The Common Model of Cognition (CMC) provides an abstract characterization of the structure and processing required by a cognitive architecture for human-like minds. We propose a unified approach to integrating metacognition within the CMC. We propose that metacog-nition involves reasoning over explicit representations of an agent's cognitive capabilities and processes in working memory. Our proposal exploits the existing cognitive capabilities of the CMC, making minimal extensions in the structure and information available within working memory. We provide examples of metacognition within our proposal.

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.

Cognitive science and artificial intelligence (AI) have grown up together as fields. The computational models of human minds developed in cognitive science have long served as benchmarks to articulate what it means for a system to be flexibly intelligent. Recent advances in AI, particularly around large language models (LLMs), are creating new opportunities to reciprocate this influence. Already, LLMs are being offered as scalable "cognitive models" of human behavior [Binz et al., 2024], automatic analysts of unstructured psychological text [Rathje et al., 2024], and components in neurosymbolic cognitive architectures (e.g., [W ong et al., 2023]). However, most applications of LLMs to cognitive science have so far focused on individual cognition.

This monograph presents a modular cognitive architecture for artificial intelligence grounded in the formal modeling of belief as structured semantic state. Belief states are defined as dynamic ensembles of linguistic expressions embedded within a navigable manifold, where operators enable assimilation, abstraction, nullification, memory, and introspection. Drawing from philosophy, cognitive science, and neuroscience, we develop a layered framework that enables self-regulating epistemic agents capable of reflective, goal-directed thought. At the core of this framework is the epistemic vacuum: a class of semantically inert cognitive states that serves as the conceptual origin of belief space. From this foundation, the Null Tower arises as a generative structure recursively built through internal representational capacities. The theoretical constructs are designed to be implementable in both symbolic and neural systems, including large language models, hybrid agents, and adaptive memory architectures. This work offers a foundational substrate for constructing agents that reason, remember, and regulate their beliefs in structured, interpretable ways.

Current artificial intelligence systems -- from large language models to autonomous robots -- excel at narrow tasks but lack key qualities of sentient beings: intrinsic motivation, affective interiority, autobiographical sense of self, deep creativity, and abili ties to autonomously evolve and adapt over time. Here we introduce Sentience Quest, an open research initiative to develop more capable artificial general intelligence lifeforms (AGIL) that achieve these grand challenges with an embodied, emotionally adaptive, self - determining, living AI, with core drives that ethically align with human s and the future of life. Our vision builds on ideas from cognitive science and neuroscience -- from Baars' Global Workspace Theory and Damasio's somatic mind, to Tononi's Integrated Information Theory and Hofstadter's narrative self -- synthesizing these into a novel cognitive architecture. We describe an approach that integrates intrinsic drives (e.g., survival, social bonding, curiosity), a global "Story Weaver" workspace for internal narrative and adaptive goal pursuit, and a hybrid neuro - symbolic memory that logs the AI's life events as structured "story objects." Implemented in humanoid robots like Sophia, this architecture enables adaptive behavior grounded in a human - like body, in pursuit of experiential learning homologous to human experiences. Early resu lts are promising, with a driver - based goal system generating self - motivated actions, a narrative memory allowing the robot to refer to its own experiences, and integrated information measures (Φ) quantifying evolving cognitive integration. We discuss ethi cal implications, exploring how co - evolution with humans via an information - centric ethics ("SuperGood" principle) may guide both developers and AI systems to ensure value alignment. Sentience Quest is presented as a call to action: a collaborative, open - source effort to imbue machines with accelerating sentience in a safe, transparent, and beneficial manner. 2

Successful agent-human partnerships require that any agent generated information is understandable to the human, and that the human can easily steer the agent towards a goal. Such effective communication requires the agent to develop a finer-level notion of what is understandable to the human. State-of-the-art agents, including LLMs, lack this detailed notion of understandability because they only capture average human sensibilities from the training data, and therefore afford limited steerability (e.g., requiring non-trivial prompt engineering). In this paper, instead of only relying on data, we argue for developing generalizable, domain-agnostic measures of understandability that can be used as directives for these agents. Existing research on understandability measures is fragmented, we survey various such efforts across domains, and lay a cognitive-science-rooted groundwork for more coherent and domain-agnostic research investigations in future.

This paper introduces function alignment, a novel theory of mind and intelligence that is both intuitively compelling and structurally grounded. It explicitly models how meaning, interpretation, and analogy emerge from interactions among layered representations, forming a coherent framework capable not only of modeling minds but also of serving as a blueprint for building them. One of the key theoretical insights derived from function alignment is bounded interpretability, which provides a unified explanation for previously fragmented ideas in cognitive science, such as bounded rationality, symbol grounding, and analogy-making. Beyond modeling, the function alignment framework bridges disciplines often kept apart, linking computational architecture, psychological theory, and even contemplative traditions such as Zen. Rather than building on any philosophical systems, it offers a structural foundation upon which multiple ways of understanding the mind may be reconstructed.

Second Workshop on Metacognitive Prediction of AI Behavior Exploring Cognitive Attributes in Financial Decision-Making Mallika Mainali, Drexel University, Philadelphia, P A, 19104, USA Rosina O. Weber, Drexel University, Philadelphia, P A, 19104, USA Abstract--Cognitive attributes are fundamental to metacognition, shaping how individuals process information, evaluate choices, and make decisions. T o develop metacognitive artificial intelligence (AI) models that reflect human reasoning, it is essential to account for the attributes that influence reasoning patterns and decision-maker behavior, often leading to different or even conflicting choices. This makes it crucial to incorporate cognitive attributes in designing AI models that align with human decision-making processes, especially in high-stakes domains such as finance, where decisions have significant real-world consequences. However, existing AI alignment research has primarily focused on value alignment, often overlooking the role of individual cognitive attributes that distinguish decision-makers. T o address this issue, this paper (1) analyzes the literature on cognitive attributes, (2) establishes five criteria for defining them, and (3) categorizes 19 domain-specific cognitive attributes relevant to financial decision-making.

This research addresses the question, which characteristics a cognitive architecture must have to leverage the benefits of natural language in Co-Constructive Task Learning (CCTL). To provide context, we first discuss Interactive Task Learning (ITL), the mechanisms of the human memory system, and the significance of natural language and multi-modality. Next, we examine the current state of cognitive architectures, analyzing their capabilities to inform a concept of CCTL grounded in multiple sources. We then integrate insights from various research domains to develop a unified framework. Finally, we conclude by identifying the remaining challenges and requirements necessary to achieve CCTL in Human-Robot Interaction (HRI).

For AI agents to emulate human behavior, they must be able to perceive, meaningfully interpret, store, and use large amounts of information about the world, themselves, and other agents. Metacognition is a necessary component of all of these processes. In this paper, we briefly a) introduce content-centric computational cognitive (C4) modeling for next-generation AI agents; b) review the long history of developing C4 agents at RPI's LEIA (Language-Endowed Intelligent Agents) Lab; c) discuss our current work on extending LEIAs' cognitive capabilities to cognitive robotic applications developed using a neuro symbolic processing model; and d) sketch plans for future developments in this paradigm that aim to overcome underappreciated limitations of currently popular, LLM-driven methods in AI.