Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.
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Existence of weak solutions is proved for the unsteady Darcy-Brinkman problem coupled to miscible reactive flows, with global existence when initial concentration is between 0 and 1, finite-time blow-up when above 1, uniqueness in 2D, and finite-element simulations confirming the behavior.
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.
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Existence and uniqueness of solutions of unsteady Darcy-Brinkman problem for modelling miscible reactive flows in porous media
Existence of weak solutions is proved for the unsteady Darcy-Brinkman problem coupled to miscible reactive flows, with global existence when initial concentration is between 0 and 1, finite-time blow-up when above 1, uniqueness in 2D, and finite-element simulations confirming the behavior.