Materials science research requires multi-step reasoning and precise material informatics retrieval, where minor errors can propagate into significant failures in downstream experiments. Despite their general success, Large Language Models (LLMs) often struggle with hallucinations, handling domain-specific data effectively (e.g., crystal structures), and integrating experimental workflows. To address these challenges, we introduce LLaMP, a hierarchical multi-agent framework designed to emulate the materials science research workflow. The high-level supervisor agent decomposes user requests into sub-tasks and coordinates with specialized assistant agents. These assistant agents handle domain-specific tasks, such as retrieving and processing data from the Materials Project (MP) or conducting simulations as needed. This pipeline facilitates iterative refinement of material property retrieval and enables the simulation of real-world research workflows. To ensure reliability, we propose a novel metric combining uncertainty and confidence estimate to evaluate the self-consistency of responses from LLaMP and baseline methods. Our experiments demonstrate LLaMP’s superior performance in material property retrieval, crystal structure editing, and annealing molecular dynamics simulations using pre-trained interatomic potentials. Unlike prior work focused solely on material property prediction or discovery, LLaMP serves as a foundation for autonomous materials research by combining grounded informatics and enabling iterative experimental processes. Code and live demo are available at https://github.com/chiang-yuan/llamp.

Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role-not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions-natural language commands that reference objects or conditions absent from the environment. We propose — Instruct-Verify-and-Act (IVA) — a unified framework that (i) detects when an instruction cannot be executed due to a false premise, (ii) engages in language-based clarification or correction, and (iii) grounds plausible alternatives in perception and action. Towards this end, we construct a large-scale instruction tuning setup with structured language prompts and train a VLA model capable of handling both accurate and erroneous requests. Our approach leverages a contextually augmented, semi-synthetic dataset containing paired positive and false-premise instructions, enabling robust detection and natural language correction. Our experiments show that IVA can improves false premise detection accuracy by 58.89% over baselines, while increasing successful responses in false-premise scenarios by 27.89%.

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