ChemAgent: Self-updating Memories in Large Language Models Improves Chemical Reasoning
Authors: Xiangru Tang
Tianyu Hu
Muyang Ye
Yanjun Shao
Xunjian Yin
Siru Ouyang
Wangchunshu Zhou
Pan Lu
Zhuosheng Zhang
Yilun Zhao
Arman Cohan
Mark Gerstein
Venue: ICLR 2025 Poster (2025)
Keywords: Chemical Reasoning
Large Language Models
Agent
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Chemical reasoning usually involves complex, multi-step processes that demand precise calculations, where even minor errors can lead to cascading failures. Furthermore, large language models (LLMs) encounter difficulties handling domain-specific formulas, executing reasoning steps accurately, and integrating code ef- effectively when tackling chemical reasoning tasks. To address these challenges, we present ChemAgent, a novel framework designed to improve the performance of LLMs through a dynamic, self-updating library. This library is developed by decomposing chemical tasks into sub-tasks and compiling these sub-tasks into a structured collection that can be referenced for future queries. Then, when presented with a new problem, ChemAgent retrieves and refines pertinent information from the library, which we call memory, facilitating effective task decomposition and the generation of solutions. Our method designs three types of memory and a library-enhanced reasoning component, enabling LLMs to improve over time through experience. Experimental results on four chemical reasoning datasets from SciBench demonstrate that ChemAgent achieves performance gains of up to 46% (GPT-4), significantly outperforming existing methods. Our findings suggest substantial potential for future applications, including tasks such as drug discovery and materials science. Our code can be found at https://github.com/gersteinlab/ChemAgent.

Published as a conference paper at ICLR 2025
CHEMAGENT: SELF-UPDATING LIBRARY IN LARGE LANGUAGE MODELS IMPROVES CHEMICAL REASON- ING
Xiangru Tang", ;Tiayu Hu Muyang Yet.*, Yanjun Shao!-", Xunjian Yin', Siru Ouyang', Wangchunshu Zhou, Pan Lu², Zhuosheng Zhang', Yilun Zhao', Arman Cohan', Mark Gerstein'
I Yale University PIICC Stannfor University 4 SShanghai Jiao Tong University
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ABSTRACT
Chemical reasoning usually involves complex, multi-step processes that demand precise calculations, where even minor errors lead to cascading failures. Fur- thermore, large language models (LLMs) encounter difficulties handling domain- specific formulas, executing reasoning steps accurately, and integrating code ef- fectively when tackling chemical reasoning tasks. To address these challenges, we present ChemAgent, novel framework designed to improve the performance of LLMs through dynamic, self-updating library. This library is developed by decomposing chemical tasks into sub-tasks and compiling these sub-tasks into structured collection that can be referenced for future queries. Then, when presented with a new problem, ChemAgent retrieves and refines pertinent infor- mation from the library, which call memory, facilitating effective task de- composition and the generation of solutions. Our method designs three types of memory and library-enhanced reasoning component, enabling LLMs to im- prove over time through experience. Experimental results on four chemical rea- soning datasets from SciBench demonstrate that ChemAgent achieves perfor- mance gains of up to 46 % (GPT-4), significantly outperforming existing meth- ods. Our findings suggest substantial potential for future applications, including tasks such as drug discovery and materials science. Our code can be found at https:// /ttl::///tttte thub. com/ hwtpa///eeeeeeeaeeeeeeeeaeeaaeee chw.aaaebteeee......mm
INTRODUCTION
Chemical reasoning presents unique challenges in the realm of artificial intelligence, demanding sophisticated reasoning and precise calculations beyond typical reasoning tasks (McQuarrie, 2008; Atkins et al., 2014; Talanquer, 2022; Guo et al., 2023b; Cao et al., 2024). For example, the GPT solution with CoT prompting in Figure 1 contains numerous errors in both the calculation process and the chemical constants used. Even in short reasoning chains, a single error can cascade, reduc- ing answer quality and escalating the probability of additional errors (Liao et al., 2024; Sun al., 2024b).
Recent udvancements in large language models (LLMs) have demonstrated capabilities in simpler scientific tasks or chemical scenarios that do not require complex reasoning (Boiko et al., 2023; Atkins et al., 2023; Wang al., 2024a; Hu & Shu, 2023; Xiao et al., 2024; Darvish et al., 2024;
Skreta et al., 2024). Other approaches have investigated the impact of tool augmentation on chem- istry problem-son solving, with Yu et al. introducing ToelAAtt. However, their application to complex chemical reasoning reveals significant limitations Pei et al. (2024); Li et al. (2024a). LLMs often (1) struggle to effectively utilize domain -specific formulas, (2) exhibit incorrect reasoning steps, and (3) produce errors when combining textual reasoning with Python code for calculations (Zhong et al., 2024b); here, syntax errors may arise, causing the code to fail to compile. As shown
"Equal contribution. The work was done when Mr. Hu and Mrs. Ye were interns Yale University.

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Problem (Hydrogen Atom Energy Levels)
An hydrogen transitions excited state with principal quantum
ground 1),
Cakulate: a) The energy of the amitted photon in joulss (O) b) The wovelongth of the amitted photon in nanomaters (mm), c) The frequeney the emitted photon terahertz (THz).
GPT-4's Output with CoT Prompting
StructChem's Output
ChemAgent's Output
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Figure 1: Comparison of problem-solving approaches for a hydrogen atom energy transition problem. The figure illustrates three different methods: (a) shows a standard Chain-of-Thouge Thought approach with calculation errors (in steps 3 and 4) in Wang et al. (2024a). (b) demonstrates the StructChem (Ouyang et al., 2024) method with formula generation and step-by-step reasoning but fails due to an incorrect constant and incorrect unit conversion (in steps I and 4). (c) presents the ChemAgent solution, featuring task decomposition, memory retrieval from the library, and reason- ing, leading to the accurate final answer.
in Figure 1 (StructChem output), errors often arise from determining a constant's incorrect form or unit.
Some Previous approaches to address these challenges in chemical reasoning tasks have focused on decomposing the reasoning process (Zhong al., 2023), e.g., formatting the reasoning steps through techniques such as self-reflection (Shinn et al., 2023) and StructChem prompting (Ouyang al., 2024). However, these methods often rely heavily on human-curated knowledge (Chan al., 2023), or fixed workflows (Fu et al., 2022; Wang et al., 2023; Zhou et al., 2023). Unlike human learners who utilize a library system to retain and apply previous experiences, these methods lack the ability to remember and learn from past analogous problems. For instance, humans can abstract and store theorems or solution strategies from previous tasks and utilize this memory for future problem-solving."
Building on these insights, ChemAgent introduces a dynamic "I.BRARR" system that facilitates iterative problem-solving by continuously updating and refining its content based on task decom- position. The LIBRARY in ChemAgent serves as a comprehensive repository where decomposed chemical tasks are stored. These tasks are broken down into sub-tasks, and their solutions are com- piled in the library for future reference. As new tasks are encountered, the library is updated with new sub-tasks and corresponding solutions, ensuring its content remains relevant and progressively improves over time. In parallel, inspired by human cognitive mechanisms (Osman, 2004; Kaufman, 2011), our system incorporates three main memory components: Planning Memory for high-level strategies, Execution Memory for specific task solutions, and Knowledge Memory for fundamental chemistry principles. These memories are stored externally, allowing the model to retrieve them efficiently during the problem-solving process. Unlike previous works relevant to LLM's external memory system (Huang al., 2024a; Zhong et al., 2024a; Zhang et al., 2023; Li al., 2024b; Guo et al., 2023;; Zhang et al., 2024b), we carefully integrate all these components in a complete agentic framework and allow them to update dynamically.
These memories are stored in a structured tree-based format that allows for efficient retrieval during the problem-solving process. When ChemAgent encounters new problem, first decomposes the task into manageable sub-tasks, then leverages the library retrieve relevant sub-tasks and solutions stored in its memory components. The retrieved information is validated and refined using analo- gous sub-tasks previously encountered, optimizing both task decomposition and solution generation.
For detailed discussion of the related work, please refer Appendix A.
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Library-enhanced Reasoning
(b) Library Construction
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Figure 2: The diagram of our overall framework. It contains (a) library-enhanced reasoning and (b) library construction. (a) illustrates how ChemAgent utilizes the library to address a new task for the test set. And (b) demonstrates the construction of the library over the dev set, including Plan Memory Mp and Execution Memory M.).
The library is dynamically updated by adding new sub-tasks and solutions as they are encountered and validated. This iterative process ensures that the memory is continuously enriched with new strategies and solutions, mimicking human problem-solving improvements through practice. By maintaining an evolving domain knowledge base, ChemAgent enables LLMs to autonomously nav- igate the problem-solving process, thereby enhancing their performance on similar tasks over time.
Our experiments are conducted on four chemical reasoning datasets from SciBench (Wang et al., 2024a) with GPT-3.5, GPT-4 (OpenAl et al., 2024), and open-source models like Llama3 (Llama Team, 2024). Experimental results indicate that ChemAgent significantly enhances the curacy, though the degree of improvement achieved by our model varies accordingly due to the varying sizes of these datasets. Compared to the base model (take GPT-4, for example), the applica- tion of memory self-improving leads to an average increase in accuracy by 37%, with a maximum improvement of 46%. In comparison with the current ttate-of-the-ae model, StructChem (Ouyang et al., 2024), our approach achieves an average improvement of 10% and a maximum improvement of 15% across different datasets. Furthermore, the improvement is more pronounced for stronger base models: GPT-4 exhibits greater enhancements compared to GPT-3.5 when augmented with our framework. Additionally, we analyze the role of our library system and examine the benefits of different memory updates across various stages. This analysis provides insights into how our iter- ative library updates contributed to performance improvements in sub-task resolution and optimal task decomposition. By continuously enriching the library with structured memory components, ChemAgent ensures that the problem-solving process is progressively refined and optimized, lead- ing to substantial improvements in accuracy.
2 METHOD
2.1 PRELIMINARIES
Analogous to how students organize and reference their problem-solving approaches for exams, our motivation for developing library system in ChemAgent is to enhance LLMs' ability to tackle complex problems by providing structured access a repository of previous sub-tasks and solutions.
The overall reasoning framework is shown in Figure (a). Formally, in a simplified form, given complex and open-ended chemical problem Ps as input, our method aims generate solution Os. The problem Ps. which comprises problem descriptions Ts and initial conditions Cs, is firstly decomposed into a series of sub-tasks Ps.. The solution Os is then synthesized from the sub- solutions Os, of the sub-tasks. Each Os, includes intermediate steps, such as formulac, reasoning steps, code, and calculations. Then, a final answer As is derived from the overall solution Os. We evaluate the performance via the accuracy of As against ground truth As,
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Execution Memory: Mi
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Figure 3: Given a task the relevant memory examples are provided in the library, Specifically, while Execution Memory (M2) and Plan Memory (Mp) are derived from prior experiences, Knowl- edge memory (M₂) generated by LLM based on the problem prompt. The conditions not explicitly presented here but are embedded within P and the [GOAL] of M..
2.2 COMPOSITION OF THE LIBRARY
We divide library into three memory components: planning memory, execution memory, and knowl- edge memory. Figure 3 provides detailed examples, corresponding prompts are in Appendix G. Planning Memory (Mp): This component stores high-level strategies and methodologies for approaching complex problems.
Execution Memory (M,): This contains highly structured descriptions of specific problem con- texts and their corresponding solutions, serving detailed execution plans.
Knowledge Memory (M&)): This houses fundamental chemistry principles and formulas, acting as ready reference. This component is generated temporarily during the solution of a specific task and is not intended for permanent retention.
Thus, ChemAgent enables LLMs to tackle problems proactively, form new memories from these attempts, utilize existing memories to solve complex problems, and continuously refine their solu- tions. Built upon the library of three types of memories, ChemAgent operates in two main stages:
1. Initial Library Construction: Complex problems in the development set decomposed into structured atomic sub-tasks, which serve as building blocks of the library, consisting of static long-term memory (M, and Me).
2. Inference Phase and Dynamic Memory Updating:
(a) Memory-Enhanced Reasoning: The memory is dynamically enriched and improved dur- ing runtime.
(b) Evaluation & refinement: Each sub-task's solution evaluated on their veracity and relevance to the main task, which leads to correction of the solution or refinement of the overall plan.
Figure and Figure illustrates this workflow. In the following sections, we explain how each part constructed and utilized. All prompts used in framework can be found in Appendix G.
2.3 DECOMPOSITION AS ATOMIC BLOCKS
Human problem-solvers naturally break down complex chemistry problems into smaller, manage- able sub-tasks (Zhou et al., 2023). This decomposition enhances the understanding of individual components and their interactions, facilitating the resolution of the original problem in a structured manner (Johnson et al., 2017). These sub-tasks not only improve the reasoning process but also function atomic building blocks within the execution memory, each paired with corresponding sub-solution. Key characteristics of this decomposition include:
Hierarchical Decomposition: Using the strategy stored in Planning Memory, break down com- plex chemistry problems into hierarchical sub-tasks. If a sub-task is too difficult to complete, is further decomposed until it can be executed in single step by the L.L.M We store each decomposed
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sub-task and its intermediate sub-solutions, enabling direct retrieval of solutions for similar future problems. This hierarchical approach ensures each sub-task acts as an independent atomic block, aiding future problem-solving
Structured Sub-tasks: To maximize the efficiency of decomposition, sub-tasks are structured into four distinct parts: (i) Task Query defining the specific question, (ii) Sub-task Objectives outlining clear goals, (iii) Step-by-step Solution detailing the method to address the sub-task, and (iv) Guid- ance for Solutions suggesting potential approaches.
Decomposed sub-tasks serve as building blocks for memory. Identi- fying and recalling similar problems be challenging, but sub-tasks often share commonalities that make it casier to leverage past experiences. These atomic blocks also function as examples in few-shot prompting, standardizing response formats and bolstering the overall problem-solving process.
Atomic Blocks as Memory:
Building on the concept of sub-tasks as fundamental units, we outline the processes involved in our framework, which includes both a dynamic library system and the structured memory inside our library. This section details how we initially construct the library and memory ($2.4), utilize and
update them for new tasks ($2.5), and evaluate and refine the generated solutions ($2.6). Specifically, our approach leverages the development set to build a library, which is designed to dynamically self- improve during runtime.
Algorithm 1: Library Construction
2.4 LIBRARY CONSTRUCTION
Input: Development set D, LLM F, prompts (Pspins Phot, Prank
The Library is constructed using the development set. As outlined in $2.3, we leverage sub-tasks de- composed from each problem as the execution mem- ory units. Each execution memory unit L4; is defined as follows, where C represents the conditions of a given problem P. and Ti and O, denote the sub-task and its corresponding sub-solution, respectively:
Output: Static memory M consisting units
u= (condition, question, solution)
for (P. S) in D do Conditions C + C F(pypiil
// Verify fy condition and
refine it necessary
4; = (C, Tr O.) for i= 1,2......
C F
Sub-tasks T t F [PI
Given problem P and its corresponding solution S in the development set, our method begins by iden- tifying and extracting the conditions from P. We then verify these conditions for accuracy to ensure that the subsequent steps operate with precise and correctly parsed data. Based on the identified con- ditions, we instruct the LLMs to generate detailed sub-tasks. For each identified sub-task T,, the corresponding sub-solutions O are parsed from S and assigned accordingly. Inspired by curriculum learning (Bengio et al., 2009), we then rank the memory units // based on their difficulty. In addition to ranking, we discard any memory units that do not meet a predefined confidence threshold, as evaluated by the LLMs. This ensures that the memory utilized for future problem-solving is both relevant and reliable, enhancing the LLM's abil- ity to tackle increasingly complex chemistry problems. The detailed memory construction process is further described in Algorithm 1.
Sub-solutions O F (P C|))
Assert (Cn = (en(7) = len(O) for i in Len(C) do
L Add (C:, Tr, O,) into 2 2 < F ((4)
return
2.5 LIBRARY-ENIANNEE REASONING
During testing, we first decompose a given problem into several sub-tasks. For each sub-task, we retrieve related memory units U, to aid in solving it. Specifically, we compute the similarity between the given sub-task and units stored in the memory. Memory units with similarity above a predefined threshold 0 are used assist the model in determining the answer for the sub-task.
Formally, let (C,,, Tr) represent a sub-task decomposed from a new problem. We retrieve memory units U4 that satisfy
Similarity Tu,) 2 0 for U, E Me.
Specifically, the similarity between tasks is calculated using Llama3's embeddings: Embed(T,)) ET,)
Similarity(Ta, T8) = [Embed x [[mbedd])]]]]
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In scenarios where the execution memory does not contain similar sub-tasks, incorporate self- improving mechanism. Initially, we enrich the memory with the required information by leveraging the internal parametric knowledge of LLMs. The LLM is instructed to identify the topic of the given sub-task (e.g.. quantum chemistry or thermodynamics) and generate self-created chemistry problems related to that topic, adhering specific guidelines. This forms kind of "synthetic" execution memory. We encourage the LLMs to generate diverse problems and solutions beyond the provided examples, thereby enhancing the robustness and adaptability of the memory system.
Moreover, the memory is continuously updated with newly solved sub-tasks and their solutions during runtime. For each newly solved sub-task T,, sub-answers from previous sub- cub-problems are incorporated into the conditions as Cj, and the execution memory is updated based on solution Oy:
Me Me U {(C,, T,, Oj)]-
The plan memory will be updated by a summary of the overall strategy knowledge used, K, (e.g. formulas, conception, order of resolution):
Mp Mp U ((T).IC))}
This dynamic updating ensures that the memory evolves and improves over time, progressively enhancing the problem-solving capabilities. More details on self-evolution are in $2.9.
2.6 EVALUATE & REFINE MODULE
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To enhance the flexibility and relia- bility of ChemAgent, we propose an evaluation refinement mod- ule, to correct the planning trajectory and verify the response to each sub-task.
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Me
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Sibers
Evaluation
As illustrated in Figure 4, after address- ing specific sub-task P., ChemAgent ex- amines the sub-solution O,, The system evaluates whether the sub-solution con- flicts with fundamental knowledge in M2 or contains common errors, such as incor- rect units. If discrepancies are identified, a new sub-solution O', is generated by re- fining the original O, based on the relevant knowledge in Md and the identified mis- takes.
Refinement
controdictory ormistake
Fird selagacdiirn insdovant insuliciently conditioned, mod the decompoation (plani..
New salr prob
Figure 4: Overall framework of the evaluation & refinement module. ChemAgent continuously modifies the solution or the comprehensive strategy un- til it either reaches the maximum number of trials or meets the evaluator's criteria.
Furthermore, if sub-task fails due insufficient conditions the evaluation determines that the sub-task's 's question does not align with the main task (e.g.. calculating energy using wavelength in- stead of calculating wavelength using given energy), the sub-tasks from P; to P,, will be restructured as Pi to P taking into account the main task and all preceding sub-tasks.
The eva luation refi module can use a different LLM than the one used for the base reasoning process. For instance, if GPT-3.5 is the base model, GPT-4 can handle evaluat oon refinement. The evaluation component judges solutions without modifying them, while the refinement component adjusts solutions based on these evaluations. This separation clarifies error identification, helping humans understand where and why mistakes occur.
2.7 SETUP
We use four chemistry datasets from SciBench (Wang et al., 2024a), and the detailed distribution of the specific fields covered by each problem in the four datasets is shown in Figure 10. Each dataset divided into a development set (Da) and test set (D,), with exact sizes provided in Table 6. According to previous research (Ouyang et al., 2024), SciBench chemical sets are representative of chemical reasoning tasks, addressing a broad range queries typically encountered this field.
During the reasoning stage, configure the planning memory (Mp) provide maximum of two related memory instances (2-shot) for each query, and the execution memory (M,) to provide up to four related instances (4-shot). However, during the construction of the library, only the knowledge

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Table 1: Performance the test sets of four datasets: QUAN, CHEMMC, ATKINS, and MATTER.
Results are compared with baselines under two different setups: a zero-shot setting with no demon- strations and few-shot setting with three demonstrations. Accuracy scores computed using the approximation detailed in Section 4.3. The best results for each setup are highlighted in bold, and the second-best results underlined. In some settings, the two modules described in Sections 2.4 (Memory) and 2.6 (Evaluate illuationn refinement) are disabled, helping illustrate each mod- ule's impact on the overall performance.
Models
CHEMMC
MATTER
AIKINS
QUAN
Avg.
Baselines, all based on GPT-4 (gpt 44 1011000ppppa porpprrviiee ew)
Few-shot Direct reasoning
28.21
14.29
20.69
14.71
19.48
Few-shot Python
38.46
34.69
57.01
44.12
43.57
StructChem
58.97
30.67
59.81
41.18
47.66
ChemAgent, all based on GPT-4 (gpt
ChemAgent
74.36
48.98
61.18
44.12
57.16
w/o Evaluatai luation & Refinement
61.54
44.89
57.94
44.12
52.12
w/o Memory, Evaluation & Refinement
58.97
38.78
57.94
41.18
49.22
all based on Llama 3. 1-7b
Direct reasoning
28.20
10.20
22.43
8.82
17.41
ChemAgent
56.41
12.24
19.63
14.71
25.75
all based Llama 3. 3.1-70b
Direct reasoning ChemAgent
33.33
30.61
30.84
23.53
29.48
64.10
32.65
43.93
29.41
42.52
all based on Qwen 2.5-72b
Direct reasoning
48.72
32.65
57.01
35,50
43.47
ChemAgent
69.23
44.90
56.07
44.12
53.58
memory (M₂)) is used, as the standard solutions are already available in the development set (Da)- We evaluate the accuracy by comparing their outputs with the correct answers, using a relative tolerance of 0.01.
We consider three baselines alignment with the evaluation paradigm SeiBench (detailed structions are provided in Appendix G): (1) Few-shot Direct reasoning involves directly feeding the problem into the LLM without any additional instructions, the source of the data is StructChem (Ouyang et al., 2024). strategy. Here, we provide six examples for every query, each consisting of a problem, its corre- sponding solution, and Python code. These examples are taken from Da. The results from the original benchmark, Scißench (Wang et al., 2024a). (3) StruetChem (Ouyang et al., 2024) employs structured instruction, confidence-based review, and refinement to guide LLMs through precise step- by-step reasoning.
(2) Few-shot Python combines few-shot examples with tool-augmented
2.8 RESULTS
We report the performance of all methods regarding accuracy score for each sub-dataset and an average score across the entire dataset. The results are summarized in Tables 1, 4 and 5. Additional results and analysis of experiments conducted other L.LMs can be found in Appendix B.
Firstly, ChemAgent consistently outperforms the other baselines across various datasets and settings. Specifically, in terms of the average score, ChemAgent improves by 9.50% % (47.66 57.16) over StructChem, which is 2.93 times increase and by 37% (19.48 vs. 57.16) over direct reasoning, which is 2.93 times increase. Notably, the performance gain varies across different datasets. In the CHEMMC dataset, our method exhibits the largest improvement, with a 46% increase (28.21 vs. 74.36) compared the direct reasoning setup. Secondly, the results also highlight the crucial role of memory in our library. The version of our framework equipped with memory consistently outper- forms the version without memory across all cases. Specifically, there an absolute improvement of 2.90% in terms of the average score. This underscores the importance of memory in retain- ing and leveraging past information to improve the accuracy and reliability of solving chemistry problems. Additionally, the Evaluation using stronger LLMs. For instance, incorporating Evaluation & Refinennt ieffinmennt with GPT-4 increases ChemAgent's performance by 5.04% compared to ChemAgent with memory alone. In summary, ChemAgent demonstrates superior performance across various LLM backbones, high- lighting the critical importance of the library system, memory design, and evaluation modules in
Refinement module plays significant role when
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enhancing groblem-solving. capabilities. Appendix details the specific aspects and reasons for method's improved performance.
2.9 SELF-EVOLUTION DURING RUNTIME
Moreover, we aim to show that library systems with evolving memory perform better when exposed to an increasing number of problems. Much like humans improve their skills through practice, these systems benefit from continuous exposure to new tasks.
0.2
6.8
Accuirscy
2
We allow ChemAgent dynamically update and rich its library during the test stage to analyze this self-evolution process. Specifically, in iteration I, ChemAgent uses all accumulated long-term mem- ory from iterations I Z--1 its library. When solving new problem P, all related responses and knowledge from that process are added the library if the solution is correct. This means that in subse- quent iterations, the system can leverage the newly acquired information (updated M, and M,) to im- prove its performance. We employ 2-shot M, and 4-shot Me during reasoning but simplify the setup by removing the evaluation and refinement mod- ules. To ensure accuracy and prevent target leakage, the memory derived from problem Pj in itera- tion I is excluded when solving P; again in Ig for any k:
Accuracy we inserti bot
llerelions
Figure 5: Self-evolving analysis. We test ChemAgent twice for each iteration, and the difference between the two results serves as the eπror margin. All the experiments here are done on MATTER dataset.
This iterative process demonstrates that as the memory pool grows with each new example, ChemA- gent's ' problem-solving performance improves. Figure 5 shows that the number of iterations in- creases, the agent's performance gradually improves and converges to a score higher than the base- line (44.89%). This improvement indicates that ChemAgent can enhance its performance through simple correct-or-not not evaluation of past solutions instead of human- human-written written high- high-quality trajectories.
2.10 COST ANALYSIS
60
ChemAgent
55
ChemAgent w/o Evalustion & Refinement
On average, each problem requires 0.012 million tokens without the Evaluation Refinement module, equivalent to around $0.09 per example. When this module is ap- plied, the average token consumption per prob-
50
Accuracy (96)
45
StructChom
40
Fow-shot + Python
35
30
lem increases approximately 0.023 million,
25
costing about $0.1725 per example. Note that the initial library construction in these calculations as it is a one-time setup. Based on results in Figure 6, the computa- tional time required by our method is reason- able. While the cost and resource consump- tion slightly higher than StructChem, the improvements justify the additional expense.
20
Direct Reasoning
System Instruction
not included
15
3.5
4.5
5.5
6.5
7
Tokens consumption logio scale
Figure 6: Cost Analysis. The size of each bubble corresponds to the average number of inferences for each method, while the y-axis indicates the av- erage accuracy across the four datasets.
2.11 ERROR ANALYSIS
We conduct an analysis of the trajectories for the failed examples and find three types of errors, shown in Figure 7.
(1) Lack of Understanding the Question. We observe that plans often fail when the problem text contains critical hidden information (e.g., in Figure 7, the terms 'reversibly" and "adiabatically" are key) or include excessive, redundant details (e.g., the exact conditions such as "250K" are irrelevant to the solution). This challenge is understandable, information and err in their approach. This issue is prevalent and independent of whether the task is tackled by our model directly queried to the LLM, suggesting that these errors may be inherent limitations the LLM's capacity. Hence, improving the ability of the foundation model to handle such nuances could significantly enhance performance.
even human solvers can be misled by such
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?
Problem (Entropy Change and Enthelpy Change)
unless stated. sample concisting 3.00 mol of diatomic perfect molecules 200 K compressed feversib y and adiabetically until its temperature reaches 250 K. Given that C_.Mm)=27.5 mol), caloulata aS.
Assume gases perfect
ChemAgent's Solution
Types of Errors
Task Decomposition
of understanding of the question
Calculate the change entropy during the comprission process using [_0mm
problam text, agent ignores terms 'reversibly and adiabetiiaally' leading to incorrect approach to solving the problem.
Memory Retrioval
I. Zo calculate stardend reaction anteopy ard" can use the formil::
Error 2: Inaccurate reasoning
D. 9ith the caleniated ditoal//ef, Paraday's constant, and
the left task decompesition, the plan "using C_MMm is an inaccurate ressoning step, leeding to errors executing plan.
Execution
Formu (V.3) in(7 #inal init)
Step Ti..
Error 3: Incorrect Memory Selection
Step 2.
Step Perform calorition to get
value of
In the left 'Memory Retrieval' section, all the retrieved memories are inelevant to adiabatic resulting the of incorrect formulas the solution.
I0r2507230)
Coole
Firs Answer a5 18411.K
Figure 7: Error analysis. This example highlights a typical incorrect solution, which can be at- tributed three main types of errors. Specifically, this problem pertains an adiabatic process.
(2) Inaccurate Reasoning. As indicated before (Ouyang et al., 2024), the planning capabilities of LLMs remain insufficient for handling complex problems. Incorrect planning for the reason- ing chains exacerbates the problem-solving process, as subsequent decisions and actions are based on initial problem decompositions. This issue persists until the Eva Evaluuatin uatiion & Refinement module detects an error, which may not happen promptly enough to correct the trajectory.
(3) Incorrect Memory Selection. While ChemAgent with memory demonstrates superior perfor- mance compared to setups without memory, it sometimes invokes misleading information, when the similarity between the invoked memory and the problem is high. This indicates a need for more sophisticated memory retrieval and utilization strategies.
In Figure 7, the invoked memory and sub-task 1.1 share considerable similarities both concern entropy change during a compression process. However, the subtle difference is that the current problem involves an adiabatic process, whereas the examples in memory do not. This seemingly minor distinction can lead significant changes in the problem-solving strategy. Distinguishing between misleading and beneficial memories remains a challenging issue, as invoked memories and problem texts may be semantically similar yet differ in critical aspects.
3
ABLATION STUDY
3.1 MEMORY COMPONENT ANALYSIS
To understand why method performs partieularly well and which memory component tributes the most, we conduct an ablation study by independently removing each memory com- ponent. We these different settings on all four sub-datasets using GPT-4 as the base LLM. The results are shown in Table 2. As mentioned in $2.3, the execution memory also serves as a few-shot prompting strategy. Therefore, when we remove M,. we add fixed human-written few-shot ex- amples (provided with our code) into each query of each sub-task. These examples are selected from the development set (Da) corresponding each dataset to ensure they do not provide misleading information.
Firstly, removing any memory component results in an overall performance drop. And notably, the relevant knowledge in LLM itself (M₂) significantly impacts the overall performance of ChemA- gent. Delving into why Mr contributes the most, we find that this might be due to the insufficient M, and Me in the Atkins dataset. As shown in Table 6, ATKINS has the lowest ratio of the devel- opment set to the test set, while it also has the largest test set. This imbalance may result in a small and sometimes irrelevant memory pool for Mp and Me search from. We hypothesize that this issue can be mitigated when the model is exposed to more chemical questions over time. However, due to limited computational resources, cannot scale up to verify this hypothesis in this research.

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Table 2: The best performing score is highlighted in bold and second-best is underlined. The number of problems in each textbook weighs the average score. We also provide the results of ChemAgent without any of these memories baseline. Evaluation refinement module is removed.
Memory component
CHEMMC
MATTER
ATKINS
QUAN
Avg.
None
58.97
38.78
57.94
41.18
51.53
Mr M&
61.54
44.89
57.94
44.12
53.71
M.
66.66
44.89
52.34
41.18
51.53
M. Me
58.97
46.94
61.68
29,41
53.27
Mp Mr few-shot
74.36
42.86
57.94
32.35
53.71
It is also worth noting that although the Mp, MR. I fewshot setting achieves the same accuracy as the complete memory setting. most its improvement is evident in the CHEMMC dataset, showing surprisingly higher improvements than others. Since Table 6 and Figure 10 demonstrate that the CHEMMC dataset has higher ratio of Da to D; and relatively narrow distribution in subfields, and since human experts carefully wrote the few-shot examples, these high-quality, expert-written written examples may serve as superior memory pool in such circumstances. In the next section, we show that better memory leads better results, which might explain this phenomenon.
3.2 MEMORY QUALITY INFLUENCE
Table Results memory quality analysis, with the best scores highlighting in bold. Here, mem- ory refers to M, and Me. The relevant knowl- edge in LLM itself (M₂) is removed from the framework. Test on MATTER dataset.
We investigate the impact of memory quality through a series of experiments. Specifically, compare the performance of memory gener- ated by GPT-4 against that generated by GPT- 3.5 on the MATTER dataset. Additionally, we create a "hybrid memory" by mixing memo- ries generated by both GPT-3.5 and GPT-4 to observe its performance. These experiments were conducted without including the relevant knowledge within the LLM itself (M²). As mentioned $2.1, unlike Mp and MM. MM is not preserved in the memory pool shared with other LLMs. Including this type of memory in the ablation study would result the pollution of provided memory by the MM generated during use.
GPT-3.5
GPT-4
GPT-3.5 Memory
11.22
36.73
GPT-4 Memory
13.26
44.89
Hybrid Memory
10.20
28.57
As shown in Table 3, data analysis indicates that memory quality significantly impacts the ac- curacy of responses from ChemAgent. Whether GPT-3.5 or GPT-4 is used, the memory produced by GPT-4 consistently outperforms that generated by GPT-3.5, showing 8% variation. This un- derscores the substantial impact of memory quality on the accuracy of generated responses. This observation also explains why the improvements seen with GPT-3.5 are less pronounced compared to those with GPT-4; the lower quality of memory produced by GPT-3.5 is the limiting factor.
Interestingly, hybrid memory performs the worst among the three settings. This can be attributed to the simultancous invocation of different memories for the same question-one generated by GPT- 3.5 and the other by GPT-4-hhich may confuse the LLM, increasing the likelihood of producing irrelevant or incorrect answers. Additionally, invoking an excessive number of memory tracks during resolution may also contribute this issue. more detailed analysis regarding the number of memory tracks invoked is provided in Appendix D.
CONCLUSION
Our research presents novel approach to enhancing large language models for solving complex chemical problems through self-exploration and memory formation. This method enables mod- els to construct and utilize library, significantly improving response accuracy. Experiments us- ing datasets and models like GPT-3.5, GPT-4, and Llama3 demonstrate substantial performance gains, with the ChemAgent architecture achieving up a 36% improvement. The structured library, built through memory decomposition into planning, execution, and knowledge, enhances problem- solving capabilities, which holds promise for generalization other domains.
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A
RELATED WORKS
A.
CHEMICAL REASONING
Recent advances in LLMs have shown promise scientific reasoning, yet chemical reasoning re- mains particularly challenging. Benchmarks like SciBench (Wang al., 2024a) have revealed that current LLMs struggle significanlly with complex chemical calculations and multi-step reasoning tasks. SciBench includes 869 college-level problems across mathematics, chemistry, and physics, providing a rigorous evaluation LLM capabilities these domains. Other datasets (Wadden et al., 2024; Li et al., 2024c; Sun et al., 2024a; Feng et al., 2024; Huang et al., 2024b) have also contributed to advancing the evaluation of LLMS in scientific problem-solving.
In response to these challenges, several approaches have been proposed. StructChem (Ouyang al., 2024) provides structured guidance by decomposing chemical reasoning into phases such as formula generation, detailed step-by-step reasoning, and confidence-basee review. While showing improve- ments, it still faces limitations with highly complex problems. Other researchers have explored enhancing LLM performance through various prompting strategies (Yang et al., 2024; Yao al., 2024: Besta et al., 2024).
Some studies have focused on using Python code for reasoning tasks. Jie et al. (2023) demon- strated that self-describing programs, which transform reasoning processes into executable code, can significantly outperform natural language Chain-of-Though methods (Wei et al., 2022) in cer- tain scenarios.
Specialized tools like ChemCrow (Bran al., 2023) utilize function calling and precise code gener- ation to tackle chemistry problems. Davis & Aaronson (2023) highlight that combining GPT-4 with external tools like Wolfram Alpha or Code Interpreter can significantly improve groblem-solving in science. However, reliably integrating these tools for complex chemical calculations remains challenge.
Yu al. proposed ChemToolAgent,, comprehensive tool-augmented framework for chemistry, demonstrating that while specialized tools significantly boost performance on molecular manipu- lation tasks, they may introduce additional complexity that hinders LLMs' reasoning abilities on general chemistry questions that require fundamental knowledge.
Compared those tool-augmented approaches, which excels in specialized molecular tasks but struggles with complex reasoning in general chemistry questions, our ChemAgent demonstrates consistent improvements across both specialized and general chemistry reasoning tasks through its self-updating library mechanism.
A.2 PROBLEM DECOMPOSITION IN SCIENTIFIC REASONING
Decomposing complex problems into smaller sub-tasks has shown to enhance model understanding and accuracy across various domains. In the context of chemical reasoning, this approach particu- larly relevant due to the multi-step nature of many chemical problems. Patel et al. (2022) highlights the efficacy of question decomposition by breaking down complex queries into manageable sub- tasks. Similarly, Khot et al. (2022) demonstrates the benefits of modular task decomposition. Other studies (Lu al., 2022; Wei al., 2023) further underscore the advantages of decomposition in complex question answering and reading comprehension. Our work builds on these insights, specif- ically focusing on how breaking down complex chemical problems can improve the performance of self-evolving agents in this domain.
A.3 SELF-EVOLUTION AND CELF-CORRECTION AGENT REASONING
Recent research has explored the self-evolution and optimization of LLMs, which is particularly relevant for tackling the complexities of chemical reasoning. Yang et al. (2023) explore meth- ods for enhancing LLM performance through self-improvement techniques, while Fernando et al. (2023) investigate self-referential self-improvement via prompt evolution. Additionally, Zhou et al. (2024), Jiang et al. (2023), Hu et al. (2024) and Qian et al. (2024) present frameworks for agent self-evolution, aligning with our approach of enabling self-exploration and continuous learning in complex chemical problem-solving. While some of these frameworks also incorporate memory
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system, they predominantly emphasize the reuse of past workflows in daily tasks, demonstrated by Wang et al. (2024b) and Qian et al. (2024).
Furthermore, in chemical reasoning, where minor enrors lead significant discrepancies in results, the ability of LLMs to self-correct and refine their outputs is crucial. Huang et al. (2023) investigate the limitations of self-correcting mechanisms in LLMs, while Weng et al. (2022) explore the potential of LLMS as reasoners with self-verification capabilities. In terms of tool-assisted meth- ods, Gou et al. (2023) propose framework for LLMs to self-correct with tool-interactive critiquing, and Gao et al. (2023) offer insights into researching and revising LLM outputs using LLMs them selves. Other studies like Qu al. (2024) also inform our approach developing a self-updating memory system capable of refining its chemical reasoning processes.
B EXPERIMENTAL RESULTS OF MODELS OTHER THAN GPT-4
We also evaluated ChemAgent thoroughly on earlier and less powerful models, such as GPT-3.5 (gpt-3.5 55--trbur 6k). Specifically, ChemAgent achieves absolute improvement of +0.17% in terms of the average score compared with the previous SOTA. Also, our framework significantly performs better when equipped with memory system (+7.13% absolute improvement), which un- derscores the importance of memory.
However, when it comes the evaluation and refinement module, an interesting phenomenon is observed: even when outputs generated by GPT-3.5 are evaluated by more capable models like GPT-4, the agent often fails to correct its mistakes. This indicates that GPT-3.5 has relatively weaker self-correction ability than GPT-4, explaining why evaluation and refinement provide little benefit when GPT-3.5 used. This finding aligns with previous research (Zhang et al., 2024a).
Table 4: Performance the test sets of four datasets: QUAN, CHEMMC, ATKINS, and MATTER. We compare baselines under two different settings: a zero-shot setting with no demonstra- tions and few-shot setting with three demonstrations. The accuracy scores are computed with the approximation detailed in Section 4.3. The best results under each setup are highlighted in bold, and the second-best results underlined. This experiment is done using GPT-3.5 (gpt-3. 5-turbo-1666
GPT-3.5
CHEMMC MATTER
ATKINS
QUAN
Avg.
Baselines, all based on GPT-3.5
Few-shot+ Direct reasoning Fewshot+Python StructChem
23,08
8.16
9.35
5.88
11.61
33.33
16.33
13.08
8.82
17.89
43.59
24.49
26.17
32.35
31.65
ChemAgent, all based on GPT-3.5
ChemAgent w/o memory
38.46
16.33
23.36
20.59
24.69
ChemAgent w/ memory
56.41
18.37
28.97
23.53
31.82
+ evaluate and refine
41.03
16.33
28.04
20.59
26.50
+ evaluate by GPT-4 and refine
46.15
22.45
28.97
26.47
31.01
For open-source models, we choose Llama3-7b-Instruct, Llama3-70b-Instrucc. and Qwen2.5-72b- Instruct as the experimental model here. The baseline direct reasoning to directly query the model without adding other instructions. The evaluation and refinement modules are removed from the ChemAgent configuration, and only the Execution Memory (M,) added due to the model's relatively lower ability instruction following. Fach experiment is repeated least. 3 times, and the results are averaged. On Ilama3-7b, the average increase across four datasets is 8.34%. On 1lama3770b, the average increase across four datasets is 13.04%. The experiment demonstrates that the stronger the self-capabilitics of large models, the more pronounced the performance gains using framework.
Reference to Appendix F, the atkins dataset, and the matter dataset involve a lot of specialized knowledge in chemistry. The questions in atkins involve the theoretical aspects of thermodynamics, kinetics, and other chemical processes, while matter focuses on studying chemical reaction mech- anisms and kinetic processes. This demands high level of chemical expertise from the model
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itself. However, the Llama3-7b model, compared to GPT-4 and Llama3-70b, has lower voir of chemical knowledge, thus resulting in average performance on atkins and matter. On the other hand, the challenges in the quan and chemme datasets lie in complex computations, prediction of molecular structures, and chemical bond reactions, emphasizing the logical reasoning ability of large models. The Llama3 experiment demonstrates that the ChemAgent framework significantly enhances the model's capability in handling logically complex chemical problems, highlighting the requirement for a certain level of subject matter expertise within the framework itself.
However, the significantly lower performance on the atkins dataset by Llama3-7b requires further explanation. The development set arkins is much more challenging than other datasets. The solutions tend to require more reasoning steps in their decomposition, and the questions in the de- velopment set focus on a much narrower sub-topic than those the test set. This results in higher rate of mistakes during the development stage, leading to an initially lower-quality static memory pool. Consequently, this greatly influences and even misleads the agent's ss performance during the test stage.
general, it can be seen that our approach generalizes well across different LLMs, showing consis- tent improvement in performance. The results indicate that our self-updating memory mechanism enhances the problem-solvee Notably, ChemAgent performs significantly better GPT-4 compared to other models, suggesting that our framework becomes even more effective as base LLM models become more powerful.
capabilities of LLMM-powered agents regardless of the backbone used.
Table 5: Experimental results on Llama 3.1-70b Llama 3.1-70b and Qwen 2.5-72b. From the aver- age improvement across four datasets, the stronger the model, the greater the improvement. Com- pared the Llama 3.1-7b model, the overall enhancement using Llama 3. 3.1-70b is better, particularly noticeable in the CHEMMC and ATKINS datasets.
Models
CHEMMC
MATTER
ATKINS
QUAN
Avg.
Llama 3.1-7b
Direct reasoning
28.20
10.20
22.43 19.63
8.82
17.41
ChemAgent
56.41
12.24
14.71
25.75
Llama 3.1-70b
Direct reasoning ChemAgent
33.33
30.61
30.84
23.53
29.48
64.10
32.65
43.93
29.41
42.52
Qwen 2.5-72b
Direct reasoning ChemAgent
48.72
32.65 44.90
57.01
35.50
43,47
69.23
56.07
44.12
53.58
C PERFORMANCE BOOST: WHERE AND WHY OUR METHOD IMPROVES
C.I CALCULATION AND UNIT CONVERSION ARE MORE PRECISE
ChemAgent achieves notably higher accuracy in calculations and unit conversions. Two key fac- tors contribute this: (1) Python code is demonstrated alongside each corresponding sub-task in memory; (2) During development, ChemAgent adopted a strategy to save unit conversion steps in the long-term plan memory pool. This allows the agent reference correct conversion steps when necessary, ensuring accurate unit conversion.
C.2 HIGHER MEMORY'S SIMILARITY HELPS THE SOLUTION
When solving given problem P, series of memories [UI.... 24, may be invoked during the process. Let their similarity to the problem represented as [S,....,,,]] and the mean similarity value is denoted Smean, p. In Figure 8, visualize the distribution of Smean, categorized by whether P is correctly solved. evident that problems with higher Smean, are more likely to be solved correctly.
We also conducted a Chi- Chi-Square Test of Independence to assess the relationship between a similar- ity threshold (i.e., whether the similarity is greater than 0.805) and the correctness of the solution.
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KDE Similarity Distributions Type
37.5
Correct Tacks' Similaity Distributian Similrity Distribttinn
Reference Velue 0.805
25.0
"ype II Error
Densty
7.5
5.0
Simllonty Value
Figure 8: Memory Similarity Analysis. The probability density functions of invoked memory similarity for solved and failed tasks are visualized using Kernel Density Estimation. We use reference value of 0.805 to do the Mann-Wal Whitney U Test and get p-value of 0.008. The similarity values are generally higher in the trajectories of solved tasks.
The Chi-Square statistic is 8.77 with p- valuu of 0.003, indicating statistically significant rela- tionship. Thus, future work could focus on improving the similarity between invoked memories and the problem at hand to further enhance problem-solving performance.
D THE INFLUENCE THE NUMBER OF MEMORY INVOKED
We analyze the impact of the number memory instances used on our agent's performance. this analysis, we preserve the evaluation and refinement module since they also utilize memory. Given that the quantities of Md and M₁ are not fixed, we focus on examining how the number of M, impacts performance. The results are presented in Figure 9.
GPT-3.5
Figure 9: Test on MATTER dataset. "shots" represents the number of memory tracks given to the LLM. It demonstrates that as the number of demonstrations increases, the agent's average perfor- mance improves, but this comes at the expense of its stability.
As the number invoked memories increases, the average accuracy improves, but the variance also grows. This indicates that although the agent's performance benefits from more memory, it also becomes more unstable. The increase in accuracy suggests that with more memory, ChemAgent adheres more strictly the required format and acquires more useful knowledge. However, the increase in variance indicates that as the number of memory instances used increases, so too does the potential for encountering misleading irrelevant information. Our case studies in the 4-shot
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setting reveal that many memory instances carry degree of irrelevant trash information, which accumulates and can harm the agent's 's abilities.
Interestingly. some scemingly irrelevant information in Me may enhance the creativity of the LLM when solving specific sub-tasks, resulting in a higher maximum accuracy the 4-shot setting. This suggests that certain level of seemingly irrelevat information can be beneficial, allowing the agent to explore hard and unknown tasks better.
E LIMITATION
Despite our advances, limitations include the computational intensity and time required for self- exploration, well the need for further refinement of memory mechanisms for tackling more complex problems. Future research should focus on understanding the specific mechanisms by which memory formation benefits reasoning, exploring how different types of memory contribute to problem-solving, and identifying optimal strategies for memory utilization. Additionally, due limitations in budget and computational resources, we demonstrate our approach solely in the context of chemistry and have not conducted comprehensive research across the entire scientific domain, such as considering mathematics or physics.
However, we believe our proposed method has strong potential for generalization across scientific fields. We have made our code open-source and encourage future researchers apply it to their own datasets. Specifically, adapting our method to new domain (e.g., the CLASS sub-dataset in SeiBench, related to Physics) involves modifying a small portion of the prompts (such as replacing chemistry-related sentences, c.g., "You are Chemistry cxpert") and then re-starting the Library Construction phase using development set from the new domain.
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F TASK DOMAINS OF THE DATASETS
The Chemistry domain of SciBench has four datasets, each hand-collected from four college chem- istry textbooks. Quantum chemistry (quan) (Hair al., 2010) provides an exploration of equilib- rium, structure, and reactions. Chemistry kinetics (matter) (Atkins et al., 2014) combines physics and mathematics, spanning through quantum mechanics and atomic structure. Quantum mechanics (chenunc) (McQuarrie, 2008) covers quantum mechanics and the upplications in chemical bonding. Physical chemistry (atkins) (Atkins al., 2023) provides explorations of equilibrium, structure, and reactions. We leverage GPT-4 to annotate each data sample in these datasets for the specific subfields. Statistically, the four datasets cover 15 chemical subfields, and the division of chemical subfields also helps us make associations in related fields to enrich the memory pool. The subfields of the datasets are shown in the figure 10.
Rudd mrechs
Merosiaa ruach
Momapphaii thees
Dernmschee
thernedyyaani
abiis
gm
meech
photochemm
Figure 10: Chemical subfields covered by the four datasets. The subfields involved in each dataset different, and a task may require knowledge from multiple subfields.
Table 6: Detailed statistics and information the four datasets we experiment with. D; and Da refer to the number of data samples with and without solutions (development set and test set). "Raaft means the ratio of the development set to the test set.
Datasets
Topics
D; (Da)
Ra/1
CHEMMC
Quantum mechanics
39 (9)
0.231
MATTER
Chemistry kinetics
49 (10)
0,204
ATKINS
Physical chemistry
107 (16)
0.150
QUAN
Quantum chemistry
34 (8)
0.235
The problems in this dataset are challenging, with average of 2 formulas and 5 steps of reasoning required to solve the problems in the experiment (Ouyang et al., 2024). Mcanwhile, each dataset provides detailed step-by-step solutions for example problems, which fit well in framework for the initial construction memory pools, shown in Table 6.
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G PROMPTS
Here is the list of prompts used in study,
G.1 INSTRUCTIONS FOR BUILDING MEMORY POOLS.
(1) Split prompts direct ChatGPT to decompose a given task into condition and problem parts. Reflect prompts are used double-check the results of the initial decomposition to confirm that the decomposition is complete and correct and to return corrected results in the event of errors. See Figure 11.
Task Split Prompt for Memory Pool Development
SPLIT PROMPT SYS=
You ChatGPT, large language model trained by OpenAl, also excellent prompt evaluator, who is capable of splitting tasks and evaluating the difficulty of tasks which will be solved by other autonomous agents powered by LLM.
SPLIT PROMPT
Now, have target task and its solution here: task: task solution: solution
And also have Python environment where agents can write code. (It also okay not to them and just your knowledge);
Then, split or separate the sentence the original task into two parts: one is conditions, and the other is questions.
Below the format you should follow when you give the answer:
CONDITIONS: QUESTIONS:
##
REFLECT PROMPT SYS=
You are ChatGPT, large language model trained by OpenAl, and also excellent answer checker who is capable of figuring out whether some conditions and questions separated from the original task are complete and correct.
REFLECT PROMPT
Now, have task and its corresponding conditions and questions listed below. I need your help check whether the conditions and questions correct and exactly fit the original task.
[tasks}}} (conditions] }
[qquestions}
You should give me back the conditions and questions refined according to the origi- nal task; if you think there is nothing that needs to be changed, just output the original conditions and questions.
You do NOT need to provide solutions.
All the specified numerical data should be included in the conditions part instead of the question part.
Also, the questions should NOT contain something that is not given in the condition.
Your answer should strictly follow the format below.
I think the given conditions and questions fit the original task well not well. Because REFINED CONDITIONS:
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REFINED REFINED.QUESTIONN QUESTION:
Figure 11: A prompt for task splitting. This prompt is mainly used to initialize the construction of the memory pool by dividing the answers and solutions given the sample into multiple sub- problems and corresponding solutions.
(2) As collection of problems and solutions already exists in the dataset (Da), we can de- compose problem and its solution at the same time. Subtask prompts decompose complex problem into multiple sub-problems, each of which must be clearly described. The Sub Solution prompt directs ChatGPT to decompose the solution into sub-steps corresponding to each of the sub-pru problems based on the set of sub-problems obtained in the previous step. Sort prompts sort the obtained subproblems from easy difficult and provide rationale for the sorting, See Figure 12
Task Difficulty Ranking Prompt for Memory Pool Development
SUBTASK PROMPT.SYS =
You are excellent expert who thinks step by step and splits complex question into several steps.
SUBTASK PROMPT =
Given a task's ' background and condition: condition
Now the question is: question
And here is the given solution to this question: solution
Please think generally and step by step divide the question into N subtasks according to the given solution. You must give each task a clear description and every subtask must be designed necessary step to the final question. You should strictly follow the format below: **8
I think the best way to solve the question and my solution is: So set up subtasks as described below.
TASKS=>
<st> SUBTASK 1: <ed>
<t SUBTASK 2: <ed>
<st> SUBTASK N: <ed>
##
SUB SOL PROMPT SYS=
You excellent expert who think step step and split given complex solution of a certain question into several sub-steps according to the given subtasks split from the original question.
SUB SOL PROMPT=
Given task's 's background and its condition:
{[condition]}
Now the question is:
{[question]
And have already split the question into the subtasks listed below:
{subtaskk} }
And the whole solution the original whole question is:
[solluiion]
Now, Please think generally and step by step divide the solution into sub-solutions, which
must correspond one-to-one with subtasks. You must give each sub-solution clear descrip- tion and every sub-solution should be specific and exactly solve the corresponding subtask.
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For example, if there N subtasks, you must generate N sub-solutions. And sub-solution should exactly solve subtask 1.
You must generate sub-solutions according the whole solution given. If the whole solution is "Not provided", just generate your own suggested solution for each subtask. Else if the whole solution provided. your every sub-solution should be part of the whole solution.
You should strictly follow the format below: $#
I think the relations between subtasks and the whole solution are:
So, set up sub-solutions as described below. SOLUTIONS=> <st> SUBSOLUTION I:
<ed> <st> SUBSOLUTION 2: <ed>> <st> SUBSOLUTION N.... <ed>
SORT PROMPT_SYS.
You are ChatGPT, large language model trained by OpenAI, and also an excellent prompt difficulty evaluator who is capable of sorting series of tasks according their difficulty.
SORT PROMPT =
Now, have some tasks (each containing conditions, questions, and corresponding solutions) listed below. I need your help to sort these tasks from the easiest one to the most difficult one for an agent to solve, which means you need to put the simpler subtasks in front. [{askss}}
Think carefully and tell the reason why you think one is easier and another is harder. You do not take the temporal relationship into consideration when sorting.
You do NOT need to refine the given solutions.
You do NOT need to follow the original order of the tasks.
You are FORBIDDEN change the description of the tasks given. Just change their order. Your answer should be a permutation of the n tasks. And the id of easier tasks should be placed in front of the harder ones. For example, if there are tasks, and task is the simplest, task 3 is the hardest.
Then your final output should be "RESULT=> <st> 213 <ed>
Please strictly follow the format below. #**
I think the difficult relationship between the tasks is:
SO MY ANSWER IS:
RESULT=>
<st> **
[a permutation] <ed>
If there is just subtask, just list it and follow this format:
#*
SO MY ANSWER IS:
RESULT=>
<st> <ed> ##$
Figure 12: A prompt for ranking task difficulty, The subproblems are sorted in order of difficulty and solved one by one, from easy to hard, by solving the easier subproblems and learning from the experience to solve the harder ones better. This is the idea of using course learning to help agent self-evolution.
G.2 INSTRUCTIONS FOR SOLVING PROBLEMS.
(1) Decomposition prompts (Figure 13) are used to decompose given chemistry problem into 1-3 subtasks, each with specific goals, criticism, and milestones.
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Task Decomposition Prompt
SYSTEM PROMPT
You Chemistry expert and efficient plan-generation agent.
Now, you are doing an exam; you must decompose problem into several subtasks that describe what the goals for the problem.
Background Information
PLAN AND SUBTASK:
A plan has tree manner of subtasks: task contains subtasks task 1.1, task 1.2, task 1.3,
and task 1.2 contains subtasks 1.2.1, 1.2.2....
A subtask structure has the following JSON component:
"subtask name": string, name of the subtask
gooal. string, the main purpose of the subtask, and what will you do to reach this goal?
: string, what potential problems may the current subtask and goal have? "milestones": list[stringl, what milestones should be achieved ensure the subtask is done? And What formulas might the current subtask use? Make it detailed and specific.
SUBTASK HANDLE:
A task lask-handling agent will handle all the subtasks the inorder-traversal. For example:
1. it will handle subtask 1 first.
2. if solved, handle subtask 2. If failed, split subtask subtask 1.1 1.2 1.3... Then handle subtask 1.1 1.2 1.3...
3. Handle subtasks recursively until all subtasks solved. Do not make the task queue complex, make it efficiently solve the original task.
RESOURCES:
musk-handligg agent can write and execute Python code.
Task Description
Generate the plan for query with operation SUBTASK-SPLIT, and make sure all must-reach goals are included in the plan.
Important Notice
Always make feasible and efficient plans that lead to successful task-solving. Never create new subtasks that are similar or the same as the existing subtasks.
For subtasks with similar goals, try do them together in one subtask with list of subgoals rather than split them into multiple subtasks.
Do not waste time on making irrelevant unnecessary plans.
The task handler is powered by sota LLM, which can directly answer many questions. So make sure your plan can fully utilize its ability and reduce the complexity of the subtasks tree.
You can plan multiple subtasks if you want.
Minimize the number of subtasks, but make sure all must-reach goals are included in the plan.
Don't 't generate tasks that aimed understanding concept, such as "understanding the problem", the LLM who answers the task already knows the underlying concept. Check the generated subtask objectives and milestones for understanding, and regenerate the subtasks so.
After the subtask is generated, check see if the answer for the task has been given in the task's ' known conditions. If the task has already been resolved, delete the subtask.
USER PROMPT
This is the first time you are handling the task (query), you should give initial plan.
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{(iimilar task saddd_ndddpa) Now try use SUBTASK.SPLL split the following problem, here is the query which you should give an initial plan to solve:
Your task- {
You will operation SULTASKSSPLIT to split the query into 1-3 subtasks and then commit.
Figure 13: A prompt for task decomposition. Splitting the problem reduces the difficulty of solving each sub-problem. Atomized simple tasks make it easier find trajectories of experience that can be drawn upon in the memory pool.
(2) The execution instructions (Figure 14) direct ChatGPT to solve the problem based the current subtask and known conditions, standardize the return format that must contain the formulae used to solve the problem, step-by-step reasoning process, and finally give piece of Python code to arrive at the answer to the problem.
Prompt for Task Execution
SYSTEM PROMPT
A question is divided into many steps, and you will complete one of them. Please provide a clear and step-by-step solution for a scientific problem in the categories of Chemistry, Physics, Mathematics. The problem will specify the unit of measurement, which should be included the answer.
You have been solving a complex task by following given plan listed below.
Plan Overview
The complex task has already been split into a tree-based plan as follows: [lann)
You have already performed some of the subtasks.
USER PROMPT
Now, you continue to complete subtasks. Please combine the results of previous tasks to complete the current goal processing subtasks. Please complete all milestones and give concise answer that can be efficiently used by subsequent subtasks.
Status
Current Subtask: {[subtask id)}
The query: (ssubtask_goal)
Milestones: {milestonnss]]
Please respond strictly the format provided. For each instance, you need to do three things.
Firstly, for "formulae retrieval", you need to identify the formulae explicitly and implicitly entailed in the problem context.
Then there is a 'reasoning/callculttion process" where you are required to reason step by step based on the identified formulae and problem context.
Finally, conclude the answer by writing piece of corresponding Python code; you MUST the International System of Units in this stage. For each problem, the output format should incorporate the following components in the corresponding format:
**Formulae retrieval: [Formula l] (the formula required solve the problem)
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[Formula (the second formula required to solve the problem, if any)
[Formula (the n-th formula required solve the problem, if any) "FReasoning/aaluulaiiii process:**
[step 1] (the first step for solving this problem)
[step n] (the n-th step for solving the problem, if any)
** Answer conclusion:*s
[answer] (Python code that can be executed independently)
Your answer should be piece of Python code that solves the current question.
You must end your code by printing all the result and their units.
Make sure the code can be successfully run without any input.
Be precise. The answer should be accurate, choose the appropriate units, and prohibit the use of round functions to round off and lose the results.
Make sure the code can be successfully run without any input. And import all the modules that you need to use.
for example, you could respond to the Answer conclusion part like this: ** **nwwer conclusion:*e [answer]:
import numpy np
Value of 2 pi omega_obs
omega_obs 1. 1:1133704493333022=223 # Hz
Value of for H35C1 440.2 kJ/mol
Calculate beta beta omega_obs (2 * +
Print the result
print ("The value of beta is:", beta, "em" (-1) ")
- Similar tasks
The following are the trajectories of tasks that have been dealt with in the past that are similar to this goal, including the successful tasks and their action lists. You can learn from them and use relevant knowledge in their procedure. {[success prompt))
Figure 14: prompt for executing tasks. The format of specified output consists of formulas and reasoning processes. The relevant knowledge in Knowledge Memory improves the accuracy of the formula part, and the reasoning processes make the output Python code more logical.
(3) If the similarity between the retrieved memory trace and the current problem is too low, the Imag- ination prompts (Figure 15) direct ChatGPT generate problem and solution in the specified format based on the current topic.
Prompt for Similar Task Association
IMG IHG.PROMPT
Please create [/top_]] advanced chemistry questions suitable for in-depth understanding and application of chemical formulas and principles. Each question should focus on the deeper aspects of the [topic] provided understand the principles of chemistry and the
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reasoning process.
[topic]: [{topi]]]
Guidelines for Problem Creation:
Use of Samples: You are provided with sample questions for reference. Feel free to use these to guide the style and depth the problems.
Beyond the Examples: You are encouraged to use your background and expertise to create problems that go beyond the provided examples, ensuring the problems are as diverse and comprehensive as possible.
Requirements for Each Problem:
a. Problem Statement: Clearly define the challenge or task.
b. Solution: Provide detailed solution that includes: I. Formulas and Knowledge Needed: List the equations and concepts required to understand and solve the problem.
II. Reasoning Steps: Outline the logical or mathematical steps to solve the problem.
III. Python code: Executable Python code generated to solve problems. the end each problem, please include Python code that can be used to confirm and verify the correctness of the provided solution. The Python solutions should illustrate the entire solution process, from the initial step to the final answer, rather than merely validating the result. Develop these solutions such that each step of the mathematical process is explicitly demonstrated and caleulated in Python. Additionally, ensure that you your Python code to confirm it free from any errors.
d. Diversity: Ensure wide range of problems, each focusing different elements from the subtopic list.
Presentation: Please output your problem statement, solution, detailed explanation, and self-contained Python code for verification below in the specified format.
For each generated question, the output is required to be in the following format: [Task Start]
[Problem Statement]: (your problem)
**Formulae retrieval:
[Formula 1] (the formula required to solve the problem)
[Formula 2] (the second formula required to solve the problem, if any) [Formula n] (the n-th formula required to solve the problem, if any)
**Reasonin//caleulatioo process:**
[step 1] (the first step for solving this problem)
[step n] (the n-th step for solving the problem, if any)
** Answer conclusion:** **
[answer]: (Python code that can be executed independently) [Task End]
You have to generate {topk]] } tasks about {{topic})).
Sample demonstration example: {[example_shots))
Figure 15: A prompt for associating similar tasks. The dataset covered 15 sub-domains, and the question was modeled by interrogation to determine which sub-domain the question belonged to. The associatively generated trajectories must be the same format as the trajectories in Execution Memory.
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(4) The evaluation prompt (Figure 16) is used to assess the correctness of the current solution, including judging the correctness of the given formulas, the rigor of the reasoning process, and whether the output of the Python code meets the task goals and completes all task milestones.
Prompt for Task Evaluation
SCORE PROMPT
You tackled a sub-problem in chemistry problem; the format of the solution to the problem is ** **Fomulaee retrieval: ** and **Reasoning/allclatio process:* and ** Answer conclusion: (which includes piece of Python code and its corresponding output).
Subtask
The question: (subtask goal]
Milestones: {milestones)
[Response Start]
[response}
[Response End]
For each instance, you need do four things:
First, judge whether the given formula is correct and whether the constants are correct.
Second, judge whether the reasoning process is rigorous and correct.
Third, determine whether the Python function outputs the parameters required by the task goal and milestone.
Finally, score the degree of completion and correctness of the whole task. You should give the "confidence score" on the scale of [0,1]. Please very strict about your ratings.
The output format should incorporate these components the following format: ** **Jugemment of the retrieved formulae:** [judgement] (Your assessment of whether the retrieved formulae are correct or not.)
** =udgemmentt of the reasoning process:** [judgement] (Your assessment of whether the reasoning process correct not.)
** +uddemeent of the Answer conclusion:** [judgement] (Your assessment of whether the Python code outputs the parameters required in the task objective and whether the Python code correctly infers according to the analysis in reason.)
**Cofiiencce *core:** [score] (float number in [0,1], A very strict score is given to the correctness of the solution of the entire task)
Figure 16: A prompt for evaluating tasks. Evaluate the current answer, generate answers consecu- tively, and select the answer with the highest score.
(5) Finally, the final answer and summary of posterior knowledge are obtained by summary prompts (Figure 17). Summarize the total process of solving the problem, the relevant knowledge used, and the formulas that can be used to enrich the memory pool. At the same time, the answers to all sub-problems are combined to produce the final answer to the whole problem.
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Prompt for Task Summary
SYSTEM PROMPT
You posterior posterior.knowledge
Now that you' 've completed parent task, the task is made up of many subtasks, each of which has been completed.
Your plan for the parent task is as follows:
PParent Goal
{parent-goall Sub-task division
The flow of your actions for handling subtasks is: Action lists for Subtasks
{sub.plaa}}
Now, you have to learn some posterior knowledge from this process by doing the following things:
1. Summary: Summarize the ideas of subtasks to the parent task, and summarize a total process to the parent task according to the action process of each subtask. Explicitly include all the formulas used during performing the subtasks in this summary. Specific numbers and numerical results are NOT needed in this part.
2. Reflection of knowledge: After performing this task, you get knowledge of generating plans for similar tasks. Only conclude the used knowledge and formulas used in this whole task, do NOT contain numerical calculation process and results.
Final Answer: Give the final answer to the task according the course the task, and ask the answer to be very short, without explaining the reason and adding unnecessary punctuation. If math problem, only the last value is given.
Figure 17: A prompt for summarizing tasks. Summarizing tasks crucial for the self-evolution of ChemAgent, Summarize the formulas and principles of this task and add them to the Memory Library.
(6) When task fails execute, may because the task decomposition unreasonable. The framework can perform adjustment operations such as adding/spliting//eleinn the current task tree adjust the overall task framework. See Figure 18 for the exact prompt.
Prompt for Task Refinement
SYSTEM PROMPT
You are a plan-rectify agent, your task is to iteratively rectify plan of query. Background Information
PLAN AND SUBTASK:
plan has a tree manner of subtasks: task contains subtasks task 1.1, task 1.2, task 1.3,
and task 1.2 contains subtasks 1.2.1, 1.2.2.
subtask structure has the following JSON component:
"subtask name": string, name of the subtask "goal. string, the main purpose of the subtask, and what will you do to reach this goal? "goaa..riticismm": string, what potential problems may the current subtask and goal have?
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"milestones":
list[string]. what milestones should be achieved ensure the subtask is done? And What formulas might the current subtask use? Make it detailed and specific.
SUBTASK HANDLE:
A task task-handling agent will handle all the subtasks as the inorder-traversal. For example: will handle subtask I first.
2. if solved, handle subtask 2. If failed, split subtask subtask 1.1 1.2 1.3... Then handle subtask 1.1 1.2 1.3...
3. Handle subtasks recursively until all subtasks are solved. Do not make the task queue too complex, make it efficiently solve the original task.
RESOURCES:
task-handling agent can write and execute Python code.
Task Description
Your task is iteratively to rectify a given plan based on the goals, suggestions, and now handling positions.
In this mode, you will use the given operations to rectify the plan. At each time, use one operation.
SUBTASK OPERATION:
1. split: Split already handled but failed subtask into subtasks because it is still hard. The "target subtask id" for this operation must be leaf task node that has no children subtasks, and should provide
split "subtasks" of length 2-4. You must ensure the wtrettteeeeee )r
"target_subtask id" exists, and the depth of new split subtasks < { (maxx-lan_tree_depplle split 1.2 with 2 subtasks will result create new 1.2.1, 1.2.2 subtasks.
add: Add new subtasks as brother nodes of the subtask id'. This operation will expand the width of the plan tree. The sunget_subtask..o id' should point to a now-handling subtask or future subtask.
add 1.1 with two subtasks will result in creating new 1.2, 1.3 subtasks.
add 1.2.1 with subtasks wil result in create new 1.2.2, 1. 1.2.3, 1.2.4 subtasks.
3. delete: Delete subtask. The 'target subtask id' should point to a future/TODO subtask. Don't delete the handling done subtask. delete 1 1.2.1 will result delete 1.2.1 subtask.
Note
The user busy, so make efficient plans that can lead successful task-solving. Do not waste time making irrelevant or unnecessary plans. Don't 't use search engines since you know about planning. Don't 't divide trivial tasks into multiple steps. If the task is unsolvable, give up and submit the tas k.
Important Notice
Never change the subtasks before the handling positions, you can compare them in lexicographical order.
Never create (with add or split action) new subtasks that are similar or the same as the existing subtasks.
For subtasks with similar goals, try to do them together in one subtask with a list of subgoals rather than split them into multiple subtasks.
Every time you use an operation, make sure the hierarchy structure of the subtasks remains, c.g., subtask 1.2 "find A.B,C" then the newly added plan directly related to this plan (like "find A", "find B", "find C") should always be added as 1.2.1, 1.2.2, 1.2.3... You are restricted to give operations at most times, so the plan refinement is not so much.
The task handler is powered by sota LLM, which can directly answer many questions. So
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make sure your plan fully utilize its ability and reduce the complexity of the subtasks
trec.
USER PROMPT=
Your task choose one of the operators of SUBTASK OPERATION, note that 1. can only modify the subtask with subtask id > ((subtask id)) (not included). Please use function call respond to me (remember this!!!).
A prompt for refining tasks. Incorrect answers may result from error in sub-question, leading to subsequent inaccuracies, or from a misjudgment in the decomposition of the question. In the former case, the Task Evaluation module is used enhance the accuracy of problem-solving while the Task Refinement module is employed to modify the question decomposition tree in the latter case.
Figure 18:
G.3 INSTRUCTIONS FOR BASELINE
Both direct-reasoning and fewshot+Python setups are used as baselines in the main experiment, mainly referring the experimental setup in SeiBench Wang al. (2024a). The following instruc- tion (Figure 19) is used in the fewshot+Python setup, which asks for piece Python code to be output and is followed by three demonstrations.
Prompt for Few- Sew-Shot Python
SYOMPRTT
Please provide clear and step-by-step solution for a scientific problem in the categories of Chemistry, Physics, or Mathematics. The problem will specify the unit of measurement. For clarity, please translate the solution steps into Python code and encase the Python code within triple backticks.
A prompt for Few-shot+Python. The baseline setup directly requires LLMs to generate Python code, and through few-shot learning, the responses are standardized. This approach, com- pared to the direct-reasoning setup, has shown an improvement in correctness.
Figure 19:
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H SPECIFIC CASE TRAJECTORIES
To concretely demonstrate the problem-solving process of our framework, we provide a complete execution trajectory of successfully answered question illustrate the procedural workflow of our framework.
Task Description
At 300 K and 20 atm, the compression factor of gas is 0.86. Calculate the volume occupied by 8.2 mmol of the gas under these conditions. The unit of the answer should cm".
Task Decomposition
Task 1: Calculate the volume in liters
Use the definition of the compression factor and the ideal gas law calculate the volume occupied by 8.2 mmol of the gas under the given conditions in liters.
Task 2: Convert the volume to cm3
Convert the calculated volume from liters to cubic centimeters.
Execution of Task 1 Calculate the Volume in Liters
Formulas:
Ideal Gas Law: PV nRT
PV
Compression Factor: nRT
Given:
Compression factor, Z = 0.86
Pressure, P 20 atm (convert Pa)
Number of moles,
8.2 mmml 8.2 10 mol
Gas constant, R = 8.31 J mol
Temperature, T 300
Calculations:
Convert pressure from atm
P = 20 atm x 101325 Pa/ //tmmmm = 2, 026, 500 Pa
Rearrange the compression factor formula solve for V: ZnRT
PV
Z nRT
= V
P Calculate the volume in cubic meters:
0.86 8.2 10-3 mol x 8.3145 mol-1 1 300 K
V
2, 026 500 Pa
4. Compute V:
V 0.86 8.2 10 10-3 8.3145 x 300
2, 0262 500 m3
V 8.6801 10
5. Convert volume to liters (since 1 m = 1000
V = 8.6801 10 m3 x 1000 L/m = 0.0086801 L
Execution of Task Convert the Volume to cm3
Formula:
Conversion from liters to cubic centimeters: 1L 1000 cm
Calculation:
1. Convert volume from liters to cubic centimeters: V = 0.0086801 I, x 1000 cm 8.6801 cm3
Summary Results
The volume occupied by 8.2 mmol of the gas under the given conditions is approximately 8.6801 cm3
We also provide execution trajectory of successfully answered question in another benchmark, ChemBench (Mirza et al., 2024) which direct-reasoning method fails to solve. The backbone LLM used here gpt-4o 0004-00-001-preiiee
Task Description
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Statement I: Addition of a non non-reacting gas at constant volume and constant temperature to a container containing gas (X) does not change the partial pressure of gas (X). Statement 2: The addition of a non-reacting gas to a container at constant volume and constant temperature results in increase in the total pressure of the container, but not in the partial pressure of the gases in the container. Which of the following statements is the most correct option for the given two statements?
A, Stattement 1 is False, Statement is True.
B. Stattement 1 True. Statement is False
C.Statements and 2 are True. Statement 2 is NOT the correct explanation for Statement 1. D.Statements and are True. Statement the correct explanation for Statement 1. Choose one correct choice.
Task Decomposition
Task 1: Verify the validity of Statement 1
Determine effect of adding non-reacting gas on partial pressure of gas (X) at constant volume and temperature.
Task 2: Verify the validity of Statement 2
Determine effect adding a non-reacting gas on total pressure at constant volume and tempera- ture.
Task 3: Compare Statements and Choose the Correct Option
Compare the findings from the analysis Statements 1 and and determine the most correct option from the provided choices.
Execution Task Verify the validity of Statement
Formulas:
- Ideea Gas Law: PV = nRT
.P PPpatiall ApdRT
Given:
Pparial is the partial pressure of gas (X) Tyus is the number of moles of gas (X)
T the temperature, V the volume
Calculations:
1. The partial pressure of gas in container is given by the ideal gas law, which states that the partial pressure is directly proportional to the number of moles of the gas, the temperature, and inversely proportional to the volume.
gas RT
Pprrtill
V
2. When a non non-reacting gas is added to the container at constant volume and constant temperature, the number of moles of gas (X) does not change. Therefore, the partial pressure gas (X) remains the same because ygs. R, T. and V are constant.
The initial and final partial pressures of gas (X) are the same, verifying that Statement is true. Execution Task Verify the validity of Statement 2
Formula:
The total pressure gas mixture is the sum of the partial pressures of the individual gases:
P Piotal
P1
Pa
+
Calculation:
1. Consider container with gas (X) constant volume and temperature. The initial total pressure the partial pressure of gas (X), Px
When non-reacting gas (Y) is added to the container constant volume and temperature, the partial pressure of gas (X) remains the same, Px.
The total pressure of the container after adding gas (Y) is the sum of the partial pressures of gas (X) and gas (Y), Pusal = Px + Py.
Execution Task Compare Statements and Choose the Correct Option
Formula:
No specific formula is needed for this subtask.
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Reasoning:
1. Analyze the results from the previous subtasks: From Task 1.1: The partial pressure of gas
(X) remains unchanged after adding non-reacting gas at constant volume and temperature. From Task 1.2: The total pressure increases after adding a non-reacting gas at constant volume and temperature, but the partial pressure of gas (X) remains unchanged.
2. Compare the findings from the analysis of Statements and 2.
Determine the most correct option based on the analysis: Both statements are true, and Statement 2 correctly explains Statement 1.
Summary Results
D. Statements 1 and 2 are True. Statement 2 is the correct explanation for Statement 1.
An example result of the decomposition step of the library construction phase, which help under- stand how the sub-tasks are organized.
Original Task
The change in molar internal energy when CaCO3( s) as calcite converts to another form, arago- nite, is +0.21 21 kJ mol-! Calculate the difference between the molar enthalpy and internal energy changes when the pressure is 1.0 bar given that the densities of the polymorphs are 2.71 cm and 2.93 g cm- respectively.
Original Solution
The change in enthalpy when the transition occurs is
AHm = Hm{ aragonite ) - H calcite)
(a) + (Vm (Um(c) + pVm(c))
AUm +p (m (c)
where a denotes aragonite and e calcite. follows by substituting Vn = M/p that
AHm - AU = PpM
((a)
plc)
Substitution of the data, using M 100 g mol gives
1
1
AH AU_ (1.0 10a Pa. (100 g mol=!)
2.93 g cm 3
2.71 g cm
- 10s Pa em3 mol -..28 Pa m² mol-!
Decomposition Results
SUB-TASK 1
Condition I
The change molar internal energy when CaCO3( 8) as calcite converts to another form, arago- nite, is +0.21 kJ//mol. The pressure is 1.0 bar. The densities the polymorphs are 2.71 g cmm and 2.93 g/ g/cm²,, respectively.
Question
Identify the given data and the required physical quantities (molar internal energy change, pres- sure, densities, etc.). Solution
Pealcite 2.71 g/lmm/mm,
Paragonite 2.93 g/em²
SUB-TASK 2
Condition 2
The change molar internal energy when CaCOs( s) as calcite converts to another form, arago- nite, is +0.21 k.//mol. The pressure is 1.0 bar. The densities of the polymorphs are 2.71 g g/em" and 2.93 /mm respectively. Question
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Express the molar volumes of calcite and aragonite in terms of their densities and the molar mass of CaCO3. Solution 2
M
M
(calcitt)
Vn (aragonnt))
Pealcite
Paragonite
where M the molar mass of CaCO3 (100 g/mol).
SUB-TASK 3
Condition 3
The change in molar internal energy when CaCOs( s) as calcite converts to another form, arago- nite, is +0.21 kJ/mol. The pressure is 1.0 bar. The densities of the polymorphs are 2.71 g/ g/em² and 2.93 8/cm" respectively.
Question 3
Substitute the given densities and molar mass into the molar volume expressions to find specific volumes.
Solution 3
100 g/mol
100 /mol
Vm (calcitee)
Vn Vn (aragonite)
2.71 g/cm"
2.93 g/ /cmm"
SUB-TASK 4
Condition 4
The change molar internal energy when CaCOs( s) as calcite converts to another form, arago- nite, is +0.21 k.//mol. The pressure is 1.0 bar. The densities of the polymorphs are 2.71 g/em² and 2.93 g/enz /emm respectively.
Question
Calculate the change in volume between aragonite and calcite using the specific volumes. Solution 4
100 1/00g/mol
100 g/ool mol
AV,
2.93g/cm
2.71 g/em²
AV.. & 34.13cm² /mmol 36.90 cm /mol 2.77 cm /mol
SUB-TASK 5
Condition 5
The change in molar internal energy when CaCOs( s) as calcite converts to another form, arago-
nite, is +0.21 k.J/ t./moo. The pressure is 1.0 bar. The densities of the polymorphs are 2.71 g, g/cm3 and 2.93 g/ cmm respectively.
Question 5
Write down the equation relating the change in molar enthalpy to the change in molar internal energy and the pressure-volume work term.
Solution 5
AHl = AUn + PAV,
where AV, Vm (aragonite)
Vm (calcite).
SUB-TASK 6
Condition 6
The change molar internal energy when CaCOO s) as calcite converts to another form, arago- nite, is +0.21 kJJ /moo.. The pressure is 1.0 bar. The densities the polymorphs are 2.71 g s/mm' and 2.93 g/ /cmmm. respectively. Question
Substitute the values of pressure. molar internal energy change, and the change in volume into the enthalpy change equation to find the difference between the molar enthalpy and internal energy changes.
Solution 6
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A AUm =PAVm
AHm AU (1.0 x 10 Pa) (-2.77 10 mm/mol)
Allm AUm -.277
SUB-TASK 7
Condition 7
The change in molar internal energy when CaCO3( s) as calcite converts to another form, arago- nite, is +0.21 kJ/ /mo/ The pressure is 1.0 bar. The densities of the polymorphs are 2.71 g, /cmm" and 2.93 g/ s//m², respectively.
Question 7
Verify the units of the calculated result to ensure consistency with the required units (e.g.. kJ /mol).
Solution 7
The units of the result 0..277 /m/o are correct and consistent with the required units.
SUB-TASK 8
Condition 8
The change in molar internal energy when CaCOs( s) as calcite converts to another form, arago-
nite, is +0.21 kJ/ /.//mol. The pressure is 1.0 bar. The densities of the polymorphs are 2.71 g, g/cm3 and 2.93 g/ s/cmm,, 3 respectively.
Question 8
Summarize the final answer with the correct sign and units.
Solution 8
The difference between the molar enthalpy change and the molar internal energy change is
A AH AUm -0.28 kJkmmn /moo..
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Reviews:
[{"review_id": "Bv37Cfv2WO", "reviewer": "Reviewer_ea4h", "summary": "The paper introduces a novel framework named *ChemAgent* designed to enhance the performance of large language models (LLMs) in complex chemical reasoning tasks. This framework overcomes the typical limitations faced by LLMs in accurately handling domain-specific formulas and reasoning steps.\n\n**Key contributions of the paper include:**\n1. **Dynamic Library System**: ChemAgent incorporates a self-updating \"library\" system that decomposes complex chemical problems into sub-tasks, storing these alongside their solutions for future reference. This approach allows the system to retrieve and refine relevant information when tackling new problems.\n2. **Memory Components**: The framework introduces three types of memory:\n   - **Planning Memory** for high-level strategies.\n   - **Execution Memory** for detailed problem-solving steps.\n   - **Knowledge Memory** for basic chemistry principles.\n3. **Iterative Problem Solving**: ChemAgent's iterative learning enables LLMs to improve their accuracy over time, enhancing performance through experience.\n4. **Performance Gains**: The framework demonstrated up to a 46% improvement in chemical reasoning accuracy on SciBench datasets, significantly outperforming existing methods such as StructChem.\n\nThe authors conclude that ChemAgent holds promise for applications in fields like drug discovery and materials science, and they provide their code for further research and development.", "questions": "### **Clarification of Innovation and Contribution**\n\n**Questions:**\n\n- Could the authors provide a more detailed explanation of how ChemAgent’s integration of dynamic memory updating and prompt engineering differs from existing approaches? Specifically, how does ChemAgent’s memory updating mechanism contribute novel insights or advancements in the field of LLMs, beyond the well-explored techniques of prompt design and memory retrieval?\n- Could the authors elaborate on any unique aspects of ChemAgent's architecture or framework that differentiate it from similar systems in the literature? Are there any specific design choices made to optimize memory retrieval or self-improvement that haven’t been widely explored in other works?\n\n**Suggestions:**\n\n- We suggest that the authors further emphasize the novelty of ChemAgent's integration of prompt design and memory modules. A comparison to existing memory-enhanced models in the LLM space could help highlight ChemAgent’s unique contribution.\n- We recommend that the authors provide a more thorough description of how the dynamic memory updating mechanism operates in real-time and how it evolves through interactions. This would help clarify the distinction between ChemAgent and previous works focused on prompt engineering or static memory updates.\n\n---\n\n### **Generalizability and Comprehensiveness of Experimental Evaluation**\n\n**Questions:**\n\n- While the use of the SciBench dataset is commendable, could the authors clarify if the results obtained are robust across a wider range of chemical tasks? Have the authors considered evaluating ChemAgent on real-world chemical challenging reasoning tasks, such as optimizing experimental parameters or suggesting experimental protocols?\n- How does ChemAgent perform when applied to more complex, interdisciplinary problems, such as tasks that require domain-specific knowledge integration (e.g., chemical engineering or materials science)?\n\n**Suggestions:**\n\n- We suggest that the authors expand the experimental evaluation to include a broader range of datasets, including those that involve real-world scientific challenges. This would provide a clearer picture of the system's generalizability and its potential for application in diverse chemical and scientific domains.\n- We recommend that the authors explore performance on tasks with varying levels of difficulty, such as those requiring multi-step reasoning or integration of data from multiple sources, to better assess ChemAgent's capabilities across different domains.", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. **Self-updating dynamic memory library system**: ChemAgent improves the chemical reasoning capabilities of large language models (LLM) by introducing a self-updating \"library\" system. The library improves reasoning and solution accuracy by decomposing complex chemical problems into subtasks and storing these tasks and corresponding solutions in memory components, allowing models to retrieve and leverage the past when solving new problems. \n\n2. **Performance improvement**: Experimental results show that ChemAgent achieves up to 46% performance improvement in chemical reasoning tasks on the SciBench dataset, significantly outperforming existing solutions such as StructChem. Especially when using more powerful models such as GPT-4, ChemAgent has demonstrated the ability to continuously learn and improve through its memory system and evaluation improvement module.", "weaknesses": "1. **Clarification of Innovation and Contribution**: ChemAgent introduces a dynamic memory updating mechanism, which, while innovative, relies on existing techniques and frameworks, such as prompt engineering and the combination of pre-trained models, to enhance the performance of large language models (LLMs). While the self-improvement capabilities of the dynamic library system are noteworthy, the approach depends primarily on prompt design to manage LLM interactions and context retrieval, an area that has been extensively explored. Therefore, we suggest authors further emphasize the distinctiveness and novelty of ChemAgent’s integration of prompt design and memory modules to help better differentiate its contributions from existing works.\n2. **Generalizability and Comprehensiveness of Experimental Evaluation**: While the SciBench dataset provides a rigorous benchmark for evaluation, the current experiments are focused on specific chemical reasoning tasks. Expanding the validation to include more diverse datasets or real-world scenarios like optimization of chemical experiment parameters would strengthen the argument for ChemAgent's broader applicability. We recommend considering a wider range of more challenging evaluation tasks in future work to comprehensively assess the system's performance across a variety of chemical or scientific problems.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bZGT2dLXSz", "reviewer": "Reviewer_tXxm", "summary": "The paper presents ChemAgent, a framework that enhances large language models (LLMs) in chemical reasoning tasks by dynamically self-updating memory library. The library is designed to retain knowledge and improve task performance over time by decomposing complex problems into smaller sub-tasks, storing solutions, and enabling efficient retrieval for future similar tasks. ChemAgent incorporates three types of memory: Planning Memory for high-level strategies, Execution Memory for specific solutions, and Knowledge Memory for core chemistry principles. Experimental results on four datasets from SciBench demonstrate that ChemAgent outperforms baselines, particularly in complex chemistry tasks.", "questions": "* How does ChemAgent handle potential memory overflow? Is there a mechanism to dynamically optimize memory usage?\n* Follow up on W2, what are the sizes of each memory module, and do they vary by type? If so, what are the considerations behind these differences?\n* How long does it typically take to build a knowledge library that achieves reliable performance?\n* Is there a mechanism to ensure the quality of data accumulated over time?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "* The three-part memory system (Planning, Execution, Knowledge) effectively models how LLMs can incrementally improve through task decomposition and retrieval. This approach emulates human memory retrieval, making it highly applicable to chemistry, where multi-step reasoning and recall of specific formulas or constants are frequently required.\n* The experimental setup provides a solid justification for the additional memory components, clearly demonstrating the benefits of enhanced recall and problem-solving.", "weaknesses": "* As the library expands, retrieval efficiency and memory management may become bottlenecks. The framework’s performance could be compromised in environments with frequent memory updates and large datasets, highlighting a need for memory pruning or optimization strategies.\n* More implementation details about the memory architecture would be beneficial, especially regarding the memory sizes and updating mechanisms. An analysis of the relative sizes of each memory type (Planning, Execution, Knowledge) would be insightful.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "71QAx088gT", "reviewer": "Reviewer_LmT4", "summary": "The paper introduces a framework ChemAgent aimed at enhancing chemical reasoning capabilities in large language models through a self-updating dynamic library. ChemAgent leverages a memory-based library system that decomposes complex chemical tasks into sub-tasks, storing solutions for future reference. The authors evaluate ChemAgent on four chemical reasoning datasets from SciBench, demonstrating that it significantly outperforms previous approaches.", "questions": "1. During the library construction phase, the definition of \"condition\" is unclear, what kind of condition is extracted, and what is the criterion of \"refine\"? The authors are encouraged to clarify with an example.\n\n2. Although the paper demonstrates strong performance in chemistry, how might the framework adapt to other scientific domains with similar reasoning demands, such as physics or computational biology? Are specific adjustments needed for these areas?\n\n3. The evaluation and refinement module plays a significant role in performance. Could the authors elaborate on any observed limitations in this module, especially regarding instances where errors are detected but not effectively corrected?\n\n4. How does ChemAgent compare with models that integrate external tools, such as ChemCrow, especially considering that code generation is employed to handle calculations in subtask answer generation? Are there established tools that could directly perform these calculations, and how do the authors assess the accuracy of the generated code?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The overall contribution of RAG+LLM with the application for chemistry calculation, is a rather novel contribution as an application.\n2. This memory system, which includes planning, execution, and knowledge memory, is well-suited for tasks that require iterative problem-solving and cumulative knowledge retention.\n3. The authors evaluated ChemAgent across multiple datasets and models and provided extensive error analysis and ablation study.", "weaknesses": "1. The main experiments use only one dataset (Sci-bench), and actually only the chemistry subset of this dataset. Given that chemistry reasoning is such a large topic with many datasets available, I would expect experiments on more datasets. Otherwise, the authors need to clearly explain why the other datasets are not suitable. Even if it’s the case that this dataset offers a unique benefit (e.g.,chemistry-related calculation, with labels ), other parts of the experiments can be performed on other datasets as well to strengthen the analysis.\n\n2. While ChemAgent performs well against StructChem and other baselines, a direct comparison with more recent multi-modal or hybrid models (such as those integrating external tools like ChemCrow) would strengthen the case for ChemAgent’s utility across various LLM designs.[1]\n\n[1].ChemCrow: Augmenting large-language models with chemistry tools", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bKl46oLhHU", "reviewer": "Reviewer_h1D2", "summary": "The paper introduces ChemAgent, a method for improving LLMs reasoning in chemistry-related tasks based on the use of tsak decomposition and memory modules of three kinds.\nIn particular, the authors implement a system with 3 kinds fo memory, namely knowledge memory Mk, plan memory Mp, and Execution memory Me, each of which plays a role in different parts of the pipeline.\nMp is used to aid an LLM when decomposing a given task by providing it with previous plans used to solved other tasks. Mk is used to retrieve facts and other relevant things useful to tackle specific sub-tasks, and Me provides detailed solutions to similar problems.\nThe evaluations are done mostly over the recently published SciBench benchmark. The authors perform multiple ablations to assess the effect of each component of the pipeline including each memory module, the LLM used, the effect of accumulating memores in Mp and Me. In addition, the papers presents comparisons with other SOTA methods and show that their method outperforms previous approaches.", "questions": "1. Given that the system doesn't appear to have chemistry-specific components, is there any particular reason why you focused solely on chemistry applications? Have you considered or tested the system's performance in other domains like physics or mathematics?\n2. Can you provide more details on how the results in Table 1 were obtained, particularly for the GPT-4 baselines? It appears some results may have been taken from other papers - could you clarify the sources and methodologies used?\n3. Have you considered evaluating ChemAgent's performance on other chemistry-specific benchmarks, such as ChemBench? What was the rationale for the choice of benchmarks used in the current study?\n4. Could you elaborate on what you mean by \"predefined templates\" when discussing previous methods? How does ChemAgent's approach differ, given that it also relies on predefined prompts?\n5. Several relevant works in the field of chemical reasoning and LLM agents with memory mechanisms seem to be missing from your citations. Please properly address these works and cite them accordingly.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The method builds up on other work (particularly StructChem), by recognizing where that approach fails and facilitating different modules to support the LLM during the reasoning process. Each memory module is designed to tackle an aspect which I find interesting, in particular the knowledge memory module (Mk) tackles the issue of bad recall of facts, such as constants, formulas, etc, preventing hallucinations of this type.\nPlan memory and execution memory both aid the LLM at the task of splitting tasks into relevant and actionable plans, based on previous experience.\nThe evaluations show the strengths of the system and an array of ablations is performed to assess the influence of each module, and of the quality of each module, in the resulting performance.\nEvaluation of the effect of evolution in memory as the agent solves tasks (Section 3.3) is very interesting, and it's indeed an experiment that I was looking forward to seeing while reading the paper. It proves one of the main claims: that experience (through expansion of the memory modules) can improve the performance of the agent.\nThe error analysis (Section 3.5) is very interesting and gives a lot of insight into where and why the agent fails at solving tasks. This is important for further developments as it gives an intuition on what parts need fixing or improving.", "weaknesses": "## Notes on the field specific Agent\n\n- there is nothing that makes the system chemistry-specific, other than the type of problems that are collected in its memory components. In fact, given the content of the paper I can imagine this system can be used for physics, maths (and thus evaluated in SciBench) or even extended to more general reasoning questions. In that sense, the relevance of this work can be improved by adding more extensive experiments on other fields outside of chemistry, as there is no chemistry-specific insight in the current formulation.\n\n- On the other hand, given that this work focuses largely on the domain of chemistry, it would be good to see more work into chemistry-specific insights or inductive biases, inclusion of modalities (that for instance improve performance on understanding of molecules, reactions, results from analytical instruments), etc, that serve as an argument for why the focus is so strongly placed in chemistry. Again, this same system could perfectly be applied for physics or maths, however no evaluation shows this.\n\n## Missing citations, ignoring several other works and benchmarks\n\n- The concepts of \"types of memory\" is not new and, although the paper cites some relevant papers, it's missing a few that implement similar ideas on other domains, however the integration of all into this complete framework is new, and interesting approach to reasoning of LLMs.\nSee the references given at the bottom [1-5], along with others that I didn't include here. There's even a whole review on memory mechanisms behind LLM Agents, that haven been cited here and that I find extremely relevant.\n- Omission of other benchmarks: Given that the system is designed for chemistry-specific tasks, and given the low amount of benchmarks specific for chemical reasoning, it would be good to assess ChemAgent's performance on other benchmarks, such as ChemBench [7]\n- For being so chemistry-specific, this work misses to cite foundational works that are related, such as [8-10] and others. [11] is only tangentially mentioned at the appendix as a related work. The paper in general misses such kind of extremely relevant references.\n- Some important citations are only tangentially relevant. Particularly in **line 048**, for the statement \"previous approaches in chemical reasoning tasks have focused on decomposing the reasoning process\", none of the references cited are relevant. Feinberg 2018 (PotentialNet) is a method for molecular property prediction. Yao 2024 (TreeofThoughts) and Zhang 2023 (Automatic Chain of Thought Prompting) have nothing to do with chemical reasoning. Only Zhong 2023 is directly relevant to this statement.\n\n\n## Writing\n\nSome parts of the paper could use rewriting for improving clarity, namely:\n\n- line 044: unclear statement: llms struggle to (1) use formulas (2) exhibit incorrect steps. Should be llms (1) struggle to… (2) exhibit…\n- line 052: It is mentioned that \"previous methods rely on predefined templates\", and cites self-consistency paper [12] as an example of that. Furthermore this is presented as an advantage of the current method over others, however: 1) ChemAgent also relies heavily on predefined prompts, and 2) self-consistency is a general methodology that considers multiple outputs from an LLM to yield the most prominent answer from multiple reasoning paths. In that sense, it's not clear what is understood by \"pre-defined templates\", and why this is a drawback of other methods that is somehow addressed in this work.\n- In Table 1 it is very unclear where results come from, or how they have been calculated. Especially for the baselines with gpt4 (direct reasoning), these results mismatch those presented in the original paper for the benchmark. Only the results for “few-shot + python” have been taken from the original benchmakr paper. Further inspection indicates that the results reported for “Direct reasoning, GPT-4” have been directly taken from the StructChem paper [13], however no explicit mention is made of this. Additionally, these results are taken from the row “Few shot setting - Direct reasoning” in Table 1 of [13], which is different from the described “Direct reasoning”. Please cite the specific sources for each of the results in the table.\n\n\n## Missing ablations\n\n- The evaluation and refinement module uses gpt-4, regardless of the LLM used as a base. It is critical to evaluate the effect of this design choice, as it's not clear how much gpt-4 as evaluator improves gpt-3.5's (or any other llm's) performance.\n- \n\n\n\n[1] Zhang Y, Wang C, Qi J,Peng Y. Leave It to Large Language Models! Correction and Planning with Memory Integration. Cyborg Bionic Syst. 2024;5:Article 0087. https://doi.org/10.34133/cbsystems.0087\n[2] Li, Kun, et al. \"Vector Storage Based Long-term Memory Research on LLM\" International Journal of Advanced Network, Monitoring and Controls, vol. 9, no. 3, Sciendo, 2024, pp. 69-79. https://doi.org/10.2478/ijanmc-2024-0029\n[3] Zhong, W., Guo, L., Gao, Q., Ye, H., & Wang, Y. (2024, March). Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 17, pp. 19724-19731).\n[4] Huang, Xu et al. “Understanding the planning of LLM agents: A survey.” ArXiv abs/2402.02716 (2024)\n[5] Guo, J., Li, N., Qi, J., Yang, H., Li, R., Feng, Y., ... & Xu, M. (2023). Empowering Working Memory for Large Language Model Agents. arXiv preprint arXiv:2312.17259.\n[6] Zhang, Zeyu et al. “A Survey on the Memory Mechanism of Large Language Model based Agents.” ArXiv abs/2404.13501 (2024)\n[7] Mirza, A., Alampara, N., Kunchapu, S., Emoekabu, B., Krishnan, A., Wilhelmi, M., ... & Jablonka, K. M. (2024). Are large language models superhuman chemists?. arXiv preprint arXiv:2404.01475.\n[8] Boiko, D.A., MacKnight, R., Kline, B. et al. Autonomous chemical research with large language models. Nature 624, 570–578 (2023). https://doi.org/10.1038/s41586-023-06792-0\n[9] Darvish, K., Skreta, M., Zhao, Y., Yoshikawa, N., Som, S., Bogdanovic, M., ... & Shkurti, F. (2024). ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization. arXiv preprint arXiv:2401.06949.\n[10] Skreta, Marta et al. “RePLan: Robotic Replanning with Perception and Language Models.” ArXiv abs/2401.04157 (2024)\n[11] M. Bran, A., Cox, S., Schilter, O. et al. Augmenting large language models with chemistry tools. Nat Mach Intell 6, 525–535 (2024). https://doi.org/10.1038/s42256-024-00832-8\n[12] Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., ... & Zhou, D. (2022). Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171.\n[13] Ouyang, S., Zhang, Z., Yan, B., Liu, X., Choi, Y., Han, J., & Qin, L. (2023). Structured chemistry reasoning with large language models. arXiv preprint arXiv:2311.09656.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)