多模型分工把研究成本砍到 6%
用 GPT-6 Luna 抽事实、Sol 聚类、Grok 4.7 找结构洞、Opus 5.5 写大纲，声称把每月 6.5 万美元研究开销压到 4200 美元。
Claude Opus 5.5, GPT-6 Sol, and Grok 4.7 cut a $65,000 monthly research burn to $4,200 Most enterprise research setups let autonomous agents write outlines while crawling the web: By turn 10, the model locks into an early thesis By turn 20, it re-crawls the web just to confirm its own bias By turn 30, it burns thousands in API spend on a shallow draft that ignored counter-evidence arXiv:2602.13830 fixes this by decoupling exploration from document synthesis: 1. Evidence Bank + Knowledge Graph (Tier 1 & 2) GPT-6 Luna ingests 64k tokens per second into raw facts. GPT-6 Sol clusters entities across Louvain communities without caring about document layout 2. SBM Structural Hole Falsifier Grok 4.7 calculates link probabilities across clusters. Spotting an empirical gap below 0.1 triggers an adversarial search chain before any chapter is drafted 3. Outline Synthesis (Tier 3) Claude Opus 5.5 compiles the final hierarchical DAG. It pulls verified data through cross-tier tethers but never browses the web directly The financial delta: - Enterprise research burn: from $65,000/mo down to $4,200/mo - Token efficiency: 76.4% reduction in redundant web calls - Open-ended benchmark (RACE): 53.08 Stop paying frontier models to search for their own hallucinations Inspect the interactive telemetry engine below ↓
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