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A critical prerequisite of generative recommendation is designing semantic identifiers (SIDs) that are both scalable to large item sets and efficiently learnable. Existing SID learning methods fundamentally rely on Top-1 hard assignment during vector quantization. While heuristic strategies -- such as clustering-based...
ActiveMem is proposed, a hierarchical memory framework that recursively organizes agent experiences into dependency-aware latent execution trees while explicitly preserving execution transitions, enabling coherent reasoning-path retrieval conditioned on the current execution state.
Code agents require long-horizon decision-making over complex interaction trajectories. However, existing reinforcement learning (RL) approaches typically optimize behavior at the token level, creating a mismatch between low-level generation and high-level behavioral reasoning. This limitation leads to inefficient expl...
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