symbolic latency abstraction to model latency across nodes to produce a "formal" upper bound. How do you empirically measure latency? Do you artificially truncate the latency probability distribution domains to achieve an upper bound? Can you also model bandwidth to calculate total operation time?

Program control structure, termination, timing, observable data distribution, resource exhaustion, and power consumption can all leak information. Security label types enforce information flow policies. Confidentiality prevents flow to inappropriate destinations; dually, integrity prevents flow from inappropriate sources. Label creep occurs when the security labels of data monotonically increase during execution.

Three blue extraterrestrials teleport human artifacts into their spaceship from a debris field. They engage in inscrutable animated banter, squabble, and amuse themselves by playing with the artifacts. Fragments of the demolished planet Earth drift past the spaceship window. A lighthearted fifteen minutes of fun and indifference at the end of all human conflict, hope, despair, love, and life.

Choreographies describe interactions between processes in distributed systems. Inference rules can translate a choreography into the many possible traces (message passing sequences) that arise from concurrency. Choreographic programming language constructs avoid starvation and promote compliance and conformance. Through endpoint projection they produce independent executable build artifacts per process.

Differential privacy usually balances the utility of a dataset with the privacy of the individuals in it. In the secure messaging system Vuvuzela, differential privacy balances the privacy of the users with the efficiency of the system in terms of compute, latency, and network traffic. The percent confidence a powerful adversary can gain over many rounds that Alice and Bob ever communicated translates nicely into the privacy parameter ε.