LLM Agents - SkrewAI

LLM Agents

New Baseline Tackles LLM Long-Term Memory Challenge

Researchers introduce a simple yet effective approach to managing conversational memory in LLM agents, addressing context window limitations through structured memory organization and retrieval mechanisms.

AI Safety

Kill Switch Defense Against Malicious LLM Web Agents

New research presents a technical framework for detecting and neutralizing malicious web-based LLM agents through real-time monitoring and intervention systems, addressing growing AI safety concerns.

LLM Agents

Agent-R1: End-to-End RL Trains Powerful LLM Agents

New research introduces Agent-R1, an end-to-end reinforcement learning framework that trains LLM agents without supervised fine-tuning. Demonstrates superior performance on complex reasoning and coding tasks through novel reward modeling.

LLM Agents

Vector vs Graph vs Event Logs: Memory Systems for LLM Agents

Technical comparison of memory architectures for AI agents: vector databases for semantic search, graph structures for relationships, and event logs for temporal reasoning. Each approach offers distinct advantages for agent performance.

LLM Agents

How LLM Agents Learn to Adapt at Test Time

New research introduces grounded test-time adaptation for LLM agents, enabling dynamic learning during deployment by anchoring adaptations to factual knowledge and environmental feedback.

Voice AI

Building Agentic Voice AI: Multi-Step Reasoning Systems

Learn how to architect voice AI assistants with autonomous reasoning capabilities, combining speech processing, LLM agents, and planning frameworks for intelligent multi-step responses.

LLM Agents

Deep Ideation: LLM Agents Navigate Concept Networks

Researchers introduce Deep Ideation, a framework that guides LLM agents through scientific concept networks to generate novel research ideas, demonstrating how structured knowledge graphs can enhance AI creativity in scientific discovery.