Large reasoning models (LRMs) have garnered significant attention from researchers owing to their exceptional capability in addressing complex tasks. Motivated by the observed human-like behaviors in their reasoning processes, this paper introduces a comprehensive taxonomy to characterize atomic reasoning steps and probe the ``psyche'' of LRM intelligence. Specifically, it comprises five groups and seventeen categories derived from human mental processes, thereby grounding the understanding of LRMs in an interdisciplinary perspective. The taxonomy is then applied for an in-depth understanding of current LRMs, resulting in a distinct labeled dataset that comprises 277,534 atomic reasoning steps. Using this resource, we analyze contemporary LRMs and distill several actionable takeaways for improving training and post-training of reasoning models. Notably, our analysis reveals that prevailing post-answer ``double-checks'' (self-monitoring evaluations) are largely superficial and rarely yield substantive revisions. Thus, incentivizing comprehensive multi-step reflection, rather than simple self-monitoring, may offer a more effective path forward. To complement the taxonomy, an automatic annotation framework, named CAPO, is proposed to leverage large language models (LLMs) for generating the taxonomy-based annotations. Experimental results demonstrate that CAPO achieves higher consistency with human experts compared to baselines, facilitating a scalable and comprehensive analysis of LRMs from a human cognitive perspective. Together, the taxonomy, CAPO, and the derived insights provide a principled, scalable path toward understanding and advancing LRM reasoning.

The Belief-Desire-Intention (BDI) model is a cornerstone for representing rational agency in artificial intelligence and cognitive sciences. Yet, its integration into structured, semantically interoperable knowledge representations remains limited. This paper presents a formal BDI Ontology, conceived as a modular Ontology Design Pattern (ODP) that captures the cognitive architecture of agents through beliefs, desires, intentions, and their dynamic interrelations. The ontology ensures semantic precision and reusability by aligning with foundational ontologies and best practices in modular design. Two complementary lines of experimentation demonstrate its applicability: (i) coupling the ontology with Large Language Models (LLMs) via Logic Augmented Generation (LAG) to assess the contribution of ontological grounding to inferential coherence and consistency; and (ii) integrating the ontology within the Semas reasoning platform, which implements the Triples-to-Beliefs-to-Triples (T2B2T) paradigm, enabling a bidirectional flow between RDF triples and agent mental states. Together, these experiments illustrate how the BDI Ontology acts as both a conceptual and operational bridge between declarative and procedural intelligence, paving the way for cognitively grounded, explainable, and semantically interoperable multi-agent and neuro-symbolic systems operating within the Web of Data.

Personal narratives are stories authors construct to make meaning of their experiences. Style, the distinctive way authors use language to express themselves, is fundamental to how these narratives convey subjective experiences. Yet there is a lack of a formal framework for systematically analyzing these stylistic choices. We present a novel approach that formalizes style in personal narratives as patterns in the linguistic choices authors make when communicating subjective experiences. Our framework integrates three domains: functional linguistics establishes language as a system of meaningful choices, computer science provides methods for automatically extracting and analyzing sequential patterns, and these patterns are linked to psychological observations. Using language models, we automatically extract linguistic features such as processes, participants, and circumstances. We apply our framework to hundreds of dream narratives, including a case study on a war veteran with post-traumatic stress disorder. Analysis of his narratives uncovers distinctive patterns, particularly how verbal processes dominate over mental ones, illustrating the relationship between linguistic choices and psychological states.

What is the state of artificial intelligence replication of human intelligence? The man-machine replicability assumption that human mental processes could in principle be replicated on a computer motivated and inspired the founding thinkers on human-like and human-level Artificial Intelligence (AI). Alan Turing was among the first to presume that AI could imitate how humans think, act, feel, speak, and decide. As a result, such an assumption is prevalent across the field of Artificial Intelligence (AI) research, with its many subfields, as ANNs, Symbolic AI, ML, DL, Computer Vision, NLP, Robotics, AGI, ASI, etc. The validity of this man-machine replicability conception has rarely been challenged, be it functional structure replication (of a mental process, e.g. a decision-making process) or output replication (of the result of a mental process, e.g. a decision).

Artificial intelligence is one of the fastest growing technologies in terms of sheer volume of patent filings at the United States Trademark and Patent Office. Between 2002 and 2018, annual AI patent applications increased by more than 100 percent. This increase evidences the importance for applicants to develop a strategy for building a patent portfolio around their artificial intelligence technology. In this article, we take a look at a few considerations when crafting claims for a patent filing which may help ensure that an AI patent application stands out. In the wake of the Supreme Court's 2014 decision in Alice, software-based innovations are subject to higher scrutiny during examination.

We use the word causality as a means of understanding cognition but we don't really understand its distinctions. Let's look at what C.S.Peirce had to say about causality. What @yudapearl says is that to understand a system one needs to hypothesize a model of the system and then see how this model is in agreement. Statistics is just one of the methods of testing. But it's not how one formulates the original model.

Claims 1 and 8 as recited are not practically performed in the human mind. As discussed above, the claims recite monitoring operation of machines using neural networks, logic decision trees, confidence assessments, fuzzy logic, smart agent profiling, and case-based reasoning. . . .

Claims 1 and 8 as recited are not practically performed in the human mind. As discussed above, the claims recite monitoring operation of machines using neural networks, logic decision trees, confidence assessments, fuzzy logic, smart agent profiling, and case-based reasoning. . . .

Olympic skaters can launch, perform multiple aerial turns, and land gracefully, anticipating imperfections and reacting quickly to correct course. To make such elegant movements, the brain must have an internal model of the body to control, predict, and make almost-instantaneous adjustments to motor commands. So-called "internal models" are a fundamental concept in engineering and have long been suggested to underlie control of movement by the brain, but what about processes that occur in the absence of movement, such as contemplation, anticipation, planning? Using a novel combination of task design, data analysis, and modeling, MIT neuroscientist Mehrdad Jazayeri and colleagues now provide compelling evidence that the core elements of an internal model also control purely mental processes. "During my thesis, I realized that I'm interested not so much in how our senses react to sensory inputs, but instead in how my internal model of the world helps me make sense of those inputs," says Jazayeri, the Robert A. Swanson Career Development Professor of Life Sciences, a member of MIT's McGovern Institute for Brain Research, and the senior author of the study.

For some die-hard tech evangelists, using neural interfaces to merge with AI is the inevitable next step in humankind's evolution. But a group of 27 neuroscientists, ethicists, and machine learning experts have highlighted the myriad ethical pitfalls that could be waiting. To be clear, it's not just futurologists banking on the convergence of these emerging technologies. The Morningside Group estimates that private spending on neurotechnology is in the region of $100 million a year and growing fast, while in the US alone public funding since 2013 has passed the $500 million mark. The group is made up of representatives from international brain research projects, tech companies like Google and neural interface startup Kernel, and academics from the US, Canada, Europe, Israel, China, Japan, and Australia.