AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery

• URL: http://arxiv.org/abs/2511.11257v1
• Date: Fri, 14 Nov 2025 12:53:57 GMT
• Title: AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery
• Authors: Yuqi Yin, Yibo Fu, Siyuan Wang, Peng Sun, Hongyu Wang, Xiaohui Wang, Lei Zheng, Zhiyong Li, Zhirong Liu, Jianji Wang, Zhaoxi Sun,
• Abstract summary: We introduce AIonopedia, to the best of our knowledge, the first agent for IL discovery.<n> powered by an LLM-augmented multimodal domain foundation model for ILs.<n>Trained and evaluated on a newly curated and comprehensive IL dataset, our model delivers superior performance.
• Score: 21.04373426924859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The discovery of novel Ionic Liquids (ILs) is hindered by critical challenges in property prediction, including limited data, poor model accuracy, and fragmented workflows. Leveraging the power of Large Language Models (LLMs), we introduce AIonopedia, to the best of our knowledge, the first LLM agent for IL discovery. Powered by an LLM-augmented multimodal domain foundation model for ILs, AIonopedia enables accurate property predictions and incorporates a hierarchical search architecture for molecular screening and design. Trained and evaluated on a newly curated and comprehensive IL dataset, our model delivers superior performance. Complementing these results, evaluations on literature-reported systems indicate that the agent can perform effective IL modification. Moving beyond offline tests, the practical efficacy was further confirmed through real-world wet-lab validation, in which the agent demonstrated exceptional generalization capabilities on challenging out-of-distribution tasks, underscoring its ability to accelerate real-world IL discovery.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference [5.052013621974765]
  Large Language Models (LLMs) possess strong representation and reasoning capabilities, but their application to structure-based drug design (SBDD) is limited by insufficient understanding of protein structures and unpredictable molecular generation.<n>We propose Exploration-Augmented Latent Inference for LLMs (ELILLM), a framework that reinterprets the LLM generation process as an encoding, latent space exploration, and decoding workflow.<n>ELILLM explicitly explores portions of the design problem beyond the model's current knowledge while using a decoding module to handle familiar regions, generating chemically valid and synthetically reasonable molecules.
  arXiv  Detail & Related papers  (2026-01-20T08:10:48Z)
• Detailed balance in large language model-driven agents [1.2687030176231846]
  Large language model (LLM)-driven agents are emerging as a powerful new paradigm for solving complex problems.<n>This Letter proposes a method to estimate the underlying generative directionality of LLMs embedded within agents.
  arXiv  Detail & Related papers  (2025-12-10T20:04:23Z)
• LimRank: Less is More for Reasoning-Intensive Information Reranking [58.32304478331711]
  Existing approaches typically rely on large-scale fine-tuning to adapt LLMs for information reranking tasks.<n>In this work, we demonstrate that modern LLMs can be effectively adapted using only minimal, high-quality supervision.
  arXiv  Detail & Related papers  (2025-10-27T17:19:37Z)
• Speed Always Wins: A Survey on Efficient Architectures for Large Language Models [51.817121227562964]
  Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models.<n> Transformer models, as the foundation of modern LLMs, offer a strong baseline with excellent scaling properties.<n>The traditional transformer architecture requires substantial computations and poses significant obstacles for large-scale training and practical deployment.
  arXiv  Detail & Related papers  (2025-08-13T14:13:46Z)
• Agentic Reinforced Policy Optimization [66.96989268893932]
  Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
  arXiv  Detail & Related papers  (2025-07-26T07:53:11Z)
• LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback [121.78866929908871]
  Large Action Models (LAMs) for AI Agents offer incredible potential but face challenges due to the need for high-quality training data.<n>We present LAM SIMULATOR, a comprehensive framework designed for online exploration of agentic tasks with high-quality feedback.<n>Our framework features a dynamic task query generator, an extensive collection of tools, and an interactive environment where Large Language Model (LLM) Agents can call tools and receive real-time feedback.
  arXiv  Detail & Related papers  (2025-06-02T22:36:02Z)
• Structural Entropy Guided Agent for Detecting and Repairing Knowledge Deficiencies in LLMs [11.724887822269528]
  Large language models (LLMs) have achieved unprecedented performance by leveraging vast pretraining corpora.<n>Their performance remains suboptimal in knowledge-intensive domains such as medicine and scientific research.<n>We propose a novel Structural Entropy-guided Knowledge Navigator (SENATOR) framework that addresses the intrinsic knowledge deficiencies of LLMs.
  arXiv  Detail & Related papers  (2025-05-12T02:21:36Z)
• Autonomous Microscopy Experiments through Large Language Model Agents [4.241267255764773]
  Large language models (LLMs) are revolutionizing self driving laboratories (SDLs) for materials research.<n>We introduce Artificially Intelligent Lab Assistant (AILA), a framework automating atomic force microscopy through LLM driven agents.<n>We find that state of the art models struggle with basic tasks and coordination scenarios.
  arXiv  Detail & Related papers  (2024-12-18T09:35:28Z)
• On the limits of agency in agent-based models [13.130587222524305]
  Agent-based modeling offers powerful insights into complex systems, but its practical utility has been limited by computational constraints. Recent advancements in large language models (LLMs) could enhance ABMs with adaptive agents, but their integration into large-scale simulations remains challenging. We present LLM archetypes, a technique that balances behavioral complexity with computational efficiency, allowing for nuanced agent behavior in large-scale simulations.
  arXiv  Detail & Related papers  (2024-09-14T04:17:24Z)
• Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning [28.077228879886402]
  Reinforcement Learning (RL) suffers from sample inefficiency in reward domains, and the problem is further pronounced in case of transitions. To improve the sample efficiency, reward shaping is a well-studied approach to introduce intrinsic rewards that can help the RL agent converge to an optimal policy faster.
  arXiv  Detail & Related papers  (2024-05-24T03:53:57Z)
• Less for More: Enhanced Feedback-aligned Mixed LLMs for Molecule Caption Generation and Fine-Grained NLI Evaluation [11.778576032848482]
  This work enhances scientific language models by improving their inference and evaluation capabilities with minimal or no additional training.<n>We reveal intriguing insights into the behaviour and suitability of such methods while significantly surpassing state-of-the-art models.<n>We propose a novel atomic-level evaluation method leveraging off-the-shelf Natural Language Inference (NLI) models for use in the unseen chemical domain.
  arXiv  Detail & Related papers  (2024-05-22T20:40:53Z)
• PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics [51.17512229589]
  PoLLMgraph is a model-based white-box detection and forecasting approach for large language models. We show that hallucination can be effectively detected by analyzing the LLM's internal state transition dynamics. Our work paves a new way for model-based white-box analysis of LLMs, motivating the research community to further explore, understand, and refine the intricate dynamics of LLM behaviors.
  arXiv  Detail & Related papers  (2024-04-06T20:02:20Z)
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
  We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
  arXiv  Detail & Related papers  (2023-08-23T09:45:29Z)

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.