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Agentic AI inference can be accelerated by 2-6x with history-free speculative decoding that eliminates KV cache overhead and boosts acceptance rates by up to 70%.
Distilling LLM agents by supervising semantic actions, not token sequences, unlocks significant performance gains and bypasses tokenization constraints.
An agent can perform city-scale 3D object grounding by actively exploring a multi-view environment, bypassing the need for precomputed proposals and achieving state-of-the-art results.