Unverified50% confidenceTradeoffExact time
Slowave's decay mechanism may carry the risk of accidentally deleting memories that are truly important but temporarily unused, and the accuracy of the agent's self-evaluation of memory usefulness also needs to be verified through real-world testing.
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50%
Confidence
Long-term
Relevance
9/16/2026
First Seen
Sources
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Related Claims
UnverifiedSlowave's memory cycle is: remember → recall → use → feedback → reinforce/weaken → decay.78% similarUnverifiedBecause the agent itself evaluates whether a memory is useful, Slowave no longer needs a separate LLM judge to maintain memories, thereby avoiding token waste and the split-brain problem.77% similarUnverifiedThe project has a built-in experimental VRAM offloading mechanism (demonstrated in workflow 06) that can significantly reduce memory usage, but memory spikes still occur during the process.75% similarUnverifiedAn intermittently faulty memory module may alternately produce correct and incorrect computation results, causing sporadic deviations in gradient calculations during neural network training, affecting model convergence without triggering hardware alerts.74% similar
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