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, 2026
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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.
citing papers explorer
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"What Are You Really Trying to Do?": Co-Creating Life Goals from Everyday Computer Use
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.
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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.
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Multi-Turn Neural Transparency: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift
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.
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An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing
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.
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When the Chain Breaks: Interactive Diagnosis of LLM Chain-of-Thought Reasoning Errors
ReasonDiag combines automated error detection with interactive visualizations to help users identify and diagnose errors in LLM chain-of-thought reasoning traces.