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This analysis supports a correctness-prediction bottleneck hypothesis: current routers primarily learn global-average model performance trends rather than fine-grained, query-specific routing signals, and collectively fail on queries that require instance-specific routing decisions.
RegEXROUTE is introduced, a pipeline that uses sparse autoencoders (SAEs) to discover interpretable regular-expression (regex) features and establishes explicit, interpretable text features as a practical basis for designing and understanding LLM routers.
This work introduces a controlled and MAS-demanding diagnostic benchmark for representative MAS efficiency methods and shows that many reported gains are setup-dependent and may arise from structural collapse, disabled tool pathways, or starting systems where random pruning already preserves accuracy, rather than robus...
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