# CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces

arXiv: https://arxiv.org/abs/2605.23887 · HTML: https://mlsanity.com/paper/2605.23887/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 75 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 22 of 22)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2110.14049/) (2021)
- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [Individual Privacy Accounting via a Renyi Filter](https://mlsanity.com/paper/2008.11193/) (2020)
- [Algorithms for Differentially Private Multi-Armed Bandits](https://mlsanity.com/paper/1511.08681/) (2015)
- [The Price of Differential Privacy For Online Learning](https://mlsanity.com/paper/1701.07953/) (2017)
- [FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search](https://mlsanity.com/paper/2105.09613/) (2021)
- [Data Pricing in Machine Learning Pipelines](https://mlsanity.com/paper/2108.07915/) (2021)
- [RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge](https://mlsanity.com/paper/2510.13590/) (2025)
- [Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs](https://mlsanity.com/paper/1904.05530/) (2019)
- [A Joint Exponential Mechanism For Differentially Private Top-k](https://mlsanity.com/paper/2201.12333/) (2022)
- [The Price of Selection in Differential Privacy](https://mlsanity.com/paper/1702.02970/) (2017)
- [Enhancing HNSW Index for Real-Time Updates: Addressing Unreachable Points and Performance Degradation](https://mlsanity.com/paper/2407.07871/) (2024)
- [NaviX: A Native Vector Index Design for Graph DBMSs With Robust Predicate-Agnostic Search Performance](https://mlsanity.com/paper/2506.23397/) (2025)
- [PSGraph: Differentially Private Streaming Graph Synthesis by Considering Temporal Dynamics](https://mlsanity.com/paper/2412.11369/) (2024)
- [CleANN: Efficient Full Dynamism in Graph-based Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2507.19802/) (2025)
- [Differentially Private Learned Indexes](https://mlsanity.com/paper/2410.21164/) (2024)
- [Practical and Accurate Local Edge Differentially Private Graph Algorithms](https://mlsanity.com/paper/2506.20828/) (2025)

## Abstract (arXiv)

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 bound of Big-O of Pq lambda delta t, with a monotone-envelope guarantee reducing bound looseness to 1.8 to 3.2 times observed loss. Layer two conditions Shapley valuation on detected changepoints and provides finite-sample error guarantees under noise. Layer three uses EXP3-IX to achieve Big-O of the square root of T log T regret while enforcing epsilon and delta differential privacy via moments accounting. CHRONOS releases a privatized affinity matrix per epoch using the Gaussian mechanism; all retrieval and ranking are post-processing, incurring no extra privacy cost. We provide multi-epoch settlement, scalability analysis for 500 sellers, and comparisons against accelerated baselines. Across four benchmarks, CHRONOS shows 0.937 recall at ten, 2.74 queries per second, 161 ms latency, and total epsilon of 4.25 at delta of 10 to the power of negative 6 under zCDP composition. These results indicate a competitive operating point. A limitation is that at this privacy level, released valuations remain noise-dominated; utility derives primarily from public index routing and adaptive scheduling driven by low-sensitivity statistics.