Tootfinder

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…

This https://arxiv.org/abs/2505.02945 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

The Cognitive Foundations of Economic Exchange: A Modular Framework Grounded in Behavioral Evidence
A key challenge in multi-agent AI is modeling social cooperation under realistic behavioral constraints. Many foundational concepts in economics and ethics such as "trust" or "morality" are often defined informally, without operational criteria or cognitive grounding, which limits their testability and implementation in artificial agents. Drawing on converging empirical evidence from primate behavior, infant cognition, and economic anthropology, we propose a conceptual framework composed of thr…

This https://arxiv.org/abs/2502.00698 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models
IQ testing has served as a foundational methodology for evaluating human cognitive capabilities, deliberately decoupling assessment from linguistic background, language proficiency, or domain-specific knowledge to isolate core competencies in abstraction and reasoning. Yet, artificial intelligence research currently lacks systematic benchmarks to quantify these critical cognitive capabilities in multimodal systems. To address this crucial gap, we propose MM-IQ, a comprehensive evaluation framew…

Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence
Kordel K. France, Rohith Peddi, Nik Dennler, Ovidiu Daescu
https://arxiv.org/abs/2506.00398

Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence
Despite extraordinary progress in artificial intelligence (AI), modern systems remain incomplete representations of human cognition. Vision, audition, and language have received disproportionate attention due to well-defined benchmarks, standardized datasets, and consensus-driven scientific foundations. In contrast, olfaction - a high-bandwidth, evolutionarily critical sense - has been largely overlooked. This omission presents a foundational gap in the construction of truly embodied and ethica…

Finance as Extended Biology: Reciprocity as the Cognitive Substrate of Financial Behavior
Egil Diau
https://arxiv.org/abs/2506.00099 https://

Finance as Extended Biology: Reciprocity as the Cognitive Substrate of Financial Behavior
A central challenge in economics and artificial intelligence is explaining how financial behaviors-such as credit, insurance, and trade-emerge from decentralized social interaction. We argue that these functions are not institutionally engineered, but arise from a single behavioral substrate: reciprocity. Observed across primates and foundational to early human societies, reciprocity governed the exchange of goods, regulation of obligation, and maintenance of cooperation-without formal rules. T…

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…

This https://arxiv.org/abs/2504.06354 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Multihead self-attention in cortico-thalamic circuits
Both biological cortico-thalamic networks and artificial transformer networks use canonical computations to perform a wide range of cognitive tasks. In this work, we propose that the structure of cortico-thalamic circuits is well suited to realize a computation analogous to multihead self-attention, the main algorithmic innovation of transformers. We start with the concept of a cortical unit module or microcolumn, and propose that superficial and deep pyramidal cells carry distinct computationa…

World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
Changyuan Zhao, Ruichen Zhang, Jiacheng Wang, Gaosheng Zhao, Dusit Niyato, Geng Sun, Shiwen Mao, Dong In Kim
https://arxiv.org/abs/2506.00417

World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
World models are emerging as a transformative paradigm in artificial intelligence, enabling agents to construct internal representations of their environments for predictive reasoning, planning, and decision-making. By learning latent dynamics, world models provide a sample-efficient framework that is especially valuable in data-constrained or safety-critical scenarios. In this paper, we present a comprehensive overview of world models, highlighting their architecture, training paradigms, and a…

Distinguishing Fact from Fiction: Student Traits, Attitudes, and AI Hallucination Detection in Business School Assessment
Canh Thien Dang, An Nguyen
https://arxiv.org/abs/2506.00050

Distinguishing Fact from Fiction: Student Traits, Attitudes, and AI Hallucination Detection in Business School Assessment
As artificial intelligence (AI) becomes integral to the society, the ability to critically evaluate AI-generated content is increasingly vital. On the context of management education, we examine how academic skills, cognitive traits, and AI scepticism influence students' ability to detect factually incorrect AI-generated responses (hallucinations) in a high-stakes assessment at a UK business school (n=211, Year 2 economics and management students). We find that only 20% successfully identified …

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,…