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Long-horizon LLM agents can achieve 96.9% task success by learning to adapt their external execution support through trainable harness policies.
G2Rec captures user interest prototypes more accurately than existing methods, enabling generative recommendation systems to operate without ground-truth user interests.
KVEraser achieves a 3-4x speedup over full recomputation while maintaining high performance in long-context tasks, revolutionizing how we handle context updates in LLMs.
