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About this paper
Xinlin Wang, Mats Brorsson
This study explores the deployment trade-offs of small language models (SLMs) in agentic paradigms for financial applications, comparing base models, single-agent, and multi-agent systems. Single-agent systems achieve the highest effectiveness with an NRQ of 4.85, while multi-agent systems reduce energy per token by 71% compared to base SLMs, despite introducing higher latency and lower reliability.
Key insights
- Increasing architectural complexity through agent paradigms introduces a trade-off: improved effectiveness and energy efficiency per token often come at the cost of higher latency and reduced system reliability.
- The 'scaling law' hypothesis is challenged in practical settings; larger Base SLMs can exhibit an 'efficiency trap' (higher energy, lower effectiveness), suggesting that architectural design is more critical than raw parameter count for performance gains.
- The primary bottleneck for modern AI deployment shifts from raw generative capability to effective context management and robust instruction adherence, as evidenced by prevalent 'Context Length Exceeded' and 'Delegation Failure' errors in agentic systems.
Summary by alphaXiv.
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