Grounding LLMs in Scientific Discovery via Embodied Actions
#Large Language Models #EmbodiedAct #Scientific Discovery #Perception-Execution Loop #Physical Simulation #MATLAB #Engineering Design #Anomaly Detection
📌 Key Takeaways
- Researchers developed EmbodiedAct framework to bridge gap between theoretical reasoning and physical simulation in LLMs
- Existing LLM solutions operate passively without runtime perception, missing transient anomalies
- EmbodiedAct creates tight perception-execution loop for active engagement with scientific simulations
- Framework significantly outperforms existing methods in reliability, stability, and accuracy
📖 Full Retelling
Researchers led by Bo Zhang have developed EmbodiedAct, a groundbreaking framework that transforms established scientific software into active embodied agents by grounding Large Language Models in embodied actions with a tight perception-execution loop, as detailed in their paper submitted to arXiv on February 24, 2026, addressing the critical limitation of existing LLM solutions that operate in a passive 'execute-then-response' manner and lack runtime perception for scientific discovery. The paper highlights how Large Language Models have shown significant potential in scientific discovery but struggle to bridge the gap between theoretical reasoning and verifiable physical simulation, with existing solutions obscuring agents to transient anomalies such as numerical instability or diverging oscillations. The researchers instantiated EmbodiedAct within MATLAB and evaluated it on complex engineering design and scientific modeling tasks, with extensive experiments demonstrating that the framework significantly outperforms existing baselines, achieving state-of-the-art performance by ensuring satisfactory reliability and stability in long-horizon simulations and enhanced accuracy in scientific modeling.
🏷️ Themes
Artificial Intelligence, Scientific Computing, Machine Learning
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
MATLAB
Numerical computing environment and programming language
MATLAB (Matrix Laboratory) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs writt...
Entity Intersection Graph
Connections for Large language model:
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Artificial intelligence
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Reinforcement learning
3 shared
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Educational technology
2 shared
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Benchmark
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OpenAI
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Mentioned Entities
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.20639 [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 Abstract: Large Language Models 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: 24 pages, 7 figures, 7 tables. Preprint Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20639 [cs.AI] (or arXiv:2602.20639v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.20639 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bo Zhang [ view email ] [v1] Tue, 24 Feb 2026 07:37:18 UTC (4,704 KB) Full-text links: Access Paper: 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 TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-02 ...
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