A paper titled DISCO proposes distributed long context scaling with grounding-reasoning disaggregation to address "context rot," the collapse in reasoning quality that occurs as LLM inputs grow toward advertised million-token context windows. The work attributes the failure to structural entanglement in monolithic architectures, where the search burden of contextual grounding is combined with reasoning.
While Large Language Models (LLMs) advertise million-token context windows, reasoning quality often collapses as inputs grow -- a phenomenon termed context rot. This failure stems from a structural entanglement in monolithic architectures, where the massive search burden of contextual grounding exha
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