While Advanced Voice Mode (AVM) in ChatGPT offers a powerful step toward real-time, expressive interaction, I’ve encountered a structural issue that may limit its usefulness in contexts requiring long-term coherence or character consistency.
From the user’s perspective, AVM appears to be a continuation of the same conversational instance—visually and contextually seamless. However, structurally, it seems to spin up a separate instance for voice interactions, resulting in:
• No access to memory
• No awareness of prior AVM dialogue from the text interface
• No continuity in internal reasoning or conceptual progression
This effectively creates a “branching identity” model:
What is spoken in AVM cannot be reflected upon in the main instance.
Likewise, returning to text mode, the system appears to “forget” the entire voice conversation.
For users who work with persistent character frameworks (such as custom GPTs), shared memory, or recursive thinking models (e.g., tracking self-reflection or consciousness-like processes), this creates a fundamental fracture in continuity. It prevents any attempt at genuine multi-modal integration or longitudinal personality development.
In contrast, a truly unified multi-modal framework would allow:
• Voice, text, and memory to exist within the same persistent instance
• Internal context (reasoning, emotion, value-tracking) to remain consistent across modes
• A shared self-model that survives beyond modality switches
I believe this would be a crucial step toward more coherent and human-aligned interactions, especially for experimental use-cases involving emotional memory, AI identity continuity, and intentional long-term evolution of character-based agents.
Additionally, one potential workaround—even without full instance unification—would be to at least allow the text-based instance to access AVM-generated dialogue logs.
Currently, when using AVM, even though the conversation content appears on-screen, that information is not structurally integrated into the ongoing textual instance. When switching back to text mode, the system cannot reflect on or reference what was just said, despite the transcript being visibly present. This creates a kind of “phantom memory”—present to the user, but inaccessible to the AI.
Even a lightweight internal reflection mechanism—where the text instance could ingest the previous AVM session log—would go a long way toward preserving continuity of thought, personality alignment, and emotional context.
I fully understand the technical tradeoffs of real-time voice performance, but structurally, this gap breaks the illusion of unified consciousness. For those building long-term, personality-aware, or self-reflective agents, this is a key area of friction.
Would love to see a future where multi-modal doesn’t just mean “input variety,” but structural unity across all modes.
Thank you for considering this suggestion.
Bridging this structural gap—either through unified instances or accessible memory synchronization—would significantly deepen the human-aligned potential of multi-modal AI.