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ThetaEvolve explores test-time learning
Published on July 11, 2026, ThetaEvolve is an open-source framework for test-time learning on open-ended optimization problems. Its description outlines components, without reporting quantitative results.
Published on July 11, 2026, ThetaEvolve is described as an open-source framework that extends AlphaEvolve with in-context learning and test-time reinforcement learning. It targets open-ended optimization problems, where agents can explore solutions during execution.
The stated design uses a single LLM, a program database, batch sampling, lazy penalties, and optional reward shaping. The description provides no quantitative results. To verify the details, consult the original publication and project materials; compare the implementation and experiments with the claims summarized here.
Original source: Framework ThetaEvolve para aprendizado em tempo de teste em problemas abertos