Latent-DARM: Bridging Discrete Diffusion And Autoregressive Models For Reasoning
arXiv:2603.09184v1 Announce Type: new Abstract: Most multi-agent systems rely exclusively on autoregressive language models (ARMs) that are based on sequential generation. Although effective for fluent text, ARMs limit global reasoning and plan revision. On the other hand, Discrete Diffusion Language Models (DDLMs) enable non-sequential, globally revisable generation and have shown strong planning capabilities, but their limited text fluency hinders direct collaboration with ARMs. We introduce Latent-DARM, a latent-space communication framework bridging DDLM (planners) and ARM (executors), maximizing collaborative benefits. Across mathematical, scientific, and commonsense reasoning benchmarks, Latent-DARM outperforms text-based interfaces on average, improving accuracy from 27.0% to 36.0% on DART-5 and from 0.0% to 14.0% on AIME2024. Latent-DARM approaches the results of state-of-the-art reasoning models while using less than 2.2% of its token budget. This work advances multi-agent co
arXiv:2603.09184v1 Announce Type: new Abstract: Most multi-agent systems rely exclusively on autoregressive language models (ARMs) that are based on sequential generation. Although effective for fluent text, ARMs limit global reasoning and plan revision. On the other hand, Discrete Diffusion Language Models (DDLMs) enable non-sequential, globally revisable generation and have shown strong planning capabilities, but their limited text fluency hinders direct collaboration with ARMs. We introduce Latent-DARM, a latent-space communication framework bridging DDLM (planners) and ARM (executors), maximizing collaborative benefits. Across mathematical, scientific, and commonsense reasoning benchmarks, Latent-DARM outperforms text-based interfaces on average, improving accuracy from 27.0% to 36.0% on DART-5 and from 0.0% to 14.0% on AIME2024. Latent-DARM approaches the results of state-of-the-art reasoning models while using less than 2.2% of its token budget. This work advances multi-agent collaboration among agents with heterogeneous models.
Executive Summary
This article introduces Latent-DARM, a novel framework that bridges the gap between Discrete Diffusion Language Models (DDLMs) and Autoregressive Models (ARMs) to facilitate multi-agent collaboration. The proposed framework enables planners (DDLMs) to communicate with executors (ARMs) through a latent space, maximizing their collaborative benefits. Experimental results demonstrate that Latent-DARM outperforms text-based interfaces on various reasoning benchmarks, achieving accuracy improvements of up to 36.0% and approaching the results of state-of-the-art reasoning models while using significantly less computational resources. This work has far-reaching implications for multi-agent systems, particularly in scenarios where heterogeneous models are required to collaborate effectively.
Key Points
- ▸ Latent-DARM bridges the gap between DDLMs and ARMs for multi-agent collaboration
- ▸ The framework enables planners (DDLMs) to communicate with executors (ARMs) through a latent space
- ▸ Experimental results demonstrate significant accuracy improvements and efficiency gains
Merits
Strength in Heterogeneous Model Collaboration
Latent-DARM effectively enables collaboration between DDLMs and ARMs, leveraging their respective strengths in planning and execution.
Efficient Computational Resources
The framework achieves significant accuracy improvements while using less than 2.2% of its token budget, making it an efficient solution for multi-agent collaboration.
Demerits
Limited Explanation of Mathematical Derivations
The article could benefit from a more detailed explanation of the mathematical derivations underlying the Latent-DARM framework, particularly in the context of DDLMs and ARMs.
Scalability and Robustness Concerns
The authors should address potential scalability and robustness concerns, as the framework's performance may degrade in more complex or dynamic scenarios.
Expert Commentary
Latent-DARM represents a significant advancement in multi-agent collaboration, particularly in scenarios where heterogeneous models are required to collaborate effectively. The framework's ability to bridge the gap between planning and execution models has far-reaching implications for the development of artificial general intelligence systems. However, further research is needed to address potential scalability and robustness concerns. Additionally, the authors should provide a more detailed explanation of the mathematical derivations underlying the framework.
Recommendations
- ✓ Future research should focus on scaling up the Latent-DARM framework to handle more complex and dynamic scenarios.
- ✓ The authors should provide a more detailed explanation of the mathematical derivations underlying the framework, particularly in the context of DDLMs and ARMs.