Unverified50% confidenceTradeoffExact time
Multi-agent systems require multiple rounds of LLM inference calls, which may face problems of excessive latency and high API call costs in high-frequency trading scenarios.
1
Sources
50%
Confidence
Long-term
Relevance
8/11/2026
First Seen
Sources
TradingAgents：多智能体LLM金融交易框架深度解析
Related Claims
Unverified多模态能力的引入会显著增加推理延迟、显存占用和标注成本，完整的多模态生产系统远比单纯文本系统复杂74% similarUnverifiedEvery annotation requires an LLM call, and for high-frequency fine-tuning scenarios, API costs and response latency are practical constraints.72% similarUnverifiedLLM-based multi-agent systems face challenges including model output randomness, context window limitations, hallucination propagation amplification, and coordination overhead.72% similarUnverified多Agent系统的速度瓶颈主要来自Agent间通信开销、任务调度复杂性和API调用并发限制三个方面72% similarUnverified在旧的同步、批处理架构上叠加异步、实时的AI决策层会产生数据延迟、上下文丢失和逻辑冲突等系统性问题71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/732461API
curl https://kongchang.com/api/v1/knowledge/claims/732461MCP
get_claim(id=732461)