Symbiotic Minds: HyperFused Emotion Recognition Models for Adaptive Human–AI Interaction
Bibliographic record
Abstract
Symbiotic Minds introduces a HyperFused framework for adaptive human–AI interaction, capable of concurrently recognizing emotions across multiple modalities and agents. The system integrates facial, audio, gestural, and contextual cues to decode nuanced emotional states in real time, effectively overcoming the limitations of single-modality and sequential fusion approaches. Through a dedicated multi-agent learning module, the framework models inter-agent emotional dependencies, enabling precise tracking of group-level emotional dynamics in complex social settings. Furthermore, a reinforcement learning component dynamically refines AI responses based on observed emotions, ensuring context-aware and socially intelligent interactions. Experiments conducted using the EmotiW Group Videos dataset reveal that the HyperFused model outperforms conventional baselines, demonstrating significant gains in accuracy, precision, recall, and F1-score. The adaptive AI response module exhibits high interaction appropriateness with minimal response latency, validating the framework’s real-time applicability. By merging multi-modal fusion, multi-agent reasoning, and reinforcement learning, the proposed system establishes a deployable solution for symbiotic human–AI ecosystems, capable of perceiving and responding intelligently to collective emotional cues.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".