AIBullisharXiv – CS AI · 9h ago7/10
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Closing the Loop on Latent Reasoning via Test-Time Reconstruction
Researchers introduce ReLAT, a test-time training method that improves latent reasoning in large language models by reconstructing the original query from intermediate latent states, ensuring task-relevant information is preserved. The approach demonstrates significant performance gains across mathematical reasoning, QA, and code generation tasks, with Qwen3-8B achieving a 16.6-point improvement on AIME 2024.