Multi-hop reasoning collapses by 1M tokens; with a learning system it doesn't

A study found that multi-hop reasoning in large language models collapses by 1 million tokens, but a learning system prevents this decline. The research highlights a critical limitation in current AI models for long-context tasks.

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