arXiv:2605.23887v1 Announce Type: cross Abstract: Temporal knowledge-graph data marketplaces face three coupled failures in static designs: stale hybrid index shortcuts reduce recall as edges evolve, stationary Shapley pricing misattributes value after distribution shifts, and uncoordinated agents over-consume a shared differential-privacy budget. We present CHRONOS, a three-layer architecture providing a unified treatment of these challenges with explicit public and private separation. Layer one applies neural-ODE temporal decay to shortcut edges, providing a per-query expected recall-loss bo
The proliferation of dynamic data sources and the increasing demand for secure, efficient data sharing in AI-driven systems highlight the limitations of static marketplace designs.
This work addresses fundamental architectural challenges in data marketplaces, crucial for optimizing AI agent performance, ensuring data privacy, and accurately valuing evolving data streams.
The CHRONOS architecture proposes a dynamic, temporally-aware approach to data indexing, pricing, and privacy budget management, moving beyond static, failure-prone designs.
- · AI data marketplace operators
- · Developers of multi-agent AI systems
- · Data privacy solution providers
- · Organizations with evolving data assets
- · Static data marketplace architectures
- · Uncoordinated data agents
- · Inflexible data valuation models
Improved recall and more accurate pricing within dynamic AI data marketplaces using temporal considerations.
Enhanced efficiency and trust in data sharing, potentially accelerating the development and deployment of sophisticated multi-agent AI systems.
The establishment of new industry standards for data marketplace design, emphasizing temporal awareness and robust privacy mechanisms as core features.
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