brain-inspired

This paper introduces a probability-wave framework for modeling adaptive agents and demonstrates its effectiveness in explaining human decision patterns in stock market data, suggesting integration with conventional AI for more efficient AGI systems.

The author proposes an AI agent architecture inspired by human brain processes, using belief updates and decision loops, and seeks feedback from the AI community on its practicality and improvements.

THOR introduces a brain-inspired reasoning framework using theta-gamma hierarchical oscillation to mitigate attention decay and error accumulation in multi-hop question answering, achieving improved accuracy and robustness on benchmarks.

A new brain-inspired hardware design enables faster and more energy-efficient anomaly detection for AI systems, drawing on principles of neuromorphic computing.

EverMind team under Shanda Group founder Chen Tianqiao has open-sourced EverMemOS (EverOS), a brain-inspired intelligent memory operating system designed to solve the forgetting and context-limited problems of large language models by simulating human memory mechanisms, providing long-term memory infrastructure for AI Agents.

A brain-inspired AI architecture promises to deliver faster computing while consuming far less power, potentially advancing energy-efficient AI hardware.

Introduces FERNme, an open-source memory layer for AI agents that uses a fuzzy Hebbian graph to simulate associative memory, supporting features like zero-LLM writes, persistence, forgetting, and user ownership.

Introduces a novel cognitive architecture that learns without backpropagation, GPUs, or forgetting, mimicking biological learning.

Researchers develop a brain-inspired phototransistor that senses and stores data, potentially reducing AI energy consumption.

A comprehensive survey on foundation agents, proposing a modular brain-inspired architecture and covering self-enhancement mechanisms, multi-agent collaboration, and AI safety.