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Traffic forecasting benchmarks commonly fix the sensor set, although deployed sensing networks change their coverage over time. We present EvoXXLTraffic, a sensor-evolving extension of the XXLTraffic dataset family, with yearly sensor catalogs, persistent station IDs, flow matrices, and geographic graph snapshots across nine California PeMS districts, spanning up to 27 years. The benchmark separates all-sensor forecasting from supervised adaptation to sensors absent from the preceding year. We evaluate 13 baseline adaptations on PEMS04 with five seeds and PEMS05 with three seeds, and seven of them on the other seven districts with three seeds. Each yearly task uses 31 stored days, explicit observation masks, and chronological evaluation. Under the frozen configurations, PECPM has the lowest full-horizon mean MAE on both all-sensor and new-node populations on PEMS04, while Online-AN leads both populations on PEMS05. Among the seven methods run on every district, Online-AN has the lowest all-sensor mean MAE in eight of the nine districts, and Online-NN has the lowest new-node mean MAE in six. A matched control on PEMS05 and PEMS06 finds that training on the evolving catalog changes mean MAE on the sensors present in every 2018-2025 catalog by less than 1% in either direction. Method ordering depends on district, evaluation population, horizon, and metric, and seed variation does not establish statistical significance. The benchmark documents implementation adaptations and uses retrospective geographic graphs, limiting claims about original-paper rankings and prospective deployment. Inherited fixed-sensor gap and hourly/daily tasks provide complementary temporal settings. Code and results: https://github.com/cruiseresearchgroup/TSAS26-EvoXXLTraffic. Data: https://huggingface.co/datasets/yind7/EvoXXLTraffic.

We study regret minimization in episodic Adversarial Markov Decision Processes (AMDPs) with unknown transitions and bandit feedback. In tabular AMDPs with $S$ states, $A$ actions, and horizon $H$, the best known $K$-episode regret is $\tilde O(HS\sqrt{AK})$, whose $\sqrt S$ gap to the $Ω(H\sqrt{SAK})$ lower bound has long remained open. In linear AMDPs with $d$-dimensional features, after a five-year line of improvements starting from $K^{14/15}$, computationally efficient algorithms without a simulator or exploratory assumptions still only attain $\tilde O(K^{3/4})$ regret. A common challenge in both settings is to estimate the Q-function of a changing policy: past data comes from earlier policies that differ from (and are correlated with) the current one, while freezing the policy for future episodes worsens the dependency on $K$. We instead propose a general technique called introduced feedback delays: we estimate the Q-function of the policy using future episodes while still updating the policy, as if the feedback were delayed. While feedback delays are usually an obstacle imposed by the environment, here they are introduced voluntarily and, perhaps surprisingly, come at no extra cost. We develop Geometric Resampling with Rejection (GRR) to account for policy changes during the delay, and threshold the visitation probabilities to avoid long delays on rarely visited state-action pairs. With this unified technique, our policy optimization algorithms achieve, with high probability, $\tilde O(\text{poly}(H)\sqrt{SAK})$ regret in tabular AMDPs, resolving the open problem; and $\tilde O(\text{poly}(d,H)\sqrt K)$ regret in linear AMDPs, giving the first efficient algorithm optimal in $K$. They also naturally extend to delayed AMDPs with a total feedback delay $\mathfrak D$, yielding the first bounds optimal in $S$ and $\mathfrak D$ (tabular) and in $K$ and $\mathfrak D$ (linear).

Large foundation models such as GPT-6 Astra and Claude Fable 5.1 have recently been shown to control robots directly from visual observations. They nevertheless remain brittle on long-horizon, contact-rich tasks in real-world environments. Further progress depends on two complementary resources: demonstrations of human behavior that supervise how tasks are performed, and interactive environments in which policies can be rolled out, evaluated, and improved in closed loop. Language models have advanced along well-characterized scaling laws; the corresponding question for physical AI is how capability scales with environments and behaviors, which first requires identifying which data to scale and how to produce and reuse such data continuously. To address this question, we introduce Ropedia Xperience 2, comprising 1,589 released metric-scale 3D scans and a collection of 4,230 egocentric recordings of 74 tasks (131 hours). We further show that each capture can be repurposed for robot learning: scenes are converted into physically interactive simulation environments, human motion is retargeted to whole-body robot trajectories, and additional robot behaviors are obtained through virtual teleoperation and automated long-horizon trajectory generation. We propose this joint capture of environments and behaviors as a gold standard for physical AI data collection, laying a data foundation for establishing scaling laws over environments and behaviors.