Unverified50% confidenceTradeoffExact time
The longer inference time of LLMs makes MCP-driven game agents better suited for turn-based or slower-paced combat scenarios.
1
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50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
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Related Claims
UnverifiedLLM在长时间自主运行中容易出现目标漂移（Goal Drift），通过每步微小决策在数十轮迭代后累积出显著语义漂移66% similarUnverified只有在LLM推理比embedding加向量检索慢数十倍以上时，语义缓存才能同时实现降本与提速65% similarUnverifieddLLM缺失KV Cache机制，每轮去噪都需对全序列重新计算注意力，导致实际速度提升远低于理论值63% similarUnverifiedFor deterministic tasks (such as initial screening for work experience ≥ 3 years), a rule engine rather than an LLM should be used, because rule engine results are 100% reproducible and execute at the microsecond level, whereas using an LLM wastes computing power and may cause misjudgments due to hallucinations.63% similar
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