做数据市场、时序知识图谱或多智能体系统的研究者值得关注——CHRONOS 把索引、定价和隐私三个痛点一起解决了，实验数据扎实，可以直接作为基线或参考架构。
CHRONOS 提出了一种三层架构，统一解决时序知识图谱数据市场中索引过时、定价失效和隐私预算过度消耗三个耦合问题。第一层使用神经ODE对边进行时序衰减，提供每查询预期召回损失上界；第二层基于检测到的变化点调整Shapley估值，并给出有限样本误差保证；第三层采用EXP3-IX算法实现次线性遗憾，同时通过矩会计满足差分隐私。实验表明，CHRONOS在四个基准上达到0.937召回率、2.74 QPS、161ms延迟，总隐私预算ε=4.25。该架构为动态数据市场提供了首个兼顾时效性、公平性和隐私性的协调方案。
CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces
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.