How a 60-Year-Old Control Theory Tool Explains Modern AI Memory Systems
A Kalman filter, developed six decades ago in control theory, solves a problem common to signal processing, AI agent memory, and computer vision: how to update beliefs using noisy new data while…
Read the full articleYou might also wanna read
Proactive Memory Agent Improves Long-Horizon AI Task Performance by Addressing Behavioral State Decay
In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and
arxiv.org·8h ago
Robot Memory System Enables AI Robots to Learn from Past Experiences
Robot Memory - Persistent memory system for AI robots. MCP Server + hybrid search + spatial retrieval. - robotmem/robotmem
github.com·4mo ago
Agent Memory Is Distributed State Management, Not Magic
Agent memory is not magic. It is distributed state with caches, logs, consistency windows, synchronization, and memory curation.
The Evolution of AI: From Static Benchmarks to Inference-Time Search for Autonomous Agents
Benchmarking at inference time as a way to achieve your agent's goals
adlrocha.substack.com·6mo ago
A Field Guide to Production-Ready AI Agents: Context Windows, Security, and Drift Monitoring
A field guide to production-ready AI agents covering context-window discipline, skill composition, capability-based security, and drift tele
Trust Scores for AI Agents: A Framework for Graduated Permissions Based on Behavioral Evaluation
Behavior is the new credential. See how trust scoring helps enterprises evaluate AI agents continuously and unlock safer, more scalable auto
Comments
Sign in to join the conversation.
No comments yet. Be the first.