arXiv:2510.06186v1 Announce Type: cross Abstract: Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing works largely adopt one-shot settings, ignoring the iterative and feedback-driven nature…
arXiv:2510.05335v1 Announce Type: new Abstract: We present M-Reason, a demonstration system for transparent, agent-based reasoning and evidence integration in the biomedical domain, with a focus on cancer research. M-Reason leverages recent advances in large language models (LLMs) and modular agent…
arXiv:2506.06727v3 Announce Type: replace Abstract: Large Multimodal Models have achieved remarkable progress in integrating vision and language, enabling strong performance across perception, reasoning, and domain-specific tasks. However, their capacity to reason over multiple, visually similar inputs remains insufficiently explored. Such…
arXiv:2510.05338v1 Announce Type: new Abstract: In this review, we assess the use of Bayesian methods in model predictive control (MPC), focusing on neural-network-based modeling, control design, and uncertainty quantification. We systematically analyze individual studies and how they are implemented in…
arXiv:2510.04173v2 Announce Type: replace Abstract: Open Agent Specification (Agent Spec) is a declarative language that allows AI agents and their workflows to be defined in a way that is compatible across different AI frameworks, promoting portability and interoperability within AI…
arXiv:2510.05363v1 Announce Type: new Abstract: Adapting Foundation Models to new domains with limited training data is challenging and computationally expensive. While prior work has demonstrated the effectiveness of using domain-specific exemplars as in-context demonstrations, we investigate whether representing exemplars purely…
arXiv:2410.21657v4 Announce Type: replace-cross Abstract: Physics based numerical climate models serve as critical tools for evaluating the effects of climate change and projecting future climate scenarios. However, the reliance on numerical simulations of physical equations renders them computationally intensive and…
arXiv:2510.05378v1 Announce Type: new Abstract: Meaningful human-AI collaboration requires more than processing language, it demands a better understanding of symbols and their constructed meanings. While humans naturally interpret symbols through social interaction, AI systems treat them as patterns with compressed…
arXiv:2503.23278v3 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) is an emerging open standard that defines a unified, bi-directional communication and dynamic discovery protocol between AI models and external tools or resources, aiming to enhance interoperability and reduce fragmentation…
arXiv:2510.05402v1 Announce Type: new Abstract: Predicting the final hardness of steel after heat treatment is a challenging regression task due to the many-to-one nature of the process — different combinations of input parameters (such as temperature, duration, and chemical composition)…
