TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?
What this work does
Combines self-supervised adaptation with stored source feature moments and online alignment, using contrastive supervision and feature queues.
Connection to discovery
A direct reason to validate that a test-time training signal aligns with discovery quality instead of assuming any self-supervised update helps.
Evidence
Inspected a failure example and assumptions behind a bound relating auxiliary-task information to main-task accuracy.
Scope and limitations
The theorem requires distribution/feature conditions that need not hold for LLM agents. Source-moment storage differs from a fully source-information-free setup.
THE RESEARCH CONVERSATION
Discussion & reactions
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