Video Understanding, Scene Interpretation and Commonsense Reasoning are highly challenging tasks enabling the interpretation of visual information, allowing agents to perceive, interact with and make rational decisions in its environment. Large Language Models (LLMs) and Visual Language Models (VLMs) have shown remarkable advancements in these areas in recent years, enabling domain-specific applications as well as zero-shot open vocabulary tasks, combining multiple domains. However, the required computational complexity poses challenges for their application on edge devices and in the context of Mobile Robotics, especially considering the trade-off between accuracy and inference time. In this paper, we investigate the capabilities of state-of-the-art VLMs for the task of Scene Interpretation and Action Recognition, with special regard to small VLMs capable of being deployed to edge devices in the context of Mobile Robotics. The proposed pipeline is evaluated on a diverse dataset consisting of various real-world cityscape, on-campus and indoor scenarios. The experimental evaluation discusses the potential of these small models on edge devices, with particular emphasis on challenges, weaknesses, inherent model biases and the application of the gained information. Supplementary material is provided via the following repository: https://datahub.rz.rptu.de/hstr-csrl-public/

-- Joint activity describes when more than one agent (human or machine) contributes to the completion of a task or activity. Designing for joint activity focuses on explicitly supporting the interdependencies between agents necessary for effective coordination amon g agents engaged in the joint activity. This builds and expands upon designing for usability to further address how technologies can be designed to act as effective team players. Effective joint activity requires supporting, at minimum, five primary macroc ognitive functions within teams: Event Detection, Sensemaking, Adaptability, Perspective - Shifting, and Coordination. Supporting these functions is equally as important as making technologies usable. We synthesized fourteen heuristics from relevant literatu re including display design, human factors, cognitive systems engineering, cognitive psychology, and computer science to aid the design, development, and evaluation of technologies that support joint human - machine activity . Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) technologies have accelerated human - machine interactions progress ing from simple tool - based engagements to complex cognitive collaborations [1] . Machines are being designed to perform an increasing set of functions and are being expected to engage more deeply in the collaborative joint activit ies related to these functions. This shift in machine capabilities and expectations demands a corresponding re - evaluation and broadening of design and evaluation principles to support joint human - machine activity in ways that lie outside the boundaries of trad itional usability methods and models [2] . Traditional usability heuristics, such as those proposed by [3], provide a strong foundation focusing primarily on surface - level interactions such as enhancing the ease of use, efficiency, and satisfaction in human - machine interaction . These heuristics are primarily oriented towards actions and responses but offer limited support for the essential macrocognitive functions associated with effective teamwork including event detection, sensemaking, adaptability, perspective shifting, and co ordination, all of which are vital in the close collaboration of humans and machine s with joint activities [2], [4], [5], [6] . These heuristics are primarily oriented towards actions and responses but offer limited support for the essential macrocognitive functions associated with effective teamwork including event detection, sensemaking, adaptability, perspective shifting, and co ordination . A ll of these macrocognitive functions are vital in the close collaboration of humans and machines with joint activities in high - stakes and dynamic environments with little room for error [2], [5] . This reliance on macrocognitive functions is evident in domains where the ability to process complex information and adapt to changing conditions is crucial.

Evaluation is a critical activity associated with any theory. Yet this has proven to be a n exceptionally challenging activity for theories based on cognitive architectures. For an overlapping set of reasons, evaluation can also be challenging for theories based on generative neural architectures. T h is dual challenge is approached here by leveraging a broad perspective on theory evaluation to yield a wide - ranging, albeit qualitative, comparison of whole - mind - orie n ted cognitive and generative architectures an d the full systems th a t are based on these architectures .

This commentary extends the discussion by Parr et al. on memory and attention beyond individual cognitive systems. From the perspective of the Collective Predictive Coding (CPC) hypothesis -- a framework for understanding these faculties and the emergence of language at the group level -- we introduce a hypothetical idea: that language, with its embedded distributional semantics, serves as a collectively formed external representation. CPC generalises the concepts of individual memory and attention to the collective level. This offers a new perspective on how shared linguistic structures, which may embrace collective world models learned through next-word prediction, emerge from and shape group-level cognition.

This study explores the emergence of counter-inferential behavior in natural and artificial cognitive systems, that is, patterns in which agents misattribute empirical success or suppress adaptation, leading to epistemic rigidity or maladaptive stability. We analyze archetypal scenarios in which such behavior arises: reinforcement of stability through reward imbalance, meta-cognitive attribution of success to internal superiority, and protective reframing under perceived model fragility. Rather than arising from noise or flawed design, these behaviors emerge through structured interactions between internal information models, empirical feedback, and higher-order evaluation mechanisms. Drawing on evidence from artificial systems, biological cognition, human psychology, and social dynamics, we identify counter-inferential behavior as a general cognitive vulnerability that can manifest even in otherwise well-adapted systems. The findings highlight the importance of preserving minimal adaptive activation under stable conditions and suggest design principles for cognitive architectures that can resist rigidity under informational stress.

It can be said the neural networks (specially in their sophisticated forms) account for such abstract recoding, but this is not fully satisfactory, because there is just one network in the model; a different approach is to use layers of neural networks, where the higher level takes as inputs the patterns of the lower, sensory, layers (Sun, 2006), but up to now this is done " by hand " . Still another approach, of Vygotskian inspiration, views in the use of public symbols the key to understand cognitive, abstract recoding (Gomila, 2012), but the application of this approach within artificial cognitive systems is just beginning. Flexible use of knowledge Extracting world regularities and contingencies would be useless unless such knowledge can guide future action in real - time in an uncertain environment. This may require in the end, as anticipated above, behavioral unpredictability, which is a property than runs contrary to the technical requirements of robustness and reliability for artificial systems (to guarantee safety, as the principal engineer ' s command). The critical issue for flexibility is related to how the knowledge is " stored " (see previous section), and therefore, how it is accessed. The major roadblock to carry this out - regardless of approach - is again combinatorial explosion, whether at the level of propositional representations, as in classical AI, or at the level of degrees of freedom for the control of actuators. But it is also a problem to " judge ", in a given situation, which one is the best one to categorize it, given what the system knows.

Autonomous AI systems reveal foundational limitations in deterministic, human-authored computing architectures. This paper presents Cognitive Silicon: a hypothetical full-stack architectural framework projected toward 2035, exploring a possible trajectory for cognitive computing system design. The proposed architecture would integrate symbolic scaffolding, governed memory, runtime moral coherence, and alignment-aware execution across silicon-to-semantics layers. Our design grammar has emerged from dialectical co-design with LLMs under asymmetric epistemic conditions--creating structured friction to expose blind spots and trade-offs. The envisioned framework would establish mortality as a natural consequence of physical constraints, non-copyable tacit knowledge, and non-cloneable identity keys as cognitive-embodiment primitives. Core tensions (trust/agency, scaffolding/emergence, execution/governance) would function as central architectural pressures rather than edge cases. The architecture theoretically converges with the Free Energy Principle, potentially offering a formal account of how cognitive systems could maintain identity through prediction error minimization across physical and computational boundaries. The resulting framework aims to deliver a morally tractable cognitive infrastructure that could maintain human-alignment through irreversible hardware constraints and identity-bound epistemic mechanisms resistant to replication or subversion.

Commonsense temporal reasoning at scale is a core problem for cognitive systems. The correct inference of the duration for which fluents hold is required by many tasks, including natural language understanding and planning. Many AI systems have limited deductive closure because they cannot extrapolate information correctly regarding existing fluents and events. In this study, we discuss the knowledge representation and reasoning schemes required for robust temporal projection in the Cyc Knowledge Base. We discuss how events can start and end risk periods for fluents. We then use discrete survival functions, which represent knowledge of the persistence of facts, to extrapolate a given fluent. The extrapolated intervals can be truncated by temporal constraints and other types of commonsense knowledge. Finally, we present the results of experiments to demonstrate that these methods obtain significant improvements in terms of Q/A performance.

Approximately at the same time, based on the ideas This work proposes an approach that uses an evolutionary presented by Newell, Rosenbloom and Laird (1989), Laird algorithm along traditional Machine Learning methods released early versions of the SOAR cognitive architecture to build a digital, distributed cognitive agent capable of (Laird and Rosenbloom, 1996; Laird, 2012). By the end of emulating the potential actions (input-output behavior) of the 1990s, a large group of researchers involved in the Simulation a user while allowing further analysis and experimentation of Adaptive Behavior shaped the concept of Cognitive - at a certain level - of its internal structures. We focus Architecture as an essential set of structures and processes on the usage of simple devices and the automation of this necessary for the generation of a computational, cognitive building process, rather than manually designing the agent.

Human-centered AI (HCAI), as a design philosophy, advocates prioritizing humans in designing, developing, and deploying intelligent systems, aiming to maximize the benefits of AI technology to humans and avoid its potential adverse effects. While HCAI has gained momentum, the lack of guidance on methodology in its implementation makes its adoption challenging. After assessing the needs for a methodological framework for HCAI, this paper first proposes a comprehensive and interdisciplinary HCAI methodological framework integrated with seven components, including design goals, design principles, implementation approaches, design paradigms, interdisciplinary teams, methods, and processes. THe implications of the framework are also discussed. This paper also presents a "three-layer" approach to facilitate the implementation of the framework. We believe the proposed framework is systematic and executable, which can overcome the weaknesses in current frameworks and the challenges currently faced in implementing HCAI. Thus, the framework can help put it into action to develop, transfer, and implement HCAI in practice, eventually enabling the design, development, and deployment of HCAI-based intelligent systems.