The Self-Corrective Multi-Agent Refinement Framework is proposed, which decouples physics-aware error correction from code generation through specialized agents and nearly triples the physical-assertion pass rate of the best proprietary baseline and retains its advantage under cross-engine transfer.
Tianyidan Xie, Pei-Yu Wang, Jia-Xin Hu et al.· arXiv.org· 7 citations· ⚡2
Linguistic Trajectory Encoding (LTE) is introduced, which compresses dynamic object motion histories via a hybrid representation combining natural language descriptions, sparse spatial anchors, and visual anchors, and adapts compression to motion complexity by anchoring periods without reliable observations to the last...
Tianyidan Xie, Shen-Yi Wang, Qiang Tang et al.· 0 citations
Evaluation of state-of-the-art open-source and closed-source MLLMs reveals that current MLLMs function as lossy summarizers rather than faithful memorizers, highlighting the need for architectures with genuine long-term spatiotemporal memory.