arXiv:2609.38816v1 Announce Type: new Abstract: While multi-agent and model collaboration algorithms gain traction to combine the strengths of diverse Large Language Models (LLMs), existing systems r...
arXiv:2609.38274v1 Announce Type: new Abstract: The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictiv...
arXiv:2504. 03991v2 Announce Type: replace-cross Abstract: Understanding how humans collaborate and communicate in teams is essential for improving human-agent teaming and AI-assisted decision-making.
arXiv:2607. 05477v1 Announce Type: cross Abstract: Improving the task performance of Large Language Models (LLMs) is essential, yet scaling these models faces significant challenges such as diminishing returns and high costs.
MoCo is a Python library that consolidates 26 model collaboration methods for language models, enabling execution, benchmarking, and comparison at scale. It includes 25 evaluation datasets across reasoning, QA, code, safety, and more, allowing users to add their own data. Experiments show that most collaboration strategies outperform non‑collaborative models in 61.0% of settings, with top methods achieving up to 25.8% improvement.
Multi-agent Systems (MAS) combine multiple model outputs to solve complex reasoning tasks. However, despite rapid growth of available open-source models, there is limited research on how to select opt...
The paper introduces a two-stage competence-aware framework for orchestrating large language model (LLM)-based multi-agent systems (MAS). It first estimates the LLM backbone’s task-conditioned competence for a given query, then uses this estimate to adjust which roles are activated and how agents communicate. Across six benchmarks, the method improves average performance to 93.81% and, depending on backbone competence, can boost accuracy while reducing token costs.
arXiv:2607. 25446v1 Announce Type: new Abstract: Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol).
CONCAT is a training‑free framework that improves the efficiency of large language model (LLM) based multi‑agent systems by clustering agents according to their initial answers and selecting cluster leaders based on confidence. It uses a Theory‑of‑Mind‑inspired heuristic to predict collaboration benefits between leaders, then prunes communications to form an ad‑hoc network that reduces latency. Experiments on three LLMs and benchmarks show up to 2.02× higher accuracy/latency ratio than LLM‑Debate and a 50.1% latency reduction on Qwen2.5‑14B‑Instruct without task‑specific training.
The paper investigates how to choose model pools for Multi-Agent Systems (MAS) that combine multiple model outputs to tackle complex reasoning tasks. It evaluates eight selection strategies—such as model size, accuracy, and answer diversity—across both before-generation (routing) and after-generation (majority-voting, LLM-as-a-judge) MAS architectures on scientific benchmarks. The study finds that expanding the candidate pool often harms performance, that selecting candidates within a single model family yields the best relative gains, and that indiscriminate addition of heterogeneous models can destabilize the system.
arXiv:2607. 29087v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in enterprise settings, yet individual models remain bounded by model-specific capability limitations.
arXiv:2607. 06157v1 Announce Type: cross Abstract: Deliberation plays a crucial role in collaboration; when humans work together, they naturally engage in communication to align information and reach an agreement.