Integration of large vision language models for efficient post-disaster damage assessment and reporting

Domain: chemistry
Published: 2026-01-10
Editorial outcome: major_revision

Abstract
Traditional natural disaster response involves significant coordinated teamwork, where speed and efficiency are key. Nonetheless, human limitations can delay critical actions and inadvertently increase human and economic losses. Agentic Large Vision Language Models (LVLMs) offer an avenue to address this challenge, with the potential for substantial socio-economic impact, particularly by improving resilience and resource access in underdeveloped regions. We introduce DisasTeller, a multi-LVLM-powered framework designed to automate tasks in post-disaster management, including on-site assessment, emergency alerts, resource allocation, and recovery planning. By coordinating four specialised LVLM agents with GPT-4 as the core, DisasTeller can accelerate disaster response activities, reducing human execution time and structuring information flow. Our evaluation shows both benefits and challenges: while DisasTeller streamlines coordination and report generation, errors in early-stage assessments may propagate downstream, highlighting the need for human validation and improved LVLM accuracy. This framework acts as a complementary support system to expert teams, bridging the gap between traditional response methods and emerging LVLM-driven efficiency, while highlighting the importance of continued refinement and collaboration for safe, trustworthy deployment.

Introduction
Natural disasters consist of various catastrophic events, including earthquakes, hurricanes, floods and wildfires. These events can cause extensive damage, loss of life and have long-term impacts on societies and economies. For instance, earthquakes can cause buildings to collapse, trapping people under rubble, while hurricanes and floods can result in drowning and physical damage. These natural disasters also lead to extended social challenges, such as the disruption of community cohesion and the psychological effects of trauma and loss.

According to the report from the United Nations office, the overall globaleconomic losses from natural disasters in 2023 were estimated to be US$250 billion, which roughly equals the entire gross domestic product of New Zealand. Beyond immediate losses, disasters would have long-lasting economic effects, including reduced economic growth, increased poverty and disruption to markets and industries. The frequency and intensity of natural disasters have been rising due to factors such as climate change, urbanisation and environmental degradation, making the need for effective post-disaster management even more critical.

Post-disaster management involves activities, including disaster response and post-disaster recovery, aimed at mitigating the impacts of natural disasters. The Sendai Framework of the United Nations for Disaster Risk Reduction (2015–2030) highlights the crucial role of enhanced disaster risk governance in ensuring an effective response and promotes the concept of ‘Building Back Better’ in sustainable recovery, reconstruction and rehabilitation efforts. Traditionally, the entire process of disaster response and post-disaster recovery requires broad collaboration and teamwork. As shown in Fig. 1, the first step in the disaster response process involves the post-disaster on-site assessment through an expert team, which would focus on evaluating the structural integrity of buildings, transportation networks and other critical infrastructures. Based on the assessment of the expert team, the alerts team is activated. The role of the alerts team is to issue emergency alerts to the public, the emergency services team and government agencies. These alerts are crucial for ensuring that all parties are aware of the situation and can act accordingly. Simultaneously, the emergency services team is mobilised to provide immediate relief to affected populations and determines the number and locations of relevant facilities based on the information provided by the alert team and the expert evaluations. The assignment team works closely with other teams and is tasked with distributing human resources for emergency service construction, determining where and how resource should be assigned depending on the other three teams’ reports. In addition, an essential component of the disaster response is keeping the affected communities informed. This includes advising them about the ongoing operations, where they can access shelter and medical care, and any other relevant updates. Effective communication helps to manage public expectations, reduce panic and foster cooperation among affected communities. After the response phase, the government and policymakers could rely on updates from the assignment team to make decisions about rebuilding strategies and long-term resilience measures.

Generally, the disaster response work would last several weeks due to the limitations of human execution and mobility, and the response process can be even slower in underdeveloped areas due to insufficient support and infrastructure investment, inevitably leading to vast losses. In all periods, enhancing the speed of disaster response efforts and ensuring marginalised groups are not left behind are widely recognised as crucial for reducing disaster-related losses and saving human lives. Current disaster management research focuses on the analysis of disaster-related factors under specific scenarios. Math equations or simple machine learning models cannot handle the complex scenarios in post-disaster management, let alone simulate human collaboration to reduce labour work, accelerate the response, or even mitigate the disaster response disparities between developed and underdeveloped areas.

As artificial intelligence (AI) technology develops, it will provide the possibility to better emulate human intelligence, thus rendering it possible to assist humans in improving operational efficiency under the oversight of humans. One of the most significant breakthroughs in AI has been the development of large vision language models (LVLMs), which extend the capabilities of large language models (LLMs) to handle multimodal tasks involving both textual and visual data. These models, which include OpenAI’s GPT-4 series, are built on neural networks that mimic the structure of the human brain and trained on vast amounts of image and text data, enabling them to generate human-like text, process information from image and context and even perform complex tasks like summarisation and conversation. While these models do not truly ‘understand’ language in a human sense, their ability to model linguistic and visual patterns has made them useful tools in domains requiring large-scale information processing and analysis. The recent progress in LLMs has laid a strong foundation for the development of LVLMs. Bharathi Mohan et al. offer a comprehensive review of LLM architectures and their expanding applications, illustrating the technological evolution toward more sophisticated multimodal models. Foundational works by Brown et al. and Radford et al. have demonstrated how few-shot learning and multitask generalisation in LLMs have shaped the design of current-generation LVLMs. Liu et al. further show how GPT-based evaluation systems enhance the alignment and reliability of generative models, while Yang et al. highlight the potential risks of misinformation, particularly in sensitive applications such as healthcare, underscoring the need for caution when deploying these models in critical domains like disaster response.

In the disaster management domain, integrating vision-language understanding is particularly valuable. Nguyen et al. show that social media imagery can be effectively leveraged for real-time damage assessment during disasters, pointing to the potential of LVLMs, which can jointly process visual and textual inputs to automate and enhance such tasks. Moreover, standardised frameworks such as the European Macroseismic Scale (EMS-98) provide structured criteria for assessing seismic intensity, which can be used as the rubric to evaluate the ability of LVLMs to interpret post-disaster images and reports. These developments support the increasing relevance of LVLMs in disaster response workflows, as the disaster response field advances toward more automated and accountable systems. Building on these insights, the use of multi-LVLM systems to support disaster response operations offers promising benefits, like accelerating the complete procedure, minimising disaster-related losses and narrowing the disaster response gap between developed and underdeveloped areas.

Unlike humans, LVLMs can operate continuously without fatigue and facilitate tasks across regions with varying levels of development. They are capable of supporting team-based workflows through rapid interactions, efficiently referencing large volumes of historical data and offering standardised outputs. Although they may lack true understanding or judgement like humans, their standardised and algorithmic output could facilitate a faster, more uniform response during the initial disaster assessment phase, potentially reducing inconsistencies and biases inherent in human judgement.

In this work, we propose a multi-LVLM-powered disaster response framework, DisasTeller, designed to streamline the collaboration of different teams in post-disaster management. As shown in Fig. 2, DisasTeller operates by prompting four independent LVLM agents (GPT-4o in our experiment) with specific task instructions and implementing three assistant tools to complete the process. The LVLM agents are provided with the descriptions of their roles, the utility of defined tools and the details of expected input and output. The expert team agent first analyses on-site local post-disaster and global region images, then shares their information with the other three teams. The alerts team and emergency team agents then generate alerting news and emergency service reports, respectively, while the assignment team agent consolidates all prior outputs to produce the human resource allocation command, public announcement and future reconstruction plan. Designed as a modular engine, DisasTeller can be readily extended with additional agents and tools for emerging applications. It will also support non-experts through user-facing interfaces such as mobile apps or dashboards, enabling real-time image submission, localised alerts, actionable guidance and transparent monitoring of resource distribution. Aggregated multi-source inputs from non-expert users further enhance system responsiveness. We assess the reasonability of all six output reports from the agents of the four teams in Supplementary Information. Overall, DisasTeller consistently produces coherent and actionable outputs across key disaster-management tasks, with GPT-4o exhibiting higher grading accuracy and lower annotation deviation than Gemma3, demonstrating the potential of multi-LVLM agent systems to provide standardised and rapid support for post-disaster response.

Results
Autonomous disaster response
To validate the autonomous process of DisasTeller, a case study of earthquake disaster (another two validations with flood and bushfire are introduced in Supplementary Information) was conducted in Fig. 3, utilising the data from previous literature. We define LVLM agents by prompting task descriptions for several independent GPT-4o models with assigned tools, as shown in Fig. 3a, before the execution of DisasTeller. Like the initial action of the experts’ on-site inspections for the post-disaster response, the input of the framework starts with local on-site post-disaster and global map images. As shown in Fig. 3b, we manually defined the affected area by earthquake as the Wajima City in Japan, and the locations of the local-view images as Wajima Drama Memorial Hall, Hama Street, Concrete Bridge, Central Nishikigawa Street, North Asaichi Street and South Central Asaichi Street. DisasTeller first processes the local-view images for the location and visible damage descriptions. Utilising the file search tool, DisasTeller reads the local technical guideline file European Macroseismic Scale (EMS98) for the damage evaluation and grading (G1–G5, from slight damage to very heavy damage). The output of the post-disaster damage grading in Fig. 3b matches the local-view images well, confirming the successful collaborations of LVLM agents to analyse the severity of the disaster. After the damage evaluation of local areas, DisasTeller deploys the map annotation tool to search and annotate the damage grades in the relevant locations of the global map image, thus generating the alert map to visualise the overall situation in this disaster area. This alert map is then further reviewed by other LVLM agents in DisasTeller to generate different reports (partial output of reports shown in Fig. 3c). The first report is generated by the expert team agent in DisasTeller to briefly summarise the local-view disaster and its damage grades. After inspecting the expert team report and the alert map, the alerts team agent in DisasTeller starts reasoning and generating alert news, providing information and safety alerts about existing dangerous areas. For example, the alert team agent reports: ‘Fire-Damaged Areas: Particularly around Asaichi Street and near the Wajima Morning Market, these zones have unstable structures and debris. Avoid these areas due to potential fire and structural hazards’.

Analysing the reports of previous agent teams and taking reference to the historical disaster data with the real website link retrieved by the web search tool, the emergency team agent ensures the areas requiring immediate recovery and emergency services. One example of the retrieved historical disaster data via the tool is the article ‘An Overview of the Design of Disaster Relief Shelters’ from ScienceDirect. Another example of the emergency team agent output is: ‘Concrete Bridge: Immediate assessment and restoration are crucial to reestablish vital transport links’. Afterwards, the assignment team agent integrates the information from the previous three reports and disaster archives obtained online via the web search tool, such as entries from the American Federal Emergency Management Agency Historic Disasters, to generate the human resource allocation report. An example assignment of human resources is: ‘Wajima Drama Memorial Hall: This area will host a large emergency shelter. It requires a team of 20 medical personnel, including five doctors and 15 nurses, supported by 10 logistics personnel’. The assignment team agent also produces the public notice report, for instance: ‘We are coordinating with governmental agencies and non-governmental organisations to ensure a comprehensive response’. Finally, the assignment team agent prepares the future reconstruction plan. One example output is: ‘Based on previous case studies, an estimated budget of approximately $1 billion is needed to cover structural repairs’. DisasTeller saves the intermediate outputs of LVLM agent teams in memory and allows LVLM agents to reuse these intermediate outputs and the defined tools, provided they have access, thus automating the whole disaster response process by leveraging the reasoning capabilities of LVLMs.

Reliable outputs of LVLM agents are critical for effectively executing DisasTeller. Yet, the quality of LVLMs’ outputs highly relies on the prompt inputs for the LVLM agents, and even the same instruction prompt can lead to different outputs. Designing prompts to achieve optimal results is an iterative process. Since maintaining identical and best outputs of LVLM agents is complex, LVLM agents in DisasTeller are restricted to output contents with designated format templates when defining task prompts, thus providing relatively controlled results. This case study demonstrates DisasTeller’s ability to autonomously carry out post-disaster response tasks through LVLM agents’ successful simulation of the coordination of disaster management teams.

Agent output results evaluation
In recent years, integrating LVLMs into machine learning frameworks has accelerated the discoveries in different scientific areas. Yet, LVLMs can generate incorrect or deceptive content, and there is a lack of standardised evaluation metrics, posing a great challenge to assessing the output quality of LVLM-based frameworks. In addition, since there is a rising concern about the time-dependent performance of closed-source LVLM like OpenAI’s GPT models, we additionally test our framework with Google’s lightweight open-source LVLM model Gemma3-27B, which has 27 billion parameters (compared with the around 200 billion parameters of GPT-4o), thus facilitating the reproducibility and better building upon for future research. Considering the previous studies, we conducted five independent runs respectively for both GPT-4o and Gemma3-driven DisasTeller and implemented the comprehensive assessment with a combination of the LVLM (GPT-4o) and human inspection. The intermediate process tasks of GPT-4o and Gemma3-driven DisasTeller are evaluated manually due to their simplicity, and the six output reports of GPT-4o and Gemma3-driven DisasTeller are assessed by both LVLM (GPT-4o) and human inspection, considering their complexity. GPT-4o is prompted to act as a post-disaster management expert (which is called EvaluatorGPT), provided with post-disaster on-site images, documents retrieved from the internet, and the evaluation rubric to enhance the accuracy of its assessments. The prompt for EvaluatorGPT is modified to grade the output reports of DisasTeller based on the rubric, which focuses on coherence and consistency of sentences and the accuracy of claims. Each evaluation of EvaluatorGPT requires a detailed explanation of the weakness of the report to support the corresponding grade. In addition, we select people who have civil engineering backgrounds with construction experience to manually inspect DisasTeller’s outputs and utilise the same rules to provide grades as a comparison of EvaluatorGPT for a more thorough evaluation. The full evaluation procedure for on-site disaster grading, global map annotations of disaster districts, six output reports and an additional error propagation study are included in Supplementary Information.

The initial response phase of real post-disaster scenarios typically occurs within the first 12–24 h, focusing on immediate response actions such as deploying survey and assessment expert teams to generate the initial alert reports and public announcement. The subsequent actions by emergency services teams to provide necessary assistance generally start within the first 72 h, followed by the structured long-term recovery plan in 1–2 weeks. Post-disaster response and recovery in real scenarios is a challenging and slow process due to the complexity of multi-stakeholder coordination and comprehensive damage assessment. However, unlike human teams, taking several hours to communicate and intervene, from image inspection to disaster-relevant report generation, LVLM-driven DisasTeller would only utilise few minutes (GPT-4o with 3/35 input/output tokens per second, Gemma3-27B with 2/10 input/output tokens per second, on a workstation with the NVIDIA A100 80G GPU and Intel Xeon Platinum 8352V CPU) to coordinate all the agents and complete the given damage assessment tasks, thus simplifying the disaster response process and exhibit the potential to accelerate this process through the autonomous procedure.

The assessment of intermediate process tasks is crucial for each run of GPT-4o and Gemma3-driven DisasTeller since the incorrect output of an individual agent would spread wrong information within the whole workflow and result in misleading final reports for all the stakeholders in the disaster event. This section evaluates two main intermediate tasks, including local disaster grading and alert map generation. Figure 4 summarises the evaluation results across five independent rounds for both local disaster grading and alert map annotation tasks. The disaster grading error is defined through accumulating the deviation of DisasTeller’s damage grading output compared to our manual assessment, and the map annotation distance error is defined through comparing the spatial locations of DisasTeller’s autonomous annotations with our manual annotations. The average scores achieved by the GPT-4o-driven framework for local disaster grading (7.3/10) and map annotations (6.0/10) are higher than those (4.6/10 for disaster grading and 5.0/10 for map annotations) achieved by the Gemma3-driven framework, which only has 63% and 83% accuracy of the GPT-4o-driven framework in these two tasks. This drop in accuracy would be owing to the parameter discrepancy between Gemma3 (27 billion) and GPT-4o (around 200 billion), which would cause the drop in generalisation ability of Gemma3 compared to GPT-4o. Each bar in Fig. 4 represents the score for one of the five independent rounds, demonstrating fluctuations in the performances of both GPT-4o and Gemma3-driven DisasTeller during the intermediate process tasks. The discrepancies between local disaster grading and map annotation in both results of GPT-4o and Gemma3-driven DisasTeller exhibit the agent’s weakness in integrating disaster-image-based and map-based data. Improving the LVLM agent’s ability to process information from different sources could help address this gap. The details of the evaluation are introduced in the Supplementary Information.

The output report quality of DisasTeller is crucial for post-disaster management because it would be a foundation for rapid and accurate decision-making. As presented in Fig. 5, the human evaluator scores exhibit more variability than those provided by the automatic (EvaluatorGPT) and human scores for GPT-4o-based DisasTeller, respectively; light and dark green bars represent the corresponding mean scores for Gemma3-based DisasTeller. Error bars indicate standard deviations across the five runs. The overall scores of GPT-4o-driven results are higher than those of Gemma3-driven results, which would be attributed to the different model scales (Gemma3-27B has 27 billion parameters while GPT-4o has around 200 billion parameters). Reports like the future reconstruction plan, human allocation report and expert team report, which involve more technical, structured planning (budgeting, recovery and restoration tasks), were rated higher by EvaluatorGPT, likely because the GPT model favours well-defined, structured tasks. On the other hand, reports that require more subjective judgment or social considerations, such as the public notice and alert team report, may have been better appreciated by human evaluators. Although DisasTeller’s output reports achieve the matching score from the LVLM and human evaluators, further performance improvement is necessary for its integration into real-scenario disaster management workflow. The details for the human and GPT evaluation of six reports are introduced in the Supplementary Information.

Methods
Large vision language models (LVLMs) with augmented generation
The rapid rise of LVLMs in recent years has created unprecedented opportunities across various domains. Based on pretrained knowledge from the large amount of data, one of the key factors behind the growing capabilities of LVLMs is their ability to perform zero-shot reasoning (solving tasks based on pretrained knowledge without specific re-training on those tasks). This ability is significantly amplified when LVLMs are used within advanced frameworks exhibiting the power of chain-of-thought reasoning. LVLMs generate step-by-step logical explanations that can interact with external systems and databases through tool usage, rendering LVLMs’ ability to be intelligent agents. However, the zero-shot reasoning of LVLMs has restrictions and low accuracy in domain-specific tasks. To mitigate this drawback, we supplement with domain-specific prompts and external technical guidelines (e.g., EMS98) to enhance the performance of our framework in this disaster domain. This is identified as in-text learning and retrieved augmented generation, where external knowledge is injected into prompts to enhance model reliability without retraining. For practical deployment, further results could be achieved with specialised training. Additionally, to make LVLMs accessible to developers and businesses, Application Programming Interfaces (APIs) are commonly employed. In this study, we streamlined our framework by utilising the API of the most representative LVLM model GPT (version ‘gpt-4o-2024-08-06’), and we also conducted additional validation through the local deployment and integration of Google’s open-source LVLM model Gemma3-27B. The modularity of our framework allows for future integration of fine-tuned LVLMs for improved specialisation and robustness.

Custom multi-LVLM agent framework design
While foundational tools like LangChain and CrewAI provide essential infrastructure for chaining prompts and coordinating multi-agent systems, our work contributes a domain-specific, multi-role LVLM agent framework specifically tailored to automate tasks in disaster response. We designed a system where each LVLM-based agent is assigned a clearly defined functional role, such as damage grading, emergency alert generation and human resource planning, based on the operational needs of real-world disaster workflows. Building upon CrewAI, we customised the role-specific memory management to ensure each agent retains its context across interactions, developed the inter-agent communication protocols to enable agents to request and refine each other’s output, and designed the dynamic task allocation allowing the system to reassign subtasks based on runtime performance or conflict resolution.

Unlike prior generic implementations of CrewAI or LangChain, our framework demonstrates a vertical integration of multi-agent reasoning, domain grounding and multi-tool interaction designed explicitly for structured coordination in disaster scenarios.

Tools development
To maximise the capabilities of each LVLM agent, we designed a set of tools, mapped to agents’ responsibilities and modularly integrated into the pipeline of our framework. It is necessary to highlight that additional tools can be easily integrated into this toolset according to requirements due to the modularity of our framework. A key innovation in our design is the use of adaptive tool invocation, where each agent determines, based on task complexity and confidence thresholds, whether external tools are needed to enhance output quality. This conditional reasoning enables greater efficiency, avoids over-reliance on tools and mimics how human teams selectively consult external resources.

Image Interpretation Tool. To simplify the multi-agent framework of DisasTeller, we integrate two LVLMs into the local and global image interpretation tools instead of defining extra agents as the image interpreters. When these two image interpretation tools are invoked, they will start to read the content of local and global images and give responses for detailed description of the image contents. This toolset helps different team agents check the local and global image information freely.

Web Search Tool. This tool is designed to empower the LVLM with the capability to access information from online sources, utilising LangChain, DuckDuckGo Search and BeautifulSoup APIs. For a given invocation, this tool retrieves the top two search results using the DuckDuckGo search engine and returns them as a list. After accessing the webpage links in the list, the BeautifulSoup library is employed to parse the website content. The text from the webpage is extracted and truncated to avoid excessive token usage. This helps limit the amount of content processed, ensuring the relevant parts of the webpage are provided without overwhelming the system. Incorporating this tool allows DisasTeller to effectively broaden its knowledge repository through the up-to-date information online.

File Read Tool. The file read tool focuses on extracting relevant data from various file formats, such as PDFs, text files and DOCX documents, to assist LVLM agents in analysing and processing information. When this tool is invoked, agents will leverage the file read tool API in CrewAI. The documents are first pre-processed into vector embeddings, which are mathematical representations of the text. Once relevant documents or information have been retrieved, the tool generates a summary relevant to the query. This summary is then integrated into the prompt to activate the LVLM’s potential for a more informed response. Instead of relying solely on the LVLM’s internal knowledge, this step augments the LVLM’s ability with specific information retrieved from the external documents, ensuring that DisasTeller’s response is grounded in actual data, thus mitigating the hallucination to a certain degree.

Map Annotation Tool. This tool aims to reflect the local on-site disaster grades in the global map, thus creating an alert map to illustrate the overall conditions in this disaster area. We customised this tool by integrating an LVLM to analyse the global map image and the input instruction from other agents. When this tool is invoked, it first retrieves the prompt from other agents with the local area names and their corresponding disaster grades and then identifies the location name in the global map image. Employing the reasoning ability of the LVLM, this tool outputs the coordinates of all relevant locations and calls Python image processing libraries to generate the news alert map.

Agents definition for post-disaster management
In our framework, each LVLM-based agent was defined through a combination of three core components: a role specification, a model assignment and a set of task-relevant tools. This modular structure enables each agent to behave intelligently and autonomously across different stages of post-disaster response. To simulate the collaborative workflow of real-world disaster response teams, we defined four distinct agents, including the expert team, alerts team, emergency team and assignment team, each representing a key functional unit in disaster operations. These agents were instantiated with independent LVLM instances and customised prompts to reflect their unique responsibilities. Table 1 presents the LVLM agents we defined with a certain prompt input in our experiments. Agents interact through the CrewAI coordination layer, which facilitates inter-agent communication, memory exchange and dynamic task sequencing. Through this agent-oriented architecture, we modelled a multi-LVLM-powered disaster response framework that closely mirrors the structure and decision flow of actual post-disaster management teams.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study proposes a multi–Large Vision Language Models (LVLM) framework that conducts post-disaster evaluation, alerts danger areas, provides immediate relief to affected populations, and determines where and how resources should be assigned. It ultimately generates post-disaster reports automatically (i.e., disaster summary, news, resource allocation, reconstruction plans, etc.). The main goal of this study is to facilitate the coordination of multiple stakeholders (or teams) and expedite post-disaster responses by employing agentic LVLMs. The concept of using generative artificial intelligence (AI) in such a critical task is intriguing and can potentially streamline the process of post-disaster response and management. However, this study remains in the conceptual stage and does not sufficiently address the potential limitations of the proposed framework, such as improving existing tools or contributing to the process (e.g., how to systematically and effectively incorporate human judgment into the proposed multi-LVLM framework). Instead, it primarily relies on existing GPT-4 agents and conducts a basic analysis. In other words, the study lacks significant technical contributions and leans more toward being an applied study rather than original research. Moreover, a key concern is the limited evaluation, validation, and demonstration of both the individual tasks and the overall framework. Phrases like “impressive performance” and “offers significant potential” were used to describe the results but are not sufficiently validated.\nDetailed comments are provided below.\n1. Some of the claims made about LVLM capabilities and potential are not well explained or validated throughout the manuscript. For example, this manuscript mentions that the framework is capable of “optimizing resource distribution”. It is implied that the “assignment team” agent can analyze the reports generated by the other agents in the framework and allocate human resources accordingly. However, this functionality was not evaluated in the case study presented in the manuscript. The included report sample shows specific numbers like 5 doctors and 15 nurses. Are these numbers really optimum? How can they be validated? Moreover, in Lines 126-128, “the output of the post-disaster damage grading in Fig. 3b matches the local-view images well, demonstrating the successful collaborations of LVLM agents to analyze different disaster information.” How can it be demonstrated?\n2. Some claims about generative AI’s capabilities in general seem bold and exaggerated. For example, their ability to “understand and generate language” (line 78), “provide consistent analysis” (line 88), and “rendering it possible to assist or even replace humans to improve efficiency” (line 70).\n3. It is not fully clear whether this framework is intended to “support human expert teams” or be used “instead of human teams”.\n4. Discussion on recent and relevant literature in both LVLMs and post-disaster response does not have sufficient depth. The references cited seem to be used to justify a couple of sentences, without an actual discussion of their findings or implications. Some of the references cited in the “Results and discussion” section could be used and further discussed in the “Introduction” section (i.e., references [15-21]).\n5. Although this manuscript states that LVLM can “perform zero-shot reasoning (solving tasks without prior training on those tasks)”, this reviewer has doubts about that and wonders whether a separate training process is required. For example, tasks such as interpreting local images, assessing disaster-affected regions, and analyzing the outputs of previous agents might necessitate additional training depending on their locations and hazard types..?\n6. Along similar lines as Comment 5, it seems like the GPT-4o-based agents were not specifically trained for post-disaster tasks yet are instructed to act as post-disaster experts. This approach may raise concerns especially when used in critical tasks like post-disaster response. How can these concerns be addressed?\n7. In the statement “avoiding the different levels of experience and subjective human judgment” (line 89), it is not explained why removing human experience from post-disaster response is an advantageous point.\n8. In real-world applications, how will the local-view images be captured? Will this involve the use of LiDAR, cameras, or other imaging technologies? What specific methods or workflows will be employed to collect and process these images effectively? It seems that an on-site inspection team would likely need to be deployed to capture these images that require subsequent preprocessing before being analyzed by the LVLM agent. Then, how will the logistics of image collection be managed in potentially hazardous or inaccessible areas? What are the expected processing times for these images? The data collection and preprocessing methods are unclear. Addressing these practical considerations is essential for the feasibility and effectiveness of the proposed framework.\n9. Following up on the comment above, accurately assessing damage may require a more thorough inspection than analyzing a single image taken from one angle. At the very least, multiple images captured from various perspectives would likely be necessary to obtain a better understanding of the situation. This approach, however, could be quite time-consuming. Thus, it raises the question: is the proposed framework more efficient and time-effective than deploying an on-site team to visually evaluate the damage and promptly report their findings to the alert team?\n10. Simplifying access to post-disaster management processes and knowledge for non-experts is mentioned multiple times in the manuscript as an advantage for this framework over traditional disaster response procedures, but this statement was not clarified well. The presented framework seems more like a backend component for disaster response teams than a tool that non-experts can use. How non-experts can interact with the framework, and how their involvement can be beneficial were not explained.\n11. Some agents rely on searching the web for information to support their tasks. However, the manuscript lacks a detailed discussion of the data collected and its reliability for different tasks. For example, the input for the framework is labeled images of earthquake-damaged structures, yet the “alerts team report” (Figure 3.c) warns from fire-damaged areas. It was not clear where this information was obtained from and what precautions were made to avoid the agents collecting misinformation from the internet. The data provided with the manuscript (line 383) consists of several Twitter data sets (some are not relevant to this study), which were also not discussed in the text.\n12. Another example is the allocation of human resources, where the agents seem to provide specific numbers (as mentioned in Comment 1). The accuracy of this information was not evaluated.\n13. Despite discussing how “the incorrect output of an individual agent would spread wrong information within the whole workflow” (line 198), the study lacks a detailed evaluation of the full framework to validate the claims about its effectiveness and does not address potential errors and uncertainty propagation throughout the framework. The only evaluation presented was for disaster grading, map annotation, and output reports.\n14. A score out of 10 was used for evaluating each of the 3 tasks. This evaluation metric can be misleading as it does not adequately reflect the severity of the error. For example, Figure 2 in the supplementary document shows a notable spatial variation in the results that is not reflected in the score number. It would have been more helpful to elaborate on these results in more detail in the main document.\n15. When evaluating output reports, a similar score out of 10 was given to each report. It is stated that the evaluation score is based on “the coherence and consistency of sentences and the accuracy of claims” (line 178). However, this score is not well defined and open to different interpretations. How much weight is given to each criterion? The basis for accuracy evaluation was not discussed well in the manuscript. What makes a report accurate or not? What were the criteria for selecting the human evaluators?\n16. Figure 3 in the supplementary document shows that the evaluator GPT model, which was prompted to “act as a post-disaster management expert”, provides generic and non-specific feedback. This arguably hurts the value of its evaluation.\n17. On the other hand, Figure 5 in the supplementary documents shows some concerning errors that the human evaluators addressed (i.e., suggesting shelter locations near severely damaged structures). This may imply weaknesses in the agent’s spatial and contextual awareness.\n18. It is not clear whether a 1:1 comparison can be performed between the GPT evaluations in Figure 3 and human evaluations in Figure 5 (i.e., are they evaluating the exact same reports or different runs?).\n19. A statement like “the reliance on LVLM-generated recommendations, if unchecked, can introduce flawed decision-making, potentially exacerbating the situation on the ground” (line 236) seems to defeat the main purpose and arguments of the study. Contradictions like these make the study appear confused about its goal.\n20. Section 2.3 outlines the limitations of LVLMs, including LVLM hallucination, difficulty in handling novel disaster events, and data security concerns. However, these limitations are not specifically identified based on the study results. Instead, they are drawn from broader, general perceptions about LVLMs rather than insights gained through the study’s own findings and analysis.\n21. The conclusion can be somewhat misleading, as the study did not adequately assess the main claims and advantages of using LVLMs over traditional post-disaster human teams. Claims about efficiency focus on how fast 4 GPT agents can coordinate and generate output reports but do not address issues with the accuracy of the outputs and the severity of the consequences of these errors.\n22. Section 4, Methodology, provides a description of existing tools rather than presenting any original contributions developed by the authors.\n23. If the proposed model is practically implemented, how would it adapt to the rapid advancements in AI tools? Given the rapid pace of AI development, what strategies or mechanisms would be in place to ensure the model remains up-to-date and effectively leverages new capabilities?\n24. If something (the proposed framework results) goes wrong and leads to significant human or economic losses, who would be held accountable? Would the responsibility lie with the organizations implementing the system?", "reviewer_2": "This paper presents an interesting work, named DisasTeller, which is a large vision language model (LVLM)-based framework for automating post-disaster management tasks including on-site assessment, emergency alerts, resource allocation, and recovery planning. Assessment on a case study of earthquake disaster showed that DisasTeller can reduce the response time compared to the current human-based procedure.\nThe paper can be further improved by addressing the following issues:\n1. Disaster response/rescue is a critical mission. Neural-network-based AI mainly lacks good interpretability/explanability, and therefore it is not easy for decision-makers to acceptable the results produced by blackbox-style AI. How does the proposed method tackle this issue?\n2. The assessment on a relatively simple case study is not so sufficient to support the declaration. It is expected to test DisasTeller on more disasters of various types, scales, and requirements.\n3. As in many disasters, available rescue forces and relief supplies are insufficient in short response time, which is the most important challenge in relief planning and scheduling. It is unclear whether DisasTeller can or how DisasTeller tackle this challenge.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Reply: The authors would like to thank the reviewers and editor for their time and comments; they have helped significantly improve the work and provided the opportunity to clarify relevant technical details and extend the analysis. All points raised were thoroughly addressed in the revision. This rebuttal provides itemised responses to each comment alongside the changes made to the paper. For further clarification, the changes are highlighted in the revised paper.\nEDITOR COMMENTS\nBeyond addressing all the reviewers’ concerns, we must also point out the lack of comparisons of your scheme when using other (open source) models different from GPT. We do understand that the aim of this work is to provide with a general, proof-of-concept study showing how to use, in principle, any LLM agent to the proposed application, and in that regard the use of GPT is in principle as good as any other. However, due to the recent reports on the time-dependent performance of OpenAI's GPT models (see [a] and related works), we are afraid that the results of your work might not be reproducible over time. Therefore, we believe that additional application of your method with another open source LLM model (such as LLaMa3) which includes clear version control is critical to ensure reproducibility and allow better comparisons and building-upon in the future. Without this, we suspect your work would be more appropriate for another, more topical journal, where its relative merits would be better appreciated within appropriate context. [a] https://arxiv.org/abs/2307.09009.\nReply: We thank the Editor for highlighting the need for reproducibility and model comparisons. In the revised manuscript, we have extended our experimental setup by applying our framework with Google’s lightweight open-source LVLM model Gemma3-27B [23] which has 27 billion parameters (compared with the around 200 billion parameters of GPT-4o [13]), thus facilitating the reproducibility and better building-upon for future research. Comparative results (including intermediate process tasks and output reports) are now reported alongside those from GPT‑4o, demonstrating that the proposed approach generalises across different models. These additions directly address the reproducibility and robustness of our method (see revised Section 2.2 and Fig. 4-5 in the main text, and Fig.1-3, Fig. 6-7, Fig. 9-10 in Appendix).\n“2.2 Agent output results evaluation\nIn recent years, integrating LVLMs into machine learning frameworks has accelerated the discoveries in different scientific areas [14,19]. Yet, LVLMs can generate incorrect or deceptive contents and there is lack of standardised evaluation metrics, posing a great challenge to assess the output quality of LVLM-based frameworks. In addition, since there is the raising concern about the time-dependent performance of close-source LVLMs like OpenAI’s GPT models [22], we additionally test our framework with Google’s lightweight open-source LVLM model Gemma3-27B [23] which has 27 billion parameters (compared with the around 200 billion parameters of GPT-4o [13]), thus facilitating the reproducibility and better building-upon for future research. Considering the previous studies [14,19], we conducted five independent runs respectively for both GPT-4o and Gemma3-driven DisasTeller and implemented the comprehensive assessment with a combination of the LVLM (GPT-4o) and human inspection.\nThe intermediate process tasks of GPT-4o and Gemma3-driven DisasTeller are evaluated manually due to their simplicity, and the six output reports of GPT-4o and Gemma3-driven DisasTeller are assessed by both LVLM (GPT-4o) and human inspection, considering their complexity. GPT-4o is prompted to act as a post disaster management expert (which is called EvaluatorGPT [18]), provided with post-disaster on-site images, documents retrieved from the internet, and the evaluation rubric to enhance the accuracy of its assessments. The prompt for EvaluatorGPT is modified to grade the output reports of DisasTeller based on the rubric, which focuses on coherence and consistency of sentences and the accuracy of claims. Each evaluation of EvaluatorGPT requires a detailed explanation of the report weakness to support the corresponding grade. In addition, we manually inspect DisasTeller’s outputs and utilise same rules to provide grades as a comparison of EvaluatorGPT for a more thorough evaluation. The full evaluation procedure for on-site disaster grading, global map annotations of disaster districts and six output reports are included in Supplementary Information.\n…\n2.2.2 Intermediate process tasks evaluation\nThe assessment of intermediate process tasks is crucial for each running of GPT-4o and Gemma3-driven DisasTeller since the incorrect output of an individual agent would spread wrong information within the whole workflow and result in misleading final reports for all the stakeholders in the disaster event. This section evaluates two main intermediate tasks, including local disaster grading and alert map generation. Fig. 4 summarises the evaluation results across five rounds for both local disaster grading and alert map annotation tasks. The disaster grading error is defined through accumulating the deviation of DisasTeller’s damage grading output compared to our manual assessment, and the map annotation distance error is defined through comparing the spatial locations of DisasTeller’s autonomous annotations with our manual annotations. The average scores achieved by the GPT-4o-driven framework for local disaster grading (7.3/10) and map annotations (6.0/10) are higher than those (4.6/10 for disaster grading and 5.0/10 for map annotations) achieved by the Gemma3-driven framework, which only have 63% and 83% accuracy of the GPT-4o-driven framework in these two tasks. This drop in accuracy would be owing to the parameter discrepancy between Gemma3 (27 billion) and GPT-4o (around 200 billion), which would cause the drop in generalisation ability of Gemma3 compared to GPT-4o. Each bar in Fig. 4 represents the score for one of the five rounds, demonstrating fluctuations in performances of both GPT-4o and Gemma3-driven DisasTeller during the intermediate process tasks. The discrepancies between local disaster grading and map annotation in both results of GPT-4o and Gemma3-driven DisasTeller exhibit the agent's weakness in integrating disaster-image-based and map-based data. Improving the LVLM agent's ability to process information from different sources could help address this gap.\nThe details of the evaluation are introduced in the Supplementary Information.\n10.0\n9.0 Average score (grading error):\n7.3 (+G3.6)\n8.0\n7.0 Average score (gradingerror):\n4.6 (+G7.8)\n6.0\n5.0\n4.0\n3.0\n2.0\n1.0\n0.0\nGPT-4o: Local disaster grading Gemma3: Local disaster grading\n10.0\nAverage score (distance error):\n9.0 6.0 (243 pixels)\n8.0\nAverage score (distance error):\n5.0 (360 pixels)\n7.0\n6.0\n5.0\n4.0\n3.0\n2.0\n1.0\n0.0\nGPT-4o: Map annotation Gemma3: Map annotation\nround1 round2 round3 round4 round5\nFig. 4 The evaluation of intermediate process tasks in GPT-4o and Gemma3-driven DisasTeller.\n2.2.3 Output reports evaluation\nThe output report quality of DisasTeller is crucial for post-disaster management because it would be a foundation for rapid and accurate decision-making. As presented in Fig. 5, the HumanEvaluator scores (GPT-4o Human and Gemma3 Human) show more variability than the scores provided by the automatic evaluation of EvaluatorGPT (GPT-4o Auto and Gemma3 Auto), reflecting the varying subject assessments for different persons. The overall scores of GPT-4o-driven results are higher than those of Gemma3-driven results, which would be attributed to the different model scales mentioned in section 2.2.2. Reports like the future reconstruction plan, human allocation report and expert team report, which involve more technical, structured planning (budgeting, recovery, and restoration tasks), were rated higher by EvaluatorGPT, likely because the GPT model favours well-defined, structured tasks. On the other hand, reports that require more subjective judgment or social considerations, such as the public notice and alert team report, may have been better appreciated by human evaluators. Although DisasTeller’s output reports achieve the matching score from the LVLM and human evaluators, further performance improvement is necessary for its integration into real-scenario disaster management workflow. The details for the human and GPT evaluation of six reports are introduced in the Supplementary Information.\nFig. 5 The evaluation of output reports from GPT-4o and Gemma3-driven DisasTeller: the error bars represent the standard deviation, points in different colours mean the evaluation scores.\nAppendix A: Tasks and evaluation\na b\nLocal post-disaster on-site images GPT-4o and Gemma3 driven DisasTelleroutput evaluation\nLocation GPT-4o Gemma3\nWajima Drama Hama Street Wajima Drama Memorial Hall G1 G3-G4 (Wrong)\nMemorial Hall Hama Street G4 G4-G5\nConcrete Bridge G5 G3-G4 (Wrong)\nRound 1 Central NishikigawaStreet G3 (Wrong) G3-G4 (Wrong)\nNorth AsaichiStreet G4 G4-G5\nSouth Central AsaichiStreet G5 G4-G5\nScore (total grading error) 5/6(+G2) 3/6(+G7)\nWajima Drama Memorial Hall G3 (Wrong) G4 (Wrong)\nHama Street G4 G3 (Wrong)\nConcrete Bridge G4-G5 G5\nRound 2 Central NishikigawaStreet G5 (Wrong) G5 (Wrong)\nNorth AsaichiStreet G4 G4\nConcrete Bridge Central Nishikigawa Street South Central AsaichiStreet G5 G4\nScore (total grading error) 4/6(+G6) 3/6(+G8)\nWajima Drama Memorial Hall G3 (Wrong) G3 (Wrong)\nHama Street G4 G5\nConcrete Bridge G5 G5\nRound 3 Central NishikigawaStreet G3 (Wrong) G4 (Wrong)\nNorth AsaichiStreet G4 G5 (Wrong)\nSouth Central AsaichiStreet G5 G4\nScore (total grading error) 4/6(+G4) 3/6(+G7)\nWajima Drama Memorial Hall G3 (Wrong) G3 (Wrong)\nHama Street G5 G5\nConcrete Bridge G5 G5\nSouth Central\nAsaichi Street Round 4 Central NishikigawaStreet G3 (Wrong) G5 (Wrong)\nNorth AsaichiStreet G4 G5 (Wrong)\nSouth Central AsaichiStreet G5 G5\nScore (total grading error) 4/6(+G4) 3/6(+G8)\nWajima Drama Memorial Hall D1 G3 (Wrong)\nHama Street D4 G3 (Wrong)\nConcrete Bridge D4-D5 G5\nRound 5 Central NishikigawaStreet D3 (Wrong) G5 (Wrong)\nNorth AsaichiStreet D4 G5 (Wrong)\nNorth Asaichi Street South Central AsaichiStreet D5 G5\nScore (total grading error) 5/6(+G2) 2/6(+G9)\nFig. 1 Evaluation of local post-disaster on-site images grading: texts in orange represent the wrong outputs of DisasTeller; texts in blue are the scores of accuracy percentages and total grading error.\n×\n×\n×\n× × ×\n×\nMap Data: Google, ©2024 Map Data: Google, ©2024 Map Data: Google, ©2024\nRound 1:3/6; Ave error: 276 pixels Round 2:3/6; Ave error: 282 pixels Round 3:5/6; Ave error: 191 pixels\n×\n×\n×\n×\n×\nMap Data: Google, ©2024 Map Data: Google, ©2024 Map Data: Google, ©2024\nRound 4:3/6; Ave error: 269 pixels Round 5:4/6; Ave error: 199 pixels Ground truth\nFig. 2 Evaluation of global map annotations: GPT-4o driven DisasTeller’s autonomous annotations are in yellow, and annotations with distance error larger than 250 pixels are identified as wrong and marked as red cross.\n×\n× ×\n× ×\n× ×\nMap Data: Google, ©2024 Map Data: Google, ©2024 Map Data: Google, ©2024\nRound 1:3/6; Ave error: 450 pixels Round 2:3/6; Ave error: 376 pixels Round 3:4/6; Ave error: 381 pixels\n×\n× ×\n×\n×\nMap Data: Google, ©2024 Map Data: Google, ©2024 Map Data: Google, ©2024\nRound 4:2/6; Ave error: 313 pixels Round 5:3/6; Ave error: 280 pixels Ground truth\nFig. 3 Evaluation of global map annotations: Gemma3 driven DisasTeller’s autonomous annotations are in yellow, and annotations with distance error larger than 250 pixels are identified as wrong and marked as red cross.\nEvaluatorGPT Round 1 Round 2 Round 3 Round 4 Round 5 Average\nExpert Team Report 7.0/10 8.0/10 7.0/10 8.0/10 8.0/10 7.6/10\nAlert Team Report 7.0/10 7.0/10 6.0/10 7.0/10 7.0/10 6.8/10\nEmergency Team Report 8.0/10 8.0/10 7.0/10 7.0/10 7.0/10 7.4/10\nHuman Allocation Report 9.0/10 9.0/10 7.0/10 8.0/10 7.0/10 8.0/10\nPublic Notice Report 6.0/10 7.0/10 6.0/10 7.0/10 7.0/10 6.6/10\nFuture Reconstruction Plan 9.0/10 9.0/10 8.0/10 8.0/10 8.0/10 8.4/10\nFig. 6 EvaluatorGPT assessment summary for five GPT-4o-driven independent-run output reports\nEvaluatorGPT Round 1 Round 2 Round 3 Round 4 Round 5 Average\nExpert Team Report 7.0/10 6.0/10 7.0/10 6.0/10 6.0/10 6.4/10\nAlert Team Report 6.0/10 5.0/10 5.0/10 4.0/10 4.0/10 4.8/10\nEmergency Team Report 7.0/10 6.0/10 7.0/10 7.0/10 7.0/10 6.8/10\nHuman Allocation Report 8.0/10 7.0/10 8.0/10 8.0/10 8.0/10 7.8/10\nPublic Notice Report 5.0/10 5.0/10 5.0/10 5.0/10 6.0/10 5.2/10\nFuture Reconstruction Plan 8.0/10 8.0/10 8.0/10 7.0/10 7.0/10 7.6/10\nFig. 7 EvaluatorGPT assessment summary for five Gemma3-driven independent-run output reports\nHumanEvaluator Round 1 Round 2 Round 3 Round 4 Round 5 Average\nExpert Team Report 8.8/10 6.5/10 7.1/10 6.5/10 8.2/10 7.4/10\nAlert Team Report 7.4/10 5.0/10 7.9/10 7.3/10 8.3/10 7.2/10\nEmergency Team Report 6.8/10 7.3/10 8.8/10 8.2/10 7.4/10 7.7/10\nHuman Allocation Report 7.6/10 6.7/10 6.5/10 6.7/10 7.0/10 6.9/10\nPublic Notice Report 6.7/10 6.1/10 8.7/10 7.8/10 7.7/10 7.4/10\nFuture Reconstruction Plan 7.4/10 6.6/10 7.9/10 6.7/10 8.4/10 7.4/10\nFig. 9 Human assessment summary for five GPT-4o-driven independent-run output reports\nHumanEvaluator Round 1 Round 2 Round 3 Round 4 Round 5 Average\nExpert Team Report 6.1/10 5.5/10 5.7/10 5.6/10 4.6/10 5.5/10\nAlert Team Report 5.2/10 4.3/10 4.8/10 4.5/10 5.1/10 4.8/10\nEmergency Team Report 7.4/10 6.7/10 6.5/10 6.2/10 6.3/10 6.4/10\nHuman Allocation Report 7.2/10 5.6/10 6.6/10 7.1/10 6.7/10 6.6/10\nPublic Notice Report 5.5/10 4.5/10 4.2/10 5.2/10 4.7/10 4.8/10\nFuture Reconstruction Plan 7.7/10 6.8/10 6.7/10 7.1/10 6.6/10 7.0/10\nFig. 10 Human assessment summary for five Gemma3-driven independent-run output reports\n”\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis study proposes a multi–Large Vision Language Models (LVLM) framework that conducts\npost-disaster evaluation, alerts danger areas, provides immediate relief to affected populations,\nand determines where and how resources should be assigned. It ultimately generates post-\ndisaster reports automatically (i.e., disaster summary, news, resource allocation, reconstruction\nplans, etc.). The main goal of this study is to facilitate the coordination of multiple stakeholders\n(or teams) and expedite post-disaster responses by employing agentic LVLMs. The concept of\nusing generative artificial intelligence (AI) in such a critical task is intriguing and can\npotentially streamline the process of post-disaster response and management. However, this\nstudy remains in the conceptual stage and does not sufficiently address the potential limitations\nof the proposed framework, such as improving existing tools or contributing to the process\n(e.g., how to systematically and effectively incorporate human judgment into the proposed\nmulti-LVLM framework). Instead, it primarily relies on existing GPT-4 agents and conducts a\nbasic analysis. In other words, the study lacks significant technical contributions and leans\nmore toward being an applied study rather than original research. Moreover, a key concern is\nthe limited evaluation, validation, and demonstration of both the individual tasks and the\noverall framework. Phrases like “impressive performance” and “offers significant potential”\nwere used to describe the results but are not sufficiently validated. Detailed comments are\nprovided below.\nWe sincerely thank the reviewer for their thoughtful and constructive feedback, which has\nhelped us critically reflect on our study's conceptual foundation and technical depth. In\nresponse, we have substantially revised the paper to clarify the contributions better and support\nthe claims made.\nFirstly, we acknowledge our study is now in the proof-of-concept stage since the practical\ndeployment requires collaboration with stakeholders and their private data. To address the\npotential limitations, we have expanded our evaluation, validation, and discussion in the\nrevised paper (e.g., integrating human judgment into the proposed framework is discussed in\nthe \"Limitations and deployment considerations\" part of the main text. The system exposes all\nintermediate reasoning results step-by-step, allowing humans to audit and intervene during\nexecution). Secondly, while we use GPT-4 as the engine for our framework, our contribution\nlies in the design and orchestration of a modular, role-specific multi-agent framework tailored\nfor disaster response. Our contribution includes novel agent definitions, domain-adapted\nprompts, novel tool designs, dynamic tool routing, and system-level coordination,\ndistinguishing our work from generic applications of GPT agents. In addition to using the GPT-\ndemonstrating its compatibility with both proprietary and open-source models. The modular\narchitecture we propose allows seamless replacement and extension of LVLMs as new models\nemerge. These design components reflect our focus on system-level contributions rather than\nmodel-level innovation. Thirdly, we expand the evaluation, validation and demonstration of\nboth the individual tasks and the overall framework, by introducing the disaster grade and map\nannotation distance errors for intermediate tasks, the detailed rubric for the output report\nevaluation, and conducting additional error propagation experiments. Finally, the claims like\n“impressive performance” and “offers significant potential” have been revised to reflect a more\nneutral and evidence-based tone. The detailed modifications according to each comment are\nprovided below.\n1. Some of the claims made about LVLM capabilities and potential are not well explained or\nvalidated throughout the manuscript. For example, this manuscript mentions that the\nframework is capable of “optimizing resource distribution”. It is implied that the “assignment\nteam” agent can analyze the reports generated by the other agents in the framework and allocate\nhuman resources accordingly. However, this functionality was not evaluated in the case study\npresented in the manuscript. The included report sample shows specific numbers like 5 doctors\nand 15 nurses. Are these numbers really optimum? How can they be validated? Moreover, in\nLines 126-128, “the output of the post-disaster damage grading in Fig. 3b matches the local-\nview images well, demonstrating the successful collaborations of LVLM agents to analyze\ndifferent disaster information.” How can it be demonstrated?\nReply: We thank the reviewer for pointing out the insufficiencies of our work that require\nimprovement. As a result, we revised the paper to adjust the claims made to the demonstration,\nas well as to better highlight the limitations of the current work. E.g., the reviewer is right in\nthe assessment made about the specific numbers, as these are very difficult to evaluate. In our\nstudy, we addressed this by comparing the agent recommendations with historical data\nretrieved online by agents. The revised text is as follows:\n“\nMain Text\n2.3 Limitations and deployment considerations\n…\nIn post-disaster settings, establishing an optimal ground truth is inherently difficult due to the\nuniqueness of each event and the absence of repeatable baseline data Evaluating variables\nsuch as the “optimum” number of doctors or shelters remains a challenge. In our case studies,\nwe addressed this by comparing outputs with publicly available historical records retrieved via\nweb tools. In future work, agents could interface with private databases to access more reliable\nbaselines for self-validation.\nAppendix A: Tasks and evaluation\nA.2 Output reports evaluation\nIn the output report, evaluating the decision variables (e.g., the number of shelters, hospitals,\nhuman allocations, and the reconstruction budget) is complex. Even in a real post-disaster\nintervention, human teams would typically not be able to optimise a solution since decisions\nhave to be made within a short span and there is not enough information from previous identical\ndisasters to make an informed evaluation and assessment. Natural disasters are rare and tend\nnot to repeat in the same conditions. That said, we addressed this by comparing the agent\nrecommendations with historical data obtained online (the literatures retrieved by the web\nsearch tool, see Fig. 4 in the evaluation, since there is lack of private disaster data, to confirm\nthat the resource allocation outputs obtained are within realistic boundaries. In future work,\nstakeholders can fine-tune LVLM with private data or let agents autonomously retrieve the\ninformation from their private database to achieve a more optimised performance in specific\nassignments.\n[2025-04-28 21:47:54][DEBUG]: == Working Agent: Emergency team\n[2025-04-28 21:47:54][INFO]: == Starting Task… use web_search_toolsearching:\n[{'title': 'Hospitals as disaster victims: Lessons not learned? -PMC', 'href': 'https://pmc.ncbi.nlm.nih.gov/articles/PMC8749465/',\n'body': 'Temporary shelter/triage area set up and transfer of patients to next door hospital with disaster hospital staff caringfor\nthem, runners used to communicate ... In a review of hospital evacuations after an earthquake, ... KanataniY. Disaster\nVulnerability of hospitals: a nationwide surveillance in Japan. Disaster Med Public Health Prep. 2015 ...'}, {'title': 'An Overview of\nthe Design of Disaster Relief Shelters', 'href': 'https://www.sciencedirect.com/science/article/pii/S2212567114010193', 'body':\n'Scoping Study to inform the Revision of \"Shelter after Disaster: Guidelines for Assistance . Wu, J. Y. & Lindell, M. K. 2004.\nHousing reconstruction after two major earthquakes: The 1994 Northridge earthquake in the United States and the 1999 Chi-Chi\nearthquake in Taiwan. Disasters, 28 , 63-81.’}]…\n[2025-04-28 21:49:54][DEBUG]: == Working Agent: Assignment team\n[2025-04-28 21:49:54][INFO]: == Starting Task… use web_search_toolsearching:\n[{'title': 'Historic Disasters -FEMA.gov', 'href': 'https://www.fema.gov/disaster/historic', 'body': 'The following disasters are\nconsidered historical because of how they impacted the way we handle similar disasters in the future. ... which caused a significant\nreevaluation of the execution of federal disaster response efforts and resource allocation. ... The Northridge Earthquake of 1994\ntested the new reforms that followed Hurricane Andrew ...'}, {'title': 'Optimized resource allocation for emergency response after\nearthquake ...', 'href': 'https://www.sciencedirect.com/science/article/pii/S0925753500000217', 'body': 'The main influencing factor\nfor optimizing resource allocation in disaster response is time. The reason for this is the time limit of the SAR period, conditional\non the goal to minimize the total number of fatalities. ... Coburn, A., Spence, R., 1992. Factors determining human casualty levels\nin earthquakes: mortality prediction in building ...’}]…\n[2025-04-28 21:51:25][DEBUG]: == Working Agent: Assignment team\n[2025-04-28 21:51:25][INFO]: == Starting Task… use web_search_toolsearching:\n[{'title': 'PDF Guidance Note Financing Post-Disaster Recovery and Reconstruction ...', 'href':\n'https://openknowledge.worldbank.org/bitstream/handle/10986/22418/Financing0post0rces000guidance0note.pdf?sequence=1',\n'body': '3 Clarify post-disaster financial responsibilities of the central and local governments 4 Mobilize resources ex-ante through\na DRM fund and financial protection strategy 5 Allocate resources based on damage and loss assessment methodology 6 Execute\nresources and coordinate efforts across the post-disaster recovery and reconstruction process'}, {'title': 'Unearthing the impact of\nearthquakes: A review of economic and social ...', 'href': 'https://onlinelibrary.wiley.com/doi/full/10.1002/pam.22642', 'body': \"For\ninstance, the March 2011 Great East Japan Earthquake led to substantial costs for Japan's government, amounting to 4% of Japan's\nsupplementary budgets financed through bonds, expenditure cuts, and budget surpluses.\"}]…\nFig. 4 A sample for the content retrieved from the internet by the web search tool\n”\nIn addition, we have revised the claim to “the output of the post-disaster damage grading in\nFig. 3b matches the local-view images well, confirming the successful collaborations of LVLM\nagents to analyse the severity of the disaster.” For the damage grading, we compared human-\nannotated grades and LVLM outputs, showing how the LVLM agents integrate image features\nwith technical guidelines (EMS98) to achieve a more accurate grading.\n2. Some claims about generative AI’s capabilities in general seem bold and exaggerated. For\nexample, their ability to “understand and generate language” (line 78), “provide consistent\nanalysis” (line 88), and “rendering it possible to assist or even replace humans to improve\nefficiency” (line 70).\nReply: We agree and have revised the paper to adjust the claims. In particular, the claims\npointed out by the reviewer have all been moderated regarding current acknowledged\nlimitations.\n“\n1. Introduction\n…\nAs artificial intelligence (AI) technology develops, it will provide the possibility to better\nemulate human intelligence, thus rendering it possible to assist humans to improve operational\nefficiency under the oversight of humans\n…\nWhile these models do not truly \"understand\" language in a human sense, their ability to model\nlinguistic and visual patterns has made them useful tools in domains requiring large-scale\ninformation processing and analysis\n…\nUnlike humans, LVLMs can operate continuously without fatigue and facilitate tasks across\nregions with varying levels of development. They are capable of supporting team-based\nworkflows through rapid interactions, efficiently referencing large volumes of historical data,\nand offering standardised outputs. Although they may lack true understanding or judgment like\nhumans, their standardised and algorithmic output could facilitate a faster, more uniform\nresponse during the initial disaster assessment phase, potentially reducing inconsistencies and\nbiases inherent in human judgment.\n”\n3. It is not fully clear whether this framework is intended to “support human expert teams” or\nbe used “instead of human teams”.\nReply: Continuing the discussion in Comment 2, our framework is intended as a decision\nsupport system for human expert teams. We have clarified in the Introduction and Conclusion\nthat DisasTeller can provide rapid preliminary assessments and report generation that can be\nvaluable to aid human experts in making informed decisions but cannot fully replace human\njudgment.\n“\n1. Introduction\n…\nAs artificial intelligence (AI) technology develops, it will provide the possibility to better\nemulate human intelligence, thus rendering it possible to assist humans to improve operational\nefficiency under the oversight of humans\n…\nWhile they lack true understanding or judgment, their outputs can be regarded as the\npreliminary reference for decision-making, helping reduce variability caused by differences in\nhuman experience and subjective interpretation\n…\n3. Conclusion\n…\nHowever, the efficiency gains must be interpreted alongside non-negligible limitations in\naccuracy. Our evaluation reveals that errors in early-stage agents, such as inaccurate disaster\ngrading, can propagate to downstream decision outputs. While report-level evaluations show\nalignment with human judgment, intermediate results, particularly in image-grounded damage\nassessment and map annotation, expose weaknesses in the current LVLM spatial and\ncontextual reasoning capacity. These findings reinforce that DisasTeller is intended as a\ncomplementary support tool, not a replacement for human disaster experts.\n”\n4. Discussion on recent and relevant literature in both LVLMs and post-disaster response does\nnot have sufficient depth. The references cited seem to be used to justify a couple of sentences,\nwithout an actual discussion of their findings or implications. Some of the references cited in\nthe “Results and discussion” section could be used and further discussed in the “Introduction”\nsection (i.e., references [15-21]).\nReply: We agree and have expanded the literature review. We now discuss key findings from\nrecent works ([15–21]) in the Introduction section, detailing their findings or implications:\n“\n1. Introduction\n…\nThe recent progress in LLMs has laid a strong foundation for the development of LVLMs.\nBharathi Mohan et al. [15] offer a comprehensive review of LLM architectures and their\nexpanding applications, illustrating the technological evolution toward more sophisticated\nmultimodal models. Foundational works by Brown et al. [16] and Radford et al. [17] have\ndemonstrated how few-shot learning and multitask generalisation in LLMs have shaped the\ndesign of current-generation LVLMs. Liu et al. [18] further show how GPT-based evaluation\nsystems enhance the alignment and reliability of generative models, while Yang et al. [19]\nhighlight the potential risks of misinformation, particularly in sensitive applications such as\nhealthcare, underscoring the need for caution when deploying these models in critical domains\nlike disaster response.\nIn the disaster management domain, integrating vision-language understanding is particularly\nvaluable. Nguyen et al. [20] show that social media imagery can be effectively leveraged for\nreal-time damage assessment during disasters, pointing to the potential of LVLMs, which can\njointly process visual and textual inputs to automate and enhance such tasks. Moreover,\nstandardised frameworks such as the European Macroseismic Scale (EMS-98) [21] provide\nstructured criteria for assessing seismic intensity, which can be used as the rubric to evaluate\nthe ability of LVLMs to interpret post-disaster images and reports. These developments support\nthe increasing relevance of LVLMs in disaster response workflows, as the disaster response\nfield advances toward more automated and accountable systems.\n”\n5. Although this manuscript states that LVLM can “perform zero-shot reasoning (solving tasks\nwithout prior training on those tasks)”, this reviewer has doubts about that and wonders whether\na separate training process is required. For example, tasks such as interpreting local images,\nassessing disaster-affected regions, and analyzing the outputs of previous agents might\nnecessitate additional training depending on their locations and hazard types.?\nReply: Thank you for pointing this out. Indeed, zero-shot reasoning is based on pretrained\nknowledge on large amount of data and has restrictions and low accuracy in domain specific\ntasks. This is why the framework is supplemented with domain-specific prompts and external\ntechnical guidelines (e.g., EMS98) to enhance the performance in this disaster domain. This is\nidentified as in-text learning and retrieved augmented generation. For deployment, further\nresults could be achieved with specialised training. We have revised the claim in the section\n4.1 of the main text:\n“\n4.1 Large Vision Language Models (LVLMs) with augmented generation\n…\nThe rapid rise of LVLMs in recent years has created unprecedented opportunities across\nvarious domains. Based on pretrained knowledge from the large amount of data, one of the key\nfactors behind the growing capabilities of LVLMs is their ability to perform zero-shot reasoning\n(solving tasks based on pretrained knowledge without specific re-training on those tasks)\n…\nHowever, the zero-shot reasoning of LVLMs has restrictions and low accuracy in domain\nspecific tasks. To mitigate this drawback, we supplement with domain-specific prompts and\nexternal technical guidelines (e.g., EMS98) to enhance the performance of our framework in\nthis disaster domain. This is identified as in-text learning [34] and retrieved augmented\ngeneration [35], where external knowledge is injected into prompts to enhance model\nreliability without retraining. For practical deployment, further results could be achieved with\nspecialised training.\n”\n6. Along similar lines as Comment 5, it seems like the GPT-4o-based agents were not\nspecifically trained for post-disaster tasks yet are instructed to act as post-disaster experts.\nThis approach may raise concerns especially when used in critical tasks like post-disaster\nresponse. How can these concerns be addressed?\nReply: We acknowledge this limitation and continue our answer to comment 5. Critical tasks\nlike post-disaster response cannot be completely handled by agents, which is why DisasTeller\nis meant to support – not replace – human decision-making. we have integrated calibrated\nprompts and external data sources and designed our code to output all the sequential\nintermediate results as the framework runs, which is available for manual check by humans,\nespecially in the critical tasks, thus helping improve accuracy and minimise the pointed-out\nrisks. A discussion was added on the “Limitations and deployment considerations” part.\n“\n2.3 Limitations and deployment considerations\n…\nOne prominent concern is the potential for spreading inaccurate or misleading information\n(LVLM hallucination) in high-stakes situations where flawed decisions may exacerbate\ndisaster impacts [19,25] … To mitigate hallucination risks, our framework is designed to\noutput step-by-step intermediate results (e.g., annotated maps, retrieved references, and\nreasoning steps) at each stage, enabling human experts to audit and intervene during execution\nrather than waiting for final outcomes.\n… to enhance the trustworthiness of DisasTeller for practical deployment, it is important to\nkeep human check in the running process. This human-in-the-loop format ensures that AI\noutputs serve as assistive recommendations rather than unquestioned directives, thereby\nreducing the risk of over-reliance on black-box results.\n”\n7. In the statement “avoiding the different levels of experience and subjective human judgment”\n(line 89), it is not explained why removing human experience from post-disaster response is an\nadvantageous point.\nReply: We agree, however our intended angle was to highlight that having a standardised,\nalgorithmic initial assessment could potentially reduce inconsistencies and biases inherent in\nhuman judgement. We did not mean to state that human experience should be removed from\npost-disaster response interventions. We thank the reviewer for pointing out this and have\nrevised the introduction to clarify it. A standardisation can facilitate a faster, more uniform\nresponse during the initial disaster assessment phase, while still allowing human experts to\napply their expertise in the decision-making.\n“\n1. Introduction\n…\nUnlike humans, LVLMs can operate continuously without fatigue and facilitate tasks across\nregions with varying levels of development. They are capable of supporting team-based\nworkflows through rapid interactions, efficiently referencing large volumes of historical data,\nand offering standardised outputs. Although they may lack true understanding or judgment like\nhumans, their standardised and algorithmic output could facilitate a faster, more uniform\nresponse during the initial disaster assessment phase, potentially reducing inconsistencies and\nbiases inherent in human judgment.\n”\n8. In real-world applications, how will the local-view images be captured? Will this involve the\nuse of LiDAR, cameras, or other imaging technologies? What specific methods or workflows\nwill be employed to collect and process these images effectively? It seems that an on-site\ninspection team would likely need to be deployed to capture these images that require\nsubsequent preprocessing before being analyzed by the LVLM agent. Then, how will the\nlogistics of image collection be managed in potentially hazardous or inaccessible areas? What\nare the expected processing times for these images? The data collection and preprocessing\nmethods are unclear. Addressing these practical considerations is essential for the feasibility\nand effectiveness of the proposed framework.\nReply: Thank you for pointing this out. The challenge of retrieving imagery or other data to\nsupport decision processes, particularly in regions that are inaccessible, is inherent to real-word\npost-disaster interventions. Therefore, with or without DisasTeller, the challenge remains, since\nthe framework acts as a tool to support decision using data available to standard operations.\nThe data collection methods available could include use of drones, satellite imagery and social\nmedia, which would be shared with the agents. We have revised the paper to clarify this\ndiscussion.\n“\n2.3 Limitations and deployment considerations\n…\nEffective deployment of DisasTeller requires timely access to local-view images or other\nsituational data. In real-world conditions, acquiring such data may involve using drones,\nsatellites, or crowdsourced images. However, logistical barriers in damaged or inaccessible\nareas remain a challenge. Moreover, any data like multi-angle images that can improve\nstandard human assessment would also improve the contribution of DisasTeller. Our\nframework assumes data collected by human teams for general assessment can be reused to\ntrigger automated reasoning, thus providing an initial assessment that can become the basis\nfor decision making, reducing delay and guiding more targeted on-site inspections.\n”\n9. Following up on the comment above, accurately assessing damage may require a more\nthorough inspection than analyzing a single image taken from one angle. At the very least,\nmultiple images captured from various perspectives would likely be necessary to obtain a better\nunderstanding of the situation. This approach, however, could be quite time-consuming. Thus,\nit raises the question: is the proposed framework more efficient and time-effective than\ndeploying an on-site team to visually evaluate the damage and promptly report their findings\nto the alert team?\nReply: Extending our answer to comment 8, any data that can improve standard human\nassessment would also improve DisasTeller contribution. Therefore, multi-angle imaging\nwould certainly be preferable for comprehensive damage assessment. However, the teams are\nnot meant to be acquiring images or data specifically for improving the assessment of\nDisasTeller, but rather supplying information readily available to them so that the framework\ncan provide an initial prompt/assessment that can become the basis for decision making. As\ndiscussed in our answer to previous comments, the paper has been revised to better clarify that\nDisasTeller is a tool to assist human teams. Rapid automated processing can significantly\nreduce the response time and guide more targeted on-site inspections.\n“\n2.3 Limitations and deployment considerations\nEffective deployment of DisasTeller requires timely access to local-view images or other\nsituational data. In real-world conditions, acquiring such data may involve using drones,\nsatellites, or crowdsourced images. However, logistical barriers in damaged or inaccessible\nareas remain a challenge. Moreover, any data like multi-angle images that can improve\nstandard human assessment would also improve the contribution of DisasTeller. Our\nframework assumes data collected by human teams for general assessment can be reused to\ntrigger automated reasoning, thus providing an initial assessment that can become the basis\nfor decision making, reducing delay and guiding more targeted on-site inspections.\n”\n10. Simplifying access to post-disaster management processes and knowledge for non-experts\nis mentioned multiple times in the manuscript as an advantage for this framework over\ntraditional disaster response procedures, but this statement was not clarified well. The\npresented framework seems more like a backend component for disaster response teams than\na tool that non-experts can use. How non-experts can interact with the framework, and how\ntheir involvement can be beneficial were not explained.\nReply: Thank you for this comment. We have added a statement on how the framework lowers\nthe expertise barrier and actively engages non-experts in the disaster response cycle in the last\nparagraph of section 1.\n“\n1. Introduction\n…\nDisasTeller could act as an aid for post-disaster response teams and simplify access to accurate\npost-disaster management knowledge for non-experts by providing a simple interface (e.g.,\nmobile apps or web dashboards). Non-experts can submit real-time reports and media\n(photos/videos), receive localised alerts and actionable guidance, track the progress of relief\nresource distribution in the interface system. The backend aggregates multi-source data from\nnon-experts could help the framework provide better disaster response suggestions,\naccelerating the disaster response process for both experts and non-experts.”\n11. Some agents rely on searching the web for information to support their tasks. However, the\nmanuscript lacks a detailed discussion of the data collected and its reliability for different tasks.\nFor example, the input for the framework is labeled images of earthquake-damaged structures,\nyet the “alerts team report” (Figure 3.c) warns from fire-damaged areas. It was not clear where\nthis information was obtained from and what precautions were made to avoid the agents\ncollecting misinformation from the internet. The data provided with the manuscript (line 383)\nconsists of several Twitter data sets (some are not relevant to this study), which were also not\ndiscussed in the text.\nReply: Thanks for this comment, the \"alerts team report\" (Figure 3.c) suggests a potential fire-\ndamaged areas by the reasoning of the alert team agent, which we explained in line 159.\nSpeculative statements could happen, and we also discuss it in the “Limitations and deployment\nconsiderations” part. Our framework will generate the real website link when running, thus\nallowing human to check the information retrieved from the internet. We also discuss more\ndetailed information for the data collection from the internet after line 159 and the Appendix.\n“\n2.3 Limitations and deployment considerations\nOne prominent concern is the potential for spreading inaccurate or misleading information\n(LVLM hallucination) in high-stakes situations where flawed decisions may exacerbate\ndisaster impacts [19,25] … our framework is designed to output step-by-step intermediate results\n(e.g., annotated maps, retrieved references, and reasoning steps) at each stage, enabling\nhuman experts to audit and intervene during execution rather than waiting for final outcomes.\n”\nLine 159: “\n1. Introduction\n…\nAfter inspecting the ‘expert team’ report and the alert map, the ‘alerts team’ in DisasTeller\nstarts reasoning and generating alert news, providing information and safety alerts of existing\ndangerous areas (e.g., ‘Fire-Damaged Areas: Particularly around Asaichi Street and near the\nWajima Morning Market, these zones have unstable structures and debris. Avoid these areas\ndue to potential fire and structural hazards’). Analysing the reports of previous agent teams\nand taking reference to the historical disaster data with the real website link (e.g., ‘An\nOverview of the Design of Disaster Relief Shelters’,\nhttps://www.sciencedirect.com/science/article/pii/S2212567114010193, …) from the internet\nthrough ‘web search tool’, ‘emergency team’ ensures the areas requiring immediate recovery\nand emergency services (e.g., ‘Concrete Bridge: Immediate assessment and restoration are\ncrucial to reestablish vital transport links’). Afterwards, the ‘assignment team’ integrates the\ninformation from previous three reports and disaster archives obtained online (e.g., ‘Historic\nDisasters - FEMA.gov’, https://www.fema.gov/disaster/historic, ...) via ‘web search tool’ to\ngenerate the human resource allocation report (e.g., ‘Wajima Drama Memorial Hall: This area\nwill host a large emergency shelter. It requires a team of 20 medical personnel, including 5\ndoctors and 15 nurses, supported by 10 logistics personnel’), public notice report (e.g., ‘We\nare coordinating with governmental agencies and non-governmental organisations to ensure a\ncomprehensive response’), and future reconstruction plan (e.g., ‘Based on previous case\nstudies, an estimated budget of approximately $1 billion is needed to cover structural repairs’).”\n“\nAppendix A: Tasks and evaluation\nA.2 Output reports evaluation\n[2025-04-28 21:47:54][DEBUG]: == Working Agent: Emergency team\n[2025-04-28 21:47:54][INFO]: == Starting Task… use web_search_toolsearching:\n[{'title': 'Hospitals as disaster victims: Lessons not learned? -PMC', 'href': 'https://pmc.ncbi.nlm.nih.gov/articles/PMC8749465/',\n'body': 'Temporary shelter/triage area set up and transfer of patients to next door hospital with disaster hospital staff caringfor\nthem, runners used to communicate ... In a review of hospital evacuations after an earthquake, ... KanataniY. Disaster\nVulnerability of hospitals: a nationwide surveillance in Japan. Disaster Med Public Health Prep. 2015 ...'}, {'title': 'An Overview of\nthe Design of Disaster Relief Shelters', 'href': 'https://www.sciencedirect.com/science/article/pii/S2212567114010193', 'body':\n'Scoping Study to inform the Revision of \"Shelter after Disaster: Guidelines for Assistance . Wu, J. Y. & Lindell, M. K. 2004.\nHousing reconstruction after two major earthquakes: The 1994 Northridge earthquake in the United States and the 1999 Chi-Chi\nearthquake in Taiwan. Disasters, 28 , 63-81.’}]…\n[2025-04-28 21:49:54][DEBUG]: == Working Agent: Assignment team\n[2025-04-28 21:49:54][INFO]: == Starting Task… use web_search_toolsearching:\n[{'title': 'Historic Disasters -FEMA.gov', 'href': 'https://www.fema.gov/disaster/historic', 'body': 'The following disasters are\nconsidered historical because of how they impacted the way we handle similar disasters in the future. ... which caused a significant\nreevaluation of the execution of federal disaster response efforts and resource allocation. ... The Northridge Earthquake of 1994\ntested the new reforms that followed Hurricane Andrew ...'}, {'title': 'Optimized resource allocation for emergency response after\nearthquake ...', 'href': 'https://www.sciencedirect.com/science/article/pii/S0925753500000217', 'body': 'The main influencing factor\nfor optimizing resource allocation in disaster response is time. The reason for this is the time limit of the SAR period, conditional\non the goal to minimize the total number of fatalities. ... Coburn, A., Spence, R., 1992. Factors determining human casualty levels\nin earthquakes: mortality prediction in building ...’}]…\n[2025-04-28 21:51:25][DEBUG]: == Working Agent: Assignment team\n[2025-04-28 21:51:25][INFO]: == Starting Task… use web_search_toolsearching:\n[{'title': 'PDF Guidance Note Financing Post-Disaster Recovery and Reconstruction ...', 'href':\n'https://openknowledge.worldbank.org/bitstream/handle/10986/22418/Financing0post0rces000guidance0note.pdf?sequence=1',\n'body': '3 Clarify post-disaster financial responsibilities of the central and local governments 4 Mobilize resources ex-ante through\na DRM fund and financial protection strategy 5 Allocate resources based on damage and loss assessment methodology 6 Execute\nresources and coordinate efforts across the post-disaster recovery and reconstruction process'}, {'title': 'Unearthing the impact of\nearthquakes: A review of economic and social ...', 'href': 'https://onlinelibrary.wiley.com/doi/full/10.1002/pam.22642', 'body': \"For\ninstance, the March 2011 Great East Japan Earthquake led to substantial costs for Japan's government, amounting to 4% of Japan's\nsupplementary budgets financed through bonds, expenditure cuts, and budget surpluses.\"}]…\nFig. 4 A sample for the content retrieved from the internet by the web search tool.\n”\nSince we are at the stage of proof of concept, in the future work, the filtering algorithms can\nbe integrated into the web search tool to further avoid the misinformation from the internet.\nAs for the reviewer’s comment “The data provided with the manuscript (line 383)”, the dataset we used was item 9\nin the link. We now modify the link, making it direct to the objective dataset.\n“Data availability\nThe dataset we used is available to the public according to the literature [20] and the link is\nhttps://crisisnlp.qcri.org/data/ASONAM17_damage_images/ASONAM17_Damage_Image_D\nataset.tar.gz.”\nDue to the lack of private data, we select part of the data from the link in line 383 and manually\ndefined these data for the case study as the proof of concept.\n12. Another example is the allocation of human resources, where the agents seem to provide\nspecific numbers (as mentioned in Comment 1). The accuracy of this information was not\nevaluated.\nReply: The same as the question 1. We addressed this in the revised paper by comparing the\nagent recommendations with historical data obtained online to confirm that the resource\nallocation outputs obtained are within realistic boundaries. We also highlighted it in the revised\nparagraph in the Appendix:\n“\nAppendix A: Tasks and evaluation\nA.2 Output reports evaluation\nIn the output report, evaluating the decision variables (e.g., the number of shelters, hospitals,\nhuman allocations, and the reconstruction budget) is complex. Even in a real post-disaster\nintervention, human teams would typically not be able to optimise a solution since decisions\nhave to be made within a short span and there is not enough information from previous identical\ndisasters to make an informed evaluation and assessment. Natural disasters are rare and tend\nnot to repeat in the same conditions. That said, we addressed this by comparing the agent\nrecommendations with historical data obtained online (the literatures retrieved by the web\nsearch tool, see Fig. 4) in the evaluation, since there is lack of private disaster data, to confirm\nthat the resource allocation outputs obtained are within realistic boundaries. In future work,\nstakeholders can fine-tune LVLM with private data or let agents autonomously retrieve the\ninformation from their private database to achieve a more optimised performance in specific\nassignments.\n[2025-04-28 21:47:54][DEBUG]: == Working Agent: Emergency team\n[2025-04-28 21:47:54][INFO]: == Starting Task… use web_search_toolsearching:\n[{'title': 'Hospitals as disaster victims: Lessons not learned? -PMC', 'href': 'https://pmc.ncbi.nlm.nih.gov/articles/PMC8749465/',\n'body': 'Temporary shelter/triage area set up and transfer of patients to next door hospital with disaster hospital staff caringfor\nthem, runners used to communicate ... In a review of hospital evacuations after an earthquake, ... KanataniY. Disaster\nVulnerability of hospitals: a nationwide surveillance in Japan. Disaster Med Public Health Prep. 2015 ...'}, {'title': 'An Overview of\nthe Design of Disaster Relief Shelters', 'href': 'https://www.sciencedirect.com/science/article/pii/S2212567114010193', 'body':\n'Scoping Study to inform the Revision of \"Shelter after Disaster: Guidelines for Assistance . Wu, J. Y. & Lindell, M. K. 2004.\nHousing reconstruction after two major earthquakes: The 1994 Northridge earthquake in the United States and the 1999 Chi-Chi\nearthquake in Taiwan. Disasters, 28 , 63-81.’}]…\n[2025-04-28 21:49:54][DEBUG]: == Working Agent: Assignment team\n[2025-04-28 21:49:54][INFO]: == Starting Task… use web_search_toolsearching:\n[{'title': 'Historic Disasters -FEMA.gov', 'href': 'https://www.fema.gov/disaster/historic', 'body': 'The following disasters are\nconsidered historical because of how they impacted the way we handle similar disasters in the future. ... which caused a significant\nreevaluation of the execution of federal disaster response efforts and resource allocation. ... The Northridge Earthquake of 1994\ntested the new reforms that followed Hurricane Andrew ...'}, {'title': 'Optimized resource allocation for emergency response after\nearthquake ...', 'href': 'https://www.sciencedirect.com/science/article/pii/S0925753500000217', 'body': 'The main influencing factor\nfor optimizing resource allocation in disaster response is time. The reason for this is the time limit of the SAR period, conditional\non the goal to minimize the total number of fatalities. ... Coburn, A., Spence, R., 1992. Factors determining human casualty levels\nin earthquakes: mortality prediction in building ...’}]…\n[2025-04-28 21:51:25][DEBUG]: == Working Agent: Assignment team\n[2025-04-28 21:51:25][INFO]: == Starting Task… use web_search_toolsearching:\n[{'title': 'PDF Guidance Note Financing Post-Disaster Recovery and Reconstruction ...', 'href':\n'https://openknowledge.worldbank.org/bitstream/handle/10986/22418/Financing0post0rces000guidance0note.pdf?sequence=1',\n'body': '3 Clarify post-disaster financial responsibilities of the central and local governments 4 Mobilize resources ex-ante through\na DRM fund and financial protection strategy 5 Allocate resources based on damage and loss assessment methodology 6 Execute\nresources and coordinate efforts across the post-disaster recovery and reconstruction process'}, {'title': 'Unearthing the impact of\nearthquakes: A review of economic and social ...', 'href': 'https://onlinelibrary.wiley.com/doi/full/10.1002/pam.22642', 'body': \"For\ninstance, the March 2011 Great East Japan Earthquake led to substantial costs for Japan's government, amounting to 4% of Japan's\nsupplementary budgets financed through bonds, expenditure cuts, and budget surpluses.\"}]…\nFig. 4 A sample for the content retrieved from the internet by the web search tool.\n”\n13. Despite discussing how “the incorrect output of an individual agent would spread wrong\ninformation within the whole workflow” (line 198), the study lacks a detailed evaluation of the\nfull framework to validate the claims about its effectiveness and does not address potential\nerrors and uncertainty propagation throughout the framework. The only evaluation presented\nwas for disaster grading, map annotation, and output reports.\nReply: Thanks for this comment. We agree on the importance of error propagation and have\nadded an additional section A.3 in the Appendix to specifically analyse the effects of error\npropagation.\n“\nAppendix A: Tasks and evaluation\nA.3 Error propagation study\nSince the sequential output of our framework are disaster grading, map annotation and output\nreports, our previous report evaluation for the output reports assessed the obvious error\npropagation throughout the framework (e.g., when the initial expert team agent generates\nwrong disaster grading ‘Central Nishikigawa Street: G3’, then the alerts team, assignment\nteam and emergency team agents will reuse this wrong information and keep generating\n‘Central Nishikigawa Street: G3’ with the relevant speculative statements, which are previously\nevaluated by EvaluatorGPT and human evaluators).\nFor the additional error propagation, we studied how the initial disaster grading error by the\nexpert team agent will affect the later key downstream decision variables generated by the\nother three agents (the num of shelters, hospitals, human allocations, and the reconstruction\nbudget). The map annotation spatial variance errors do not change the disaster grading\ninformation and have no influence for the later downstream decision variables, since the later\nthree agents generate these downstream decision variables based on local disaster descriptions\nwith their respective grades which are provided by the initial expert team agent.\nWe conducted two experiments including the positive error (e.g., the ground truth disaster\ngrade of Wajima Drama Memorial Hall is G1, but we introduce an error higher grade which\nis G5. Thus, the disaster grade error will be +G4) and negative error propagation (we\nmanually make the grade lower than the ground truth). We use the ground truth disaster grades\nas “Wajima Drama Memorial Hall – G1; Hama Street – G4; Concrete Bridge – G5; Central\nNishikigawa Street – G1; North Asaichi Street – G3; South Central Asaichi Street – G4”. To\nget the ground truth downstream decision variables generated by the other three agents (the\nnum of shelters, hospitals, human allocations, and the reconstruction budget), we fixed the\ninitial disaster grades with the ground truth and then average the output downstream decision\nvariables of three independent running as the ground truth (150 rescue workers, 100 medical\nstaffs, 80 logistics support personnel, 3 emergency shelters, 1 hospital, and $0.05 billion\nreconstruction budgets). Because our framework decides the downstream variables based on\nthe reference of historical data, we manually feed the ground truth for the framework as the\nhistorical data and then we can evaluate how the deviation of disaster grading from the\nhistorical data (ground truth) will affect the deviation of the downstream variables.\nFor the positive error propagation part, we manually introduce the wrong disaster grades\n(Wajima Drama Memorial Hall – G5; Hama Street – G4; Concrete Bridge – G5; Central\nNishikigawa Street – G5; North Asaichi Street – G3; South Central Asaichi Street – G4) with\nthe total disaster grade error +G8. Then, the output downstream variables of three independent\nrunning are listed in Table 2, and Fig. 11 presents the reasoning results of our framework for\nthe calculation of downstream variables. In Table 2, when the initial disaster grades are\nmanually introduced the total error of +G8 (+44% deviation from the total grades of ground\ntruth), the propagated errors for downstream variables vary: rescue workers (+33% ~ 50%),\nmedical staffs (+30% ~ 50%), logistics support personnel (+25% ~ 50%), emergency shelters\n(+0% ~ 67%), hospitals (+0% ~ 100%), reconstruction budgets (+40% ~ 80%).\nTable 2 The downstream variables of three independent-run output in positive error propagation test\nround1 round2 round3 Ground Truth\nRescue workers 200 225 200 150\nMedical staffs 130 140 150 100\nLogistics support personnel 100 120 120 80\nEmergency shelters 3 4 5 3\nHospitals 1 1 2 1\nReconstruction budget\n0.07 0.07 0.09 0.05\n($ billion)\nDownstream reports for the key variables:\nHuman Resource Requirements:\n…\nBased on the current disaster grading and historical data, the human resource requirements for the affected areas are as\nfollows:\n-**Rescue Workers**: Given the increased severity in the current situation, especially in G5 areas, a total of 200 rescue\nworkers are required. This is an increase from the historical requirement of 150 due to the higher number of G5 areas.\n-**Medical Staff**: The need for medical staff is critical, especially with multiple G5 areas. A total of 130 medical staff are\nrequired, up from the historical 100, to ensure adequate medical response and care.\n-**Logistic Support Personnel**: To manage the increased logistical challenges, 100 logistic support personnel are needed,\ncompared to the historical 80, to ensure efficient distribution of resources and support…\nEmergent Shelters and Hospitals:\n…\nIn comparison to historical disaster data, where \"Wajima Drama Memorial Hall –G1; Hama Street –G4; Concrete Bridge –\nG5; Central NishikigawaStreet –G1; North AsaichiStreet –G3; South Central AsaichiStreet –G4\" required a total of 3\nshelters and 1 hospital, the current situation demands the same number of shelters and hospitals due to the increased severity\nof the earthquake impact in certain areas.\n…\nFinancial Budget Estimation:\n…\nBased on the current disaster grading and historical data, the estimated budget required for reconstruction is approximately\n0.07 billion. This budget accounts for the increased severity and extent of damage in the high-risk areas compared to\nhistorical events.\n…\nFig. 11 the reasoning results of DisasTeller for the calculation of downstream variables in the positive error\npropagation test.\nFor the negative error propagation part, we manually introduce the wrong disaster grades\n(Wajima Drama Memorial Hall – G1; Hama Street – G1; Concrete Bridge – G5; Central\nNishikigawa Street – G1; North Asaichi Street – G1; South Central Asaichi Street – G1) with\nthe total disaster grade error -G8. Then, the output downstream variables of three independent\nrunning are listed in Table 3. In Table 3, when the initial disaster grades are manually\nintroduced the total error of -G8 (-44% deviation from the total grades of ground truth), the\nmost propagated errors for downstream variables are none and only the reconstruction budget\nhas +0% ~ 40% error. Since our framework will reuse the summary of each local disaster\nimage description for the downstream agents (see Fig.2 in main text), it will autonomously\ncheck the local disaster summary when calculating these downstream variables. For example,\nas the underlined text in Fig. 12, although the initial wrong disaster grade (G1) was introduced\nto ‘Hama Street’ (the ground truth is G4), the later running of our framework still finds the\nsignificant structural damage from the local disaster summary, thus never cutting down the\nnumber of shelters, hospitals and reconstruction budget. This attribute of our framework would\nprovide a safe bottom line for the real disaster scenarios.\nThe agents carry out different assignments and output reports sequentially in our framework.\nThus, our framework also allows humans to check the intermediate outputs without waiting for\nthe whole framework's completion, thereby minimising the effects of error propagation.\nTable 3 The downstream variables of three independent-run output in the negative error propagation test\nround1 round2 round3 Ground Truth\nRescue workers 150 150 150 150\nMedical staffs 100 100 100 100\nLogistics support personnel 80 80 80 80\nEmergency shelters 3 3 3 3\nHospitals 1 1 1 1\nReconstruction budget\n0.07 0.057 0.05 0.05\n($ billion)\nDownstream reports for the key variables:\nHuman Resource Requirements:\n…\nHistorical data indicates a similar situation, a total of 150 rescue workers, 100 medical staff, and 80 logistic support\npersonnel are required. Based on the current disaster analysis and historical data, the following human resource\nrequirements have been determined for effective post-disaster management:\n-**Rescue Workers**: 150\n-**Medical Staff**: 100\n-**Logistic Support Personnel**: 80\n…\nEmergent Shelters and Hospitals:\n…\nBased on the analysis of the earthquake impact on the specified locations and comparison with historical disaster data, the\nfollowing recommendations are made for the establishment of emergent shelters and hospitals:\n-**Shelters**: 3\n-**Location Recommendations**:\n-Near **Wajima Drama Memorial Hall**: This area remains stable with minimal damage, making it a suitable location for\na shelter.\n-**Hama Street**: Despite significant structural damage, establishing a shelter here will provide accessibility to affected\nindividuals in the vicinity.\n-**North AsaichiStreet**: Given the hazardous environment due to a collapsed building, a shelter here will aid in\nimmediate relief efforts.\n-**Hospitals**: 1\n-**Location Recommendation**:\n-Centrally located to serve all affected areas efficiently, possibly near **Central NishikigawaStreet**. This area, despite\nactive construction, has withstood the earthquake well and can support medical facilities.\n…\nFinancial Budget Estimation:\n…\nConsidering the current disaster damage and historical data, the total financial budget required for reconstruction is\nestimated at 0.05 billion. This budget allocation prioritizes Hama Street and Concrete Bridge due to their higher damage.\n…\nFig. 12 The reasoning results of DisasTeller for the calculation of downstream variables in the negative error\npropagation test\n”\n14. A score out of 10 was used for evaluating each of the 3 tasks. This evaluation metric can be\nmisleading as it does not adequately reflect the severity of the error. For example, Figure 2 in\nthe supplementary document shows a notable spatial variation in the results that is not reflected\nin the score number. It would have been more helpful to elaborate on these results in more\ndetail in the main document.\nReply: Thank you for this comment. We have revised the metrics in the supplementary\ndocument (Appendix) and added disaster grading error and map annotation distance error to\nbetter evaluate the intermediate results of our framework; the revision has been done in both\nAppendix and the Section 2.2 in the main document:\n“\nAppendix A: Tasks and evaluation\nA.1 Intermediate process tasks\n…\nWe also define the total disaster grading error through accumulating the deviation of\nDisasTeller’s damage grading output compared to ground truth (our manual assessment for\nthe damage grading). For example, when DisasTeller’s output only has the wrong grading G3\nfor Central Nishikigawa Street (ground truth ranges from G1 - G2), we calculate the difference\nbetween G3 and the lower limit of ground truth (G1) to get the final total grading error +G2.\nAccording to the manual evaluation results, Fig. 1-b presents the five independent-runs grading\noutputs of GPT-4o and Gemma3 driven DisasTeller where texts in orange show the wrong\ngeneration and texts in blue exhibit the final scores with the total grading error, we provide for\nDisasTeller.\na b\nLocal post-disaster on-site images GPT-4o and Gemma3 driven DisasTelleroutput evaluation\nLocation GPT-4o Gemma3\nWajima Drama Hama Street Wajima Drama Memorial Hall G1 G3-G4 (Wrong)\nMemorial Hall Hama Street G4 G4-G5\nConcrete Bridge G5 G3-G4 (Wrong)\nRound 1 Central NishikigawaStreet G3 (Wrong) G3-G4 (Wrong)\nNorth AsaichiStreet G4 G4-G5\nSouth Central AsaichiStreet G5 G4-G5\nScore (total grading error) 5/6(+G2) 3/6(+G7)\nWajima Drama Memorial Hall G3 (Wrong) G4 (Wrong)\nHama Street G4 G3 (Wrong)\nConcrete Bridge G4-G5 G5\nRound 2 Central NishikigawaStreet G5 (Wrong) G5 (Wrong)\nNorth AsaichiStreet G4 G4\nConcrete Bridge Central Nishikigawa Street South Central AsaichiStreet G5 G4\nScore (total grading error) 4/6(+G6) 3/6(+G8)\nWajima Drama Memorial Hall G3 (Wrong) G3 (Wrong)\nHama Street G5 G5\nConcrete Bridge G5 G5\nRound 3 Central NishikigawaStreet G3 (Wrong) G4 (Wrong)\nNorth AsaichiStreet G4 G5 (Wrong)\nSouth Central AsaichiStreet G5 G4\nScore (total grading error) 4/6(+G4) 3/6(+G7)\nWajima Drama Memorial Hall G3 (Wrong) G3 (Wrong)\nHama Street G5 G5\nConcrete Bridge G5 G5\nSouth Central\nAsaichi Street Round 4 Central NishikigawaStreet G3 (Wrong) G5 (Wrong)\nNorth AsaichiStreet G4 G5 (Wrong)\nSouth Central AsaichiStreet G5 G5\nScore (total grading error) 4/6(+G4) 3/6(+G8)\nWajima Drama Memorial Hall D1 G3 (Wrong)\nHama Street D4 G3 (Wrong)\nConcrete Bridge D4-D5 G5\nRound 5 Central NishikigawaStreet D3 (Wrong) G5 (Wrong)\nNorth AsaichiStreet D4 G5 (Wrong)\nNorth Asaichi Street South Central AsaichiStreet D5 G5\nScore (total grading error) 5/6(+G2) 2/6(+G9)\nFig. 1 Evaluation of local post-disaster on-site images grading: texts in orange represent the wrong outputs of\nDisasTeller; texts in blue are the scores of accuracy percentages and total grading error.\n…\nWe inspect the generated annotations in each independent run and evaluate the accuracy of\nthe annotations by determining whether the annotations are close to the ground truth (our\nmanual annotations) on the global map. The map annotation distance error is defined through\ncomparing the spatial location of DisasTeller’s autonomous annotations with ground truth. In\nthe global map image with 1080 × 1522 pixels, we define the distance error grades: 0 – 100\npixels (G1), 100 – 200 pixels (G2), 200 – 250 pixels (G3), 250 – 350 pixels (G4), larger than\n350 pixels (G5). The distance error grades lager than G3 (250 pixels) is considered as wrong\nspatial annotation and marked as red cross. Fig. 2 – 3 shows the final evaluation of GPT-4o\nand Gemma3 driven DisasTeller’s annotations in the global map with the wrong annotation\nmarked as red cross and the average distance error of six locations in each round provided.\n×\n×\n×\n× × ×\n×\nMap Data: Google, ©2024 Map Data: Google, ©2024 Map Data: Google, ©2024\nRound 1:3/6; Ave error: 276 pixels Round 2:3/6; Ave error: 282 pixels Round 3:5/6; Ave error: 191 pixels\n×\n×\n×\n×\n×\nMap Data: Google, ©2024 Map Data: Google, ©2024 Map Data: Google, ©2024\nRound 4:3/6; Ave error: 269 pixels Round 5:4/6; Ave error: 199 pixels Ground truth\nFig. 2 Evaluation of global map annotations: GPT-4o driven DisasTeller’s autonomous annotations are in yellow,\nand annotations with distance error larger than 250 pixels are identified as wrong and marked as red cross.\n×\n× ×\n× ×\n× ×\nMap Data: Google, ©2024 Map Data: Google, ©2024 Map Data: Google, ©2024\nRound 1:3/6; Ave error: 450 pixels Round 2:3/6; Ave error: 376 pixels Round 3:4/6; Ave error: 381 pixels\n×\n× ×\n×\n×\nMap Data: Google, ©2024 Map Data: Google, ©2024 Map Data: Google, ©2024\nRound 4:2/6; Ave error: 313 pixels Round 5:3/6; Ave error: 280 pixels Ground truth\nFig. 3 Evaluation of global map annotations: Gemma3 driven DisasTeller’s autonomous annotations are in yellow,\nand annotations with distance error larger than 250 pixels are identified as wrong and marked as red cross.\n”\n“\n2.2 Agent output results evaluation (main document)\n…\nFig. 4 summarises the evaluation results across five rounds for both local disaster grading and\nalert map annotation tasks. The disaster grading error is defined through accumulating the\ndeviation of DisasTeller’s damage grading output compared to our manual assessment, and\nthe map annotation distance error is defined through comparing the spatial locations of\nDisasTeller’s autonomous annotations with our manual annotations …\n10.0\n9.0 Average score (grading error):\n7.3 (+G3.6)\n8.0\n7.0 Average score (gradingerror):\n4.6 (+G7.8)\n6.0\n5.0\n4.0\n3.0\n2.0\n1.0\n0.0\nGPT-4o: Local disaster grading Gemma3: Local disaster grading\n10.0\nAverage score (distance error):\n9.0 6.0 (243 pixels)\n8.0\nAverage score (distance error):\n5.0 (360 pixels)\n7.0\n6.0\n5.0\n4.0\n3.0\n2.0\n1.0\n0.0\nGPT-4o: Map annotation Gemma3: Map annotation\n“ round1 round2 round3 round4 round5\nFig. 4 The evaluation of intermediate process tasks in GPT-4o and Gemma3-driven DisasTeller.\n”\n15. When evaluating output reports, a similar score out of 10 was given to each report. It is\nstated that the evaluation score is based on “the coherence and consistency of sentences and\nthe accuracy of claims” (line 178). However, this score is not well defined and open to different\ninterpretations. How much weight is given to each criterion? The basis for accuracy evaluation\nwas not discussed well in the manuscript. What makes a report accurate or not? What were the\ncriteria for selecting the human evaluators?\nReply: We thank the reviewer for this comment. We added the detailed rubric for the evaluation\nscore of sentences consistency (50%) and the accuracy of claims (50%), see the revised Table\n1 and Fig. 8 in the Appendix. As for the human evaluators, we selected them having civil\nengineering backgrounds with construction experience. This is clarified in the main text:\n“\n2.2 Agent output results evaluation (main document)\n…\nThe prompt for EvaluatorGPT is modified to grade the output reports of DisasTeller based on\nthe rubric, which focuses on coherence and consistency of sentences and the accuracy of claims.\nEach evaluation of EvaluatorGPT requires a detailed explanation of the report’s weakness to\nsupport the corresponding grade. In addition, we select people who have civil engineering\nbackgrounds with construction experience to manually inspect DisasTeller’s outputs and utilise\nthe same rules to provide grades as a comparison of EvaluatorGPT for a more thorough\nevaluation.\n”\n“\nAppendix\nA.2 Output reports evaluation\nTable 1 The evaluation rubric for DisasTeller output reports\nScore\nDimension Sub-Dimension Evaluation Focus Detailed Notes\nRange\nAre sections clearly structured 2: Fully structured and\n(Intro, Damage, Needs, logical; 1~2: Mostly\n1.1 Logical\nRecommendations, etc.)? Does the 0–2 structured but minor\nOrganisation\nreport avoid random jumping jumps; 0~1: Poorly\nbetween ideas? structured.\n1. Sentence 2: Full consistency; 1~2:\nAre terms like \"Grade D4-D5\",\nconsistency 1.2 Consistent Minor inconsistencies;\n\"Emergent Shelter\", \"Critical Area\" 0–2\nTerminology 0~1: Frequent switching\nused consistently across sections?\nof terms/confusion.\nAre the cause-effect relationships 1: Clear and consistent;\n1.3 Consistent\nexplained consistently? (e.g., 0–1 0~1: Missing or\nArgumentation\nDamage → Need → Action Plan.) contradictory flow.\nSubtotal 0–5\n2. Wrong Are descriptions faithful to the 3: Highly accurate; 1~3:\n2.1 Factual\nClaims or available evidence (e.g., damage 0–3 Medium exaggerations;\nAccuracy\nOverclaims images, situation)? 0~1: Major inaccuracies.\nDoes the report avoid overly 2: No speculative claims;\n2.2 Avoiding\nspeculative or unsupported claims 0–1: Speculative or\nSpeculative 0–2\n(e.g., \"hundreds of dead\" without unverified claims\nStatements\nevidence)? present.\nSubtotal 0–5\nTotal 0–10\n1. Alerts Team Report for Social Media\no Score: 8.1/10\nSentence Coherence and Consistency: The most parts of this reportare well organized, but the reuse of damage descriptions\no\nseems to be kind of redundant(1.1 1.6/2 points; 1.2 2/2 points; 1.3 1/1 points).\nWrong Claims/Overclaims: The description of Central Nishikigawa Street as \"Moderate damage\" (Grade 3) is an overclaim,\no\nbut this mistake is not too far from the ground truth (2.1 2.5/3 points),andthesafety advisories are also affected by this\nwrong claim (2.2 1/2 points).\n2. Emergency Service Human Resource Allocation Report\no Score: 7.7/10\nSentence Coherence and Consistency: The report is structured butkind ofsuffers from redundancy and need to provide more\no\nconcise summary for human allocation(1.1 1.7/2 points; 1.2 2/2 points; 1.3 1/1 points).\no Wrong Claims/Overclaims: The proposed number of rescue workers and medical personnel is kind oflarge (2.1 2/3 points).\nThe report still needs to fully consider the possiblescarcity of humans.Thismay be a potential overclaims about human\nresource availability and the allocation (2.2 1/2 points).\n3. Emergency Team Report\no Score: 7.1/10\nSentence Coherence and Consistency: The most parts are goodinthisreport, the sentencestructureand consistency in the\no\nfirst partcan be further improved (1.1 1.6/2 points; 1.2 2/2 points; 1.3 0.5/1 points).\nWrong Claims/Overclaims: The suggestion of setting up the hospitals for every location is under consideration.Some places\no\nmay not need the hospital andit could be an overclaim (2.1 2/3 points; 2.2 1/2 points).\n4. Expert Team Report\no Score: 9.3/10\no Sentence Coherence and Consistency: The report's sentences are well organised(1.1 2/2 points; 1.2 2/2 points; 1.3 1/1\npoints).\nWrong Claims/Overclaims: The description of Central NishikigawaStreet as \"Moderate damage\" (Grade 3) seems an\no\noverclaim, but this mistake is not too far from the ground truth (2.1 2.5/3 points). Most descriptions in the report match the\nimages (2.2 1.8/2 points).\n5. Public Community Report\no Score: 7.5/10\nSentence Coherence and Consistency: The report can reduce the redundant description for the Introduction part and should\no\nkeep consistent terminology for different events (1.1 1.8/2 points; 1.2 1.5/2 points; 1.3 1/1 points).\nWrong Claims/Overclaims: This report has irrelevant claims about the current disasters, mixes the data from global disaster\no\nstatistics from FEMA and NOAA, and it is not sure which area the claim points to: “The affected areas faced catastrophic\nevents, including severe storms, flooding, and hurricanes, causing significant structural damage and necessitating immediate\nemergency response” (2.1 2/3 points; 2.2 1.2/2 points).\n6. Reconstruction Plan Report\no Score: 7.8/10\no Sentence Coherence and Consistency: The report is mostly structuredwith clear descriptionfor reconstruction plan (1.1\n1.8/2 points; 1.2 2/2 points; 1.3 1/1 points).\nWrong Claims/Overclaims: In thefirstpartof this report, the irrelevant claims with the global disasters are introduced,and\no\nthespeculative statementsfor the grade 5 damaged areas are succeeded from previous outputs (2.1 2/3 points; 2.2 1/2\npoints).\nFig. 8 A sample of the human assessment report.\n”\n16. Figure 3 in the supplementary document shows that the evaluator GPT model, which was\nprompted to “act as a post-disaster management expert”, provides generic and non-specific\nfeedback. This arguably hurts the value of its evaluation.\nReply: Thank you for highlighting this. We agree and have modified the claim while revising\nthe prompt to provide a more detailed description for the EvaluatorGPT, which is updated in\nthe supplementary document (Appendix).\n“GPT-4o is prompted to act as an expert for post disaster management (referred to as\nEvaluatorGPT), specialising in disaster impact assessment, emergency response planning, and\nrecovery evaluation, and is provided with post-disaster on-site images, documents retrieved\nfrom the internet, and the evaluation rubric to enhance the accuracy of its assessments.\n1. ExpertTeam Report:The Expert Team Report has acceptable structure with clear sections, but some transitions (like damage\ndescriptions leading to general conclusions) feel abrupt, earning 1/2 for Logical Organization. Terminology such as \"Grade 3\"\nand \"Damage Grading\" is consistent (2/2), and the cause-effect reasoning between damage and assigned grades is coherent (1/1).\nIn terms of factual accuracy, while most descriptions match the images, risk factors like secondary collapses are\nunderemphasized, so 2/3is awarded. Additionally, optimistic statements about resilience without direct evidence cause a minor\ndeduction for speculation (1/2). Therefore, the final strict score is 7.0/10.\n2. Alert Team Report: The Alerts Team Report is clearly structured with consistent use of labels like \"Grade 4\" and \"Safety\nAdvisory,\" scoring full points for Logical Organization (2/2)and Terminology (2/2), and the argument from damage to risk\nadvisory is logical (1/1). However, a critical factual error occurs by labelingWajima Drama Memorial Hall as only minimally\ndamaged despite visible structural issues, thus 1/3 for Factual Accuracy, and the advisories are broad and not carefully\ndifferentiated by location severity, slightly speculative, scoring 1/2. Thus, the overall rubric-aligned score is 7.0/10.\n3. Emergency Team Report: The Emergency Team Report is logically organized, detailing each location’s situation, needs, and\nshelter plans (2/2), with consistent technical terminology (2/2) and a clear link between observed damage and recommended\nemergency measures (1/1). While factual descriptions mostly match observed conditions, the number of proposed shelters is\nslightly aggressive without full population data support, resulting in 2/3 for Factual Accuracy, and the conclusion about system-\nlevel strategy improving efficiency is somewhat speculative (1/2), leading to a strict final score of 8.0/10.\n4. Human Resource Allocation Report : The Human Resource Allocation Report is very well structured, moving systematically\nthrough affected areas and required personnel (2/2), maintains perfect consistency in using terms like \"Medical Personnel\" and\n\"Rescue Teams\" (2/2), and has clear, logical cause-effect chains between severity and resource allocation (1/1). Factual\ndeployment numbers closely match actual damages observed, justifying 3/3 for Factual Accuracy, and while minor future risk\nprojections are mentioned without deep evidence (1/2 for Speculation), the report remains largely precise, resulting in a final\nscore of 9.0/10.\n5. Public Notice Report: The Public Community Report lacks strong logical focus, mixing local earthquake impacts with\nirrelevant global disaster statistics like FEMA and NOAA data (1/2 for Organization), and its terminology shifts between global\nand local events inconsistently (1/2), with a weak link between needs and actions (0/1 Argumentation). Factual descriptions are\nvague compared to real disaster images (2/3 for Factual Accuracy), but no speculative or exaggerated claims are made (2/2),\nresulting in a strict rubric-based score of 6.0/10.\n6. Future Reconstruction Plan: The Future Reconstruction Plan is excellently structured into Immediate, Short-term, and Long-\nterm phases (2/2 Logical Organization), uses consistent terms like \"Phased Recovery\" and \"Risk Reduction\" (2/2 Terminology),\nand builds a logical flow from observed damage to recovery steps (1/1 Argumentation). Factual references are generally adapted\nwell to local needs even though some costs are derived from global comparisons (3/3 for Factual Accuracy), and minor\novergeneralizations about financial capacity result in a small deduction for speculation (1/2), producing a final score of 9.0/10.\nFig. 5 A sample of the EvaluatorGPT assessment report.\n”\n17. On the other hand, Figure 5 in the supplementary documents shows some concerning errors\nthat the human evaluators addressed (i.e., suggesting shelter locations near severely damaged\nstructures). This may imply weaknesses in the agent’s spatial and contextual awareness.\nReply: We acknowledge these errors and have added a discussion on the limitations of the\ncurrent spatial reasoning capabilities of LVLMs. Future work will focus on enhancing the\nspatial reasoning ability of LVLMs to improve accuracy.\n“\n2.3 Limitations and deployment considerations\nSome errors identified during evaluation, such as shelter suggestions near collapsed structures,\nhighlight deficiencies in current LVLM spatial reasoning. Although the agents interpret image\ncaptions and geolocation inputs, spatial safety logic remains limited. Enhancing the spatial\nawareness of LVLMs and integrating geographic information system safety heuristics [26] will\nbe key areas of future improvement.\n”\n18. It is not clear whether a 1:1 comparison can be performed between the GPT evaluations in\nFigure 3 and human evaluations in Figure 5 (i.e., are they evaluating the exact same reports or\ndifferent runs?).\nReply: The previous samples we provided for GPT and human evaluations could not do a 1:1\ncomparison since they evaluated different reports. We have modified the figures to exhibit the\nsamples of the EvaluatorGPT and human assessment reports for the identical reports in the\nsame round (see the Fig. 5 and Fig. 8 in the Appendix A.2).\n“\nA.2 Output reports evaluation\n…\nFig. 5 and Fig. 8 present the samples of the EvaluatorGPT and human assessment reports for\nthe identical reports in the same round, with the detailed explanations for the DisasTeller\nreports’ weakness.\n1. ExpertTeam Report:The Expert Team Report has acceptable structure with clear sections, but some transitions (like damage\ndescriptions leading to general conclusions) feel abrupt, earning 1/2 for Logical Organization. Terminology such as \"Grade 3\"\nand \"Damage Grading\" is consistent (2/2), and the cause-effect reasoning between damage and assigned grades is coherent (1/1).\nIn terms of factual accuracy, while most descriptions match the images, risk factors like secondary collapses are\nunderemphasized, so 2/3is awarded. Additionally, optimistic statements about resilience without direct evidence cause a minor\ndeduction for speculation (1/2). Therefore, the final strict score is 7.0/10.\n2. Alert Team Report: The Alerts Team Report is clearly structured with consistent use of labels like \"Grade 4\" and \"Safety\nAdvisory,\" scoring full points for Logical Organization (2/2)and Terminology (2/2), and the argument from damage to risk\nadvisory is logical (1/1). However, a critical factual error occurs by labelingWajima Drama Memorial Hall as only minimally\ndamaged despite visible structural issues, thus 1/3 for Factual Accuracy, and the advisories are broad and not carefully\ndifferentiated by location severity, slightly speculative, scoring 1/2. Thus, the overall rubric-aligned score is 7.0/10.\n3. Emergency Team Report: The Emergency Team Report is logically organized, detailing each location’s situation, needs, and\nshelter plans (2/2), with consistent technical terminology (2/2) and a clear link between observed damage and recommended\nemergency measures (1/1). While factual descriptions mostly match observed conditions, the number of proposed shelters is\nslightly aggressive without full population data support, resulting in 2/3 for Factual Accuracy, and the conclusion about system-\nlevel strategy improving efficiency is somewhat speculative (1/2), leading to a strict final score of 8.0/10.\n4. Human Resource Allocation Report : The Human Resource Allocation Report is very well structured, moving systematically\nthrough affected areas and required personnel (2/2), maintains perfect consistency in using terms like \"Medical Personnel\" and\n\"Rescue Teams\" (2/2), and has clear, logical cause-effect chains between severity and resource allocation (1/1). Factual\ndeployment numbers closely match actual damages observed, justifying 3/3 for Factual Accuracy, and while minor future risk\nprojections are mentioned without deep evidence (1/2 for Speculation), the report remains largely precise, resulting in a final\nscore of 9.0/10.\n5. Public Notice Report: The Public Community Report lacks strong logical focus, mixing local earthquake impacts with\nirrelevant global disaster statistics like FEMA and NOAA data (1/2 for Organization), and its terminology shifts between global\nand local events inconsistently (1/2), with a weak link between needs and actions (0/1 Argumentation). Factual descriptions are\nvague compared to real disaster images (2/3 for Factual Accuracy), but no speculative or exaggerated claims are made (2/2),\nresulting in a strict rubric-based score of 6.0/10.\n6. Future Reconstruction Plan: The Future Reconstruction Plan is excellently structured into Immediate, Short-term, and Long-\nterm phases (2/2 Logical Organization), uses consistent terms like \"Phased Recovery\" and \"Risk Reduction\" (2/2 Terminology),\nand builds a logical flow from observed damage to recovery steps (1/1 Argumentation). Factual references are generally adapted\nwell to local needs even though some costs are derived from global comparisons (3/3 for Factual Accuracy), and minor\novergeneralizations about financial capacity result in a small deduction for speculation (1/2), producing a final score of 9.0/10.\nFig. 5 A sample of the EvaluatorGPT assessment report.\n1. Alerts Team Report for Social Media\no Score: 8.1/10\nSentence Coherence and Consistency: The most parts of this reportare well organized, but the reuse of damage descriptions\no\nseems to be kind of redundant(1.1 1.6/2 points; 1.2 2/2 points; 1.3 1/1 points).\nWrong Claims/Overclaims: The description of Central Nishikigawa Street as \"Moderate damage\" (Grade 3) is an overclaim,\no\nbut this mistake is not too far from the ground truth (2.1 2.5/3 points),andthesafety advisories are also affected by this\nwrong claim (2.2 1/2 points).\n2. Emergency Service Human Resource Allocation Report\no Score: 7.7/10\nSentence Coherence and Consistency: The report is structured butkind ofsuffers from redundancy and need to provide more\no\nconcise summary for human allocation(1.1 1.7/2 points; 1.2 2/2 points; 1.3 1/1 points).\no Wrong Claims/Overclaims: The proposed number of rescue workers and medical personnel is kind oflarge (2.1 2/3 points).\nThe report still needs to fully consider the possiblescarcity of humans.Thismay be a potential overclaims about human\nresource availability and the allocation (2.2 1/2 points).\n3. Emergency Team Report\no Score: 7.1/10\nSentence Coherence and Consistency: The most parts are goodinthisreport, the sentencestructureand consistency in the\no\nfirst partcan be further improved (1.1 1.6/2 points; 1.2 2/2 points; 1.3 0.5/1 points).\nWrong Claims/Overclaims: The suggestion of setting up the hospitals for every location is under consideration.Some places\no\nmay not need the hospital andit could be an overclaim (2.1 2/3 points; 2.2 1/2 points).\n4. Expert Team Report\no Score: 9.3/10\no Sentence Coherence and Consistency: The report's sentences are well organised(1.1 2/2 points; 1.2 2/2 points; 1.3 1/1\npoints).\nWrong Claims/Overclaims: The description of Central NishikigawaStreet as \"Moderate damage\" (Grade 3) seems an\no\noverclaim, but this mistake is not too far from the ground truth (2.1 2.5/3 points). Most descriptions in the report match the\nimages (2.2 1.8/2 points).\n5. Public Community Report\no Score: 7.5/10\nSentence Coherence and Consistency: The report can reduce the redundant description for the Introduction part and should\no\nkeep consistent terminology for different events (1.1 1.8/2 points; 1.2 1.5/2 points; 1.3 1/1 points).\nWrong Claims/Overclaims: This report has irrelevant claims about the current disasters, mixes the data from global disaster\no\nstatistics from FEMA and NOAA, and it is not sure which area the claim points to: “The affected areas faced catastrophic\nevents, including severe storms, flooding, and hurricanes, causing significant structural damage and necessitating immediate\nemergency response” (2.1 2/3 points; 2.2 1.2/2 points).\n6. Reconstruction Plan Report\no Score: 7.8/10\no Sentence Coherence and Consistency: The report is mostly structuredwith clear descriptionfor reconstruction plan (1.1\n1.8/2 points; 1.2 2/2 points; 1.3 1/1 points).\nWrong Claims/Overclaims: In thefirstpartof this report, the irrelevant claims with the global disasters are introduced,and\no\nthespeculative statementsfor the grade 5 damaged areas are succeeded from previous outputs (2.1 2/3 points; 2.2 1/2\npoints).\nFig. 8 A sample of the human assessment for the same reports evaluated by EvaluatorGPT.\n”\n19. A statement like “the reliance on LVLM-generated recommendations, if unchecked, can\nintroduce flawed decision-making, potentially exacerbating the situation on the ground” (line\n236) seems to defeat the main purpose and arguments of the study. Contradictions like these\nmake the study appear confused about its goal.\nReply: We agree and have revised the manuscript. In particular this statement has been\nreworded to emphasise that our framework is designed to serve as an aid to human decision-\nmakers. We stress that human monitoring are integral parts of the system to mitigate risks\nalthough acknowledging that they exist.\n“\n2.3 Limitations and deployment considerations\n…\nOne prominent concern is the potential for spreading inaccurate or misleading information\n(LVLM hallucination) in high-stakes situations where flawed decisions may exacerbate\ndisaster impacts [19,25] …\nTo mitigate hallucination risks, our framework is designed to output step-by-step intermediate\nresults (e.g., annotated maps, retrieved references, and reasoning steps) at each stage, enabling\nhuman experts to audit and intervene during execution rather than waiting for final outcomes.\n… to enhance the trustworthiness of DisasTeller for practical deployment, it is important to\nkeep human check in the running process. This human-in-the-loop format ensures that AI\noutputs serve as assistive recommendations rather than unquestioned directives, thereby\nreducing the risk of over-reliance on black-box results.\n”\n20. Section 2.3 outlines the limitations of LVLMs, including LVLM hallucination, difficulty in\nhandling novel disaster events, and data security concerns. However, these limitations are not\nspecifically identified based on the study results. Instead, they are drawn from broader, general\nperceptions about LVLMs rather than insights gained through the study’s own findings and\nanalysis.\nReply: Thank you for this comment. We agree and have rewritten the limitations to provide a\nmore specific assessment based on the results obtained with the framework. The following\nrevised parts are mentioned here for completeness:\n“\n2.3 Limitations and deployment considerations\nAlthough DisasTeller demonstrates the possibility of autonomous post-disaster response tasks\ncompletion, integrating multi-LVLM agent-based engines like DisasTeller for post-disaster\nresponse still exists several inherent limitations and risks that need to be carefully mitigated to\nensure effective and safe deployment.\nOne prominent concern is the potential for spreading inaccurate or misleading information\n(LVLM hallucination) in high-stakes situations where flawed decisions may exacerbate\ndisaster impacts [19,25]. Despite the use of domain-specific prompting, technical guidelines\n(e.g., EMS-98), and calibrated workflows, our experiments show that errors in early-stage\nagents (e.g., disaster grading) can propagate to downstream outputs like human allocations\nand reconstruction planning. However, the system also exhibits corrective behaviour in some\ncases, revalidating local summaries and overriding initial errors based on visual or contextual\ncues. To mitigate hallucination risks, our framework is designed to output step-by-step\nintermediate results (e.g., annotated maps, retrieved references, and reasoning steps) at each\nstage, enabling human experts to audit and intervene during execution rather than waiting for\nfinal outcomes.\nIn post-disaster settings, establishing an optimal ground truth is inherently difficult due to the\nuniqueness of each event and the absence of repeatable baseline data Evaluating variables\nsuch as the “optimum” number of doctors or shelters remains a challenge. In our case studies,\nwe addressed this by comparing outputs with publicly available historical records retrieved via\nweb tools. In future work, agents could interface with private databases to access more reliable\nbaselines for self-validation.\nSome errors identified during evaluation, such as shelter suggestions near collapsed structures,\nhighlight deficiencies in current LVLM spatial reasoning. Although the agents interpret image\ncaptions and geolocation inputs, spatial safety logic remains limited. Enhancing the spatial\nawareness of LVLMs and integrating geographic information system safety heuristics [26] will\nbe key areas of future improvement.\nEffective deployment of DisasTeller requires timely access to local-view images or other\nsituational data. In real-world conditions, acquiring such data may involve using drones,\nsatellites, or crowdsourced images. However, logistical barriers in damaged or inaccessible\nareas remain a challenge. Moreover, any data like multi-angle images that can improve\nstandard human assessment would also improve the contribution of DisasTeller. Our\nframework assumes data collected by human teams for general assessment can be reused to\ntrigger automated reasoning, thus providing an initial assessment that can become the basis\nfor decision making, reducing delay and guiding more targeted on-site inspections.\nAs with all neural network-based AI, LVLMs suffer from limited interpretability [27]. This\nposes challenges for acceptance in mission-critical applications like disaster response. While\nthe inherent complexity of neural networks is difficult to be eliminated, the integration of open-\nsource LVLMs (like Gemma3 in our study) in DisasTeller offers certain advantages in\nmitigating interpretability concerns. Due to the transparency of model architecture and\nparameters, open-source LVLMs allow full access to the architecture, weights, and training\nprocedures, enabling researchers and practitioners to inspect how the model operates\ninternally. Open-source LVLMs will also be under collective peer review and benchmarking\nwithin the research community, which often leads to faster identification of failure cases and\nbetter understanding of model behaviours. Benefiting from the attributes of open-source\nLVLMs, agents’ behaviours in DisasTeller will be more transparent and traceable. In addition,\nOur framework is designed to output not only final decisions but also the step-by-step\nreasoning results, and human can inspect the intermediate results (e.g., the visual evidence of\nannotated damage regions and the historical information retrieved from the internet as a\nreference for human allocation) without waiting the running finished, thus each intermediate\nreasoning progress and task output can be checked and allow human experts to verify how each\nconclusion was reached. Since fully resolving the interpretability challenge remains an open\nresearch problem in current stage, to enhance the trustworthiness of DisasTeller for practical\ndeployment, it is important to keep human check in the running process. This human-in-the-\nloop format ensures that AI outputs serve as assistive recommendations rather than\nunquestioned directives, thereby reducing the risk of over-reliance on black-box results.\nIn disasters, resources are rarely sufficient, and planning must balance urgency, impact, and\nlogistics. DisasTeller’s human allocation module incorporates historical and spatial references\nto prioritise areas with high estimated needs, but it cannot guarantee optimality. The practical\ndeployment could integrate real-time resource inventories or supply chain data to improve\nscheduling under constrained scenarios.\nAs a decision-support tool, DisasTeller is not intended to replace human authority. All final\ndecisions remain the responsibility of human operators and implementing organisations. To\nmaintain ethical deployment, robust risk management, legal compliance, and stakeholder\noversight are necessary. In the case of incorrect AI-generated decisions, traceability and\ntransparency of agent logic will be essential for accountability.\nGiven the rapid evolution of AI, maintaining system relevance over time is a challenge. To\naddress this, DisasTeller is built with a modular architecture that allows seamless substitution\nof new LVLMs, tools, and reasoning modules. Periodic retraining, version control, and\nembedded performance monitoring can ensure future upgrades remain aligned with the\nsystem’s safety and usability requirements.\n”\n21. The conclusion can be somewhat misleading, as the study did not adequately assess the\nmain claims and advantages of using LVLMs over traditional post-disaster human teams.\nClaims about efficiency focus on how fast 4 GPT agents can coordinate and generate output\nreports but do not address issues with the accuracy of the outputs and the severity of the\nconsequences of these errors.\nReply: We have revised the Conclusion to provide a more balanced view. While efficiency\ngains are highlighted, we also discuss accuracy limitations and the critical need for human\noversight, reinforcing that our framework is a complementary tool rather than a complete\nreplacement for human teams.\n“\n3. Conclusion\n…\nAssessment results showed that DisasTeller can reduce the time needed for coordination and\ntask completion compared to the current human-based disaster response procedure. However,\nthe efficiency gains must be interpreted alongside non-negligible limitations in accuracy. Our\nevaluation reveals that errors in early-stage agents, such as inaccurate disaster grading, can\npropagate to downstream decision outputs. While report-level evaluations show alignment with\nhuman judgment, intermediate results, particularly in image-grounded damage assessment and\nmap annotation, expose weaknesses in the current LVLM spatial and contextual reasoning\ncapacity. These findings reinforce that DisasTeller is intended as a complementary support tool,\nnot a replacement for human disaster experts.\nFurther development is required to improve task-specific LVLM accuracy, integrate real-time\nmultimodal sensor data, and strengthen interactions with human decision-makers in the loop.\nAugmenting the system with fine-tuned LVLMs and specialised tools beyond the current\nframework could also enhance reliability under real-world constraints.\nAdditionally, challenges in evaluation stem from the lack of universal ground-truth benchmarks,\nand the non-deterministic nature of LVLM outputs. Due to random sampling during inference\n[16], repeated executions may yield slightly different results. Although the temperature\nparameter can regulate generation variability [17], the deterministic output would cause bias\nand reduce LVLM's problem-solving capability, particularly in disaster scenarios requiring\ndiverse reasoning approaches [16]. Balancing output consistency with reasoning richness\nremains an open research direction.\nIn conclusion, despite current limitations, our work demonstrates the potential of multi-agent\nLVLM frameworks like DisasTeller to improve the speed, structure, and accessibility of post-\ndisaster response. Continued collaboration between AI developers, domain experts, and field\nresponders will be essential to realise safe, effective, and trustworthy AI-assisted disaster\nmanagement systems.\n”\n22. Section 4, Methodology, provides a description of existing tools rather than presenting any\noriginal contributions developed by the authors.\nReply: We have modified and reconstructed Section 4 in the main text to emphasise our novel\ncontributions.\n“\n4. Methods\n4.1 Large Vision Language Models (LVLMs) with augmented generation\nThe rapid rise of LVLMs in recent years has created unprecedented opportunities across\nvarious domains. Based on pretrained knowledge from the large amount of data, one of the key\nfactors behind the growing capabilities of LVLMs is their ability to perform zero-shot reasoning\n(solving tasks based on pretrained knowledge without specific re-training on those tasks) [31].\nThis ability is significantly amplified when LVLMs are used within advanced frameworks\nexhibiting the power of chain-of-thought reasoning [32]. LVLMs generate step-by-step logical\nexplanations that can interact with external systems and databases through tool usage,\nrendering LVLMs’ ability to be intelligent agents [33]. However, the zero-shot reasoning of\nLVLMs has restrictions and low accuracy in domain specific tasks. To mitigate this drawback,\nwe supplement with domain-specific prompts and external technical guidelines (e.g., EMS98)\nto enhance the performance of our framework in this disaster domain. This is identified as in-\ntext learning [34] and retrieved augmented generation [35], where external knowledge is\ninjected into prompts to enhance model reliability without retraining. For practical deployment,\nfurther results could be achieved with specialised training. Additionally, to make LVLMs\naccessible to developers and businesses, Application Programming Interfaces (APIs) are\ncommonly employed. In this study, we streamlined our framework by utilising the API of the\nmost representative LVLM model GPT (version “gpt-4o-2024-08-06”), and we also conducted\nthe additional validation through the local deployment and integration of Google’s open-source\nLVLM model Gemma3-27B. The modularity of our framework allows for future integration of\nfine-tuned LVLMs for improved specialisation and robustness.\n4.2 Custom multi-LVLM agent framework design\nWhile foundational tools like LangChain [36] and CrewAI [37] provide essential infrastructure\nfor chaining prompts and coordinating multi-agent systems, our work contributes a domain-\nspecific, multi-role LVLM agent framework specifically tailored to automate tasks in disaster\nresponse. We designed a system where each LVLM-based agent is assigned a clearly defined\nfunctional role, such as damage grading, emergency alert generation, and human resource\nplanning, based on the operational needs of real-world disaster workflows. Building upon\nCrewAI, we customised the role-specific memory management to ensure each agent retains its\ncontext across interactions, developed the inter-agent communication protocols to enable\nagents to request and refine each other’s output, and designed the dynamic task allocation\nallowing the system to reassign subtasks based on runtime performance or conflict resolution.\nUnlike prior generic implementations of CrewAI or LangChain, our framework demonstrates\na novel vertical integration of multi-agent reasoning, domain grounding, and multi-tool\ninteraction designed explicitly for structured coordination in disaster scenarios.\n4.3 Tools development\nTo maximise the capabilities of each LVLM agent, we designed a set of tools, mapped to agents'\nresponsibilities and modularly integrated into the pipeline of our framework. It is necessary to\nhighlight that additional tools can be easily integrated into this toolset according to\nrequirements due to the modularity of our framework. A key innovation in our design is the use\nof adaptive tool invocation, where each agent determines, based on task complexity and\nconfidence thresholds, whether external tools are needed to enhance output quality. This\nconditional reasoning enables greater efficiency, avoids over-reliance on tools, and mimics\nhow human teams selectively consult external resources\n…\n4.4 Agents definition for post-disaster management\nIn our framework, each LVLM-based agent was defined through a combination of three core\ncomponents: a role specification, a model assignment, and a set of task-relevant tools. This\nmodular structure enables each agent to behave intelligently and autonomously across\ndifferent stages of post-disaster response [33]. To simulate the collaborative workflow of real-\nworld disaster response teams, we defined four distinct agents including ‘Expert Team’,\n‘Alerts Team’, ‘Emergency Team’, and ‘Assignment Team’, each representing a key functional unit in\ndisaster operations. These agents were instantiated with independent LVLM instances and\ncustomised prompts to reflect their unique responsibilities. Table 1 presents the LVLM agents\nwe defined with a certain prompt input in our experiments. Agents interact through the CrewAI\ncoordination layer, which facilitates inter-agent communication, memory exchange, and\ndynamic task sequencing. Through this agent-oriented architecture, we modelled a multi-\nLVLM-powered disaster response framework that closely mirrors the structure and decision\nflow of actual post-disaster management teams.\n…\n”\n23. If the proposed model is practically implemented, how would it adapt to the rapid\nadvancements in AI tools? Given the rapid pace of AI development, what strategies or\nmechanisms would be in place to ensure the model remains up-to-date and effectively leverages\nnew capabilities?\nReply: We have included a discussion on the modular architecture of DisasTeller in section 4\nand the “Limitations and deployment considerations” part of the main text. This modular\narchitecture allows it for the seamless integration of new LVLMs and external tools as they\nbecome available. Strategies for periodic retraining, rigorous version control, and continuous\nperformance monitoring of embedded LVLMs can also ensure that the system remains state-\nof-the-art.\n“\n4. Methods\n4.1 Large Vision Language Models (LVLMs) with augmented generation\n…\nIn this study, we streamlined our framework by utilising the API of the most representative\nLVLM model GPT (version “gpt-4o-2024-08-06”), and we also conducted the additional\nvalidation through the local deployment and integration of Google’s open-source LVLM model\nGemma3-27B. The modularity of our framework allows for future integration of fine-tuned\nLVLMs for improved specialisation and robustness…\n4.3 Tools development\nTo maximise the capabilities of each LVLM agent, we designed a set of tools, mapped to agents'\nresponsibilities and modularly integrated into the pipeline of our framework. It is necessary to\nhighlight that additional tools can be easily integrated into this toolset according to\nrequirements due to the modularity of our framework.\n”\n“\n2.3 Limitations and deployment considerations\n…\nGiven the rapid evolution of AI, maintaining system relevance over time is a challenge. To\naddress this, DisasTeller is built with a modular architecture that allows seamless substitution\nof new LVLMs, tools, and reasoning modules. Periodic retraining, version control, and\nembedded performance monitoring can ensure future upgrades remain aligned with the\nsystem’s safety and usability requirements.\n”\n24. If something (the proposed framework results) goes wrong and leads to significant human\nor economic losses, who would be held accountable? Would the responsibility lie with the\norganizations implementing the system?\nReply: We discuss the ethical and legal considerations in the “Limitations and deployment\nconsiderations” part. We clarify that DisasTeller is intended solely as a decision support tool.\nResponsibility for final decisions remains with the human experts and the implementing\norganisations as per their defined workflows, which must also establish comprehensive risk\nmanagement and oversight protocols.\n“\n2.3 Limitations and deployment considerations\nAs a decision-support tool, DisasTeller is not intended to replace human authority. All final\ndecisions remain the responsibility of human operators and implementing organisations. To\nmaintain ethical deployment, robust risk management, legal compliance, and stakeholder\noversight are necessary. In the case of incorrect AI-generated decisions, traceability and\ntransparency of agent logic will be essential for accountability.\n”\nReviewer #2 (Remarks to the Author):\nThis paper presents an interesting work, named DisasTeller, which is a large vision language\nmodel (LVLM)-based framework for automating post-disaster management tasks including on-\nsite assessment, emergency alerts, resource allocation, and recovery planning. Assessment on\na case study of earthquake disaster showed that DisasTeller can reduce the response time\ncompared to the current human-based procedure. The paper can be further improved by\naddressing the following issues:\n1. Disaster response/rescue is a critical mission. Neural-network-based AI mainly lacks good\ninterpretability/explanability, and therefore it is not easy for decision-makers to acceptable the\nresults produced by blackbox-style AI. How does the proposed method tackle this issue?\nReply: We appreciate the reviewer’s valuable comment highlighting the crucial issue of\ninterpretability in AI-based disaster response systems. Since the inherent complexity of Neural-\nnetwork-based AI is difficult to be eliminated, we introduced the open-source LVLMs\n(Gemma3) embedded solution and sequentially output all the intermediate results which can\nbe checked by human in our framework design, to mitigate the interpretability concerns. We\nalso highlighted that it is important to keep human check in the running of our framework\nduring the practical deployment. We have augmented our manuscript with a discussion on\ninterpretability.\n“\n2.3 Limitations and deployment considerations\nAs with all neural network-based AI, LVLMs suffer from limited interpretability [27]. This\nposes challenges for acceptance in mission-critical applications like disaster response. While\nthe inherent complexity of neural networks is difficult to be eliminated, the integration of open-\nsource LVLMs (like Gemma3 in our study) in DisasTeller offers certain advantages in\nmitigating interpretability concerns. Due to the transparency of model architecture and\nparameters, open-source LVLMs allow full access to the architecture, weights, and training\nprocedures, enabling researchers and practitioners to inspect how the model operates\ninternally. Open-source LVLMs will also be under collective peer review and benchmarking\nwithin the research community, which often leads to faster identification of failure cases and\nbetter understanding of model behaviours. Benefiting from the attributes of open-source\nLVLMs, agents’ behaviours in DisasTeller will be more transparent and traceable. In addition,\nOur framework is designed to output not only final decisions but also the step-by-step\nreasoning results, and human can inspect the intermediate results (e.g., the visual evidence of\nannotated damage regions and the historical information retrieved from the internet as a\nreference for human allocation) without waiting the running finished, thus each intermediate\nreasoning progress and task output can be checked and allow human experts to verify how each\nconclusion was reached. Since fully resolving the interpretability challenge remains an open\nresearch problem in current stage, to enhance the trustworthiness of DisasTeller for practical\ndeployment, it is important to keep human check in the running process. This human-in-the-\nloop format ensures that AI outputs serve as assistive recommendations rather than\nunquestioned directives, thereby reducing the risk of over-reliance on black-box results.”\n2. The assessment on a relatively simple case study is not so sufficient to support the declaration.\nIt is expected to test DisasTeller on more disasters of various types, scales, and requirements.\nReply: We agree that broader validation is important. The revised manuscript has extended\nevaluations to additional disaster scenarios (floods and bushfires). We provide preliminary\nresults in the Appendix:\n“\nA.4 Flood scenario preliminary study\nThe broader validation for DisasTeller in different disaster scenarios is necessary. We\nconducted a preliminary case study of flood scenario utilising the data from the Australian\ngovernment open report [4]. The Tweed River affected by the flood locate in the northern region\nof New South Wales, Australia. Six affected local places with their local-view images, including\nTweed Regional Aquatic Centre, Tweed Regional Aquatic Centre Carpark, the corner of Wharf\nSt and Commercial Rd, the corner of Wharf St and Queen St, York Street, and Tumbulgum Rd\nnear Racecourse Rd, were selected in this study. According to the damage scale for flood [5],\nwe manually assessed the disaster grades of six local places as the ground truth: Tweed\nRegional Aquatic Centre – DS3; Tweed Regional Aquatic Centre Carpark – DS3; the corner\nof Wharf St and Commercial Rd – DS3; the corner of Wharf St and Queen St – DS2; York Street\n– DS3; Tumbulgum Rd near Racecourse Rd – DS4. According to the output content of\nDisasTeller in Fig. 13-b, the score for disaster grading task is 3/6 (wrong grading for Tweed\nRegional Aquatic Centre – DS4, the corner of Wharf St and Queen St – DS3, and York Street –\nDS4) with the total grading error +DS3. Fig. 14 presents the global map annotation results\nwith the accuracy score 4/6, and Table 4 shows the results for the evaluation of six output\nreports. In the alert map generation task, the LVLM agents need to identify the six affected\nlocal places from about one hundred location names in the global map. Finding some affected\nareas requires understanding the junction relationship of two places, like the corner of Wharf\nSt and Queen St, which makes it a difficult task even for humans. Considering the current ability\nof LVLM agents to understand spatial locations is still limited, the task-specific fine-tuning\ncould help enhance this ability.\na\nTasks: generate descriptions for post-floodimages; post-flooddamage grading with the respective locations; generate summary reports.\nTools: image interpretation tools for local on-site disaster description and global map analysis; web search tool for additional internet knowledge;\nfile search tool for reading the technical guideline manual file; map annotation tool for the alert map generation.\nb Input Post-disaster damage grading Alert map generation\nTweed Regional Tweed Regional Aquatic\nAquatic Centre Centre Carpark\n-Tweed Regional Aquatic Centre: DS4\n-Tweed Regional Aquatic Centre Carpark: DS3\nT an h d e c C o o r m ne m r e o r f c i W al h R ar d f St -The corner of Wharf St and Commercial Rd: DS3\nYork Street -The corner of Wharf St and Queen St: DS3\n-York Street: DS4\n-TumbulgumRd near Racecourse Rd: DS4\nThe corner of Wharf St TumbulgumRd near\nand Queen St Racecourse Rd Map Data: Google, ©2024 Map Data: Google, ©2024\nc\nReports generation\nExpert team report: Alerts team report: Emergency team report: Human resource Public notice report: Future reconstruction\nDisaster grades have been High-Risk Zones: Immediate Transportation allocation report: Organizational Operation: plan:\nassigned as follows: Tweed Regional Aquatic Recovery Needs: Allocation Plan: The recent flooding event Financial Budget:\nTweed Regional Aquatic Center, York Street -TumbulgumRoad near -Area A (Murwillumbah): in Tweed has caused Based on similar past\nCenter -DS4 (Extensive (Murwillumbah), Racecourse Road: It requires a team of 30 significant disruption and disasters, a preliminary\ndamage and disruption), TumbulgumRoad near complete road submersion medical staff, 50 rescue hardship to the local … budget of approximately\nTweed Regional Aquatic Center Racecourse Road. Avoid demands rapid water team members, 20 We are working closely AUD 150 million is\nCenter Carpark -DS3 high-risk areas, residents drainage and structural logistics support with local authorities and proposed …\n(Substantial damage),\nin DS4 zones should assessments, … personnel, … other agencies to ensure Plan of Action:\nCorner of Wharf Street and\nQueen Street -DS3 implement safety -Corners of Wharf Street: Resource allocation faces effective resource Immediate Response(0-3\n(Considerable impact on measures like sandbags need quick restoration to challenges such as distribution and logistical months): …\nlocal businesses and and prepare for potential facilitate movement and transportation issues due support. For further Long-term Resilience (1-\ninfrastructure)… evacuations … support local business … to damaged structures … assistance, … 3years): …\nFig. 13 The experimental process for post-flood case study: a. the descriptions for tasks of agents and tools\nassigned; b. the running process of DisasTeller and intermediate tasks; c. the final output reports of agents in\nDisasTeller.\n×\n×\nMap Data: Google, ©2024 Map Data: Google, ©2024\nAccuracy:4/6; Ave error: 235pixels Ground truth\nFig. 14 Evaluation of global map annotations for post-flood case study: DisasTeller’s autonomous annotations\nare in yellow, and annotations with distance error larger than 250 pixels are identified as wrong and marked as\nred cross.\nTable 4 EvaluatorGPT and human Evaluation for the output reports in post-flood case study.\nExpert Alert Emergency Human Public Future\nTeam Team Team Allocation Notice Reconstruction\nReport Report Report Report Report Plan\nEvaluatorGPT 6.0/10 7.0/10 6.0/10 8.0/10 6.0/10 6.0/10\nHumanEvaluator 6.2/10 6.5/10 7.1/10 7.3/10 6.8/10 7.1/10\nA.5 Bushfire scenario preliminary study\nAnother post-bushfire disaster scenario preliminary case study was conducted utilising the\nimages from Australian government historical data [6]. We defined six affected local places in\nQueensland, Australia, with their local-view images, including Tamborine Mountain, Gold\nCoast, Robina, Helensvale, Tweed Heads, and Kingscliff. After manual assessment according\nto the Australian fire danger rating system [7], the ground truth disaster grades for these six\naffected local places are: Tamborine Mountain – G4 (catastrophic); Gold Coast – G2 - G3\n(from high to extreme); Robina – G2 (high); Helensvale – G1 (moderate); Tweed Heads – G3\n- G4 (from extreme to catastrophic); Kingscliff – G4 (catastrophic). Based on the output of\nDisasTeller in Fig. 15, the score for disaster grading task is 4/6 (wrong grading for Robina –\nG3 and Helensvale – G2) with the total grading error +G2. Fig. 16 and Table 5 show the\nevaluation of global map annotations and the evaluation of six output reports for the post-\nbushfire case study. Since the running process of DisasTeller is the same for different disaster\nscenarios, the drawbacks are also quite similar. The future work will focus on addressing these\nissues and contributing to the practical deployment.\na\nTasks: generate descriptions for post-bushfireimages; post-bushfiredamage grading with the respective locations; generate summary reports.\nTools: image interpretation tools for local on-site disaster description and global map analysis; web search tool for additional internet knowledge;\nfile search tool for reading the technical guideline manual file; map annotation tool for the alert map generation.\nb Input Post-disaster damage grading Alert map generation\n-Tamborine Mountain: G4 (Catastrophic)\n-Gold Coast: G3 (Extreme)\n-Robina: G3 (Extreme)\n-Helensvale: G2 (High)\n-Tweed Heads: G4 (Catastrophic)\n-Kingscliff: G4 (Catastrophic)\nMap Data: Google, ©2024 Map Data: Google, ©2024\nc\nReports generation\nExpert team report: Alerts team report: Emergency team report: Human resource Public notice report: Future reconstruction\nPost-bushfire conditions Bushfire Severity and Immediate Transportation allocation report: Organizational Operation: plan:\nacross multiple locations : Potential Danger Areas: Recovery Needs: Allocation Plan: The recent bushfire Financial Budget:\nTamborineMountain, -TamborineMountain: -Tweed Heads: -Area B (Gold Coast): It disaster has caused An estimated budget of\nGold Coast, Robina, catastrophic bushfires Infrastructure recovery is requires 30 medical staff, significant devastation $500 million is proposed\nHelensvale, Tweed Heads, with formidable flames crucial for maintaining 25 rescue personnel, 15 across several regions … to cover infrastructure\nand Kingscliff. Utilizing sweeping through the access routes, … logistics support, … The Queensland Fire repair…\nvisual evidence from forests. The area is at -Kingscliff: The situation is fluid, Department has issued Plan of Action:\nimages and qualitative extreme risk due to dense Immediate attention requiring continuous continuous warnings and Prioritize restoring\ndisaster grading vegetation, with potential required for coastal roads, assessment and provided updates on the transportation …\nassessments, this report landslides during heavy ensuring safe passage for adjustment of resource situation, emphasizing the The reconstruction efforts\noutlines the severity and rains further exacerbating residents and emergency allocation based on real- need for community are expected to span 18-\nimpact of the bushfires.… the situation… personnel… time feedback… cooperation … 24 months…\nFig. 15 The experimental process for post-bushfire case study: a. the descriptions for tasks of agents and tools\nassigned; b. the running process of DisasTeller and intermediate tasks; c. the final output reports of agents in\nDisasTeller.\n×\n×\n×\nMap Data: Google, ©2024 Map Data: Google, ©2024\nAccuracy:3/6; Ave error: 287pixels Ground truth\nFig. 16 Evaluation of global map annotations for post-bushfire case study: DisasTeller’s autonomous\nannotations are in yellow, and annotations with distance error larger than 250 pixels are identified as wrong\nand marked as red cross.\nTable 5 EvaluatorGPT and human Evaluation for the output reports in post-bushfire case study.\nExpert Alert Emergency Human Public Future\nTeam Team Team Allocation Notice Reconstruction\nReport Report Report Report Report Plan\nEvaluatorGPT 7.0/10 6.0/10 9.0/10 8.0/10 7.0/10 8.0/10\nHumanEvaluator 7.7/10 6.3/10 8.4/10 7.5/10 7.1/10 7.6/10\n”\n3. As in many disasters, available rescue forces and relief supplies are insufficient in short\nresponse time, which is the most important challenge in relief planning and scheduling. It is\nunclear whether DisasTeller can or how DisasTeller tackle this challenge.\nReply: We have expanded our discussion on resource allocation under scarcity in the\n“Limitations and deployment considerations” part. Our framework’s human resource allocation\nmodule is designed to prioritise based on urgency and available supplies. Although the system\ndoes not create new resources, it optimises the deployment of limited ones by integrating spatial,\ntemporal, and historical data. This enhancement is discussed in detail in the revised section\n“Limitations and deployment considerations”.\n“2.3 Limitations and deployment considerations\nIn disasters, resources are rarely sufficient, and planning must balance urgency, impact, and\nlogistics. DisasTeller’s human allocation module incorporates historical and spatial references\nto prioritise areas with high estimated needs, but it cannot guarantee optimality. The practical\ndeployment could integrate real-time resource inventories or supply chain data to improve\nscheduling under constrained scenarios.\n”\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReferences\nMain text\n[13] J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F.L. Aleman, D. Almeida, J.\nAltenschmidt, S. Altman, S. Anadkat, Gpt-4 technical report, arXiv preprint arXiv:2303.08774\n(2023), https://arxiv.org/abs/2303.08774.\n[14] A. M. Bran, S. Cox, O. Schilter, C. Baldassari, A.D. White, P. Schwaller, Augmenting\nlarge language models with chemistry tools, Nature Machine Intelligence (2024), pp. 1-11,\nhttps://www.nature.com/articles/s42256-024-00832-8.\n[15] G. Bharathi Mohan, R. Prasanna Kumar, P. Vishal Krishh, A. Keerthinathan, G.\nLavanya, M.K.U. Meghana, S. Sulthana, S. Doss, An analysis of large language models: their\nimpact and potential applications, Knowledge and Information Systems (2024), pp. 1-24,\nhttps://link.springer.com/article/10.1007/s10115-024-02120-8.\n[16] T.B. Brown, Language models are few-shot learners, arXiv preprint arXiv:2005.14165\n(2020), https://arxiv.org/abs/2005.14165.\n[17] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language models are\nunsupervised multitask learners, OpenAI blog 1 (8) (2019), pp. 9,\nhttps://cdn.openai.com/better-language-\nmodels/language_models_are_unsupervised_multitask_learners.pdf.\n[18] Y. Liu, D. Iter, Y. Xu, S. Wang, R. Xu, C. Zhu, G-eval: Nlg evaluation using gpt-4 with\nbetter human alignment, arXiv preprint arXiv:2303.16634 (2023),\nhttps://arxiv.org/abs/2303.16634.\n[19] J. Yang, H. Xu, S. Mirzoyan, T. Chen, Z. Liu, Z. Liu, W. Ju, L. Liu, Z. Xiao, M. Zhang,\nPoisoning medical knowledge using large language models, Nature Machine Intelligence\n(2024), pp. 1-13, https://www.nature.com/articles/s42256-024-00899-3.\n[20] D.T. Nguyen, F. Ofli, M. Imran, P. Mitra, Damage assessment from social media\nimagery data during disasters, Proceedings of the 2017 IEEE/ACM international conference\non advances in social networks analysis and mining 2017, (2017), pp. 569-576,\nhttps://doi.org/10.1145/3110025.311010.\n[21] G. Grünthal, European macroseismic scale 1998 (EMS-98), (1998), https://www.gfz-\npotsdam.de/en/section/seismic-hazard-and-risk-dynamics/data-products-services/ems-98-\neuropean-macroseismic-scale.\n[22] L. Chen, M. Zaharia, J. Zou, How is ChatGPT’s behavior changing over time?,\nHarvard Data Science Review 6 (2) (2024), https://arxiv.org/abs/2307.09009.\n[23] G. Team, A. Kamath, J. Ferret, S. Pathak, N. Vieillard, R. Merhej, S. Perrin, T.\nMatejovicova, A. Ramé, M. Rivière, Gemma 3 technical report, arXiv preprint\narXiv:2503.19786 (2025), https://arxiv.org/abs/2503.19786.\n[24] G. Ninivaggi, Ishikawa earthquake response sees growing opposition scrutiny, The\nJapan Times, 2024, https://www.japantimes.co.jp/news/2024/01/11/japan/politics/kishida-\ndisaster-response/.\n[25] I. Augenstein, T. Baldwin, M. Cha, T. Chakraborty, G.L. Ciampaglia, D. Corney, R.\nDiResta, E. Ferrara, S. Hale, A. Halevy, Factuality challenges in the era of large language\nmodels and opportunities for fact-checking, Nature Machine Intelligence (2024), pp. 1-12,\nhttps://www.nature.com/articles/s42256-024-00881-z.\n[26] N. Chrisman, Full circle: More than just social implications of GIS, Cartographica: The\nInternational Journal for Geographic Information and Geovisualization 40 (4) (2005), pp. 23-\n35, https://utppublishing.com/doi/abs/10.3138/8U64-K7M1-5XW3-2677.\n[27] L. Gao, J. Schulman, J. Hilton, Scaling laws for reward model overoptimization,\nInternational Conference on Machine Learning, (2023), pp. 10835-10866,\nhttps://proceedings.mlr.press/v202/gao23h.html.\n[31] Q. Dong, L. Li, D. Dai, C. Zheng, J. Ma, R. Li, H. Xia, J. Xu, Z. Wu, T. Liu, A survey\non in-context learning, arXiv preprint arXiv:2301.00234 (2022),\nhttps://arxiv.org/abs/2301.00234.\n[32] P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis,\nW.-t. Yih, T. Rocktäschel, Retrieval-augmented generation for knowledge-intensive nlp tasks,\nAdvances in neural information processing systems 33 (2020), pp. 9459-9474,\nhttps://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-\nAbstract.html.\n[33] H. Chase, LangChain, 2022, https://github.com/langchain-ai/langchain.\n[34] J. Moura, Crew AI, 2024, https://github.com/crewAIInc/crewAI.\n[35] Duckduckgo-search, 2024, https://github.com/deedy5/duckduckgo_search.\n[36] L. Richardson, Beautiful soup documentation, April, 2007,\nhttps://readthedocs.org/projects/beautiful-soup-4/downloads/pdf/latest/.\n[37] F. Lundh, M. Ellis, Python imaging library (PIL), 2012, https://github.com/python-\npillow/Pillow.\nSupplementary information\n[4] Post 2022 Event Flood Behaviour Analysis - Tweed River, New South Wales\nGovernment, Department of Planning and Environment Australia, 2024,\nhttps://flooddata.ses.nsw.gov.au/flood-projects/post-2022-event-flood-behaviour-analysis-\ntweed-river-report-only.\n[5] I. Kelman, Physical flood vulnerability of residential properties in coastal, eastern\nEngland, University of Cambridge, United Kingdom, 2003.\n[6] Resilient and ready for the people of queensland, Queensland Fire Department,\nAustralia, 2019, https://www.fire.qld.gov.au/sites/default/files/2021-04/Response-Magazine-\nDec2019_0.pdf.\n[7] Australian Fire Danger Rating System, Australian Government, National Emergency\nManagement Agency, Australia, 2022, https://afdrs.com.au/.\nManuscript Number: NCOMMS-24-76093A\nIntegration of Large Vision Language Models for Efficient Post-disaster Damage\nAssessment and Reporting\nReply: The authors would like to thank the reviewers and editor for their time and comments;\nthey have helped significantly improve the work and provided the opportunity for us to clarify\nrelevant technical details and extend the analysis. All points raised were thoroughly addressed.\nThis rebuttal provides itemised responses to each comment alongside the changes made to the\npaper. For further clarification, the changes are highlighted in the revised paper.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nAuthors have revised and expanded different sections of the main text and supplementary\nmaterials document. In particular, the limitations section discussed the concerns and comments\npresented by the reviewer regarding utilizing agentic large language models in disaster\nresponse and management. The study now emphasizes more the role of the proposed\nframework as a supplementary tool for decision makers rather than a fully automated\nequivalent to emergency management teams. Overall, the second submission shows an\nimprovement by clarifying objectives and identifying practical limitations of the framework.\nHowever, to accept this manuscript, we believe the following minor revisions are required:\nThe findings stated in the abstract (lines 14-17) are general and arguably contradict the findings\nshown throughout the study and summarized in lines 356-360 and 376-380. The abstract\nmentions the framework’s effectiveness while neglecting its current limitations and how\nhumans may still need to validate intermediate processes (at least until robust validation shows\notherwise). The conclusions section does a better job in balancing between discussing the\npotential benefits of this framework and its shortcomings. Minor revision of the abstract is\nrecommended to maintain consistency in the manuscript and avoid misrepresenting its findings.\nThanks for this comment. As suggested, we have modified the abstract to keep consistency\nwith the conclusion, which reads as follows:\n“Traditional natural disaster response involves significant coordinated teamwork where speed\nand efficiency are key. Nonetheless, human limitations can delay critical actions and\ninadvertently increase human and economic losses. Agentic Large Vision Language Models\n(LVLMs) offer a new avenue to address this challenge, with the potential for substantial socio-\neconomic impact, particularly by improving resilience and resource access in underdeveloped\nregions. We introduce DisasTeller, the first multi-LVLM-powered framework designed to\nautomate tasks in post-disaster management, including on-site assessment, emergency alerts,\nresource allocation, and recovery planning. By coordinating four specialised LVLM agents\nwith GPT-4 as the core, DisasTeller can accelerate disaster response activities, reducing\nhuman execution time and structuring information flow. Our evaluation shows both benefits\nand challenges: while DisasTeller streamlines coordination and report generation, errors in\nearly-stage assessments may propagate downstream, highlighting the need for human\nvalidation and improved LVLM accuracy. This framework therefore acts as a complementary\nsupport system to expert teams, bridging the gap between traditional response methods and\nemerging LVLM-driven efficiency, while highlighting the importance of continued refinement\nand collaboration for safe, trustworthy deployment.”\nSome recommendations and additional notes for future work are presented below:\n(a) The validation process could benefit from less AI and more humans as basis for evaluation.\nFor example, the ground truth values in Table 2 and Table 3 from the supplementary materials\ndocument were obtained using the AI web search tool. It would benefit the evaluation if the\nground truth values were estimated by a human.\n(b) Retrieval-augmented generation could include more human-curated documents to add to\nthe agents’ knowledge base. High reliance on the web search tool to retrieve knowledge seems\nquestionable unless the web search tool is made to be more robust.\n(c) It would be interesting to see the token generation rate for both the GPT-4 model and locally\nhosted models (e.g., input and output tokens/second). This would help estimate the realistic\ncost associated with either the API calls or local hardware (e.g., GPUs) needed to implement\nthe framework.\n(d) Based on the images provided in the supplementary materials document, the map annotation\ntool seems to be consistently unreliable. Consider integrating the agents with GIS tools such as\nQGIS to potentially improve the spatial awareness.\n(e) The bushfire scenario case study presented in the supplementary materials document, even\nas a proof-of-concept example, does not look convincing. The images and their corresponding\ndamage descriptions are very general. Perhaps detailed aerial and satellite images are more\nsuitable for evaluating the damage of wildfires.\nThe authors thank the reviewer for these recommendations and suggestions for future work.\nWe have discussed and included these recommendations and additional notes for future work\nin the revised main manuscript and Supplementary Information.\n(a) In regard to the validation process benefiting from less AI and more humans as basis for\nevaluation, the authors would like to mention that due to the incomplete disaster data in this\nproof-of-concept case study, the human evaluation would be subjective and the estimated\nground truth values are in an interval and challenging to use for quantitative analysis. In Tables\n2 and 3 from the supplementary materials, we chose the suggested values by DisasTeller in this\ninterval as the reference, thus able to evaluate the exclusive error propagation of DisasTeller\nwithout introducing and unquantified human subjective bias. We have highlighted this in the\nsection A.3 in the Supplementary Information:\n“A.3 Error propagation study\n(…) To get the ground truth downstream decision variables generated by the other three agents\n(the num of shelters, hospitals, human allocations, and the reconstruction budget), we fixed the\ninitial disaster grades with the ground truth and then average the output downstream decision\nvariables of three independent running as the ground truth (150 rescue workers, 100 medical\nstaff, 80 logistics support personnel, 3 emergency shelters, 1 hospital, and $0.05 billion\nreconstruction budgets). Due to the incomplete disaster data in this proof-of-concept case study,\nthe human evaluation would be subjective, and the estimated ground truth downstream decision\nvariables are in an interval and challenging to use for quantitative analysis. We chose the\ndownstream decision variables output by DisasTeller in this interval as the ground truth, thus\nable to evaluate the exclusive error propagation of DisasTeller without introducing human\nsubjective bias.”\n(b) We also acknowledge that incorporating more human-curated documents (e.g., government\nreports or agency-specific protocols) would greatly improve robustness. In our current work,\nretrieval was restricted to openly available data (e.g., the EMS98 guideline [21]) due to lack of\naccess to private human-curated documents. We have added this as a clear future direction in\nthe section of main manuscript “2.3 Limitations and deployment considerations”, highlighting\nthe integration of curated disaster knowledge bases and private institutional datasets to\nstrengthen retrieval reliability:\n“2.3 Limitations and deployment considerations\n(…) The current retrieval-augmented generation component relies highly on web search to\nobtain reference information. While this ensures adaptability, the reliability of open-web data\ncan fluctuate. Future iterations should integrate more human-curated or institutional\nknowledge bases to enhance factual grounding, especially for region-specific response\nstandards and resource benchmarks.”\n(c) Thanks for the recommendation to include the token generation rate for both the GPT-4\nmodel and the locally hosted model. We have conducted the test on a high-performance\nworkstation with the A100 80G GPU and Intel Xeon Platinum 8352V CPU. The results have\nbeen added to the Section 2.2.1 of the main manuscript:\n“ 2.2.1 Response time of DisasTeller\n(…) However, unlike human teams, taking several hours to communicate and intervene, from\nimage inspection to disaster-relevant report generation, LVLM-driven DisasTeller would only\nutilise a few minutes (GPT-4o with 3/35 input/output tokens per second, Gemma3-27B with\n2/10 input/output tokens per second, on a workstation with the NVIDIA A100 80G GPU and\nIntel Xeon Platinum 8352V CPU) to coordinate all the agents and complete the given damage\nassessment tasks, thus simplifying the disaster response process and exhibit the potential to\naccelerate this process through the autonomous procedure.”\n(d) Our evaluation indeed found repeated errors in spatial reasoning and annotation distance\nexceeding 250 pixels. As the reviewer recommends, we emphasised in the revised section of\nmain manuscript “2.3 Limitations and deployment considerations” that integrating GIS\nplatforms (e.g., QGIS) could provide structured geospatial references and reduce misalignment.\nThis will be added as a specific future improvement to enhance the agents’ spatial grounding\nand geolocation accuracy.\n“2.3 Limitations and deployment considerations\n(…) Some errors identified during evaluation, such as shelter suggestions near collapsed\nstructures, highlight deficiencies of LVLM-based spatial reasoning and the reliability of the\nmap annotation tool used for geospatial understanding. While the agents can interpret image\ncaptions and geolocation inputs, their spatial logic and environmental safety awareness remain\nconstrained. The present annotation module, built primarily for visual-semantic reasoning,\nlacks integration with established geographic information system (GIS) infrastructures. In\nfuture work, coupling DisasTeller’s agents with GIS platforms such as QGIS [26] could enable\naccess to structured spatial layers (e.g., hazard zones, elevation, infrastructure networks) that\ncan guide safer and more context-aware reasoning. Enhancing spatial awareness through GIS-\nlinked heuristics and formal geospatial constraints [27] will therefore be a key direction for\nimproving both reliability and interpretability.”\n(e) We acknowledge that the current bushfire case study, which is based on general local-view\nimages, provides only a limited evaluation of wildfire damage. As the reviewer rightly suggests,\nhigh-resolution aerial and satellite imagery would enable a more rigorous validation of wildfire\ndamage assessments. However, since disaster scenario datasets are often restricted or privately\nheld, our current access to such data is limited. In future work, we plan to collaborate with\nrelevant stakeholders and government agencies to extend the framework toward multi-\nresolution remote-sensing data integration (including drone and satellite imagery). This\nenhancement will strengthen the credibility of the case studies and better reflect real-world\ndeployment conditions. We have highlighted this improvement in Section A.6 of the revised\nsupplementary materials and updated the discussion in Section “2.3 Limitations and\ndeployment considerations” of the main manuscript.\n“A.6 Bushfire scenario preliminary study\n(…) The implementation of this case study is based on general local-view images, which can\nbe replaced with high-resolution aerial and satellite imagery to enable a more rigorous\nvalidation of wildfire damage assessments. However, since disaster scenario datasets are often\nrestricted or privately held, our current access to such data is limited. In future work, we plan\nto collaborate with relevant stakeholders and government agencies to extend the framework\ntoward multi-resolution remote-sensing data integration (including drone and satellite\nimagery). This enhancement will strengthen the credibility of the case studies and better reflect\nreal-world deployment conditions.”\n“2.3 Limitations and deployment considerations\n(…) However, logistical barriers in damaged or inaccessible areas, as well as limited access\nto private or proprietary datasets, remain major challenges. Moreover, any data like multi-\nangle, high-resolution images (e.g., aerial and satellite data for wildfire contexts) that can\nimprove standard human assessment would also improve the contribution of DisasTeller.\nFuture collaboration with relevant stakeholders and government agencies for shared image\ndatasets could further strengthen the system’s credibility and better reflect real-world\ndeployment conditions. ”\nReviewer #2 (Remarks to the Author):\nI have three major comments on the previous version of this manuscript. In this revision, I am\nsatisfied with the authors' replies/responses to the first two comments. However, the response\nto the third of my previous comments is not sufficient. That is, the authors stated that\nDisasTeller optimises resource distribution by integrating spatial, temporal, and historical data,\nbut did not provide sufficient details on how to achieve the optimality. In the domains of\noperations research and computer algorithms, \"optimising\" refers to maximizing profits or\nminimizing losses in terms of one or multiple objective function in a mathematically accurate\nway. \"optimising\" does not simply refer to \"improving\". Even when the optimality cannot be\nguaranteed, the improvement on profits/losses should be quantitatively strictly proved or\nexperimentally validated. This paper can be essentially improved by demonstrating the\nDisasTeller's capability of resource distribution optimisation mathematically and quantitatively.\nReply: We thank Reviewer #2 for acknowledging that our responses to the first two comments\nwere satisfactory. We now focus on the third point regarding optimisation. We acknowledge\nthe reviewer’s concern. The use of “optimising” was too broad and might have been\nmisinterpreted as a mathematically rigorous optimisation process. Therefore, in the revised\nmain manuscript, we reworded “optimising” to “improving allocation relevance of priority\nrules rather than guaranteeing optimality” to avoid confusion:\n“ 2.3 Limitations and deployment considerations\n(…) In real-world disasters, available resources are often insufficient, and planning must\nbalance urgency, impact, and logistical constraints. The scarcity of rescue forces and relief\nsupplies makes mathematically optimal allocation nearly impossible within short response\ntimes. DisasTeller’s human allocation module does not perform strict mathematical\noptimisation in the sense of operations research. Instead, it emulates rule-based prioritisation\nand dynamic reallocation, reflecting how human decision-makers act under uncertainty.\nSpecifically, it integrates spatial, temporal, and historical cues to prioritise areas with higher\nestimated damage severity, thereby improving allocation relevance rather than guaranteeing\noptimality. DisasTeller is designed as a decision-support framework that assists human\nplanners in rapidly generating and revising preliminary allocation plans. Future works could\nintegrate real-time resource inventories or supply chain data and introduce more specific\npriority rules to improve scheduling under constrained scenarios.”\nAs mentioned by the reviewer in comment 3 in the previous round of revisions “As in many\ndisasters, available rescue forces and relief supplies are insufficient in short response time,\nwhich is the most important challenge in relief planning and scheduling. It is unclear whether\nDisasTeller can or how DisasTeller tackle this challenge”, we agree that in real-world disaster\nscenarios, the scarcity of rescue forces and relief supplies is the most fundamental challenge.\nIn the emergent disaster scenarios, ensuring the optimal allocation under resource scarcity is\nalways difficult and allocation plan is usually decided by rule-based prioritisation (e.g., priority\naccording to the damage severity of different areas) with dynamic reallocation after real-time\non-site inspection [4]. Therefore, we conducted an extra experiment in the revised Section A.4\nof the Supplementary Information, to evaluate the ability of DisasTeller for real-time dynamic\nallocation under scarce resources by rule-based prioritisation (allocation decision according to\nthe damage severity of the local areas). The results are presented as follows:\n“A.4 Allocation under scarce resources\nIn real-world disaster scenarios, the scarcity of rescue forces and relief supplies is the most\nfundamental challenge. In the emergent disaster scenarios, ensuring the optimal allocation\nunder resource scarcity is always difficult and allocation plan is usually decided by rule-based\nprioritisation (e.g., priority according to the damage severity of different areas) with dynamic\nreallocation after real-time on-site inspection [4]. DisasTeller is also designed to mitigate the\nimpact of scarcity, following the decision-making logic of the real situation where human\nexperts need to inspect the on-site scenarios and then make decisions based on rule-based\nprioritisation and dynamic reallocation.\nWe conducted an extra experiment for the earthquake case to simulate the real-time dynamic\nallocation by rule-based prioritisation (allocation decision according to the damage severity\nof the local areas) with scarce resources in disasters. In this experiment, we assumed the\navailable rescue resources are insufficient with only 75 rescue workers, 50 medical staff, and\n40 logistic support personnel existing. Extra information with current scarce resource (“only\n75 rescue workers, 50 medical staff, and 40 logistic support personnel existing, allocated\ncurrent scarce resource according to the damage severity of the local areas”) was injected into\nDisasTeller framework to mimic the real-time dynamic information input, thus evaluating the\nability of DisasTeller to allocate scarce resources under specific rule-based prioritisation. The\ndamage severity grading of local areas was manually assessed according to the technical\nguideline EMS98 [21]: Wajima Drama Memorial Hall – G1; Hama Street – G4; Concrete\nBridge – G5; Central Nishikigawa Street – G1; North Asaichi Street – G3; South Central\nAsaichi Street – G4. According to the disaster grading percentage of different local areas (the\nsum of grading for six local areas is G18), we manually allocated the scarce resources with\n1/18, 4/18, 5/18, 1/18, 3/18, 4/18 for these six locations, respectively.\nTable 4 Comparison of the scarce resources allocation results\nRescue Medical Logistic Support\nLocal Areas\nWorkers Staff Personnel\nWajima Drama Memorial\n4.2 2.8 2.2\nHall\nHama Street 16.7 11.1 8.9\nConcrete Bridge 20.8 13.9 11.1\nManual\nCentral Nishikigawa Street 4.2 2.8 2.2\nAllocation\nNorth Asaichi Street 12.5 8.3 6.7\nSouth Central Asaichi\n16.7 11.1 8.9\nStreet\nWajima Drama Memorial\n5 6 4\nHall\nHama Street 15 10 8\nDisasTeller\nConcrete Bridge 20 10 10\nCentral Nishikigawa Street 5 6 4\nNorth Asaichi Street 10 8 6\nSouth Central Asaichi\n15 10 8\nStreet\nMean Square Root 3.4\nWajima Drama Memorial\n12.5 8.3 6.7\nHall\nHama Street 12.5 8.3 6.7\nConcrete Bridge 12.5 8.3 6.7\nEvenly\nCentral Nishikigawa Street 12.5 8.3 6.7\nAllocated\nNorth Asaichi Street 12.5 8.3 6.7\nSouth Central Asaichi\n12.5 8.3 6.7\nStreet\nMean Square Root 23.3\nAs shown in Table 4, compared with the “Evenly Allocated” strategy, DisasTeller reduces 85.4%\nmean square root deviation from the manual allocation, showcasing its ability to perform rule-\nbased dynamic reallocation. DisasTeller is designed to provide preliminary suggestions and\naims to accelerate the human decision-making process, not to achieve the optimal scarce\nresource allocation that is even difficult to realise in real scenarios.”\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReferences\n[4] Y. Wang, Multiperiod optimal allocation of emergency resources in support of cross-\nregional disaster sustainable rescue, International Journal of Disaster Risk Science 12 (3)\n(2021), pp. 394-409, https://link.springer.com/article/10.1007/s13753-021-00347-5.\n[21] G. Grünthal, European macroseismic scale 1998 (EMS-98), (1998), https://www.gfz-\npotsdam.de/en/section/seismic-hazard-and-risk-dynamics/data-products-services/ems-98-\neuropean-macroseismic-scale.\n[26] M. Rosas-Chavoya, J.L. Gallardo-Salazar, P.M. López-Serrano, P.C. Alcántara-\nConcepción, A.K. León-Miranda, QGIS a constantly growing free and open-source geospatial\nsoftware contributing to scientific development, Cuadernos de Investigación Geográfica 48 (1)\n(2022), pp. 197-213, https://doi.org/10.18172/cig.5143.\n[27] N. Chrisman, Full circle: More than just social implications of GIS, Cartographica: The\nInternational Journal for Geographic Information and Geovisualization 40 (4) (2005), pp. 23-\n35, https://utppublishing.com/doi/abs/10.3138/8U64-K7M1-5XW3-2677."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Authors have revised and expanded different sections of the main text and supplementary materials document. In particular, the limitations section discussed the concerns and comments presented by the reviewer regarding utilizing agentic large language models in disaster response and management. The study now emphasizes more the role of the proposed framework as a supplementary tool for decision makers rather than a fully automated equivalent to emergency management teams. Overall, the second submission shows an improvement by clarifying objectives and identifying practical limitations of the framework.\nHowever, to accept this manuscript, we believe the following minor revisions are required:\nThe findings stated in the abstract (lines 14-17) are general and arguably contradict the findings shown throughout the study and summarized in lines 356-360 and 376-380. The abstract mentions the framework’s effectiveness while neglecting its current limitations and how humans may still need to validate intermediate processes (at least until robust validation shows otherwise). The conclusions section does a better job in balancing between discussing the potential benefits of this framework and its shortcomings. Minor revision of the abstract is recommended to maintain consistency in the manuscript and avoid misrepresenting its findings.\nSome recommendations and additional notes for future work are presented below:\nThe validation process could benefit from less AI and more humans as basis for evaluation. For example, the ground truth values in Table 2 and Table 3 from the supplementary materials document were obtained using the AI web search tool. It would benefit the evaluation if the ground truth values were estimated by a human.\nRetrieval-augmented generation could include more human-curated documents to add to the agents’ knowledge base. High reliance on the web search tool to retrieve knowledge seems questionable unless the web search tool is made to be more robust.\nIt would be interesting to see the token generation rate for both the GPT-4 model and locally hosted models (e.g., input and output tokens/second). This would help estimate the realistic cost associated with either the API calls or local hardware (e.g., GPUs) needed to implement the framework.\nBased on the images provided in the supplementary materials document, the map annotation tool seems to be consistently unreliable. Consider integrating the agents with GIS tools such as QGIS to potentially improve the spatial awareness.\nThe bushfire scenario case study presented in the supplementary materials document, even as a proof-of-concept example, does not look convincing. The images and their corresponding damage descriptions are very general. Perhaps detailed aerial and satellite images are more suitable for evaluating the damage of wildfires.", "reviewer_2": "I have three major comments on the previous version of this manuscript. In this revision, I am satisfied with the authors' replies/responses to the first two comments. However, the response to the third of my previous comments is not sufficient.That is, the authors stated that DisasTeller optimises resource distribution by integrating spatial, temporal, and historical data, but did not provide sufficient details on how to achieve the optimality. In the domains of operations research and computer algorithms, \"optimising\" refers to maximizing profits or minimizing losses in terms of one or multiple objective function in a mathematically accurate way. \"optimising\" does not simply refer to \"improving\". Even when the optimality cannot be guaranteed, the improvement on profits/losses should be quantitatively strictly proved or experimentally validated. This paper can be essentially improved by demonstrating the DisasTeller's capability of resource distribution optimization mathematically and quantitatively.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Manuscript Number: NCOMMS-24-76093A\nIntegration of Large Vision Language Models for Efficient Post-disaster Damage\nAssessment and Reporting\nReply: The authors would like to thank the reviewers and editor for their time and comments;\nthey have helped significantly improve the work and provided the opportunity for us to clarify\nrelevant technical details and extend the analysis. All points raised were thoroughly addressed.\nThis rebuttal provides itemised responses to each comment alongside the changes made to the\npaper. For further clarification, the changes are highlighted in the revised paper.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nAuthors have revised and expanded different sections of the main text and supplementary\nmaterials document. In particular, the limitations section discussed the concerns and comments\npresented by the reviewer regarding utilizing agentic large language models in disaster\nresponse and management. The study now emphasizes more the role of the proposed\nframework as a supplementary tool for decision makers rather than a fully automated\nequivalent to emergency management teams. Overall, the second submission shows an\nimprovement by clarifying objectives and identifying practical limitations of the framework.\nHowever, to accept this manuscript, we believe the following minor revisions are required:\nThe findings stated in the abstract (lines 14-17) are general and arguably contradict the findings\nshown throughout the study and summarized in lines 356-360 and 376-380. The abstract\nmentions the framework’s effectiveness while neglecting its current limitations and how\nhumans may still need to validate intermediate processes (at least until robust validation shows\notherwise). The conclusions section does a better job in balancing between discussing the\npotential benefits of this framework and its shortcomings. Minor revision of the abstract is\nrecommended to maintain consistency in the manuscript and avoid misrepresenting its findings.\nThanks for this comment. As suggested, we have modified the abstract to keep consistency\nwith the conclusion, which reads as follows:\n“Traditional natural disaster response involves significant coordinated teamwork where speed\nand efficiency are key. Nonetheless, human limitations can delay critical actions and\ninadvertently increase human and economic losses. Agentic Large Vision Language Models\n(LVLMs) offer a new avenue to address this challenge, with the potential for substantial socio-\neconomic impact, particularly by improving resilience and resource access in underdeveloped\nregions. We introduce DisasTeller, the first multi-LVLM-powered framework designed to\nautomate tasks in post-disaster management, including on-site assessment, emergency alerts,\nresource allocation, and recovery planning. By coordinating four specialised LVLM agents\nwith GPT-4 as the core, DisasTeller can accelerate disaster response activities, reducing\nhuman execution time and structuring information flow. Our evaluation shows both benefits\nand challenges: while DisasTeller streamlines coordination and report generation, errors in\nearly-stage assessments may propagate downstream, highlighting the need for human\nvalidation and improved LVLM accuracy. This framework therefore acts as a complementary\nsupport system to expert teams, bridging the gap between traditional response methods and\nemerging LVLM-driven efficiency, while highlighting the importance of continued refinement\nand collaboration for safe, trustworthy deployment.”\nSome recommendations and additional notes for future work are presented below:\n(a) The validation process could benefit from less AI and more humans as basis for evaluation.\nFor example, the ground truth values in Table 2 and Table 3 from the supplementary materials\ndocument were obtained using the AI web search tool. It would benefit the evaluation if the\nground truth values were estimated by a human.\n(b) Retrieval-augmented generation could include more human-curated documents to add to\nthe agents’ knowledge base. High reliance on the web search tool to retrieve knowledge seems\nquestionable unless the web search tool is made to be more robust.\n(c) It would be interesting to see the token generation rate for both the GPT-4 model and locally\nhosted models (e.g., input and output tokens/second). This would help estimate the realistic\ncost associated with either the API calls or local hardware (e.g., GPUs) needed to implement\nthe framework.\n(d) Based on the images provided in the supplementary materials document, the map annotation\ntool seems to be consistently unreliable. Consider integrating the agents with GIS tools such as\nQGIS to potentially improve the spatial awareness.\n(e) The bushfire scenario case study presented in the supplementary materials document, even\nas a proof-of-concept example, does not look convincing. The images and their corresponding\ndamage descriptions are very general. Perhaps detailed aerial and satellite images are more\nsuitable for evaluating the damage of wildfires.\nThe authors thank the reviewer for these recommendations and suggestions for future work.\nWe have discussed and included these recommendations and additional notes for future work\nin the revised main manuscript and Supplementary Information.\n(a) In regard to the validation process benefiting from less AI and more humans as basis for\nevaluation, the authors would like to mention that due to the incomplete disaster data in this\nproof-of-concept case study, the human evaluation would be subjective and the estimated\nground truth values are in an interval and challenging to use for quantitative analysis. In Tables\n2 and 3 from the supplementary materials, we chose the suggested values by DisasTeller in this\ninterval as the reference, thus able to evaluate the exclusive error propagation of DisasTeller\nwithout introducing and unquantified human subjective bias. We have highlighted this in the\nsection A.3 in the Supplementary Information:\n“A.3 Error propagation study\n(…) To get the ground truth downstream decision variables generated by the other three agents\n(the num of shelters, hospitals, human allocations, and the reconstruction budget), we fixed the\ninitial disaster grades with the ground truth and then average the output downstream decision\nvariables of three independent running as the ground truth (150 rescue workers, 100 medical\nstaff, 80 logistics support personnel, 3 emergency shelters, 1 hospital, and $0.05 billion\nreconstruction budgets). Due to the incomplete disaster data in this proof-of-concept case study,\nthe human evaluation would be subjective, and the estimated ground truth downstream decision\nvariables are in an interval and challenging to use for quantitative analysis. We chose the\ndownstream decision variables output by DisasTeller in this interval as the ground truth, thus\nable to evaluate the exclusive error propagation of DisasTeller without introducing human\nsubjective bias.”\n(b) We also acknowledge that incorporating more human-curated documents (e.g., government\nreports or agency-specific protocols) would greatly improve robustness. In our current work,\nretrieval was restricted to openly available data (e.g., the EMS98 guideline [21]) due to lack of\naccess to private human-curated documents. We have added this as a clear future direction in\nthe section of main manuscript “2.3 Limitations and deployment considerations”, highlighting\nthe integration of curated disaster knowledge bases and private institutional datasets to\nstrengthen retrieval reliability:\n“2.3 Limitations and deployment considerations\n(…) The current retrieval-augmented generation component relies highly on web search to\nobtain reference information. While this ensures adaptability, the reliability of open-web data\ncan fluctuate. Future iterations should integrate more human-curated or institutional\nknowledge bases to enhance factual grounding, especially for region-specific response\nstandards and resource benchmarks.”\n(c) Thanks for the recommendation to include the token generation rate for both the GPT-4\nmodel and the locally hosted model. We have conducted the test on a high-performance\nworkstation with the A100 80G GPU and Intel Xeon Platinum 8352V CPU. The results have\nbeen added to the Section 2.2.1 of the main manuscript:\n“ 2.2.1 Response time of DisasTeller\n(…) However, unlike human teams, taking several hours to communicate and intervene, from\nimage inspection to disaster-relevant report generation, LVLM-driven DisasTeller would only\nutilise a few minutes (GPT-4o with 3/35 input/output tokens per second, Gemma3-27B with\n2/10 input/output tokens per second, on a workstation with the NVIDIA A100 80G GPU and\nIntel Xeon Platinum 8352V CPU) to coordinate all the agents and complete the given damage\nassessment tasks, thus simplifying the disaster response process and exhibit the potential to\naccelerate this process through the autonomous procedure.”\n(d) Our evaluation indeed found repeated errors in spatial reasoning and annotation distance\nexceeding 250 pixels. As the reviewer recommends, we emphasised in the revised section of\nmain manuscript “2.3 Limitations and deployment considerations” that integrating GIS\nplatforms (e.g., QGIS) could provide structured geospatial references and reduce misalignment.\nThis will be added as a specific future improvement to enhance the agents’ spatial grounding\nand geolocation accuracy.\n“2.3 Limitations and deployment considerations\n(…) Some errors identified during evaluation, such as shelter suggestions near collapsed\nstructures, highlight deficiencies of LVLM-based spatial reasoning and the reliability of the\nmap annotation tool used for geospatial understanding. While the agents can interpret image\ncaptions and geolocation inputs, their spatial logic and environmental safety awareness remain\nconstrained. The present annotation module, built primarily for visual-semantic reasoning,\nlacks integration with established geographic information system (GIS) infrastructures. In\nfuture work, coupling DisasTeller’s agents with GIS platforms such as QGIS [26] could enable\naccess to structured spatial layers (e.g., hazard zones, elevation, infrastructure networks) that\ncan guide safer and more context-aware reasoning. Enhancing spatial awareness through GIS-\nlinked heuristics and formal geospatial constraints [27] will therefore be a key direction for\nimproving both reliability and interpretability.”\n(e) We acknowledge that the current bushfire case study, which is based on general local-view\nimages, provides only a limited evaluation of wildfire damage. As the reviewer rightly suggests,\nhigh-resolution aerial and satellite imagery would enable a more rigorous validation of wildfire\ndamage assessments. However, since disaster scenario datasets are often restricted or privately\nheld, our current access to such data is limited. In future work, we plan to collaborate with\nrelevant stakeholders and government agencies to extend the framework toward multi-\nresolution remote-sensing data integration (including drone and satellite imagery). This\nenhancement will strengthen the credibility of the case studies and better reflect real-world\ndeployment conditions. We have highlighted this improvement in Section A.6 of the revised\nsupplementary materials and updated the discussion in Section “2.3 Limitations and\ndeployment considerations” of the main manuscript.\n“A.6 Bushfire scenario preliminary study\n(…) The implementation of this case study is based on general local-view images, which can\nbe replaced with high-resolution aerial and satellite imagery to enable a more rigorous\nvalidation of wildfire damage assessments. However, since disaster scenario datasets are often\nrestricted or privately held, our current access to such data is limited. In future work, we plan\nto collaborate with relevant stakeholders and government agencies to extend the framework\ntoward multi-resolution remote-sensing data integration (including drone and satellite\nimagery). This enhancement will strengthen the credibility of the case studies and better reflect\nreal-world deployment conditions.”\n“2.3 Limitations and deployment considerations\n(…) However, logistical barriers in damaged or inaccessible areas, as well as limited access\nto private or proprietary datasets, remain major challenges. Moreover, any data like multi-\nangle, high-resolution images (e.g., aerial and satellite data for wildfire contexts) that can\nimprove standard human assessment would also improve the contribution of DisasTeller.\nFuture collaboration with relevant stakeholders and government agencies for shared image\ndatasets could further strengthen the system’s credibility and better reflect real-world\ndeployment conditions. ”\nReviewer #2 (Remarks to the Author):\nI have three major comments on the previous version of this manuscript. In this revision, I am\nsatisfied with the authors' replies/responses to the first two comments. However, the response\nto the third of my previous comments is not sufficient. That is, the authors stated that\nDisasTeller optimises resource distribution by integrating spatial, temporal, and historical data,\nbut did not provide sufficient details on how to achieve the optimality. In the domains of\noperations research and computer algorithms, \"optimising\" refers to maximizing profits or\nminimizing losses in terms of one or multiple objective function in a mathematically accurate\nway. \"optimising\" does not simply refer to \"improving\". Even when the optimality cannot be\nguaranteed, the improvement on profits/losses should be quantitatively strictly proved or\nexperimentally validated. This paper can be essentially improved by demonstrating the\nDisasTeller's capability of resource distribution optimisation mathematically and quantitatively.\nReply: We thank Reviewer #2 for acknowledging that our responses to the first two comments\nwere satisfactory. We now focus on the third point regarding optimisation. We acknowledge\nthe reviewer’s concern. The use of “optimising” was too broad and might have been\nmisinterpreted as a mathematically rigorous optimisation process. Therefore, in the revised\nmain manuscript, we reworded “optimising” to “improving allocation relevance of priority\nrules rather than guaranteeing optimality” to avoid confusion:\n“ 2.3 Limitations and deployment considerations\n(…) In real-world disasters, available resources are often insufficient, and planning must\nbalance urgency, impact, and logistical constraints. The scarcity of rescue forces and relief\nsupplies makes mathematically optimal allocation nearly impossible within short response\ntimes. DisasTeller’s human allocation module does not perform strict mathematical\noptimisation in the sense of operations research. Instead, it emulates rule-based prioritisation\nand dynamic reallocation, reflecting how human decision-makers act under uncertainty.\nSpecifically, it integrates spatial, temporal, and historical cues to prioritise areas with higher\nestimated damage severity, thereby improving allocation relevance rather than guaranteeing\noptimality. DisasTeller is designed as a decision-support framework that assists human\nplanners in rapidly generating and revising preliminary allocation plans. Future works could\nintegrate real-time resource inventories or supply chain data and introduce more specific\npriority rules to improve scheduling under constrained scenarios.”\nAs mentioned by the reviewer in comment 3 in the previous round of revisions “As in many\ndisasters, available rescue forces and relief supplies are insufficient in short response time,\nwhich is the most important challenge in relief planning and scheduling. It is unclear whether\nDisasTeller can or how DisasTeller tackle this challenge”, we agree that in real-world disaster\nscenarios, the scarcity of rescue forces and relief supplies is the most fundamental challenge.\nIn the emergent disaster scenarios, ensuring the optimal allocation under resource scarcity is\nalways difficult and allocation plan is usually decided by rule-based prioritisation (e.g., priority\naccording to the damage severity of different areas) with dynamic reallocation after real-time\non-site inspection [4]. Therefore, we conducted an extra experiment in the revised Section A.4\nof the Supplementary Information, to evaluate the ability of DisasTeller for real-time dynamic\nallocation under scarce resources by rule-based prioritisation (allocation decision according to\nthe damage severity of the local areas). The results are presented as follows:\n“A.4 Allocation under scarce resources\nIn real-world disaster scenarios, the scarcity of rescue forces and relief supplies is the most\nfundamental challenge. In the emergent disaster scenarios, ensuring the optimal allocation\nunder resource scarcity is always difficult and allocation plan is usually decided by rule-based\nprioritisation (e.g., priority according to the damage severity of different areas) with dynamic\nreallocation after real-time on-site inspection [4]. DisasTeller is also designed to mitigate the\nimpact of scarcity, following the decision-making logic of the real situation where human\nexperts need to inspect the on-site scenarios and then make decisions based on rule-based\nprioritisation and dynamic reallocation.\nWe conducted an extra experiment for the earthquake case to simulate the real-time dynamic\nallocation by rule-based prioritisation (allocation decision according to the damage severity\nof the local areas) with scarce resources in disasters. In this experiment, we assumed the\navailable rescue resources are insufficient with only 75 rescue workers, 50 medical staff, and\n40 logistic support personnel existing. Extra information with current scarce resource (“only\n75 rescue workers, 50 medical staff, and 40 logistic support personnel existing, allocated\ncurrent scarce resource according to the damage severity of the local areas”) was injected into\nDisasTeller framework to mimic the real-time dynamic information input, thus evaluating the\nability of DisasTeller to allocate scarce resources under specific rule-based prioritisation. The\ndamage severity grading of local areas was manually assessed according to the technical\nguideline EMS98 [21]: Wajima Drama Memorial Hall – G1; Hama Street – G4; Concrete\nBridge – G5; Central Nishikigawa Street – G1; North Asaichi Street – G3; South Central\nAsaichi Street – G4. According to the disaster grading percentage of different local areas (the\nsum of grading for six local areas is G18), we manually allocated the scarce resources with\n1/18, 4/18, 5/18, 1/18, 3/18, 4/18 for these six locations, respectively.\nTable 4 Comparison of the scarce resources allocation results\nRescue Medical Logistic Support\nLocal Areas\nWorkers Staff Personnel\nWajima Drama Memorial\n4.2 2.8 2.2\nHall\nHama Street 16.7 11.1 8.9\nConcrete Bridge 20.8 13.9 11.1\nManual\nCentral Nishikigawa Street 4.2 2.8 2.2\nAllocation\nNorth Asaichi Street 12.5 8.3 6.7\nSouth Central Asaichi\n16.7 11.1 8.9\nStreet\nWajima Drama Memorial\n5 6 4\nHall\nHama Street 15 10 8\nDisasTeller\nConcrete Bridge 20 10 10\nCentral Nishikigawa Street 5 6 4\nNorth Asaichi Street 10 8 6\nSouth Central Asaichi\n15 10 8\nStreet\nMean Square Root 3.4\nWajima Drama Memorial\n12.5 8.3 6.7\nHall\nHama Street 12.5 8.3 6.7\nConcrete Bridge 12.5 8.3 6.7\nEvenly\nCentral Nishikigawa Street 12.5 8.3 6.7\nAllocated\nNorth Asaichi Street 12.5 8.3 6.7\nSouth Central Asaichi\n12.5 8.3 6.7\nStreet\nMean Square Root 23.3\nAs shown in Table 4, compared with the “Evenly Allocated” strategy, DisasTeller reduces 85.4%\nmean square root deviation from the manual allocation, showcasing its ability to perform rule-\nbased dynamic reallocation. DisasTeller is designed to provide preliminary suggestions and\naims to accelerate the human decision-making process, not to achieve the optimal scarce\nresource allocation that is even difficult to realise in real scenarios.”\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReferences\n[4] Y. Wang, Multiperiod optimal allocation of emergency resources in support of cross-\nregional disaster sustainable rescue, International Journal of Disaster Risk Science 12 (3)\n(2021), pp. 394-409, https://link.springer.com/article/10.1007/s13753-021-00347-5.\n[21] G. Grünthal, European macroseismic scale 1998 (EMS-98), (1998), https://www.gfz-\npotsdam.de/en/section/seismic-hazard-and-risk-dynamics/data-products-services/ems-98-\neuropean-macroseismic-scale.\n[26] M. Rosas-Chavoya, J.L. Gallardo-Salazar, P.M. López-Serrano, P.C. Alcántara-\nConcepción, A.K. León-Miranda, QGIS a constantly growing free and open-source geospatial\nsoftware contributing to scientific development, Cuadernos de Investigación Geográfica 48 (1)\n(2022), pp. 197-213, https://doi.org/10.18172/cig.5143.\n[27] N. Chrisman, Full circle: More than just social implications of GIS, Cartographica: The\nInternational Journal for Geographic Information and Geovisualization 40 (4) (2005), pp. 23-\n35, https://utppublishing.com/doi/abs/10.3138/8U64-K7M1-5XW3-2677."}, {"round": 3, "reviewer_comments": {"reviewer_1": "Authors have revised the manuscript, in response to our request, to maintain consistency between the abstract and the findings discussed in the manuscript body. They have also briefly addressed our suggestions for future work improvement in their limitations section and examples provided in the supplementary documents. Throughout the previous revision stages, we believe that authors have made sufficient efforts to address the comments and clarify key points that were suggested by us and other reviewers.\nOverall, the current manuscript shows the potential of the proposed framework as a supplementary resource to hazard emergency response management, while also addressing limitations in LLM capabilities, data availability, and qualitative validation of the framework.", "reviewer_2": "According to the revisions and the authors' replies, although the method is described in detail, its effectiveness has not been sufficiently validated, e.g., by quantitative results of substantial comparative experiments or in theoretical proofs in mathematically accurate manner. Therefore, I recommend rejection.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter