Notably, it is crucial for objects located at 772 the edges of images to maintain the closure of their bounding squares. Requiring existing MLLMs to 775 rethink may still not improve the accuracy of their responses. This may be because InternVL has been trained on more autonomous driving data. The final MLLM and prompt achieve an accuracy rate of approximately 781 90% on the entire OpenAD data. We conduct experiments by employing diverse visual Acc of and te+xtual prompts, along with various MLLMs, and select the*optimal approach.

We study the subclass of potential mean-field games in which the running interaction cost and the terminal target cost are both expressed through reproducing-kernel maximum mean discrepancy (MMD) penalties, and develop a computational framework that exploits this kernel structure. Both costs are estimated from finite-sample empirical distributions using a random Fourier U-statistic representation that is unbiased and has linear cost in the batch size. The drift of the controlled diffusion is parametrized by a neural network and trained via stochastic gradient descent. For this subclass we prove a sample-level almost-sure convergence theorem and an explicit almost-sure rate of convergence, under coupled rate conditions on the penalty parameter, the random-feature count, the sample size, and the optimization tolerance. The framework includes the kernel-MMD-penalty Schrödinger bridge problem as the special case of a vanishing interaction cost. Numerical experiments illustrate the method on the Schrödinger bridge problem in dimensions up to one hundred, and on an electric vehicle charging coordination problem with per-vehicle physical heterogeneity, where an aggregate-demand congestion cost represents price-feedback competition at the population level and the terminal MMD penalty shapes the state-of-charge distribution at the deadline.

A.1 For LEHD model467 In Table 5, we explore the effects of eliminating normalization from the attention layer in our LEHD468 model. We train three LEHD models with the same training scheme and training budget, differing469 solely in the attention layer: one with batch normalization (BN), one with instance normalization470 (IN), and one without normalization (w/o). We train all three POMO models with the same reinforcement learning method477 with POMO strategy and training budget (1000 epochs). The results show that different types of478 normalization have few effects on the POMO model.479 The results in Table 6 show that removing normalization from attention layer has little impact on the480 model with a heavy encoder and a light decoder.

We will fix them in the final version.