arXiv:2606.07074v1 Announce Type: new Abstract: Deep research agents have demonstrated remarkable capabilities in complex information-seeking tasks, yet this power comes at a steep computational cost. Driven by accuracy-focused training paradigms, current models adopt brute-force strategies characterized by blind tool dependency and performative reasoning-generating long, redundant trajectories that are far from necessary for resolving these tasks, leading to wasteful tool calls and excessive token consumption. To overcome this efficiency trap, we propose SlimSearcher, a principled framework t
The accelerating capabilities of AI agents in complex tasks necessitate addressing their inherent computational inefficiencies as a bottleneck for wider adoption and scalability.
Improving the efficiency of AI agents directly impacts the economic viability and scalability of agentic systems, potentially reducing operational costs and democratizing access.
The focus on training efficiency for web agents highlights a shift from pure accuracy to a more balanced approach considering computational resources and practical deployment.
- · AI agent developers
- · Cloud computing providers (reduced cost for users)
- · Enterprises adopting AI agents
- · Inefficient AI agent models
- · Users with limited computational budgets (without such optimisations)
Reduced computational costs and increased accessibility for complex AI agent tasks.
Faster deployment and broader application of autonomous AI agents across various industries.
Acceleration of work automation as cost-effective, powerful agents become more widespread, impacting white-collar labor markets.
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Read at arXiv cs.LG