Jenna Russell announces the public release of StoryScope, a dataset and codebase designed for narrative analysis and AI authorship research. The release includes the full code, 10,272 prompts, 51,336 AI-generated narratives (each approximately 5,000 words), and per-story features. The dataset is intended to support future research in understanding how AI systems generate long-form narratives, making it a significant resource for the computational narrative and AI evaluation communities. Russell shares this ahead of her presentation at COLM 2026.

Jean de Nyandwi shares a tweet highlighting the research paper "Lost in the Middle: How Language Models Use Long Contexts", which investigates a critical open question in language modeling: how models actually utilize long input contexts. The post emphasizes that the paper approaches this problem with significant rigor, making it a noteworthy contribution to understanding one of the biggest unknowns in language modeling — namely, whether models effectively leverage information throughout an entire long context or tend to lose track of content in the middle.

Rohan Paul shares a research paper titled "Large Language Models For Text Classification: Case Study And Comprehensive Review" and highlights that he generated a podcast episode about the paper using Google's Illuminate, a tool that converts research papers into audio discussions. The post includes a media attachment (likely the podcast audio or a visual summary) showcasing the output from Illuminate.

This is a brief social media post that points to the intersection of LLMs for text classification and AI-powered research summarization tools. It serves as a useful reference for those interested in both the academic survey of LLM-based text classification approaches and the practical use of Google's Illuminate for digesting academic content.

A research paper titled "Perfecting Human-AI Interaction at Clinical Scale" is shared by the Human-Computer Interaction Papers account on X. The paper focuses on turning production signals into safer, more human conversations, authored by Subhabrata Mukherjee, Markel Sanz Ausin, Kriti Aggarwal, Debajyoti Datta, Shanil Puri, and others. It spans multiple research areas including Human-Computer Interaction (cs.HC), Artificial Intelligence (cs.AI), Computational Linguistics (cs.CL), and Multiagent Systems (cs.MA).

The work appears to address the challenge of scaling human-AI conversational systems in clinical settings, leveraging real-world production data to improve both safety and naturalness of AI-driven interactions. This is a significant area of research as AI systems become more prevalent in healthcare and other high-stakes domains where the quality and reliability of human-AI dialogue is critical.

This article from the KM Institute explores how AI-driven knowledge management transforms the traditional knowledge lifecycle—creation, capture, organization, sharing, and reuse—by embedding intelligence directly into daily workflows. Unlike conventional KM systems that rely on manual documentation and static taxonomies, AI-driven KM leverages machine learning, natural language processing, and generative AI to continuously learn from content, context, and user behavior. The piece highlights voiceover AI as a notable enabling technology that treats spoken knowledge as a first-class asset, expanding KM beyond text into audio-first environments.

A central argument is that AI does not replace knowledge workers but fundamentally redefines their roles. Workers shift from being knowledge holders and content producers to becoming knowledge stewards, sense-makers, and strategic contributors. With AI handling routine retrieval and content generation, humans focus on validation, contextual judgment, cross-domain interpretation, and ethical oversight. The article cites McKinsey research noting that knowledge workers spend nearly 20% of their time just searching for information—a friction point AI-driven KM directly addresses.

The piece also emphasizes that AI-driven KM introduces new governance and risk considerations, including bias in AI-generated insights, over-reliance on automation, and data privacy concerns. Strong human oversight and transparent governance frameworks are presented as essential for building trust and realizing long-term value from these systems.

Diyi Yang announces an ACL 2025 tutorial focused on human-AI collaboration, highlighting the premise that humans and AI work better together. The tutorial promises an interdisciplinary overview covering the goals, evaluation methods, and societal impacts of human-AI collaboration, making it a resource for researchers and practitioners interested in the intersection of AI systems and human interaction.