Semantic Register Compression in Multi-Agent LLM Cascades

Jul 17, 2026

Researchers introduce and characterize 'semantic register compression' as a measurable failure mode in multi-agent LLM systems, where intermediate agents can systematically reduce the semantic distinctions necessary for accurate downstream decisions. In a three-agent pipeline, they find that critical evaluation reduces label separability by up to 41.7% in fact-checking, 27.2% in sentiment analysis, and 20.0% in medical triage tasks. The study demonstrates that this phenomenon is generalizable across domains and is primarily driven by oriented semantic transformation, with implications for the safety and reliability of multi-agent LLM deployments.

Why it matters: This work identifies a generalizable and quantifiable risk in multi-agent LLM systems that could impact the reliability of applications in high-stakes areas such as fact-checking and medical triage.

Full story at: arXiv Computation and Language ↗