Gen-Verse/LatentMAS

Health v2 · maintenance only

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1099 stars · 168 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LatentMAS is a training-free multi-agent reasoning framework that lets LLM agents collaborate in continuous latent space by passing hidden-state 'latent thoughts' through a shared latent working memory instead of text. It works with any Hugging Face model (optionally vLLM) and delivers higher accuracy, 70-84% fewer output tokens, and 4x faster inference than text-based multi-agent baselines.

Use cases

• run multi-agent LLM collaboration with fewer tokens
• speed up multi-agent reasoning pipelines
• make LLM agents communicate in latent space instead of text
• improve math and science reasoning accuracy with multiple agents
• reduce inference cost of multi-agent systems
• experiment with latent-space agent communication on Hugging Face models
• apply latent collaboration to scientific discovery workflows

When to choose

• you want multi-agent LLM reasoning with lower token usage and faster wall-clock time
• you need a training-free technique compatible with any Hugging Face model
• you're researching latent-space reasoning or agent communication
• you run math, science, commonsense, or code generation benchmarks with LLM agents

When to avoid

• you need a production multi-agent orchestration platform with tool use and APIs
• your models or backend aren't supported by Hugging Face or vLLM
• you require text-auditable agent communication traces
• you need a battle-tested framework with long-term community support

Facets

library · maturity active

agent-framework llm-inference machine-learning large-language-models machine-learning artificial-intelligence python latent-reasoning multi-agent-systems training-free latent-space vllm huggingface research-code icml-2026 ai-agents gpu

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