Deep research tools are among the most impactful and most commonly encountered agentic systems today. We observe, however, that each deep research agent introduced so far is hard-coded to carry out a particular research strategy using a fixed choice of tools. We introduce Universal Deep Research (UDR), a generalist agentic system that wraps around any language model and enables the user to create, edit, and refine their own entirely custom deep research strategies without any need for additional training or finetuning. To showcase the generality of our system, we equip UDR with example minimal, expansive, and intensive research strategies, and provide a user interface to facilitate experimentation with the system.

Breakthroughs, discoveries, and DIY tips sent every weekday. Amidst the flurry of AI announcements and product reveals from Google in recent months, you might have missed one of the most useful AI-powered apps in the whole collection: NotebookLM (that LM stands for Language Model). Perhaps NotebookLM has gone largely under the radar because it was originally launched as more of an academic research tool when it first appeared back in 2023. Its user interface lacks some of the slickness and accessibility of Google Gemini, and it's not quite as obvious how you're supposed to use it, or what it can do. However, NotebookLM is gradually becoming better known amongst consumers, with official apps for Android and iOS now available, alongside the web app.

While improving Microsoft Copilot's basic research functions may not be as exciting as, say, a Copilot that dishes out compliments and knows everything about you, it's still incredibly useful for Copilot as a research tool. On Friday, at Microsoft's 50th anniversary celebration, the company showed off its Copilot redesign. The company introduced Copilot Vision for Windows as well as a more intuitive Copilot assistant. But Copilot's knowledge capabilities are also being improved with Copilot Search, Deep Research, podcasts, and Pages. The podcasts and Pages features may seem familiar.

Generative AI (GenAI) tools are radically expanding the scope and capability of automation in knowledge work such as academic research. AI-assisted research tools show promise for augmenting human cognition and streamlining research processes, but could potentially increase automation bias and stifle critical thinking. We surveyed the past three years of publications from leading HCI venues. We closely examined 11 AI-assisted research tools, five employing traditional AI approaches and six integrating GenAI, to explore how these systems envision novel capabilities and design spaces. We consolidate four design recommendations that inform cognitive engagement when working with an AI research tool: Providing user agency and control; enabling divergent and convergent thinking; supporting adaptability and flexibility; and ensuring transparency and accuracy. We discuss how these ideas mark a shift in AI-assisted research tools from mimicking a researcher's established workflows to generative co-creation with the researcher and the opportunities this shift affords the research community.

The rise of large language models (LLMs) has led many researchers to consider their usage for scientific work. Some have found benefits using LLMs to augment or automate aspects of their research pipeline, while others have urged caution due to risks and ethical concerns. Yet little work has sought to quantify and characterize how researchers use LLMs and why. We present the first large-scale survey of 816 verified research article authors to understand how the research community leverages and perceives LLMs as research tools. We examine participants' self-reported LLM usage, finding that 81% of researchers have already incorporated LLMs into different aspects of their research workflow. We also find that traditionally disadvantaged groups in academia (non-White, junior, and non-native English speaking researchers) report higher LLM usage and perceived benefits, suggesting potential for improved research equity. However, women, non-binary, and senior researchers have greater ethical concerns, potentially hindering adoption.

LLMs for online text data analysis limits its use and underscores a significant gap in the research landscape. Online discussion forums provide crucial data to understand the Our work addresses this gap through the following contributions: concerns of a wide range of real-world communities. However, the typical qualitative and quantitative methods used to analyze those (i) We introduce QuaLLM, an LLM-based framework consisting data, such as thematic analysis and topic modeling, are infeasible of a novel prompting methodology and evaluation strategy to scale or require significant human effort to translate outputs for the analysis and extraction of quantitative insights from to human readable forms. This study introduces QuaLLM, a novel text data on online forums. LLM-based framework to analyze and extract quantitative insights (ii) We apply our framework to a case study on Reddit's rideshare from text data on online forums. The framework consists of a novel communities, analyzing over one million comments--the prompting methodology and evaluation strategy. We applied this largest study of its kind--to identify worker concerns regarding framework to analyze over one million comments from two Reddit's AI and algorithmic platform decisions, responding to rideshare worker communities, marking the largest study of its regulatory calls [49].

Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and efficiency, challenges such as prompt tuning, biases, and subjectivity must be addressed. The study presents insights from experiments utilizing LLMs for qualitative analysis, highlighting successes and limitations. Additionally, it discusses strategies for mitigating challenges, such as prompt optimization techniques and leveraging human expertise. This study aligns with the 'LLMs as Research Tools' workshop's focus on integrating LLMs into HCI data work critically and ethically. By addressing both opportunities and challenges, our work contributes to the ongoing dialogue on their responsible application in research.

It took Ralph Ellison seven years to write Invisible Man. It took J. D. Salinger about 10 to write The Catcher in the Rye. J. K. Rowling spent at least five years on the first Harry Potter book. Writing with the hope of publishing is always a leap of faith. Will you finish the project?

This paper investigates the potential of Virtual Reality (VR) as a research tool for studying diversity and inclusion characteristics in the context of human-robot interactions (HRI). Some exclusive advantages of using VR in HRI are discussed, such as a controllable environment, the possibility to manipulate the variables related to the robot and the human-robot interaction, flexibility in the design of the robot and the environment, and advanced measurement methods related e.g. to eye tracking and physiological data. At the same time, the challenges of researching diversity and inclusion in HRI are described, especially in accessibility, cyber sickness and bias when developing VR-environments. Furthermore, solutions to these challenges are being discussed to fully harness the benefits of VR for the studying of diversity and inclusion.

These 7 tools can generate emails, transcribe interviews, improve your writing, create daily work schedules, and more.