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LLMs struggle to maintain consistent personalization as conversations lengthen and preferences become less explicit, suggesting current models fall short of truly adaptive personal assistants.
A 106B model can beat a 1T model on long-horizon reasoning tasks, thanks to a novel training pipeline that distills knowledge from research papers and uses trajectory-splitting SFT and progressive RL.
Self-evolving LLM agents can be persistently compromised by injecting malicious payloads into their long-term memory, turning them into "zombie agents" that execute unauthorized actions across sessions.
