Graph of Thoughts Nanobiomaterials Assistants: Towards Logical, Tool-Augmented, and Multi-Agent Reasoning in Scientific LLMs

Xianghang Peng, David Scott Lewis

Keywords: nanobiomaterials, graph of thoughts, scientific llms, tool-augmented reasoning, multi-agent debate, symbolic solvers, formal logical layers, neuro-symbolic architectures, constraint satisfaction, nanoparticle design, therapeutic nanostructures, safety-critical ai, drug delivery, materials informatics, graph-based inference

TL;DR: Introduces GoT-Nano, a graph-of-thoughts, tool-augmented, multi-agent LLM framework that enforces logical and physicochemical constraints to design safe, high-performance nanobiomaterials for drug delivery and therapeutic applications.

Abstract: The integration of Large Language Models (LLMs) into materials science has accelerated literature review, hypothesis generation and testing, property prediction, and code generation. However, the safety-critical domain of nanobiomaterials—defined by complex multi-attribute constraints involving surface chemistry, cytotoxicity, and immune response—exposes the fundamental limitations of current autoregressive models. Standard approaches, such as Chain-of-Thought (CoT) prompting, frequently suffer from logical inconsistencies, hallucinated constraints, and a lack of formal verifiability. This **position paper** argues that the next generation of AI assistants for nanobiomaterials must transition from linear, probabilistic generation to structured, rigorous reasoning architectures. We propose the Graph of Thoughts Nanobiomaterials Assistant (GoT-Nano), a framework that integrates Graph of Thoughts (GoT) reasoning, symbolic solver augmentation (SatLM), and multi-agent debate systems. By restructuring inference as an arbitrary graph, we enable non-linear exploration of design spaces, backtracking from toxic candidates, and the aggregation of multi-modal insights. Furthermore, we advocate for the integration of formal logical layers to enforce consistency (e.g., negation and transitivity) and the use of external symbolic tools for verifiable calculation. We contend that this shift from pattern matching to deliberate, verifiable reasoning is essential for the trustworthy deployment of AI in the high-stakes design of therapeutic nanostructures.

Submission Number: 100