Great Work

[geniusgeek]

Great work on the MAS, I took a look at the codebase and pretty good for the reasoning tasks

what i am looking forward to as next steps are:

• native language support
• hallucination prevention and detection to prevent risks of passing and compounding hallucination in multi-agent environment
• comms system between heterogeneous agents

[jiaruzouu]

Thank you for the kind words and for taking a close look at our implementation! Your suggested next steps are exactly along the directions we’re excited about:

• Native language support: Since LatentMAS is training-free and backend-agnostic, extending it to multilingual agents is very natural and already on our roadmap.

• Hallucination prevention & detection: One key advantage of LatentMAS is the lossless latent working-memory transfer, which helps avoid error compounding from noisy text re-encoding. We’re actively exploring enhanced verification and consistency checks on top of this.

• Communication between heterogeneous agents: We are actively working toward making the LatentMAS mechanism more modular for truly heterogeneous agent systems. One promising direction is to leverage ensemble learning strategies or introduce a lightweight trainable projector to unify and align heterogeneous hidden-state representations across agents. Also, here is a great work for making different model communication possible [1].

Thanks again for the thoughtful feedback, and we would love to keep exchanging ideas and having more discussions with you!

[1] Cache-to-Cache: Direct Semantic Communication Between Large Language Models.