Agentic Reasoning for Large Language Models → Read more

Let us start with this fresh great survey from a bunch of notable authors: University of Illinois Urbana-Champaign, Meta, Amazon, Google DeepMind, UCSD, Yale. It’s about how AI reasoning shifts from just “thinking” to actually acting in real environments. You’ll learn the main things about agent types, core skills like planning and tool use, optimization methods, real-world applications, and the big open challenges ahead.

Toward Efficient Agents: Memory, Tool learning, and Planning → Read more

Focuses on cutting the real costs of AI agents – things like token usage, latency, and number of steps – without sacrificing task performance. It breaks this down across memory (compression and retrieval), tool use (reducing unnecessary calls), and planning (controlled search), compares methods, and reviews concrete benchmarks and metrics for measuring efficiency in practice.

Agent-as-a-Judge → Read more

How do agentic capabilities influence evaluation? This piece explains the move from simple “LLM-as-a-judge” setups to more capable agent-based judges. As tasks get more complex, single-pass model judgments fall short, so researchers are turning to agents that can plan, use tools, collaborate, and verify results. So this is a new roadmap roadmap for robust, verifiable AI evaluation.

A practical guide to building agents by OpenAI → Read more

This guide is for product and engineering teams who want to build their first AI agents. It shares practical lessons from real deployments, covering how to pick good use cases, design agent workflows, and make sure agents behave safely, reliably, and predictably in production.

Model AI Governance Framework for Agentic AI → Read more

Uncovers both the benefits and risks of AI agents, and lays out a practical governance framework so organizations can use them safely while keeping humans meaningfully in control.

Agentic Large Language Models, a survey → Read more

Surveys agentic LLMs across reasoning, tools, and multi-agent collaboration, highlighting their synergy. It also explores their promise, risks and applications in medicine, finance, science.

A Survey on Agentic Multimodal Large Language Models → Read more

Maps how multimodal models turn into full agents that can plan, use tools, and act in environments. It categorizes concrete methods, datasets, and benchmarks, with examples in GUI agents, robotics, healthcare, and autonomous driving.