GLMap combines explicit 3D Gaussians with multi-scale language semantics in a dual-modality structure and uses an analytical Gaussian Estimator for incremental map building, improving zero-shot performance on navigation and reasoning tasks.
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4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
TrajRAG uses a topological-polar trajectory representation and hierarchical retrieval to accumulate and reuse geometric-semantic navigation experiences, improving zero-shot ObjectNav on MP3D and HM3D benchmarks.
COSMO-Agent is a tool-augmented RL agent that trains LLMs to complete closed-loop CAD-CAE optimization using a multi-constraint reward and an industry dataset of 25 component categories, improving small models over larger ones.
citing papers explorer
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Multi-Scale Gaussian-Language Map for Zero-shot Embodied Navigation and Reasoning
GLMap combines explicit 3D Gaussians with multi-scale language semantics in a dual-modality structure and uses an analytical Gaussian Estimator for incremental map building, improving zero-shot performance on navigation and reasoning tasks.
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COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
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TrajRAG: Retrieving Geometric-Semantic Experience for Zero-Shot Object Navigation
TrajRAG uses a topological-polar trajectory representation and hierarchical retrieval to accumulate and reuse geometric-semantic navigation experiences, improving zero-shot ObjectNav on MP3D and HM3D benchmarks.
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Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
COSMO-Agent is a tool-augmented RL agent that trains LLMs to complete closed-loop CAD-CAE optimization using a multi-constraint reward and an industry dataset of 25 component categories, improving small models over larger ones.