LLM Architecture

AI Agents

Context Engineering for AI Agents: Summary, Masking & Memory

A deep dive into context engineering techniques for AI agents, exploring how LLM summarization, token masking, and memory systems help manage the context window to build more capable AI systems.

AI research

Test-Time Compute: Three Techniques Making AI Think Longer

Modern AI models use test-time compute to improve responses through extended reasoning. Three key techniques—chain-of-thought, self-consistency, and reward-guided search—are reshaping how AI systems approach complex problems.

AI Agents

MIRA: Combining Memory Systems with RL for Smarter AI Agents

New research introduces MIRA, a framework that integrates memory architectures with reinforcement learning while minimizing expensive LLM calls, advancing efficient autonomous agent design.

AI Agents

MAPLE: New Sub-Agent Architecture for AI Memory and Learning

New research introduces MAPLE, a sub-agent architecture enabling memory, learning, and personalization in agentic AI systems through modular design patterns.

AI Agents

Build Self-Organizing Memory Systems for AI Agents

Learn how to construct self-organizing memory architectures that enable AI agents to maintain context and reason across extended interactions and complex tasks.

AI Agents

MARS: A New Modular Agent Architecture for AI Research Automation

New research introduces MARS, a modular agent with reflective search capabilities designed to automate AI research tasks through intelligent decomposition and self-correction.

AI Agents

Building Memory-Driven AI Agents: A Technical Architecture Guide

Learn how to implement short-term, long-term, and episodic memory systems in AI agents, enabling persistent context and improved reasoning capabilities across sessions.

AI Agents

AI Agent Architecture Guide: Shallow, ReAct, or Deep?

Understanding when to use shallow tool-calling, ReAct reasoning loops, or deep multi-agent systems is crucial for building effective AI applications. Here's how to choose.

AI Agents

New Framework Augments LLMs with Expert Knowledge for AI Agents

Researchers propose a software engineering framework for building AI agents that combines LLM capabilities with codified human expert domain knowledge for improved reliability.

AI research

Multi-Agent AI Workflows Reshape Automated Scientific Discovery

New research proposes interactive multi-agent architectures for AI scientists, moving beyond single-model approaches to collaborative systems that could transform how AI tackles complex research problems.

LLM Architecture

MoE-LoRA Framework Advances Multi-Task LLM Specialization

New research combines Mixture-of-Experts with Low-Rank Adaptation to create specialized AI models that maintain generalist capabilities while excelling at domain-specific tasks.

AI Agents

Deep Agents: Solving Multi-Step AI Agent Failure Modes

AI agents often fail after several steps due to error compounding and context degradation. Deep Agents architecture introduces new mechanisms to maintain coherence across extended task execution.