arXiv:2605.27811v1 Announce Type: new Abstract: Auto-bidding systems aim to maximize advertiser value over long horizons under budget constraints and ratio targets such as cost-per-acquisition, yet future traffic and auction dynamics are non-stationary and uncertain. Existing approaches face distinct limitations: control-based pacing reacts to deviations but cannot anticipate future conditions, while RL and generative methods fold constraints into reward signals, obscuring violations and degrading under distribution shift. We shift the learning target from actions to responses with the Generat
The proliferation of complex, dynamic online auction environments and the increasing sophistication of AI models for optimization make this research timely.
This work addresses fundamental limitations in current auto-bidding systems, potentially leading to significantly more efficient and robust online advertising and resource allocation.
The focus shifts from reactive control or constraint-folding in reward signals to proactive generative response modeling, offering better anticipation and adherence to constraints.
- · Online advertising platforms
- · Advertisers
- · E-commerce businesses
- · AI/ML researchers
- · Inefficient bidding systems
- · Advertisers without advanced AI tools
Improved return on investment for digital advertising campaigns through more efficient budget allocation and target adherence.
Increased competition and complexity in online auction markets as AI-driven bidding becomes more sophisticated and proactive.
Spread of generative response modeling techniques to other constrained optimization problems beyond advertising, such as supply chain management or energy grid balancing.
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Read at arXiv cs.AI