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DriveMind is proposed, an MLLM-based driving framework that keeps several hypotheses about the scene alive at the same time and updates their weights as new frames arrive and improves fine-grained interactive driving abilities such as merging, emergency braking, and give-way.
This work evaluates 25 model and agent systems under a controlled tool-using protocol, then uses controlled studies to locate failures in evidence access, tool selection, memory, reasoning, interaction, and scientific execution.
Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state-of-the-art results on 10 tasks, achieving 3-10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks.
WorldCupArena is presented, a dynamic benchmark for language models and deep-research agents that can be reused for future leagues and cups, and shows only small gains in result and exact-score accuracy, but a clearer gain in Scoreline.
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