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This paper proposes CEAA, a modular cognitive architecture for embodied intelligent virtual agents that integrates high-level reasoning models with real-time embodied execution in interactive 3D environments.
This paper proposes a vector-symbolic declarative memory system for the ACT-R cognitive architecture to better represent socio-cultural influences on decision-making, validated using an implicit association test.
This paper introduces Predictive Set Theory, a formal generative framework for cognitive architecture that reconstructs cognition from set-theoretic operations and addresses limitations in predictive processing and Bayesian cognitive science.
The paper proposes Sophia, a recursive cognitive refinement architecture for modular artificial consciousness that introduces a metacognitive sublayer to recursively refine intermediate semantic states through coherence checking, contextual synthesis, and memory-aware reinterpretation.
This proposal introduces the Dynamic Concept Graph (DCG), a hybrid cognitive architecture that combines neural representation learning, symbolic knowledge structures, multimodal perception, and analogical reasoning to provide persistent, evolving world models for AI, addressing limitations such as inconsistent reasoning and lack of causal understanding in large language models.
Anthropic's new research reveals a global workspace in language models, showing a striking parallel between the conscious and subconscious divide in human brains and the internal reasoning of Claude.
The article discusses the emerging pattern of 'wiki memory' for AI agents, where raw source data is intelligently compressed into a persistent, structured knowledge layer that agents can use efficiently. It compares this to basic RAG and gives examples like DeepWiki and LLM Wiki.
Introduces a novel cognitive architecture that learns without backpropagation, GPUs, or forgetting, mimicking biological learning.
A solo-built multi-agent cognitive architecture uses hyperbolic geometry on a Poincaré ball manifold, variational free energy for belief updating, and wave interference for memory retrieval, allowing personality to emerge from memory interactions rather than scripting.
Elasticsearch blog post describes building a persistent agent memory layer with three memory types (episodic, semantic, procedural), achieving 0.89 recall on a QA eval with zero tenant leaks using hybrid recall and DLS isolation.
GENesis-AGI is an open-source cognitive architecture that extends Claude Code with layered memory, self-learning, and real-world channels for building long-running personal AI agent systems.
This paper introduces Relational Reflective Intelligence (RRI), an inference-time governance layer that uses auditable reasoning loops to stabilize human-AI reasoning, addressing cognitive vulnerabilities shared by humans and LLMs.
The article describes Skynet, an Elixir-based framework using OTP GenServers to build persistent cognitive architectures for LLM agents. It implements a layered memory stack inspired by neuroscience, addressing the amnesia problem in long-running agents.
IDPR is a framework for response-conditioned inhibitory deliberation that first generates a fast intuitive answer, then uses an inhibition controller to decide whether to invoke slow reasoning, achieving efficiency gains while maintaining accuracy.
A reflective discussion on designing AI agents that intelligently choose the type of thinking needed for a task, proposing a control layer for task classification, attention, and memory management, inspired by human cognition.
Hexis is an open-source Postgres-native cognitive architecture that wraps any LLM to provide persistent memory, autonomous behavior, and identity, enabling agents to remember and pursue goals across sessions.
Introduces PHI // DRIFT, a cognitive middleware that enhances LLMs with persistent homeostatic needs, salience-weighted memory, and a Jungian shadow module, claiming that architecture produces measurably different behavior than model scale. Preprint under review.
Garry Tan argues that AI agent builders should focus on automating routine, boring tasks (the 'cerebellum') rather than only high-level planning and reasoning (the 'prefrontal cortex'), as most agent frameworks fail by treating all cognition as high cognition.
Describes PHI // DRIFT, a cognitive architecture with seven homeostatic state variables that drift between sessions, memory scored by emotional salience and time decay, and a Jungian shadow module, built on a CPU-only mini tower and submitted as a preprint to SSRN.
A preprint on SSRN presents PHI // DRIFT, a cognitive middleware architecture for AI companions with persistent internal state and salience-weighted memory retrieval, claiming 14.8% more context per prompt versus cosine-only RAG on consumer hardware.