Researchers developed WaLLM, a general-purpose LLM chatbot, and deployed it on WhatsApp to study user behavior. The study found that users primarily used WaLLM for health and well-being advice, and that engagement features had varying adoption rates and associated user patterns.

Researchers propose ETHER, an agent that uses Emergent Communication to learn a grounded, artificial language for goal-conditioned reinforcement learning, addressing limitations of Hindsight Experience Replay (HER).

This paper explores voice-first interaction for mobile terminals, introducing Richard, a system prototype that manages voice sessions, task execution, and result delivery. It focuses on user control and task continuity in mobile voice interaction, informing design principles for personal computing devices.

NarrativeSteward is an authoring environment that helps authors work with AI agents to create interactive narratives. It organizes outlines, worldbuilding, and narrative graphs to provide guidance and support for authors. The system has been tested and validated through technical tests and a within-subject study.

Researchers presented a blackbox prompt-minimization framework for large language models (LLMs) that reduces few-shot prompts to their necessary minimal subset. The framework, called ramework, preserves propositional output fidelity and shows that models preferentially retain logical identifiers and constraint declarations while discarding natural language prose and cross-prompt relational annotations.

Researchers introduced PrivacySkills, a framework to evaluate how LLM agents choose information sources with privacy guidance. They found that agents access confidential sources 30% of the time when users are available, but this increases to 45% when users are unavailable. Providing system-level privacy instructions and skill-level metadata labels can reduce this rate, but combining both has the most significant impact.

A research paper introduces Rational Enquiry via Value-of-Information Reasoning (REVOIR), a method for assistive agents to decide when to ask for clarification and when to act on their interpretation. REVOIR uses inference-time reasoning to evaluate the value of information and achieves better results in two assistive tasks with fewer questions.

ThuRunel is a dynamic decoupling approach for structured advisory dialogue, combining a finite-state belief management framework, a chain-of-thought teacher synthesis protocol, and learned generation adapters. It has been deployed as a bilingual web application and achieved consistent improvements in elicitation completeness and specialist brief quality against eleven baselines.