Unverified50% confidenceFactExact time
In case studies on four real dark web markets, the core conclusion is that the agent's tool selection capability is more effective than simply scaling up model parameters.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/15/2026
First Seen
Valid until: 1/13/2027
Sources
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Unverified验证环节的模型选择是大模型还是专门化小智能体是一个架构权衡：大模型能力全面但成本高延迟大，小智能体轻量可控但可能覆盖不全，业界尚无定论69% similarUnverifiedCase study conclusion: agents generate candidate findings far faster than humans can verify them; the model itself matters less than how it is used; poorly scoped runs burn tokens on low-value targets, and weak review capacity dumps the load onto security teams.68% similarUnverifiedOn-device agents place noticeably different demands on models than cloud agents, and low latency and stable structured output are often more critical than the absolute level of intelligence.66% similarUnverifiedIn real production environments, fast and low-cost intelligence is often more practically valuable than slow and expensive deep reasoning.65% similarVerifiedAssigning each agent a dedicated capability boundary and prompt often outperforms having a single model handle everything, a principle that is connected to the effectiveness of chain-of-thought prompting.65% similar
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