Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple l...Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple label matching.However,previous studies have largely modeled this task as syndrome pattern classification within a fixed label space,which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning,and is also insufficient to capture the openness,semantic variability,and cross-disease reusability of syndrome manifestation expression.This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought(PR-CoT)supervision into large language models(LLMs)could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer.Methods Syndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information(X)→pathogenesis structure(Z)→syndrome pattern output(Y),where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment.Within this framework,a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders.After preprocessing,information extraction,manual proofreading,and data cleaning,the dataset comprised 4800 training cases,400 development cases,and 400 test cases.Each sample was annotated with a structured PR-CoT consisting of three progressive levels:clinical information summarization,comprehensive pathogenesis analysis,and syndrome pattern output.Supervised fine-tuning was conducted on open-source LLMs,with an end-to-end model serving as the baseline.Qwen3-32B was used as the primary experimental model,and Qwen3-14B as the scale comparison model.A progressive multidimensional evaluation framework was further established,comprising a structural parsing level,a semantic similarity level,and an expert blind review level.At the structural parsing level,syndrome pattern expressions were decomposed into structural elements and evaluated using Precision,Recall,F1 score,and Jaccard similarity.At the semantic similarity level,independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns.At the expert blind review level,three TCM experts independently evaluated model outputs on two dimensions:syndrome differentiation consistency and terminology standardization of syndrome patterns.In addition,zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets.Results At the structural parsing level,PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components.Compared with the corresponding baselines,neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision.In contrast,at the semantic similarity level,PR-CoT supervision produced stable positive gains across different model scales and evaluation systems.The average semantic score of Qwen3-32B increased from 6.4258 in the baseline model to 6.5850 after PR-CoT supervision,and that of Qwen3-14B increased from 5.8700 to 5.9642.At the expert blind review level,the overall score of Qwen3-32B(PR-CoT)was 7.0260±0.1077,higher than 6.4163±0.2889 for its baseline.In zero-shot cross-disease testing,the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets,indicating a certain degree of transferability.Conclusion The benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility,rather than in improved hard matching of structural elements.These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures,rather than as a classification task within a traditional fixed label space.By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework,this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment,interpretability,and multi-level evaluation.展开更多
War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient an...War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,viole...Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,violet LEDs are proposed as an alternative solution.Currently,phosphors that can be efficiently excited by violet light(with wavelengths from 400 to 420 nm)remain under development still.In this study,we utilize large language models to construct a comprehensive database of Eu^(2+)and Ce^(3+)doped phosphors for discovering novel violet-excited phosphors.A total of 822 phosphor data entries,including elemental compositions,crystal structures and excitation/emission wavelengths,have been extracted and validated from 9551 research papers.Compared with Ce^(3+)doped phosphors,the Eu^(2+)are in general more suited for violet-excited phosphors,as well as red-emitting phosphors.In particular,Eu^(2+)doped nitrides and sulfides are worth of exploration for violet-excited phosphors.This database is expected to be useful in the future development of phosphors for pc-WLEDs based on artificial intelligence methods.The datasets in this article are listed in Science Data Bank at http://doi.org/10.57760/sciencedb.34314.展开更多
Traditional source search algorithms are prone to local optimization,and source search methods combining crowdsourcing and human-AI collaboration suffer from low cost-efficiency due to human intervention.In this study...Traditional source search algorithms are prone to local optimization,and source search methods combining crowdsourcing and human-AI collaboration suffer from low cost-efficiency due to human intervention.In this study,we proposed a lightweight human-AI collaboration framework that utilized multi-modal large language models(MLLMs)to achieve visual-language conversion,combined chain-of-thought(CoT)reasoning to optimize decision-making,and constructed a heuristic strategy that incorporated probability distribution filtering and a balance between exploitation and exploration.The effectiveness of the framework was verified by experiments.The human-AI alignment heuristic strategy with large language model adaptation design provides a new idea to reduce manual dependency for source search task in complex scenes.展开更多
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base...[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.展开更多
文摘Objective The essence of syndrome manifestation recognition in traditional Chinese medicine(TCM)is to infer the body’s latent pathogenesis state from clinical observational information,rather than to perform simple label matching.However,previous studies have largely modeled this task as syndrome pattern classification within a fixed label space,which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning,and is also insufficient to capture the openness,semantic variability,and cross-disease reusability of syndrome manifestation expression.This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought(PR-CoT)supervision into large language models(LLMs)could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer.Methods Syndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information(X)→pathogenesis structure(Z)→syndrome pattern output(Y),where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment.Within this framework,a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders.After preprocessing,information extraction,manual proofreading,and data cleaning,the dataset comprised 4800 training cases,400 development cases,and 400 test cases.Each sample was annotated with a structured PR-CoT consisting of three progressive levels:clinical information summarization,comprehensive pathogenesis analysis,and syndrome pattern output.Supervised fine-tuning was conducted on open-source LLMs,with an end-to-end model serving as the baseline.Qwen3-32B was used as the primary experimental model,and Qwen3-14B as the scale comparison model.A progressive multidimensional evaluation framework was further established,comprising a structural parsing level,a semantic similarity level,and an expert blind review level.At the structural parsing level,syndrome pattern expressions were decomposed into structural elements and evaluated using Precision,Recall,F1 score,and Jaccard similarity.At the semantic similarity level,independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns.At the expert blind review level,three TCM experts independently evaluated model outputs on two dimensions:syndrome differentiation consistency and terminology standardization of syndrome patterns.In addition,zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets.Results At the structural parsing level,PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components.Compared with the corresponding baselines,neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision.In contrast,at the semantic similarity level,PR-CoT supervision produced stable positive gains across different model scales and evaluation systems.The average semantic score of Qwen3-32B increased from 6.4258 in the baseline model to 6.5850 after PR-CoT supervision,and that of Qwen3-14B increased from 5.8700 to 5.9642.At the expert blind review level,the overall score of Qwen3-32B(PR-CoT)was 7.0260±0.1077,higher than 6.4163±0.2889 for its baseline.In zero-shot cross-disease testing,the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets,indicating a certain degree of transferability.Conclusion The benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility,rather than in improved hard matching of structural elements.These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures,rather than as a classification task within a traditional fixed label space.By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework,this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment,interpretability,and multi-level evaluation.
文摘War rehearsals have become increasingly important in national security due to the growing complexity of international affairs.However,traditional rehearsal methods,such as military chess simulations,are inefficient and inflexible,with particularly pronounced limitations in command and decision-making.The overwhelming volume of information and high decision complexity hinder the realization of autonomous and agile command and control.To address this challenge,an intelligent warfare simulation framework named Command-Agent is proposed,which deeply integrates large language models(LLMs)with digital twin battlefields.By constructing a highly realistic battlefield environment through real-time simulation and multi-source data fusion,the natural language interaction capabilities of LLMs are leveraged to lower the command threshold and to enable autonomous command through the Observe-Orient-Decide-Act(OODA)feedback loop.Within the Command-Agent framework,a multimodel collaborative architecture is further adopted to decouple the decision-generation and command-execution functions of LLMs.By combining specialized models such as Deep Seek-R1 and MCTool,the limitations of single-model capabilities are overcome.MCTool is a lightweight execution model fine-tuned for military Function Calling tasks.The framework also introduces a Vector Knowledge Base to mitigate hallucinations commonly exhibited by LLMs.Experimental results demonstrate that Command-Agent not only enables natural language-driven simulation and control but also deeply understands commander intent.Leveraging the multi-model collaborative architecture,during red-blue UAV confrontations involving 2 to 8 UAVs,the integrated score is improved by an average of 41.8%compared to the single-agent system(MCTool),accompanied by a 161.8%optimization in the battle loss ratio.Furthermore,when compared with multi-agent systems lacking the knowledge base,the inclusion of the Vector Knowledge Base further improves overall performance by 16.8%.In comparison with the general model(Qwen2.5-7B),the fine-tuned MCTool leads by 5%in execution efficiency.Therefore,the proposed Command-Agent introduces a novel perspective to the military command system and offers a feasible solution for intelligent battlefield decision-making.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金National Key Research and Development Program of China(2021YFB3500501)。
文摘Commercial phosphor-converted white LEDs(pc-WLEDs)face two inherent limitations,namely blue light hazard and low color rendering index,due to the use of blue LEDs as excitation source.To address these challenges,violet LEDs are proposed as an alternative solution.Currently,phosphors that can be efficiently excited by violet light(with wavelengths from 400 to 420 nm)remain under development still.In this study,we utilize large language models to construct a comprehensive database of Eu^(2+)and Ce^(3+)doped phosphors for discovering novel violet-excited phosphors.A total of 822 phosphor data entries,including elemental compositions,crystal structures and excitation/emission wavelengths,have been extracted and validated from 9551 research papers.Compared with Ce^(3+)doped phosphors,the Eu^(2+)are in general more suited for violet-excited phosphors,as well as red-emitting phosphors.In particular,Eu^(2+)doped nitrides and sulfides are worth of exploration for violet-excited phosphors.This database is expected to be useful in the future development of phosphors for pc-WLEDs based on artificial intelligence methods.The datasets in this article are listed in Science Data Bank at http://doi.org/10.57760/sciencedb.34314.
基金National Natural Science Foundation of China (62202477)。
文摘Traditional source search algorithms are prone to local optimization,and source search methods combining crowdsourcing and human-AI collaboration suffer from low cost-efficiency due to human intervention.In this study,we proposed a lightweight human-AI collaboration framework that utilized multi-modal large language models(MLLMs)to achieve visual-language conversion,combined chain-of-thought(CoT)reasoning to optimize decision-making,and constructed a heuristic strategy that incorporated probability distribution filtering and a balance between exploitation and exploration.The effectiveness of the framework was verified by experiments.The human-AI alignment heuristic strategy with large language model adaptation design provides a new idea to reduce manual dependency for source search task in complex scenes.
文摘[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management.
