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Cognitive architecture

A cognitive architecture is a theoretical framework that specifies the essential fixed structures and processes of a domain-generic designed to simulate and behavior across multiple levels and domains. These architectures provide a blueprint analogous to , defining how information is represented, processed, and integrated through components such as perceptual-motor modules, memory systems (e.g., declarative and procedural), and learning mechanisms like chunking or reinforcement. The development of cognitive architectures emerged in the late 1970s and as part of efforts to create unified theories of , building on earlier cognitivist ideas to incorporate perceptual, motor, and learning elements into comprehensive models. Pioneering work includes Allen Newell's Soar architecture (introduced in the ), which emphasizes symbolic problem-solving through production rules and operator selection in problem spaces, and John Anderson's ACT-R (evolving from ACT* in the ), a modular system that integrates , procedural rules, and empirical constraints from to model tasks like language learning and . Other influential examples include CLARION (developed in the mid-1990s by Ron Sun), a hybrid architecture combining explicit symbolic and implicit connectionist representations for tasks like navigation and , and LIDA, a biologically inspired model featuring cyclic processes of , action, and . Cognitive architectures play a central role in bridging and by enabling the testing of psychological theories through detailed simulations and fostering the development of general-purpose intelligent agents. Their significance is evident in practical applications, such as intelligent tutoring systems based on (e.g., the tutor, which improved student test scores by 15%), military simulations using Soar, and broader explorations of anticipatory thinking or human-like decision-making in uncertain environments. Recent advancements, as of 2025, continue to refine these models to incorporate hybrid symbolic-subsymbolic approaches—such as integrations with large language models (e.g., VSM-ACTR)—and address challenges in scalability and biological plausibility, including new frameworks like CogTwin for adaptable AI agents.

Overview and Fundamentals

Definition and Scope

A cognitive architecture provides a for modeling human-like in computational systems, defining the fixed structures that underlie intelligent . It specifies the core representations, such as or subsymbolic forms, along with mechanisms for processing, learning, and control structures that govern and task execution. This posits a about how an agent's and processing capacity are organized to produce adaptive, goal-directed actions across diverse environments. The scope of cognitive architecture extends across multiple disciplines, including , , , and , where it serves as a unifying construct to bridge theoretical models with empirical observations of . Unlike task-specific models, cognitive architectures emphasize and universality, aiming to replicate the general-purpose nature of rather than optimizing for isolated functions. This interdisciplinary integration allows architectures to incorporate insights from psychological experiments, neural data, and computational simulations, fostering a holistic understanding of as an emergent property of structured processes. Central principles guiding cognitive architectures include universality, which ensures applicability across a broad range of cognitive tasks without domain-specific tailoring; modularity, involving distinct subsystems for functions like perception, memory, and action that interact cohesively; and implementability, requiring the framework to be computationally realizable in software or hardware for practical testing and deployment. These principles, rooted in the pursuit of unified theories of cognition, distinguish cognitive architectures from ad hoc models by prioritizing human-like generality and empirical fidelity.

Key Components

Cognitive architectures incorporate several core memory systems to model human-like storage and retrieval. Declarative memory handles factual and events, often divided into semantic components for general facts and concepts, and episodic components for personal experiences tied to specific contexts and times. stores skills and procedures as executable rules or productions, enabling automated performance of routine tasks without conscious recall. serves as a short-term for manipulating and integrating during active processing, typically limited in capacity to support focused . Processing mechanisms in cognitive architectures facilitate the transformation of inputs into outputs through specialized modules. Perception modules process sensory data from the environment, encoding it into representational formats suitable for internal use, such as visual or auditory features. Reasoning engines apply inference rules to derive new from existing representations, supporting problem-solving and . Action selectors generate behavioral responses, mapping internal states to motor commands or environmental interactions. Control structures govern the flow of information and within the architecture. Hierarchical processing organizes operations in layered goal structures, where higher-level plans decompose into subordinate actions, while allows simultaneous execution of independent modules for efficiency. Goal-directed pursues long-term objectives through and , contrasting with reactive that responds immediately to environmental cues without foresight. Learning mechanisms enable adaptation and improvement over time by modifying internal representations based on experience. adjusts behaviors through reward signals, strengthening successful actions via utility updates. refines knowledge using labeled examples, often compiling simpler rules into more efficient procedures. identifies patterns in data without guidance, facilitating generalization from raw inputs. A basic example of a supervised update in , drawn from connectionist models, is the : \Delta W = \alpha \cdot (target - output) \cdot input where W represents connection weights, \alpha is the learning rate, target is the desired output, output is the current prediction, and input is the activating signal; this rule iteratively minimizes errors to tune skill-based associations.

Historical Development

Early Foundations

The philosophical foundations of cognitive architecture trace back to functionalism in the philosophy of mind, which emerged prominently in the 1960s and views mental states as defined by their causal roles and functional organization rather than their intrinsic physical properties. This perspective, advanced by thinkers like Hilary Putnam, posits that cognition can be understood as computational processes that operate independently of the underlying hardware, much like software running on different machines, thereby enabling the modeling of the mind through abstract functional specifications. Functionalism provided a theoretical bridge between psychology and computation, emphasizing that the mind's operations could be analyzed in terms of input-output relations and internal state transitions, independent of biological implementation. In the 1960s, these ideas influenced the information-processing paradigm in , which modeled the human mind as a serial processor handling information through discrete stages including sensory perception, encoding, long-term storage, retrieval, and motor response. Pioneered by researchers drawing analogies from early , this paradigm treated as a sequence of transformations on symbolic representations, akin to programs executing on a digital computer, and laid the groundwork for computational simulations of mental processes. played a pivotal role in this era, developing the EPAM (Elementary Perceiver and Memorizer) model in collaboration with Edward A. Feigenbaum, which simulated verbal learning and through a discrimination network that builds hierarchical feature trees for stimuli and associates responses via rote memorization mechanisms. EPAM demonstrated how simple perceptual and memory processes could account for complex learning behaviors, such as paired-associate tasks, by incrementally refining a tree-structured memory based on error feedback. Building on these foundations in the , John R. Anderson introduced early versions of the (Adaptive Control of Thought) models, which emphasized production rules as the core mechanism for , representing as condition-action pairs that activate declaratively stored facts to generate intelligent behavior. In , procedural is encoded in if-then production rules that compile over time through learning, enabling the system to simulate a wide range of cognitive tasks from memory retrieval to problem-solving, while distinguishing between declarative (facts) and procedural (skills) memory components. These developments shifted focus toward integrated production systems that could mimic human performance in psychological experiments, setting the stage for more unified theories in the .

Major Milestones

In 1983, John R. Anderson published The of Cognition, which formalized the Adaptive Control of Thought (ACT) model as a production system , emphasizing rule-based mechanisms for integrating declarative and to simulate human cognitive processes. Building on earlier foundations in Herbert Simon's problem-solving research, the field advanced toward unified models in the late 1980s, exemplified by the publication of the seminal paper on Soar by Allen Newell and colleagues in , introducing it as a general for . In 1990, Newell expanded this vision in Unified Theories of Cognition, proposing Soar as a comprehensive framework that integrates problem-solving, learning, and perception across diverse cognitive domains, marking a pivotal shift toward computationally unified theories of the mind. During the 1990s, the ACT framework evolved into with modular extensions, particularly the perceptual-motor module developed by Michael D. Byrne and Anderson, which enabled detailed modeling of human interaction with the environment by incorporating timing constraints and sensorimotor interfaces. In the mid-1990s, connectionist influences began integrating into hybrid models, as seen in Ron Sun's architecture, which incorporated tweaks to distinguish implicit (subsymbolic) and explicit (symbolic) learning processes, facilitating simulations of dual-process cognition in complex tasks. These milestones, including Newell's 1990 publication, underscored the growing emphasis on architectures that bridge psychological theory with computational implementation, influencing subsequent research in cognitive modeling.

Theoretical Distinctions and Paradigms

Symbolic vs. Connectionist Approaches

The approach to cognitive architecture emphasizes explicit representations of through symbols and rule-based logic, often implemented via production systems where is encoded as conditional statements that trigger actions upon matching preconditions. This excels in tasks requiring structured reasoning, , and logical , as the transparent manipulation of discrete symbols facilitates verifiable and interpretable processes. However, its rigid, deterministic nature struggles with handling uncertainty, perceptual variability, or noisy data, as it lacks mechanisms for probabilistic or from incomplete information. In contrast, the connectionist approach models through parallel distributed processing in of interconnected nodes, akin to artificial neural networks, where knowledge emerges from weighted connections adjusted via learning algorithms rather than explicit rules. This framework demonstrates strengths in , generalization from examples, and from data, enabling robust performance on tasks like or language processing without predefined symbolic structures. Its primary weakness lies in achieving systematic reasoning or compositional understanding, as the distributed representations often fail to support precise rule application or explainability in a stepwise manner. Theoretically, symbolic approaches embody a top-down , starting from high-level abstract rules and centralized control to derive behavior, whereas connectionist models adopt a bottom-up strategy, building complex capabilities from low-level interactions and emergent patterns across distributed units. A core production rule in systems exemplifies this , activating without probabilistic elements: \text{IF } \text{condition} \text{ THEN } \text{action} This binary structure underscores the centralized, rule-driven flow, differing sharply from the probabilistic, gradient-based updates in connectionist networks that yield emergent behavior through collective node interactions. The tension between these paradigms fuels a longstanding debate, epitomized by the hypothesis proposed by Newell and , which posits that arises solely from the manipulation of formal symbols in a physical system capable of universal computation. In response, Rumelhart and McClelland's parallel distributed processing framework critiqued this view by advocating subsymbolic representations, arguing that emerges from fine-grained, distributed processes better suited to modeling human-like learning and flexibility without relying on explicit symbols. Hybrid architectures seek to mitigate these paradigm-specific limitations by integrating symbolic reasoning with connectionist learning in complementary ways.

Hybrid Architectures

Hybrid architectures in integrate symbolic reasoning, which relies on explicit rules and logical structures, with connectionist approaches that employ neural networks for and learning from data, thereby addressing the limitations of purely symbolic systems in handling uncertainty and sub-symbolic processing or purely neural systems in lacking structured inference. This combination enables more robust cognitive modeling by leveraging the strengths of both paradigms to simulate human-like . Key types of hybrid architectures include shallow hybrids, where separate symbolic and neural modules interact through interfaces or data exchange, and hybrids, which achieve tighter by embedding symbolic constraints directly into neural computations or vice versa. Shallow hybrids maintain for easier interpretation and maintenance, while hybrids facilitate seamless fusion for complex tasks requiring joint optimization. Early developments in hybrid architectures emerged in the with frameworks like CLARION, which models the duality of explicit (rule-based, conscious) and implicit (associative, ) cognitive processes through dual representational structures. CLARION's design captures interactions between these processes, enabling simulations of learning, decision-making, and adaptation that align with dual-process theories in . Post-2020 advancements in neural-symbolic AI have further refined these integrations, as seen in neuro-symbolic visual systems from 2022 that employ scene graphs for symbolic scene representation alongside transformer-based neural models for query and . These approaches parse visual data into structured graphs to support logical querying, enhancing the system's ability to reason over perceptual inputs. Recent 2024-2025 advances include the DAC-HRC , which fuses deliberative symbolic with neural modules to enable adaptive cognitive in dynamic environments. Additionally, hybrid AGI proposals have gained traction, integrating large language models for with symbolic for structured decomposition and . These architectures offer benefits such as improved to novel scenarios by combining data-driven learning with rule-based constraints, enhanced explainability through traceable steps amid neural predictions, and better handling of real-world variability via robust of probabilistic and deterministic elements. Such advantages position hybrids as a promising path for scalable cognitive systems.

Notable Cognitive Architectures

Classical Examples

One of the foundational cognitive architectures is , developed by John R. Anderson in the 1990s at . ACT-R features a modular structure with specialized modules for perceptual-motor activities, goal management, and memory, interconnected through buffers that interface with a central production system. It distinguishes between declarative memory, which stores factual knowledge as chunks activated subsymbolically based on recency and frequency, and , implemented as production rules that compile cognitive procedures. A key mechanism is rational analysis, which tunes architectural parameters to optimize predictions of by deriving them from optimality principles rather than arbitrary fitting. ACT-R has been instrumental in modeling human performance across cognitive tasks, validating its mechanisms against empirical psychological data from experiments on learning, , and . For instance, it simulates driving behaviors by integrating modules with procedural rules for hazard detection and response, achieving close matches to reaction times observed in human drivers. Similarly, language processing models in ACT-R replicate patterns in sentence comprehension and production, aligning with data from reading and speech studies. These applications demonstrate ACT-R's emphasis on unifying diverse psychological phenomena under a single framework. Another classical architecture is Soar, originating from the work of Allen Newell, John Laird, and Paul Rosenbloom at in the 1980s. Soar is built on the unified problem-space hypothesis, positing that all intelligent behavior arises from searching a problem space using operators applied to states, resolved through a single production system that handles perception, decision-making, and action in a unified manner. Its learning mechanism, chunking, creates new production rules from subgoal solutions, enabling automatic improvement without external supervision and supporting scalability to complex tasks. Soar applies these principles to planning, where it decomposes goals into hierarchical operators, and to perception, integrating sensory data into state representations for real-time environmental interaction. Soar's contributions lie in its ability to simulate psychological processes like resolution and learning curves, validated against human data in problem-solving domains such as theorem proving and tactical . It has facilitated simulations of tasks involving , like automated , and perception-driven navigation, providing empirical fits to response times and error rates in controlled studies. This architecture underscores the feasibility of a general-purpose system for cognitive modeling. EPIC, developed by David E. Kieras and David E. Meyer in the 1990s at the , focuses on integrating cognitive processing with perceptual-motor control, particularly for multitasking scenarios. It employs a central cognitive processor using production rules for , paralleled by dedicated processors for , eye movements (oculomotor), hand movements (manual motor), and auditory inputs, allowing concurrent operation without a strict central bottleneck. Eye-hand coordination is modeled through strategic scheduling, where the cognitive processor directs parallel motor actions, accounting for delays like saccadic eye movements (approximately 200 ms) and manual execution times (around 100 ms minimum). This design enables precise predictions of performance in interactive tasks. EPIC's mechanisms have been validated against psychological data from dual-task experiments, such as the psychological refractory period effect, where it accurately reproduces response time trade-offs in eye-hand activities like tracking and . It contributes to simulations of human-computer , including cockpit operations and telephone dialing, by quantifying how perceptual-motor parallelism influences overall efficiency and error rates. The subsumption architecture, introduced by at in 1986, represents a reactive contrasting symbolic approaches by emphasizing layered, distributed control for embodied agents. It structures behaviors in ascending layers, each handling specific competencies (e.g., obstacle avoidance at the base level), where higher layers subsume lower ones during activation, suppressing unnecessary reactions without central deliberation. This finite-state machine-based system connects sensors directly to actuators, promoting robustness in dynamic environments through simple, parallel rules rather than complex planning. Subsumption has been applied to mobile robotics, enabling incremental development from basic to more sophisticated interactions. Contributions of subsumption include demonstrations of reactive behaviors in real-world settings, validated through robotic implementations that achieve autonomous and without predefined world models, aligning with observations of animal-like adaptability in uncertain conditions. It influenced simulations of perception-action loops in tasks like corridor following, highlighting the value of bottom-up emergence over top-down reasoning. Collectively, these classical architectures—ACT-R, Soar, EPIC, and subsumption—advanced cognitive modeling by providing psychologically grounded frameworks that integrate symbolic and reactive elements, with extensive validation against human behavioral data in domains like , , and . Their emphasis on modular mechanisms and empirical fitting established benchmarks for simulating complex tasks, informing subsequent developments in .

Modern and Hybrid Examples

Post-2010 developments in cognitive architectures have increasingly emphasized designs that integrate neural and symbolic elements to support continuous learning and adaptation in dynamic environments. The CLARION architecture, originally proposed in the , received significant enhancements in subsequent years, particularly through refinements to its dual-process structure of explicit () and implicit (connectionist) subsystems, enabling more robust without requiring predefined . These updates facilitate interaction between subsystems for incremental skill acquisition and adjustment in changing contexts. DeepMind's introduction of memory-augmented neural architectures marked a pivotal advancement in hybrid reasoning capabilities. The (DNC), developed in 2016, augments recurrent neural networks with a differentiable external memory matrix, allowing end-to-end training via for tasks requiring algorithmic reasoning and long-term memory retention, such as and sequence copying. This design bridges connectionist learning with structured memory access, enabling the learning of complex algorithms through end-to-end training. In the 2020s, neuro-symbolic hybrids have gained prominence in , combining for with reasoning for interpretable decision-making. For instance, 2022 systems for visual question answering (VQA) on embodied agents employ Mask R-CNN for instance segmentation and generation, paired with transformer-based models to translate queries into executable programs, enabling robots to answer spatial and relational questions about their . Similarly, the DAC-HRC (Dynamically Adaptive Cognitive architecture for Human-Robot Collaboration), proposed in 2024, structures control into layered modules— for , reactive for immediate responses, adaptive for , and contextual for long-term —to support socially aware interactions in settings, improving task and user satisfaction through real-time adaptation to human behaviors. The architecture, developed by Stan Franklin and colleagues starting around 2004, is a biologically inspired hybrid model that implements through cyclic processes of , understanding, action selection, and contextualization, often incorporating consciousness-like mechanisms. It integrates symbolic and subsymbolic processing for tasks such as and cognitive modeling, with applications in simulating and in dynamic environments. By 2025, hybrid architectures targeting (AGI) have incorporated (LLMs) with symbolic planning to foster self-training reasoning. Frameworks like those applying cognitive design patterns to LLM agents integrate modular symbolic components for planning and verification, allowing models to decompose complex tasks, simulate outcomes, and refine behaviors autonomously, as seen in benchmarks for multi-step reasoning and tool use. These designs enhance reliability in open-ended scenarios by mitigating LLM hallucinations through symbolic grounding. Modern hybrids distinguish themselves through scalability enabled by backbones, which handle vast datasets efficiently, and their proficiency in processing inputs, such as vision-language integrations for real-world deployment.

Applications and Implementations

In Artificial Intelligence and Robotics

Cognitive architectures have been instrumental in advancing applications, particularly in task automation within gaming environments. For instance, the Soar architecture facilitates hierarchical planning and decision-making for AI agents in complex virtual worlds, enabling efficient problem-solving in games by integrating reasoning with learning mechanisms to adapt strategies during gameplay. In , hybrid models combining and neural components enhance comprehension and generation tasks, such as systems that maintain over extended interactions by leveraging structured knowledge representation alongside from large language models. In robotics, cognitive architectures support practical deployments for navigation and collaboration. The subsumption architecture, a foundational reactive paradigm, enables layered behaviors for obstacle avoidance and pathfinding in dynamic environments, allowing robots to prioritize immediate sensory inputs over deliberative planning for efficient reactive navigation in unstructured settings. More recent implementations, such as the DAC-hRC architecture introduced in 2024, integrate perception, reasoning, and social adaptation modules to facilitate human-robot collaboration in industrial assembly tasks, where robots adjust actions based on human intentions detected through multimodal cues, improving workflow efficiency in shared spaces. In swarm robotics, neuro-symbolic coordination frameworks from 2025 research enable decentralized decision-making among multiple agents, combining neural networks for local perception with symbolic rules for global task allocation, as demonstrated in collective exploration scenarios where robots synchronize movements without central control. Evaluation of these architectures in AI and robotics emphasizes metrics that capture operational effectiveness and flexibility. Key measures include task success rate, which quantifies the percentage of completed objectives in simulated or real-world trials, and adaptability to novel environments, assessed through transfer learning performance across varied scenarios to gauge generalization beyond training data. A representative case is robot visual question answering (VQA) systems grounded in cognitive architectures, such as identifying object relations in household environments by fusing scene graphs with for query resolution. These applications often leverage hybrid architectures to address core challenges like real-time decision-making under uncertainty, where probabilistic reasoning modules process incomplete sensor data to select actions with bounded computational overhead, ensuring robust performance in unpredictable robotic operations.

In Cognitive Science and Education

Cognitive architectures play a pivotal role in cognitive science by enabling the simulation of human cognitive processes, such as memory recall and decision-making, to test theories of the mind. For instance, the ACT-R (Adaptive Control of Thought-Rational) architecture has been extensively used to model these processes, where declarative memory is represented as chunks that activate based on recency and frequency, influencing decision outcomes. These simulations have been validated against empirical data from psychology. In education, cognitive architectures inform the design of intelligent tutoring systems that adapt to learners' cognitive states, enhancing instructional efficacy. A prominent example is the Cognitive Tutor, developed in the 1990s and refined through the 2020s, which applies principles to and instruction by modeling student knowledge as production rules and predicting errors to provide targeted feedback. Empirical studies demonstrate its impact, with students using the system showing 15-25% greater learning gains in compared to traditional methods, as measured in controlled classroom trials. Recent advancements integrate large language models (LLMs) with cognitive architectures to create personalized educational tools, particularly in domains. From 2020 to 2025, research on has explored LLM-augmented systems that leverage architectures like for scaffolding complex problem-solving, such as in physics simulations where LLMs generate explanations aligned with cognitive models of reasoning. These hybrid approaches aim to simulate individualized learning paths, improving engagement and retention in online platforms. Validation of these applications relies on rigorous empirical methods, including comparisons to human performance metrics like error rates in cognitive tasks. For example, models of the Stroop task—where participants name ink colors while ignoring conflicting word meanings—accurately predict interference effects and response times, matching human data in experimental validations. This predictive power underscores the architectures' utility in both scientific modeling and educational design.

Challenges and Future Directions

Ethical Considerations

One major ethical challenge in cognitive architectures is ensuring and explainability, as neural components often function as black boxes, making it difficult to audit processes. Hybrid architectures can mitigate this by integrating elements that provide interpretable, rule-based reasoning alongside connectionist modules, allowing for verifiable outputs in critical applications. This approach aligns with broader calls for auditable systems to build trust and enable oversight. Bias and fairness represent another critical issue, where training data in cognitive architectures may perpetuate inequalities, resulting in disparate performance across demographic groups such as , , or . For instance, biased datasets can lead to discriminatory outcomes in decision-support systems modeled on human cognition. To address this, the Recommendation on the Ethics of Artificial Intelligence emphasizes fairness through diverse data practices and ongoing bias audits, with implementation guidelines evolving from 2022 to 2025 to promote equitable AI deployment globally. Accountability remains a pressing concern in systems driven by cognitive architectures, particularly in , where errors in autonomous decisions—such as failures or mishaps—raise questions about among developers, operators, and the architectures themselves. Frameworks for in robotic systems advocate for traceable and human oversight to assign clearly, ensuring that ethical lapses do not evade . Specific ethical risks include violations in cognitive modeling, where architectures that simulate mental states from behavioral could inadvertently expose sensitive psychological profiles . Additionally, in educational applications, cognitive architectures risk enabling manipulative that personalizes content in ways that unduly influence learner behavior or . To counter these challenges, frameworks for integrating ethical rules directly into cognitive architectures have emerged, such as embedding just-war principles into robotic systems to constrain lethal or harmful actions under international norms. Discussions in 2023 highlight how such normative integrations can enforce and in , preventing misuse in high-stakes environments.

Emerging Trends

Recent advancements in cognitive architectures have increasingly incorporated large language models (LLMs) to enhance reasoning capabilities within systems, addressing limitations in traditional and connectionist approaches. By integrating LLMs, these architectures leverage the models' to simulate higher-level , such as and in dynamic environments. For instance, frameworks like Cognitive Architectures for Language Agents (CoALA) organize LLM-based agents to mimic human-like processes, enabling more flexible task execution. Similarly, synergistic combinations of LLMs with established architectures, such as Clarion, facilitate implicit-explicit knowledge processing, improving adaptability in computational cognitive modeling. A notable direction involves self-training mechanisms in LLMs to foster autonomous reasoning improvement, particularly in hybrid cognitive systems. Research demonstrates that large reasoning models can self-train using with verifiable rewards, where models generate and evaluate their own reasoning paths to refine performance without external supervision. This approach, exemplified by methods like Self-Rewarding Training (SRT), allows models to iteratively enhance logical inference, bridging gaps in narrow toward more generalizable . For example, self-training elicits concise reasoning paths through best-of-N sampling and few-shot conditioning, achieving improvements of 6-10% on reasoning benchmarks. Progress toward () emphasizes deeply interactive neural-symbolic systems that combine subsymbolic learning with explicit rule-based reasoning. These architectures aim to overcome the brittleness of pure neural networks by embedding symbolic knowledge graphs into pipelines, enabling robust generalization across domains. According to the AAAI 2025 Presidential Panel, 76.9% of surveyed experts consider integrating learning and reasoning approaches very important for AGI-level reasoning, as neural networks alone insufficiently handle abstract inference. Seminal work highlights how these systems improve reasoning in LLMs by incorporating neuro-symbolic methods, reducing hallucinations and enhancing factual accuracy in multi-step problems. Scalability in cognitive architectures has advanced through parameter-efficient fine-tuning (PEFT) techniques, which adapt large models to cognitive tasks with minimal resource overhead. PEFT methods, such as Low-Rank Adaptation (), update only a fraction of parameters—often less than 1%—while preserving pretrained knowledge, making them ideal for deploying cognitive models in resource-constrained settings like . In parallel, multimodal extensions support embodied by integrating , , and modalities into unified architectures. Surveys on world models for embodied underscore how these extensions enable agents to predict environmental dynamics from sensory inputs, facilitating real-time in physical spaces; for example, LLM-based cognitive architectures grounded in reasoning achieve higher success rates in tasks. In 2025, trends highlight the social science of LLMs, exploring their capacity to exhibit mind-like behaviors such as and social inference. Studies reveal that LLMs can simulate human social dynamics, serving as proxies for collective cognition research. Complementing this, the Voice Evaluation of Reasoning Ability () framework diagnoses gaps in LLMs by comparing text-based and voice-based reasoning, advancing diagnostics for more naturalistic cognitive evaluation. Despite these innovations, research gaps persist in benchmarks for dynamic, real-world testing of cognitive architectures. Current evaluations often rely on static datasets, lacking metrics for adaptability in unpredictable environments; emerging benchmarks like τ-Bench address this by simulating open-ended interactions, revealing drops of 30-50% in real-world variability compared to controlled settings. Comprehensive surveys call for standardized, benchmarks to validate and , ensuring architectures meet practical deployment needs.