Interaction-Symmetric Decoding for Closed-Loop Trajectory Planning

Modeling multi-modal driving behaviors under dense agent interactions remains a fundamental challenge for autonomous closed-loop trajectory planning, and recent generative planners such as Diffusion Planner and Flow Planner have emerged as the dominant paradigm. However, these planners inherit a one-sided scene conditioning pattern from their transformer-based decoders, in which trajectory tokens query scene tokens through cross-attention while no complementary channel lets scene elements assign constraint responsibility back to the trajectory hypotheses. This misaligns with the joint multi-agent formulation they adopt, in which every agent's future should be conditioned by all others symmetrically. To address this inductive-bias mismatch, this paper introduces interaction-symmetric decoding, a design principle that recasts scene-trajectory coupling as a dual assignment problem, pairing forward demand assignment (what scene information each trajectory hypothesis retrieves) with reverse constraint-responsibility assignment (which trajectory hypotheses each scene element should influence). We instantiate it in SymDiff Planner, whose Interaction-Symmetric DiT decoder couples a Demand-Responsibility Assignment module that realizes the assignment dual with an Adaptive Symmetry Modulation module that regulates the reverse assignment strength by scene context. Extensive experiments on the large-scale nuPlan benchmark show that SymDiff Planner achieves state-of-the-art closed-loop performance among learning-based planners, with the most pronounced gains on the challenging Test14-hard split dominated by dense agent interactions and long-tail driving cases. Code and pre-trained checkpoints will be released upon acceptance.