By allowing models to actively update their weights during inference, Test-Time Training (TTT) creates a "compressed memory" that solves the latency bottleneck of long-document analysis.
By combining Transformer-based sequence modeling with a novel conditional probability strategy, the approach overcomes long-standing trade-offs between maximizing expression metrics and maintaining ...