AVAGENT: Bridging Asynchronous Communication Through AI-Powered Virtual Avatars
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".