Research paper introducing Absorber LLM, a technique that applies causal synchronization mechanisms for test-time training. The approach aims to adapt and optimize language model performance during inference rather than only at training time.
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Absorber LLM: Harnessing Causal Synchronization for Test-Time Training
Absorber LLM introduces test-time training via causal synchronization, enabling language models to adapt and optimize performance during inference rather than only at training time.
Friday, April 24, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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