AVAGENT: Bridging Asynchronous Communication Through AI-Powered Virtual Avatars
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Asynchronous communication is essential in diverse contexts such as remote meetings, education, and collaboration. However, traditional replay-based systems lack interactivity and often fail to preserve critical contextual cues. This paper introduces Avagent, a novel framework for asynchronous com munication in Virtual/Augmented Reality (VR/AR) environments that leverages agentized avatars to bridge temporal and contextual gaps between participants. Unlike typical agent-mediated systems, the framework utilizes users’ data to create Avagents that reflect their actual intent, emotional states, and behaviors. By accurately capturing and replicating both verbal and nonverbal behaviors, Avagents enable more interactive and context-rich communication. This approach facilitates dynamic interactions, enhances social presence, and ensures continuity across timelines. Avagents empower new users to engage interactively with prior discussions, fostering deeper understanding and seamless collaboration with previous users. Envisioning the benefits of Avagents as an engaging and context-rich solution for asynchronous communication, this paper outlines interaction scenarios, a work-in-progress prototype, and associated challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it