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Agentic memory allows LLM agents to reuse past experience, yet retrieved memories can also distort inference even when they are benign, correctly stored, and appropriately retrieved. We study this failure mode, which we call memory over-reliance. Across benchmarks and memory architectures, we find that memory is useful...
Long-term memory systems for large language models (LLMs) have gained popularity for extending reasoning capabilities across applications. Current memory systems rely on LLM agents to organize and consolidate memory, resulting in costly, inefficient write operations. To address this limitation, we propose MemFit, a lon...
A systematic study of three representative paradigms for training-free multi-subject I2V generation: direct, parallel, and sequential generation, revealing the strengths and limitations of each paradigm and offering practical insights for designing controllable multi-subject video generation systems.
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