Hi LatentMAS team,
I am examining the architecture of LatentMAS, particularly the mechanism of information exchange through working memory via last-layer hidden embeddings (latent thoughts). Since the framework operates entirely in a continuous latent space without external text anchoring, I am curious how the system prevents semantic drift or hallucination during multi-turn auto-regressive generation and extended latent steps.
Without explicit dimensional convergence or global anchoring, the shared latent memory seems potentially vulnerable to gradual degradation and noise accumulation over long reasoning chains. I raise this question because I faced very similar challenges in continuous-space communication protocols. In my earlier work, "AI Mother Tongue: Self-Emergent Communication in MARL via Endogenous Symbol Systems" (arXiv:2507.10566, July 2025), I developed a reinforcement-learning framework that constructs an endogenous symbol dictionary precisely to enforce representational stability among agents. This symbol-grounding approach provides explicit structural alignment, which has naturally extended my framework to AI safety, latent space probing, and other domains where purely continuous, training-free latent spaces often lack verifiable trajectories.
Given that LatentMAS is a training-free framework, I would be very interested in learning how your architecture structurally or mathematically mitigates latent drift compared to gradient-based or RL-driven alignment methods. Any technical insights or empirical observations on this aspect would be greatly appreciated.
References for context:
• Paper: https://arxiv.org/abs/2507.10566
• Code: https://github.com/cyrilliu1974/AI-Mother-Tongue
• ResearchGate: https://www.researchgate.net/profile/Hung-Ming-Liu
Looking forward to your thoughts!
Best regards,
Hung-Ming Liu
Hi LatentMAS team,
I am examining the architecture of LatentMAS, particularly the mechanism of information exchange through working memory via last-layer hidden embeddings (latent thoughts). Since the framework operates entirely in a continuous latent space without external text anchoring, I am curious how the system prevents semantic drift or hallucination during multi-turn auto-regressive generation and extended latent steps.
Without explicit dimensional convergence or global anchoring, the shared latent memory seems potentially vulnerable to gradual degradation and noise accumulation over long reasoning chains. I raise this question because I faced very similar challenges in continuous-space communication protocols. In my earlier work, "AI Mother Tongue: Self-Emergent Communication in MARL via Endogenous Symbol Systems" (arXiv:2507.10566, July 2025), I developed a reinforcement-learning framework that constructs an endogenous symbol dictionary precisely to enforce representational stability among agents. This symbol-grounding approach provides explicit structural alignment, which has naturally extended my framework to AI safety, latent space probing, and other domains where purely continuous, training-free latent spaces often lack verifiable trajectories.
Given that LatentMAS is a training-free framework, I would be very interested in learning how your architecture structurally or mathematically mitigates latent drift compared to gradient-based or RL-driven alignment methods. Any technical insights or empirical observations on this aspect would be greatly appreciated.
References for context:
• Paper: https://arxiv.org/abs/2507.10566
• Code: https://github.com/cyrilliu1974/AI-Mother-Tongue
• ResearchGate: https://www.researchgate.net/profile/Hung-Ming-Liu
Looking forward to your thoughts!
Best regards,
Hung-Ming Liu