Jessie Jia | Searcharxiv

arXiv2026

Attention: What Prevents Young Adults from Speaking Up Against Cyberbullying in an LLM-Powered Social Media Simulation

Interactive, multi-agent social simulation systems have shown promise for helping users practice navigating various complex social situations across domains. This paper asks: To what extent can such systems help young adult (YA) bystanders speak up publicly against cyberbullying, a task often thwarted by complex, multi-party social dynamics? We created Upstanders' Practicum, a multi-AI-agent social media simulation powered by Large Language Models (LLMs), as a probe and observed 34 YAs freely practicing public bystander intervention across three iteratively refined versions. We found that practicing public bystander intervention in the simulation was helpful, but after participants made three attention shifts: (1) from inattention to paying true attention, (2) from self-focus ("I don't usually do this'') to attending to those directly involved, and (3) from resolving the private conflict between bully and victim ("maybe I could set up the meeting between them'') to addressing the broader audience online ("public comment is about norm-setting"). Only after these shifts did practice in the simulation start to help: participants then saw a reason to speak up publicly and, through continued practice, crafted tactful public messages without explicit instruction. These findings illuminate new design and research opportunities for bystander education beyond social skill instruction, namely, designing for true attention, for fostering a vocal upstander identity, and for seeing bystander intervention as public norm setting. In addition, we open-source Truman Agents (cornell-design-aigroup.github.io/TrumanAgents/), the first-of-its-kind multi-LLM-agent social media simulation platform that Upstanders' Practicum builds upon, for future cyberbullying and social media research.

cs.HC↗

arXiv2025

Correspondence-Oriented Imitation Learning: Flexible Visuomotor Control with 3D Conditioning

We introduce Correspondence-Oriented Imitation Learning (COIL), a conditional policy learning framework for visuomotor control with a flexible task representation in 3D. At the core of our approach, each task is defined by the intended motion of keypoints selected on objects in the scene. Instead of assuming a fixed number of keypoints or uniformly spaced time intervals, COIL supports task specifications with variable spatial and temporal granularity, adapting to different user intents and task requirements. To robustly ground this correspondence-oriented task representation into actions, we design a conditional policy with a spatio-temporal attention mechanism that effectively fuses information across multiple input modalities. The policy is trained via a scalable self-supervised pipeline using demonstrations collected in simulation, with correspondence labels automatically generated in hindsight. COIL generalizes across tasks, objects, and motion patterns, achieving superior performance compared to prior methods on real-world manipulation tasks under both sparse and dense specifications.

cs.RO↗