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Pharmaceutical University
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Durable self-preservation in agents hinges on the interplay between self-caused credit and slow work, revealing a new dimension of behavioral learning that defies traditional memory retention methods.
EVAF achieves up to 90% goal persistence in language agents, revealing that memory depth is crucial for sustained behavior beyond simple retrieval.
GaussTrace reveals how LLMs can uncover the hidden lineage of 3D models, transforming the landscape of digital asset provenance.
