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Scientific discovery with language models requires finding the best valid solution within a finite computational budget. We study how feedback from this search can improve the procedure used to conduct it. We introduce HarnessTTT, a test-time learning framework that trains a harness proposer while keeping the executor...
SkillFM (Skill Flow Matching), a generative framework that synthesizes task-conditioned textual skills directly without test-time skill retrieval, is introduced, demonstrating that latent skill generation is an effective alternative to retrieval-based skill augmentation.
It is found that the optimal distillation context depends jointly on the student model's capacity and the target task, and that no single universally effective configuration emerges.
This work presents SimpleDesign, an effective multi-modal protein design model trained directly in the data space that leverages a single-stage end-to-end objective that combines discrete cross-entropy for sequences and a regression objective for structures.
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