LLMs operate within limited context windows. As context fills, compute cost rises and answer quality severely degrades.
The agentic memory bottleneck is not the physical storage of information, but rather, how the LLM efficiently retrieves exactly what it needs, without stuffing the context window with a broad search.
Instead of asking miners to store more memories, CoreTex asks them to improve how memories are found. When a miner submits a substrate improvement, the CoreTex stack tests that update on unseen memory-search tasks. The small Qwen model evaluates and scores based on overall accuracy of context surfaced against current baseline. Changes that result in a score above a dynamically scaling threshold advance the onchain state, whereas weak, bogus, or stale updates are rejected.
The corpus evolves dynamically over time, just as real memory would. New information is injected, old information is rendered stale, conflicting information enters, various old hidden tests are retired, and new ones are added. Not only does this result in a more robust, generalized substrate composition over time, it acts as a lever to extend longevity by exposing new surface area for miners.
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