AchiralAchiral

Concepts · Humans and machines

Concepts

Foundational concepts for studying memory as a cognitive, behavioral, organizational, and computational phenomenon.

Summary Capsule

Explore ACT-R, retrieval, agent memory, and organizational context as related but distinct ideas.

This collection explains what cognitive architectures can contribute to AI-system design without treating engineering patterns as models of the human mind.

Editorial desk illustration showing a goal marker, memory cards, rule cards, and an action disk connected in a simple ACT-R-inspired loop.

Introduction to ACT-R

A research-grounded introduction to ACT-R: declarative memory, production rules, goals, buffers, and graded retrieval.

Editorial desk illustration showing a saved context card entering a broader goal, rule, permission, and action flow.

ACT-R Memory vs Agent Memory

Agent memory persists useful context; ACT-R specifies how knowledge, goals, and learned procedures jointly produce behavior.

Editorial worktable illustration showing work cards, tool trays, shared memory, model blocks, governance icons, and a feedback loop.

Architecture of a Memory-Native Organization

A reference architecture for connecting operational signals, governed shared context, model interactions, and feedback in an AI-enabled organization.

Editorial diagram showing source cards retrieved from an archive into an answer workspace, connected to a lifecycle of retaining, reviewing, retrieving, and forgetting shared context.

RAG vs AI Memory

RAG retrieves context for a response; AI memory governs what information persists, changes, and is reused over time.

Memory and continuity in AI systems

Memory is not a single technical object. In cognitive science it concerns mechanisms of learning and recall; in AI engineering it often refers to persistent context, retrieval, or state management. This collection separates those meanings before asking how they can inform practical systems.

Achiral applies these ideas to a shared operational-context layer for AI systems. That layer is an engineering design, not a model of human memory or cognition.

PerspectiveCentral question
Cognitive architectureWhat must be represented, retrieved, activated, and acted on for a system to behave with continuity rather than isolated response?
Behavioral adaptationHow does repeated experience change future choices, priorities, habits, and interventions without requiring every rule to be restated?
Organizational memoryHow do decisions, context, exceptions, and handoffs become durable shared state instead of disappearing across tools and conversations?
Computational retrievalWhen should a system search, rank, reinforce, forget, or withhold context so memory remains useful instead of becoming noise?
Governance and safetyHow can compounding memory stay permissioned, inspectable, reversible, and aligned with human review as it begins to shape action?