개요

원천: MIT CSAIL AI Lab Technical Report 날짜: 2026-04-15 지역: US 계층: L1,L2 신뢰도: 0.94

핵심 내용

Multi-Agent Orchestration in Large Language Models: Architectural Patterns

출처: MIT CSAIL AI Lab Technical Report 날짜: 2026-04-15 지역: US 계층: L1,L2 | 깊이: expert 신뢰도: 0.94 | 논제 정합: 0.5

핵심 지표

Multi-agent system memory consumption scales O(n²) with agent count and context length

요약

Experimental measurements show 5-agent system requires 3.8x memory vs single-agent baseline across SNLM/SNAC/SNLC

Vibe Coding Economy 정합성

마스터 논제 점수: 0.5

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원본: us_001 | 출처 URL: https://csail.mit.edu/publications/multi-agent-orchestration-patterns-2026.pdf

Vibe Coding Economy 정합성

마스터 논제 점수: 0.5

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원본 ID: P5_us_001