514 place 1

518 New ‘Test-Time Training’ method lets AI keep learning without exploding inference costs

VentureBeat
VentureBeat 2 place · 01/05/2026 19:00 EDT

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to digest long docs, tickets, and logs, this is a bid to get “long memory” without paying attention costs that grow with context length.The approach, called “End-to-End Test-Time Training” (TTT-E2E), reframes language modeling as a continual learning problem: Instead of memorizing facts during pre-training, models.

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