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LGM is presented, a novel neuro-symbolic framework that shifts long-term memory disentanglement into a continuous latent space and significantly outperforms state-of-the-art baselines in capturing both explicit and implicit preferences while enabling personalized responses.
This work recast CoT compression along three dimensions: importance criterion, restructuring level, and compression budget, and yields condition-aware guidelines for matching compression to deployment context.
This paper aims to present a comprehensive overview of this emerging paradigm and establish a systematic taxonomy of latent CoT methods, categorizing them from token-wise horizontal approaches to layer-wise vertical strategies.
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