A co-creation process for inferring and refining personal strivings from computer activity logs yields more representative goals and higher user agency than baselines in a 14-person week-long study.
1, 3 [Ope26] OPENAI: Reasoning models | openai api
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GROVE visualizes distributions of language model generations as overlapping paths through a text graph, with user studies showing that graph summaries aid structural judgments like diversity assessment while raw outputs remain better for details.
Multi-turn neural transparency using behavioral vectors and dynamic visualizations improves user anticipation and evaluation of LLM trait expression while reducing overconfidence, per a randomized study with 246 participants.
An agentic AI framework with LLMs generates formulations for coupled UAV product collection and MEC task scheduling, solved by hierarchical PPO that reaches 99.6% collection success and 100% deadline compliance in simulations.
ReasonDiag combines automated error detection with interactive visualizations to help users identify and diagnose errors in LLM chain-of-thought reasoning traces.
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Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations
GROVE visualizes distributions of language model generations as overlapping paths through a text graph, with user studies showing that graph summaries aid structural judgments like diversity assessment while raw outputs remain better for details.