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More Is Not Always Better: Cross-Component Interference in LLM Agent Scaffolding

arXiv cs.AI ↗ · 2026-05-08 Cached

This paper challenges the assumption that adding more scaffolding components to LLM agents always improves performance, demonstrating through systematic experiments that cross-component interference often leads to degradation. The study finds that simpler, task-specific subsets of components frequently outperform fully equipped 'all-in' agents across various model scales.

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