IAAA'26:
The 1st International Workshop on
Information Access in the Era of AI Agents

[Workshop Venue TBD]

Unlocking Smarter Information Access, For Humans and AI Agents Alike

Information access (IA) refers to retrieving, evaluating, organizing, and digesting information, with broad applications such as search engines, recommender systems, and question-answering systems used daily by millions worldwide.

Natural language processing (NLP) has long played a key role in enhancing the intelligence of IA systems. With the rise of large language models (LLMs) and AI agents, there are unprecedented opportunities to develop interactive agents that improve human IA quality and reduce their effort.

At the same time, as AI agents increasingly automate our tasks, the agents themselves have the need for IA, creating new challenges in enabling AI agents to best leverage IA tools, such as search engines to retrieve relevant information to augment generation.

These new opportunities and challenges call for research in the interdisciplinary area of IA, NLP, LLMs, and interactive AI agents. There are many general open questions to be investigated, such as:

How can we maximize the benefits of AI agents for human IA?
How can agents acquire complete user context, process it, and deliver accurate, timely, and personalized information services?
How can AI agents simulate users and understand their needs?
How can privacy be protected given the extensive user data required for personalized IA?
How can AI agents utilize existing IA technologies to inform their own decision-making and enable self-improvement?

With a broader view of IA serving both human users and AI agents, this workshop examines the current state of the art and promising future directions in IA paradigms for the era of AI agents:

For humans, IA will be enhanced in effectiveness and efficiency through AI agent assistance; and
For AI agents, they can leverage existing human IA technologies to supplement their limited pre-training knowledge, inform decisions, and autonomously improve their capabilities.

Call for Papers

We invite long (8-page) and short (4-page) paper submissions on topics including, but not limited to:

AI agent design and training for traditional human IA tasks, e.g., IR, RecSys, and QA systems
User simulation for estimating user backgrounds, goals, and information needs
Exploration of human-AI agent interaction modes in information acquisition
Privacy protection strategies for personalized IA provided by AI agents
Autonomous information acquisition by AI agents (e.g., retrieval-augmented generation; RAG) for extending or updating their static and limited parametric knowledge
Agent memory mechanisms and cognitive architectures to process and organize information
Learning paradigms for the AI agent’s self-improvement through accessing new information, e.g., deep reinforcement learning
Datasets, benchmarks, and hardware or software systems supporting new paradigms of AI-enhanced IA and IA-enhanced AI agents

Submission Instructions

We welcome two types of papers: regular workshop papers and non-archival submissions. Only regular workshop papers will be included in the workshop proceedings. The review process will be double-blind. All submissions should be in PDF format, following the [TBD] template and made through the OpenReview submission portal ([TBD]).