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PlannerForge is presented, an LLM-agent framework that extends all scenario-based testing stages (from Scenario Generation to ADS Assessment) and adds two further LLM-enhanced stages: ADS Enhancement and ADS Benchmarking.
Visual fidelity in World Models can be misleading; a model that looks better may perform worse in action robustness, challenging existing evaluation paradigms.
World models may be fundamentally misfiring by imagining future states kinematically, leading to significant performance drops without corresponding diagnostic signals.
A 4B-parameter model outperforms Gemini-3-Pro in autonomous driving by incorporating physics-informed constraints and style-aware training, suggesting specialized models can surpass larger, general-purpose models in domain-specific tasks.
