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The paper introduces BATTERY-SIM-AGENT, a simulator-in-the-loop framework where a Large Language Model (LLM) agent performs hypothesis-driven parameter estimation for high-fidelity battery models (e.g., DFN in PyBaMM). Instead of optimizing a single scalar loss, the agent consumes structured, multimodal feedback (per-objective residuals, feature mismatches, and visual overlays; Sec. 3.2) and uses dynamic memory with a trial-and-error warm-up (Sec. 3.3) to form physically grounded hypotheses and propose structured JSON parameter updates. Algorithm 1 describes a two-phase process (warm-up + main optimization). Experiments span a 200-task benchmark across five chemistries (Chen2020, O’Regan2022, Prada2013, Ecker2015, Marquis2019), multiple C-rates (0.2C/1C/2C), and two difficulty modes (regular vs. extreme) (Sec. 5.1). The agent (GPT-O3) substantially outperforms Bayesian Optimization and an OSS ablation on first-cycle calibration (Table 2) and shows strong performance on long-horizon degradation and real-world CALCE data (Table 3).
Cross‑Modal Consistency: 36/50
Textual Logical Soundness: 22/30
Visual Aesthetics & Clarity: 15/20
Overall Score: 73/100
Detailed Evaluation (≤500 words):
1. Cross‑Modal Consistency
• Major 1: The paper claims the agent “consistently” outperforms baselines across both modes, but Table 2 shows multiple Extreme‑mode cases where BO is better. Evidence: Fig. 2(b); Table 2 Extreme/Prada2013 (BO 17.05±25.3 vs O3 59.14±62.3), Extreme/Marquis2019 (BO 8.42±6.3 vs O3 48.34±90.4).
• Major 2: Table 1 title implies comparison with “Traditional BBO,” but only OSS and O3 rows are provided, omitting BO. Evidence: Table 1 header “Comparison of Traditional Black‑Box Optimization and Battery‑Sim‑Agent” with no BO row.
• Minor 1: The abstract’s “67–95% reduction” claim is not clearly supported across all chemistries in Regular mode (e.g., O’Regan: ~58%). Evidence: Sec 5.2 “67–95% reduction” vs Table 2 Regular/ORegan2022 (BO 81.73±224.0; O3 34.18±48.2).
• Minor 2: Equation (1) and symbols show spacing artifacts (“w h e r e”, “o b s”), risking notation confusion. Evidence: Sec 2.2 Eq.(1) text.
• Minor 3: Figure numbering/captions: Fig. 3 caption mentions subplots (a,b,c) by method; the provided panels match, but panel titles are minimal, potentially ambiguous to first‑time readers. Evidence: Fig. 3 (three boxplots labeled by charge_c_rate only).
2. Text Logic
• Major 1: Over‑generalized performance claim (“consistently and significantly outperforms … across both modes”) conflicts with provided Extreme‑mode numbers, weakening the core argument. Evidence: Sec 5.2 first paragraph vs Table 2 Extreme rows cited above.
• Minor 1: The contribution about persistent memory lacks a targeted ablation isolating memory’s effect. Evidence: Sec 3.3 claims vs Sec 5 baselines (no memory ablation).
• Minor 2: Some references are future‑dated (2025) system cards/surveys; novelty positioning could better distinguish prior “agentic” optimization works. Evidence: Sec 4 citations to Wei 2025, OpenAI 2025.
3. Figure Quality
• Major 1: Small fonts/legends in multi‑panel results make fine details (e.g., durations, final metrics in Fig. 4; scatter legends in Fig. 2) hard to read at print size. Evidence: Fig. 2 (legend), Fig. 4 (annotation text).
• Minor 1: Fig. 2 combines scatter and side boxplot; the boxplot y‑axis lacks explicit label in the side panel; clarify it mirrors the scatter’s metric. Evidence: Fig. 2 panels.
• Minor 2: Color palette is acceptable but similar hues between methods could be more color‑blind‑safe. Evidence: Figs. 2–3.
Key strengths:
Key weaknesses:
Recommendations:
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This paper introduces Battery-Sim-Agent, a novel framework that leverages Large Language Models (LLMs) to address the challenging inverse problem of parameter estimation in battery digital twins. The core innovation lies in reframing the parameter estimation task as a reasoning-driven, hypothesis-testing workflow, where an LLM acts as a scientist iteratively refining parameters based on multi-modal feedback from a high-fidelity battery simulator. The proposed method employs a closed-loop system where the LLM receives structured feedback, including quantitative error metrics, visual overlays of voltage curves, and qualitative descriptions of discrepancies. This feedback is then used by the LLM to formulate hypotheses about which parameters might be causing the observed discrepancies, guided by a dynamic memory module that stores expert knowledge and empirical findings. Based on its hypothesis, the LLM proposes targeted parameter updates in a structured JSON format, which are then used to adjust the simulator's parameters. The paper presents empirical evaluations on both simulated and real-world battery data. The simulated experiments involve a benchmark suite spanning diverse battery chemistries, operating conditions, and difficulty levels. The results demonstrate that Battery-Sim-Agent significantly outperforms traditional black-box optimization (BBO) methods, such as Bayesian Optimization (BO), in terms of parameter estimation accuracy. The real-world validation uses data from the CALCE dataset, showing that the framework can also achieve promising results on noisy experimental data. The authors highlight the potential of LLM agents to enhance the efficiency and accuracy of battery parameter estimation, paving the way for more reliable digital twins and accelerated battery development. The paper's significance lies in its novel approach to a critical problem in battery research, demonstrating the potential of LLMs to bridge the gap between simulation and experimental data, and to automate complex scientific workflows. However, my analysis also reveals several areas that could be strengthened, particularly in terms of methodological novelty, baseline comparisons, and the depth of analysis.
The primary strength of this paper lies in its innovative application of LLM agents to the challenging problem of battery parameter estimation. The idea of reframing the inverse problem as a hypothesis-driven scientific workflow, where the LLM acts as a scientist iteratively refining parameters, is both intuitive and promising. This approach aligns well with how human experts approach complex scientific problems, making it a natural and potentially powerful method. The use of multi-modal feedback, including quantitative error metrics, visual overlays, and qualitative descriptions, provides the LLM with a rich understanding of the simulation's performance, enabling it to make more informed decisions. The dynamic memory module, which stores expert knowledge and empirical findings, further enhances the LLM's ability to reason effectively about the parameter space. The empirical results presented in the paper are also a significant strength. The comprehensive benchmark suite, spanning diverse battery chemistries, operating conditions, and difficulty levels, demonstrates the robustness and generalizability of the proposed method. The fact that Battery-Sim-Agent significantly outperforms traditional black-box optimization methods, such as Bayesian Optimization, on these benchmarks highlights the potential of LLM agents to enhance the efficiency and accuracy of battery parameter estimation. Furthermore, the validation on real-world data from the CALCE dataset is a crucial step in demonstrating the practical applicability of the framework. The paper is also well-written and clearly explains the proposed method and its underlying principles. The authors effectively communicate the motivation behind their work and the significance of their findings. The use of figures and tables to present the results is also effective in conveying the key findings of the paper. The paper's focus on a critical problem in battery research, namely the accurate parameterization of battery digital twins, further enhances its significance. By demonstrating the potential of LLMs to bridge the gap between simulation and experimental data, this work paves the way for more reliable digital twins and accelerated battery development. The authors have successfully demonstrated the potential of LLM agents to automate complex scientific workflows, which could have broader implications for other scientific domains.
My analysis reveals several weaknesses that, while not invalidating the core contributions, warrant careful consideration. Firstly, the methodological novelty of the proposed approach is somewhat limited. While the application to battery parameter estimation is novel, the core framework relies heavily on existing LLM agent architectures. As noted by multiple reviewers, the idea of using LLMs for scientific reasoning and optimization is not entirely new, and the paper could benefit from a more detailed comparison to other similar works in different domains. The paper acknowledges the use of LLMs in "agentic science" but does not provide a deep dive into the specific architectural differences or innovations compared to existing frameworks. This lack of detailed comparison makes it difficult to assess the unique contributions of this work beyond its application to battery parameter estimation. This is supported by the paper's own description of its method as being inspired by existing LLM agent frameworks and the absence of a detailed comparison to other similar works. Secondly, the evaluation could be strengthened by including more diverse and challenging baselines. The paper primarily compares against Bayesian Optimization (BO), which is a reasonable starting point but does not fully capture the potential of more sophisticated optimization techniques. As suggested by multiple reviewers, the inclusion of gradient-based methods, such as those using automatic differentiation, would provide a more comprehensive understanding of the performance landscape. Furthermore, the paper lacks a comparison to other LLM-based optimization techniques, which would help to isolate the benefits of the proposed approach. The absence of these comparisons makes it difficult to assess the true advantage of Battery-Sim-Agent over other potential solutions. This is evident in the experimental setup, which only includes BO as a baseline and lacks a comparison to other LLM-based optimization techniques. Thirdly, the paper lacks a thorough analysis of the computational cost of the proposed method compared to traditional approaches. While the paper mentions the computational cost of traditional methods, it does not provide a direct comparison of the computational cost of Battery-Sim-Agent. This is a significant omission, as the computational cost of LLM-based methods can be substantial, and a clear understanding of the trade-offs between accuracy and computational cost is crucial for practical applications. The absence of this analysis makes it difficult to assess the practical feasibility of the proposed method. This is supported by the lack of any explicit computational cost comparison in the experimental results. Fourthly, the presentation of the method could be more detailed, particularly regarding the implementation of the dynamic memory and the specific prompts used to guide the LLM's reasoning. While the paper describes the dynamic memory module, the exact implementation details and the specific prompts used are not fully elaborated in the main text. This lack of detail makes it difficult to fully understand the inner workings of the method and to reproduce the results. This is evident in the high-level description of the dynamic memory and the absence of specific prompt details in the main text. Fifthly, the paper could benefit from a more in-depth discussion of the limitations of the approach and potential avenues for future research. While the paper demonstrates promising results, it does not fully address the potential limitations of the method, such as its sensitivity to the quality of the feedback, the potential for the LLM to get stuck in local minima, and the generalizability of the approach to different battery chemistries and operating conditions. A more thorough discussion of these limitations would provide a more balanced perspective on the potential and challenges of the proposed method. This is supported by the lack of a dedicated section discussing the limitations of the approach. Finally, the paper's reliance on simulated data for the majority of the evaluation raises concerns about the generalizability of the method to real-world scenarios. While the paper includes a real-world validation, the simulated experiments do not fully capture the complexities of real-world battery behavior, such as electrode degradation, electrolyte evaporation, and other aging phenomena. This raises questions about the robustness of the method to noisy and incomplete data, which are common in real-world battery experiments. This is supported by the fact that the majority of the evaluation is based on simulated data, and the real-world validation is limited to a small number of datasets.
To address the identified weaknesses, I recommend several concrete improvements. Firstly, the authors should provide a more detailed comparison to existing LLM agent frameworks, highlighting the specific architectural differences and innovations of Battery-Sim-Agent. This would involve a more thorough literature review and a more detailed discussion of the method's unique contributions. This could include a table comparing the proposed method to other LLM-based optimization techniques, highlighting the specific advantages and disadvantages of each approach. Secondly, the authors should expand the set of baselines used for comparison, including gradient-based methods and other LLM-based optimization techniques. This would provide a more comprehensive understanding of the performance landscape and help to isolate the benefits of the proposed approach. Specifically, the authors should consider including methods that utilize automatic differentiation to compute gradients of the simulation outputs with respect to the parameters, as these methods can be more efficient than traditional black-box optimization techniques. Furthermore, the authors should consider comparing against other LLM-based optimization techniques to isolate the benefits of the proposed approach. Thirdly, the authors should conduct a thorough analysis of the computational cost of the proposed method compared to traditional approaches. This analysis should include a breakdown of the computational resources required for each step of the method, as well as a comparison of the overall runtime. This would provide a more realistic assessment of the practical feasibility of the proposed method. Fourthly, the authors should provide more detailed information about the implementation of the dynamic memory and the specific prompts used to guide the LLM's reasoning. This would involve including pseudocode or a more detailed algorithmic description of the dynamic memory update process, as well as examples of the specific prompts used to guide the LLM's hypothesis generation and parameter update suggestions. This would improve the reproducibility of the results and allow other researchers to build upon this work. Fifthly, the authors should include a more in-depth discussion of the limitations of the approach and potential avenues for future research. This would involve a more thorough analysis of the potential limitations of the method, such as its sensitivity to the quality of the feedback, the potential for the LLM to get stuck in local minima, and the generalizability of the approach to different battery chemistries and operating conditions. This would provide a more balanced perspective on the potential and challenges of the proposed method. Finally, the authors should expand the evaluation to include more real-world data and consider the impact of aging phenomena on the parameter estimation process. This would involve incorporating datasets that capture the effects of electrode degradation, electrolyte evaporation, and other aging-related factors. Furthermore, the authors should investigate the robustness of the method to noisy and incomplete data, which are common in real-world battery experiments. This could involve adding controlled noise to the simulated data and evaluating the performance of the method under different noise levels. Additionally, the authors should consider using a wider range of real-world datasets, including those from different battery chemistries and operating conditions, to ensure the generalizability of the method. By addressing these weaknesses, the authors can significantly strengthen the paper and further solidify its contribution to the field.
Several key uncertainties and methodological choices warrant further clarification. Firstly, how does the dynamic memory module specifically store and retrieve expert knowledge and empirical findings? The paper describes the dynamic memory as a mechanism for storing past actions and outcomes, but the exact implementation details are not fully elaborated. I would like to understand the specific data structures used to represent this knowledge, and the algorithms used to retrieve relevant information based on the current feedback. Secondly, what is the sensitivity of the method to the specific prompts used to guide the LLM's reasoning? The paper mentions the use of prompts, but it does not provide a detailed analysis of how different prompt formulations might affect the performance of the method. I would like to understand how the authors arrived at the specific prompts used, and whether they explored alternative prompt formulations. Thirdly, how does the method handle the potential for the LLM to get stuck in local minima? The paper does not explicitly address this issue, but it is a common challenge in optimization problems. I would like to understand whether the authors have implemented any mechanisms to mitigate this risk, and how the performance of the method is affected by the presence of local minima. Fourthly, what is the computational cost of the proposed method compared to traditional approaches, and how does this cost scale with the complexity of the battery model and the number of parameters? The paper does not provide a detailed analysis of the computational cost, and I would like to understand the specific computational resources required for each step of the method. Fifthly, how does the method generalize to different battery chemistries and operating conditions? The paper presents results on a limited set of battery chemistries and operating conditions, and I would like to understand how the method would perform on other types of batteries and under different operating conditions. Finally, how does the method handle noisy and incomplete data, which are common in real-world battery experiments? The paper includes a real-world validation, but I would like to understand how the method would perform under different levels of noise and data incompleteness. Addressing these questions would provide a more complete understanding of the strengths and limitations of the proposed method and would help to guide future research in this area.