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[2602.20639] Grounding LLMs in Scientific Discovery via Embodied Actions
Summary
The paper presents EmbodiedAct, a framework that enhances Large Language Models (LLMs) by grounding them in embodied actions for scientific discovery, improving reliability and accuracy in simulations.
Why It Matters
This research addresses the limitations of existing LLMs in scientific applications by integrating real-time perception and execution, which could significantly advance the field of AI in scientific modeling and engineering design. The findings could lead to more robust AI systems capable of handling complex tasks in dynamic environments.
Key Takeaways
- EmbodiedAct transforms LLMs into active agents for scientific tasks.
- The framework improves reliability and stability in long-horizon simulations.
- EmbodiedAct outperforms existing models in accuracy for scientific modeling.
- Real-time perception enhances the ability to respond to transient anomalies.
- The research highlights the potential of integrating embodied actions in AI.
Computer Science > Artificial Intelligence arXiv:2602.20639 (cs) [Submitted on 24 Feb 2026] Title:Grounding LLMs in Scientific Discovery via Embodied Actions Authors:Bo Zhang, Jinfeng Zhou, Yuxuan Chen, Jianing Yin, Minlie Huang, Hongning Wang View a PDF of the paper titled Grounding LLMs in Scientific Discovery via Embodied Actions, by Bo Zhang and 5 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have shown significant potential in scientific discovery but struggle to bridge the gap between theoretical reasoning and verifiable physical simulation. Existing solutions operate in a passive "execute-then-response" loop and thus lacks runtime perception, obscuring agents to transient anomalies (e.g., numerical instability or diverging oscillations). To address this limitation, we propose EmbodiedAct, a framework that transforms established scientific software into active embodied agents by grounding LLMs in embodied actions with a tight perception-execution loop. We instantiate EmbodiedAct within MATLAB and evaluate it on complex engineering design and scientific modeling tasks. Extensive experiments show that EmbodiedAct significantly outperforms existing baselines, achieving SOTA performance by ensuring satisfactory reliability and stability in long-horizon simulations and enhanced accuracy in scientific modeling. Comments: Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20639 [cs.AI] (or arXiv:2602.20639v1 [cs.AI] for this v...
