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首先，A second pilot study tested four cross-modality memory strategies. Pre-captioning (text → text) uses only 0.9k tokens but reaches just 14.5% on image tasks and 17.2% on video tasks. Storing raw visual tokens uses 15.8k tokens and achieves 45.6% and 30.4% — noise overwhelms signal. Context-aware captioning compresses to text and improves to 52.8% and 39.5%, but loses fine-grained detail needed for verification. Selectively retaining only relevant vision tokens — Semantically-Related Visual Memory — uses 2.7k tokens and reaches 58.2% and 43.7%, the best trade-off. A third pilot study on credit assignment found that in positive trajectories (reward = 1), roughly 80% of steps contain noise that would incorrectly receive positive gradient signal under standard outcome-based RL, and that removing redundant steps from negative trajectories recovered performance entirely. These three findings directly motivate VimRAG’s three core components.，这一点在钉钉中也有详细论述
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