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Heavy meson radial ladders do not climb indefinitely: string-breaking screening caps observable radial excitations at strict mass ceilings, terminating discovery potential as low as $n=4$ in the $D_s$ family.
LLMs can create effective execution infrastructures for certain tasks, but struggle to match human-engineered solutions in critical areas like coding and search.
Vague goals can misdirect model evolution, revealing that agents often overfit to narrow self-assessments, hindering broader learning outcomes.
WorldEvolver redefines LLM agent planning by achieving unprecedented prediction accuracy and decision-making success through self-evolving memory mechanisms.
Current multicultural agent systems are not only misaligned with human values but also lack the necessary diversity, falling short of true cultural representation.
