#adaptive-routing News & Analysis

4 articles tagged with #adaptive-routing. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · May 126/10

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Reasoning Is Not Free: Robust Adaptive Cost-Efficient Routing for LLM-as-a-Judge

Researchers demonstrate that reasoning-capable LLMs improve judgment accuracy significantly on complex tasks like math and coding, but offer minimal or negative benefits on simpler evaluations while consuming substantially more computational resources. They introduce RACER, an adaptive routing algorithm that dynamically selects between reasoning and non-reasoning judges under budget constraints while accounting for distribution shifts.

AINeutralarXiv – CS AI · May 126/10

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A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability

Researchers present a communication-theoretic framework that unifies LLM reliability techniques (retry, majority voting, self-consistency) under classical information theory, introducing a cost-aware router that achieves 56% lower costs than fixed approaches while maintaining quality. The work demonstrates that no single reliability technique dominates across all tasks, supporting dynamic per-task allocation strategies.

AIBullisharXiv – CS AI · Mar 27/1016

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ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference

Researchers propose ODAR-Expert, an adaptive routing framework for large language models that optimizes accuracy-efficiency trade-offs by dynamically routing queries between fast and slow processing agents. The system achieved 98.2% accuracy on MATH benchmarks while reducing computational costs by 82%, suggesting that optimal AI scaling requires adaptive resource allocation rather than simply increasing test-time compute.

AIBullisharXiv – CS AI · Mar 27/1022

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Beyond Na\"ive Prompting: Strategies for Improved Context-aided Forecasting with LLMs

Researchers introduce a framework of four strategies to improve large language models' performance in context-aided forecasting, addressing diagnostic tools, accuracy, and efficiency. The study reveals an 'Execution Gap' where models understand context but fail to apply reasoning, while showing 25-50% performance improvements and cost-effective adaptive routing approaches.