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TPBench introduces a benchmark that isolates 'turning-point eviction' in dialogue compression by probing separately for the user's initial goal (P1), the current value of a revised slot (P2), and both together (P3), using existing MultiWOZ and SGD dialogue-state annotations. The paper finds that probe-specific evaluations rank compression methods differently, and that deleting update-carrying turns sharply degrades current-value accuracy.
This paper shows that a language model with a lossy memory that retains a wrong conclusion but drops the evidence produces confident incorrect answers, whereas an empty memory leads to abstention. The authors propose a source-first compression policy that preserves recomputable sources instead of conclusions to maintain correctability, and demonstrate the mechanism across multiple models and dialogue systems.