Update README.md

README.md

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-# RLM 
+# Recipe Language Model (RLM)
 
 ## 1. Overview
 
-Recipe Language Model (RLM): We introduce a domain-specific RLM developed through seven AI layers and interconnected robotic boxes to drive the evolution of physical AI in materials research.
+We introduce a domain-specific RLM developed through seven AI layers and interconnected robotic boxes to drive the evolution of physical AI for defining a new paradigm - Materials Intelligence.
 
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 ## 2. Introduction
 
-Materials innovation have been undergoing rapid development with the vast combinatorial exploration of recipes; however, the related research suffers from time-consuming trial-and-error synthesis and labour-intensive fabrication.
-As a promising alternative, robotics enables high-throughput experimentation and data collection; however, the resulting numerical datasets are often insufficiently analysed and fail to provide effective feedback for semantic recipe optimisation.
+Materials innovation have been undergoing rapid development with the vast combinatorial exploration of recipes; however, the related research suffers from time-consuming trial-and-error synthesis and labour-intensive fabrication. As a promising alternative, robotics enables high-throughput experimentation and data collection; however, the resulting numerical datasets are often insufficiently analysed and fail to provide effective feedback for semantic recipe optimisation.
 
-Here, we present a domain-specific recipe language model (RLM) developed for an emerging scientific tool of robotic boxes (perovskite solar cell research as a demonstration).
-For iterative fine-tuning of the RLM, seven artificial intelligence (AI) layers, including learning, generating, RecipeQA, fine-tuning, reasoning, evaluation, and optimisation, have been designed with a language agent.
-During the loops of seven AI layers, both numerical and semantic recipes were continuously learned and optimised for the RLM.
-Guided by this RLM, eleven robotic boxes executed the controllable synthesis, fabrication and characterisation of 50,764 samples.
-Simultaneously, more than 578 million tokens were generated and augmented to improve the ability to recommend a recipe and mechanistic reasoning, reaching a level comparable to that of an experienced researcher.
+Here, we present a domain-specific RLM developed for an emerging scientific tool of robotic boxes (perovskite solar cell research as a demonstration). For iterative fine-tuning of the RLM, seven AI layers, including learning, generating, RecipeQA, fine-tuning, reasoning, evaluation, and optimisation, have been designed with a language agent. During the loops of seven AI layers, both numerical and semantic recipes were continuously learned and optimised for the RLM. Guided by this RLM, eleven robotic boxes executed the controllable synthesis, fabrication and characterisation of 50,764 samples. Simultaneously, more than 578 million tokens were generated and augmented to improve the ability to recommend a recipe and mechanistic reasoning, reaching a level comparable to that of an experienced researcher.
 
-Therefore, the integration of the RLM with robotic boxes enables an AI and robotics discovery process in which specialised language modelling and modularised robotic hardware continuously improve one another, suggesting an evolution of physical AI for materials research.
-Here, we present a domain-specific recipe language model (RLM) developed for an emerging scientific tool of robotic boxes, with perovskite solar cell research as a demonstration.
-For iterative fine-tuning of the RLM, seven artificial intelligence (AI) layers, including learning, generating, RecipeQA, fine-tuning, reasoning, evaluation, and optimisation, have been designed with a language agent.
-During the loops of seven AI layers, both numerical and semantic recipes are continuously learned and optimised for the RLM.
+Therefore, the integration of the RLM with robotic boxes enables an AI and robotics discovery process in which specialised language modelling and modularised robotic hardware continuously improve one another, suggesting an evolution of physical AI for the Materials Intelligence.
 
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 ## 3. Model Description
 
-To train this domain-specific RLM, the workflow starts from encoded formulas and parameters as recipe inputs, proceeds through seven AI layers with the language agent, and produces in situ characterisation and device performance assessment as mechanistic outputs.
-The language agent encodes these machine-readable recipes into structured formulas and parameters sequences, which are translated into tokens for subsequent fine-tuning of the RLM and execution by the robotic boxes.
-As a result, the fine-tuned RLM incorporates the encoded recipes, robotics, and characterised results to form a closed recommendation–synthesis–fabrication–characterisation–mechanism loop for exploring the large space of the recipes and their underlying mechanisms.
+To train this domain-specific RLM, the workflow starts from encoded formulas and parameters as recipe inputs, proceeds through seven AI layers with the language agent, and produces in situ characterisation and device performance assessment as mechanistic outputs. The language agent encodes these machine-readable recipes into structured formulas and parameters sequences, which are translated into tokens for subsequent fine-tuning of the RLM and execution by the robotic boxes. As a result, the fine-tuned RLM incorporates the encoded recipes, robotics, and characterised results to form a closed recommendation–synthesis–fabrication–characterisation–mechanism loop for exploring the large space of the recipes and their underlying mechanisms.
 
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 ## 4. Key Features
 
-Acceleration of Materials Intelligence (MI): Simultaneously optimizes recipe recommendation (technical accuracy) and mechanistic reasoning (scientific understanding).
+Acceleration of Materials Intelligence (MI): Simultaneously optimizes recipe recommendation (technical accuracy) and mechanistic reasoning (scientific understanding) for accelerating the discovery of MI recipes.
 
-Evolution of Physical AI (PAI): Creates a synergy where the RLM enhances the robotic controllable fabrication, while the robotic outcomes continuously refine the RLM’s recipe and mechanism.
+Evolution of Physical AI (PAI): Creates a synergy where the RLM enhances the robotic controllable fabrication, while the robotic outcomes continuously refine the RLM’s recipe and mechanism to drive the evolution of PAI.
 
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@@ -97,12 +87,11 @@ This layer further aligns the model towards preference-consistent and high-perfo
 
 ## 6. Intended Use
 
-The RLM is designed as a domain-specific framework to bridge the gap between abstract scientific knowledge and physical experimentation in materials research (herein perovskite solar cell research as a demonstration).
-By integrating seven AI layers and interconnected robotic systems, its intended use is to automate and evolve physical AI through the following potential applications:
+The RLM is designed as a domain-specific model to bridge the gap between numerical machine learning, semantic recipe, and the physical experimentation in materials research (herein perovskite solar cell research with complex formulas and complicatedparameters as a demonstration). By integrating seven AI layers and interconnected robotic systems, its intended use is to automate and evolve physical AI through the following potential applications: 
 
 Autonomous Experimental Planning: The RLM translates high-level research objectives into granular, robot-executable "recipes". It functions as a high-level planner that identifies necessary actions, objects, and sequences required for materials synthesis and fabrication.
 
-Physical-AI Coevolution: It drives the "coevolution" of Large Language Models (LLMs) with physical models, refining the AI's ability to conduct domain-specific simulations while generating real-world data to steer broader intelligence.
+Physical-AI Coevolution: It drives the "coevolution" of Large Language Models (LLMs) with physical models, refining the AI's ability to conduct domain-specific simulations while generating real-world data to steer broader Materials Intelligence.
 
 Knowledge-to-Action Translation: The model leverages its seven AI layers architecture to transform textual knowledge from scientific literature into structured, actionable steps, overcoming the "erudite scholar" limitation where AI possess knowledge but lacks the capacity for physical interaction.