Research Collaboration Opportunity

 

This research work originates from real-world independent investigation into perceptual trust dynamics in human–AI voice interaction.

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Due to limited collaboration bandwidth, only a small number of vetted model teams may be research partners this quarter

 

 

 

This short diagnostic helps determine whether Perceptual Alignment Research Collaboration is relevant for your model architecture and pre-deployment evaluation stack.

 

Signals Research Collaboration May Be Relevant to Your Pre-Launch Stack

 

• Your model is moving toward native audio reasoning
• You are deploying voice agents in high-trust environments
• Your evaluation stack measures prosody but not perceptual trust calibration
• Your team has observed tonal hallucinations or tonal sycophancy

 

 

Please complete the diagnostic so we can determine fit and availability for you.

Complete in ~7 minutes.

                                                                                 

Contact Name *
Email *
phone number *
Your Company *
Your role/title *
How does your team currently quantify and fine-tune Tonal Hallucinations - specifically instances where the model’s prosodic weight (certainty/authority) diverges from the underlying factual confidence of the reasoning layer? *
Has your team observed cases where tonal authority increases user compliance even when the model's internal reasoning confidence is low? *
Does your current evaluation process distinguish between acoustic prosody quality and perceptual trust calibration? *
When a model produces a technically correct answer but delivers it with tonal certainty exceeding its reasoning confidence, which failure category does your team classify this under? *
In your transition to native audio-reasoning, are you treating Tonal Ambivalence as a noise-reduction challenge or as an attentional signal for stabilizing inference-time trust? *
What is your current protocol for red-teaming Tonal Sycophancy - the tendency for a model to adopt manipulative or overly-pleasing tone that bypasses a user’s critical judgment? *
Does your alignment data include a Human Perceptual Anchor accounting for the delta between technical correctness and felt-experience trust? *
Are you currently solving for prosodic stability at the latent reasoning level, or relying on post-inference emotional labeling? *
In production voice deployments, how does your team mitigate the risk of perceptual over-confidence - where tonal authority increases user compliance despite uncertain reasoning? *
Does your evaluation stack include fine-tuning for human trust calibration, or are prosodic outputs evaluated primarily through acoustic/emotional metrics? *
As your architecture moves toward native audio reasoning, when do you expect prosodic alignment to become a first-class safety requirement? *
Where is prosodic alignment currently handled in your stack? *
Has your team observed instances where technically correct responses were perceived as misaligned or untrustworthy due to tonal delivery? *
Which best describes your current voice deployment stage? *
If a perceptual alignment research collaboration revealed a consistent mismatch between reasoning confidence and prosodic delivery in your current model, which outcome would be most valuable to your team? *
Anything else we should know about your system or goals?