working-memory

The paper introduces Answerable Working Memory (AWM) and AWM-GRPO to enhance the quality of terminal working memory in long-document VQA agents, improving accuracy and reducing memory issues.

The article argues that AI's success in mathematics may stem from its large working memory (context windows) rather than superior reasoning, comparing it to human cognitive limitations.

Introduces the Dendritron, a replacement for the Perceptron that can add internal memory and continual learning capabilities to frozen-weight Transformer models, with open-source code provided.

This paper identifies 'context dilution' as a key failure mode in language agents performing multi-hop reasoning and introduces SLEUTH, an epistemic working memory that explicitly tracks confirmed facts, active hypotheses, and open questions, showing consistent gains across benchmarks.

The article examines the link between working memory and consciousness, discussing how working memory's limitations and the 'doorway effect' might contribute to our conscious experience.

An ex-Google engineer explains AI agent memory architecture in 12 minutes, covering working memory and three memory layers (procedural, semantic, episodic) with a summarizer to prevent token bloat, as used by Claude.

This article discusses the concept of bounded cognition in software engineering, highlighting the limitations of human memory and attention, and how software systems are built despite these constraints.

An exploration of how AI agent memory systems often miss crucial cognitive processes like working memory, drawing parallels to anterograde amnesia, and offering design guidance for more effective solutions.

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.

A developer built a real-time 3D visualization dashboard for monitoring AI agent working memory after losing $400+ to runaway agent loops, using color-coded nodes and edges to detect reasoning loops before they become costly. The post reflects on agent observability as an emerging category distinct from traditional microservice monitoring.