arXiv AI
Jul 20
When Do Multi-Agent Systems Help? An Information Bottleneck Perspective
arXiv:2607. 16133v1 Announce Type: cross Abstract: LLM powered multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks.
How Task Structure Limits Multi-Agent Success: An Information-Theoretic Analysis
arXiv:2606. 13733v1 Announce Type: cross Abstract: Multi-agent systems (MAS) were expected to overcome the limitation of single-agent systems (SAS) through collaboration.
Related stories
arXiv AI
Jun 2
Scaling Behavior of Single LLM-Driven Multi-Agent Systems
arXiv:2606. 00655v1 Announce Type: cross Abstract: The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling behavior and intrinsic collective dynamics remain underexplored.
arXiv AI
Aug 11
The Collaboration Gap: Exploration and Benchmarking of Open-World Agentic Cooperation
arXiv:2511. 02687v2 Announce Type: replace Abstract: The trajectory of AI development suggests that we will increasingly rely on agent-based systems powered by language models, composed of independently developed agents with different information, privileges, and tools.
arXiv AI
Jun 8
When Does Multi-Agent Collaboration Help? An Entropy Perspective
arXiv:2602. 04234v6 Announce Type: cross Abstract: Multi-agent systems (MAS) have emerged as a prominent paradigm for leveraging large language models (LLMs) to tackle complex tasks.
arXiv AI
Jul 16
Benefits and Limitations of Communication in Multi-Agent Reasoning
arXiv:2510. 13903v2 Announce Type: replace-cross Abstract: Chain-of-thought prompting has popularized step-by-step reasoning in large language models, yet model performance still degrades as problem complexity and context length grow.
arXiv AI
5d ago
Discovering Efficient and Explainable Communication Topologies for LLM-based Multi-Agent Systems via Causal Inference
arXiv:2608. 12921v1 Announce Type: cross Abstract: The performance of large language model (LLM)-based multi-agent systems (MAS) largely depends on effective communication topologies.
arXiv AI
Jul 15
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
arXiv:2607. 13034v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires.
arXiv AI
Aug 6
Capability-Gated Planning: Cost-to-Goal Discovery and the Limits of Myopic Experiment Selection
arXiv:2608. 05085v1 Announce Type: cross Abstract: Systems that automate scientific discovery must repeatedly decide which experiment to run, which hypothesis to test, which tool to build, and when to stop.
arXiv Machine Learning
Jun 2
Multi-Agent Computer Use
arXiv:2606. 01533v1 Announce Type: cross Abstract: Computer use agents (CUAs) today are primarily deployed as single serial agents.
arXiv AI
Jun 8
More Capable, Less Cooperative? When LLMs Fail At Zero-Cost Collaboration
arXiv:2604. 07821v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly coordinate in multi-agent systems, yet we lack an understanding of where and why cooperation fails.
arXiv AI
Jun 9
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.
arXiv AI
Aug 7
A Two-Tier Perspective on Inference-Time Parallelism in Multi-Agent LLM Systems
arXiv:2608. 05791v1 Announce Type: cross Abstract: Large language model (LLM)-driven multi-agent systems typically require multiple model invocations and complex coordination during inference, and their execution strategies directly affect system accuracy, latency, and computational cost.