Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers

• URL: http://arxiv.org/abs/2509.00103v2
• Date: Mon, 27 Oct 2025 22:13:12 GMT
• Title: Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers
• Authors: Robert MacKnight, Jose Emilio Regio, Jeffrey G. Ethier, Luke A. Baldwin, Gabe Gomes,
• Abstract summary: We demonstrate that pre-trained knowledge in large language models (LLMs) fundamentally changes this paradigm.<n>LLM-GO excels precisely where traditional methods struggle: complex categorical spaces requiring domain understanding rather than mathematical optimization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern optimization in experimental chemistry employs algorithmic search through black-box parameter spaces. Here we demonstrate that pre-trained knowledge in large language models (LLMs) fundamentally changes this paradigm. Using six fully enumerated categorical reaction datasets (768-5,684 experiments), we benchmark LLM-guided optimization (LLM-GO) against Bayesian optimization (BO) and random sampling. Frontier LLMs consistently match or exceed BO performance across five single-objective datasets, with advantages growing as parameter complexity increases and high-performing conditions become scarce (<5% of space). BO retains superiority only for explicit multi-objective trade-offs. To understand these contrasting behaviors, we introduce a topology-agnostic information theory framework quantifying sampling diversity throughout optimization campaigns. This analysis reveals that LLMs maintain systematically higher exploration Shannon entropy than BO across all datasets while achieving superior performance, with advantages most pronounced in solution-scarce parameter spaces where high-entropy exploration typically fails-suggesting that pre-trained domain knowledge enables more effective navigation of chemical parameter space rather than replacing structured exploration strategies. To enable transparent benchmarking and community validation, we release Iron Mind (https://gomes.andrew.cmu.edu/iron-mind), a no-code platform for side-by-side evaluation of human, algorithmic, and LLM optimization campaigns with public leaderboards and complete trajectories. Our findings establish that LLM-GO excels precisely where traditional methods struggle: complex categorical spaces requiring domain understanding rather than mathematical optimization.

Related papers

• A Meta-Knowledge-Augmented LLM Framework for Hyperparameter Optimization in Time-Series Forecasting [0.0]
  We introduce LLM-AutoOpt, a hybrid HPO framework that combines BO with LLM-based contextual reasoning.<n>We show that LLM-AutoOpt achieves improved predictive performance and more interpretable optimization behavior compared to BO and LLM baselines without meta-knowledge.
  arXiv  Detail & Related papers  (2026-02-01T21:26:57Z)
• MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning with Holistic Platform and Adaptive Hybrid Policy Optimization [103.74675519953898]
  Long-chain reflective reasoning is a prerequisite for solving complex real-world problems.<n>We build a benchmark consisting 1,260 samples of 42 challenging synthetic tasks.<n>We generate post-training data and explore learning paradigms for exploiting such data.
  arXiv  Detail & Related papers  (2025-10-09T17:53:58Z)
• ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System [72.63341091857959]
  We introduce ChemBOMAS: a large language model (LLM)-enhanced multi-agent system that accelerates Bayesian optimization.<n>Data-driven strategy involves an 8B-scale LLM regressor fine-tuned on a mere 1% labeled samples.<n>The knowledge-driven strategy employs a hybrid Retrieval-Augmented Generation approach to guide LLM in dividing the search space.<n>ChemBOMAS set a new state-of-the-art, accelerating optimization efficiency by up to 5-fold compared to baseline methods.
  arXiv  Detail & Related papers  (2025-09-10T16:24:08Z)
• OPT-BENCH: Evaluating LLM Agent on Large-Scale Search Spaces Optimization Problems [19.586884180343038]
  OPT-BENCH is a benchmark designed to evaluate Large Language Models (LLMs) on large-scale search space optimization problems.<n> OPT-Agent emulates human reasoning when tackling complex problems by generating, validating, and iteratively improving solutions through historical feedback.
  arXiv  Detail & Related papers  (2025-06-12T14:46:41Z)
• Reasoning BO: Enhancing Bayesian Optimization with Long-Context Reasoning Power of LLMs [13.478684527247129]
  This paper designs Reasoning BO, a novel framework that leverages reasoning models to guide the sampling process in BO.<n> Reasoning BO provides real-time sampling recommendations along with critical insights grounded in plausible scientific theories.<n>The framework demonstrates its capability to progressively refine sampling strategies through real-time insights and hypothesis evolution.
  arXiv  Detail & Related papers  (2025-05-19T08:20:40Z)
• A Survey of Direct Preference Optimization [103.59317151002693]
  Large Language Models (LLMs) have demonstrated unprecedented generative capabilities.<n>Their alignment with human values remains critical for ensuring helpful and harmless deployments.<n>Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative.
  arXiv  Detail & Related papers  (2025-03-12T08:45:15Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving.<n>Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.<n>We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Discovering Preference Optimization Algorithms with and for Large Language Models [50.843710797024805]
  offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. We perform objective discovery to automatically discover new state-of-the-art preference optimization algorithms without (expert) human intervention. Experiments demonstrate the state-of-the-art performance of DiscoPOP, a novel algorithm that adaptively blends logistic and exponential losses.
  arXiv  Detail & Related papers  (2024-06-12T16:58:41Z)
• Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark [166.40879020706151]
  This paper proposes a shift towards BP-free, zeroth-order (ZO) optimization as a solution for reducing memory costs during fine-tuning. Unlike traditional ZO-SGD methods, our work expands the exploration to a wider array of ZO optimization techniques. Our study unveils previously overlooked optimization principles, highlighting the importance of task alignment, the role of the forward gradient method, and the balance between algorithm complexity and fine-tuning performance.
  arXiv  Detail & Related papers  (2024-02-18T14:08:48Z)
• Large Language Models to Enhance Bayesian Optimization [57.474613739645605]
  We present LLAMBO, a novel approach that integrates the capabilities of Large Language Models (LLM) within Bayesian optimization. At a high level, we frame the BO problem in natural language, enabling LLMs to iteratively propose and evaluate promising solutions conditioned on historical evaluations. Our findings illustrate that LLAMBO is effective at zero-shot warmstarting, and enhances surrogate modeling and candidate sampling, especially in the early stages of search when observations are sparse.
  arXiv  Detail & Related papers  (2024-02-06T11:44:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.