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MindMemOS enables AI agents to autonomously refine their memories and skills, achieving state-of-the-art accuracy in dynamic environments.
KeepLoRA++ reveals that effective continual learning hinges on strategically managing knowledge across different layers of a model, leading to significant performance gains in complex tasks.
Memory retention strategies that account for long-term consequences can dramatically enhance the performance of language agents under tight constraints.
YouZhi-LLM achieves unprecedented concurrency and accuracy in financial LLMs by dramatically reducing KV-cache overhead, setting a new standard for deployment efficiency.
LLMs can generate Verilog that's actually useful for hardware design when guided by a closed-loop system that incorporates feedback from simulation, synthesis, and timing analysis.
