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Achieve a 40% jump in success rates on real-world contact-rich manipulation by intelligently scheduling force feedback into visual-motor policies.
Autonomous exploration by an LLM agent dramatically outperforms both rigid retrieval workflows and supervised fine-tuning for temporal knowledge graph question answering, achieving state-of-the-art results in a zero-shot setting.
