arXiv:2605.20315v1 Announce Type: new Abstract: LLM agents have recently emerged as a powerful paradigm for solving complex tasks through planning, tool use, memory retrieval, and multi-step interaction. However, these agentic workflows often introduce substantial input-side overhead, making the compute-intensive prefilling stage a key bottleneck in long-context, multi-turn inference. In this work, we propose Mix-Quant, a simple and effective phase-aware quantization framework for fast agentic inference. We first investigate FP4 quantization in agentic LLM workflows and observe that quantizing
The increasing complexity and adoption of agentic LLMs are pushing the boundaries of current inference capabilities, making optimizations for long-context, multi-turn interactions urgent.
This development addresses a key bottleneck in the practical deployment and scalability of advanced AI agents, making them more efficient and cost-effective.
The efficiency of running sophisticated AI agents improves significantly, potentially accelerating their integration into more complex workflows and applications.
- · AI compute providers (e.g., cloud platforms)
- · Developers of agentic LLMs
- · Enterprises adopting AI agents
- · AI software optimization companies
- · Companies relying on less efficient inference methods
- · Developers unable to optimize compute costs
Reduced operational costs for advanced AI applications due to more efficient inference.
Accelerated development and deployment of increasingly complex and autonomous AI agents in various industries.
Broader accessibility of powerful AI agent technologies leading to market disruption across white-collar sectors.
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Read at arXiv cs.CL