Tootfinder

DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research
Jun-Hsiang Yao, Mingzheng Li, Jiayi Liu, Yuxiao Li, Jielin Feng, Jun Han, Qibao Zheng, Jianfeng Feng, Siming Chen
https://arxiv.org/abs/2505.23730

DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research
The Digital Twin Brain (DTB) is an advanced artificial intelligence framework that integrates spiking neurons to simulate complex cognitive functions and collaborative behaviors. For domain experts, visualizing the DTB's simulation outcomes is essential to understanding complex cognitive activities. However, this task poses significant challenges due to DTB data's inherent characteristics, including its high-dimensionality, temporal dynamics, and spatial complexity. To address these challenges,…

MLC-Agent: Cognitive Model based on Memory-Learning Collaboration in LLM Empowered Agent Simulation Environment
Ming Zhang, Yiling Xuan, Qun Ma, Yuwei Guo
https://arxiv.org/abs/2507.20215

MLC-Agent: Cognitive Model based on Memory-Learning Collaboration in LLM Empowered Agent Simulation Environment
Many real-world systems, such as transportation systems, ecological systems, and Internet systems, are complex systems. As an important tool for studying complex systems, computational experiments can map them into artificial society models that are computable and reproducible within computers, thereby providing digital and computational methods for quantitative analysis. In current research, the construction of individual agent models often ignores the long-term accumulative effect of memory m…

A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge
Liang Geng
https://arxiv.org/abs/2506.00570

A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge
With the rapid penetration of artificial intelligence across industries and scenarios, a key challenge in building the next-generation intelligent core lies in effectively integrating the language understanding capabilities of foundation models with domain-specific knowledge bases in complex real-world applications. This paper proposes a multimodal cognition and embodied decision-making brain system, ``Wenlu", designed to enable secure fusion of private knowledge and public models, unified proc…

Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap
Kacper Sokol, James Fackler, Julia E Vogt
https://arxiv.org/abs/2506.05030

Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap
Artificial intelligence promises to revolutionise medicine, yet its impact remains limited because of the pervasive translational gap. We posit that the prevailing technology-centric approaches underpin this challenge, rendering such systems fundamentally incompatible with clinical practice, specifically diagnostic reasoning and decision making. Instead, we propose a novel sociotechnical conceptualisation of data-driven support tools designed to complement doctors' cognitive and epistemic activ…

A statistical physics framework for optimal learning
Francesca Mignacco, Francesco Mori
https://arxiv.org/abs/2507.07907 https://arxi…

A statistical physics framework for optimal learning
Learning is a complex dynamical process shaped by a range of interconnected decisions. Careful design of hyperparameter schedules for artificial neural networks or efficient allocation of cognitive resources by biological learners can dramatically affect performance. Yet, theoretical understanding of optimal learning strategies remains sparse, especially due to the intricate interplay between evolving meta-parameters and nonlinear learning dynamics. The search for optimal protocols is further h…

Extended Creativity: A Conceptual Framework for Understanding Human-AI Creative Relations
Andrea Gaggioli, Sabrina Bartolotta, Andrea Ubaldi, Katusha Gerardini, Eleonora Diletta Sarcinella, Alice Chirico
https://arxiv.org/abs/2506.10249

Extended Creativity: A Conceptual Framework for Understanding Human-AI Creative Relations
Artificial Intelligence holds significant potential to enhance human creativity. However, achieving this vision requires a clearer understanding of how such enhancement can be effectively realized. Adopting the perspective of distributed creativity, we identify three primary modes through which AI can contribute to creative processes: Support, where AI acts as a tool; Synergy, where AI and humans collaborate in complementary ways; and Symbiosis, where human and AI cognition become so integrated…