arXiv:2609.37017v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natu...
arXiv:2610.08820v1 Announce Type: new Abstract: Large language model-based multi-agent systems improve complex problem solving through collaboration, while latent communication directly transmits mod...
arXiv:2605.25421v2 Announce Type: replace Abstract: Communication protocol design is a central challenge in large language model-based multi-agent systems. Existing single-channel approaches face an...
LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natural-language communication. However, directly for...
arXiv:2608. 11676v1 Announce Type: new Abstract: Heterogeneous multi-agent LLM systems, where agents are powered by different model families, can outperform homogeneous configurations by reducing redundant reasoning patterns.
arXiv:2606. 05304v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language.
arXiv:2511. 20639v3 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence.
arXiv:2606. 24470v1 Announce Type: new Abstract: A real-time agent for general computer use - with games as the most demanding case - must act within tens of milliseconds while still planning over seconds.
arXiv:2511. 09149v5 Announce Type: replace-cross Abstract: While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint.
Prefill-to-Autoregressive Cross-model Transfer (PACT) is a method that lets a large language model provide prompt representations to a smaller model, aiming to combine high capability with efficient generation. The authors present PLatour, an open‑source framework that connects frozen models via lightweight, zero‑initialized linear bridges trained with task rewards and supports hybrid‑attention architectures. While PLatour outperforms open‑source baselines on generation tasks and beats prior results on mathematics and coding benchmarks with a smaller sender, controlled experiments reveal that some gains can arise from uninformative sender signals or receiver‑only training, prompting the authors to propose PACT‑Check to assess genuine communication benefits.
arXiv:2608. 13317v1 Announce Type: new Abstract: Large language model based multi-agent systems usually communicate in text, i.
MASBench is a benchmark for evaluating large language model (LLM)-based multi‑agent systems (MAS) under partial observability, a realistic constraint where agents only see limited environmental information. The benchmark is structured into three progressive task categories—Reasoning, Scheduling, and Game—and assesses three collaboration mechanisms: Protocol, Memory, and Routing. It provides deterministic evaluation metrics such as performance score, communication cost, and cost effectiveness, and includes empirical results across various LLM backbones to guide effective MAS design.