arXiv:2605.21965v1 Announce Type: new Abstract: Large language models increasingly use external tools such as web search and document retrieval to solve information-intensive tasks. However, multi-hop tool use in complex tasks introduces substantial latency, since the model must repeatedly wait for tool observations before continuing. We study how to accelerate such trajectories without changing the final trajectory the model would have taken without acceleration, assuming access to faster but less reliable speculator tools. We develop a theoretical framework for lossless speculation in multi-
The rapid adoption of large language models for complex, information-intensive tasks highlights the current bottleneck of multi-hop tool use and its associated latency.
Accelerating multi-hop retrieval agents significantly improves the efficiency and responsiveness of advanced AI applications, making them more practical for real-world deployment.
The proposed method could reduce latencies in AI agent operations, enabling smoother and faster execution of complex workflows involving external tools.
- · AI Agent Developers
- · Cloud Computing Providers
- · Companies adopting AI Agents
- · Generative AI platforms
- · Inefficient AI tool orchestration methods
- · AI solutions with high latency tolerances
Faster AI agents can execute more complex tasks in less time, increasing throughput for companies.
This efficiency gain could drive broader adoption of autonomous AI agents across various industries, creating new market opportunities.
The acceleration of AI agent capabilities may further blur the lines between human and AI-driven workflows, leading to fundamental changes in labor markets and business processes.
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Read at arXiv cs.CL