Introduction

The field of agentic information seeking is rapidly evolving, with a focus on developing autonomous agents that can efficiently navigate and synthesize large volumes of information. Recent developments have centered around enhancing the capabilities of large language models (LLMs) in web search and information seeking tasks.

General Direction

The current direction of the field is towards creating more advanced and autonomous agentic systems that can perform complex information seeking tasks. This involves developing novel training paradigms, such as iterative self-evolution frameworks, that can improve the performance of LLMs in open-search domains. Additionally, there is a growing emphasis on designing systems that can augment user interactions with content across webpages, mitigating cognitive and manual efforts.

Noteworthy Papers

Some papers have made significant contributions to the field, including:

• EvolveSearch, which proposes a novel iterative self-evolution framework that combines supervised fine-tuning and reinforcement learning to enhance agentic web search capabilities.
• WebDancer, which presents a cohesive paradigm for building end-to-end agentic information seeking agents from a data-centric and training-stage perspective.
• Orca, which explores how AI can augment users' interactions with content across webpages, supporting user-driven exploration and synthesis of web content at scale.
• WorkForceAgent-R1, which introduces a rule-based reinforcement learning framework designed to enhance single-step reasoning and planning for business-oriented web navigation tasks.