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Scaling task complexity across 280 standardized manipulation variants reveals that today's leading VLA models fundamentally collapse under fine-grained spatial ambiguity and memory-intensive sequential planning.
A lightweight, RL-trained context curator can match GPT-4o's context management abilities, slashing token consumption by 8x and opening the door to efficient long-horizon LLM agents.
