mlsanityData to 14 Oct 2026

Revisiting Gossip Protocols: A Vision for Emergent Coordination in Agentic Multi-Agent Systems

arXiv 2508.01531 ↗First posted 3 Aug 2025cs.MA cs.AIcited by 4

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

As agentic platforms scale, agents are evolving beyond static roles and fixed toolchains, creating a growing need for flexible, decentralized coordination. Today's structured communication protocols (e.g., direct agent-to-agent messaging) excel at reliability and task delegation, but they fall short in enabling emergent, swarm-like intelligence, where distributed agents continuously learn, adapt, and communicate to form collective cognition. This paper revisits gossip protocols, long valued in distributed systems for their fault tolerance and decentralization, and argues that they offer a missing layer for context-rich, adaptive communication in agentic AI. Gossip enables scalable, low-overhead dissemination of shared knowledge, but also raises unresolved challenges around semantic filtering, staleness, trustworthiness, and consistency in high-stakes environments. Rather than proposing a new framework, this work charts a research agenda for integrating gossip as a complementary substrate alongside structured protocols. We identify critical gaps in current agent-to-agent architectures, highlight where gossip could reshape assumptions about coordination, and outline open questions around intent propagation, knowledge decay, and peer-to-peer trust. Gossip is not a silver bullet, but overlooking it risks missing a key path toward resilient, reflexive, and self-organizing multi-agent systems.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 3 of its 18 references that are in the map. First posted on arXiv 3 Aug 2025.

Builds on

3 of 18 references.

References · 3

  1. 2020The Gossiping Insert-Eliminate Algorithm for Multi-Agent Banditscited by 38
  2. 2019Gossip-based Actor-Learner Architectures for Deep Reinforcement Learningcited by 16
  3. 2025A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimescited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Consensus Is All You Need: Gossip-Based Reasoning Among Large Language Models
  2. 2026Towards Adaptive, Scalable, and Robust Coordination of LLM Agents: A Dynamic Ad-Hoc Networking Perspective
  3. 2026Usable Agent Discovery for Decentralized AI Systems
  4. 2026Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems
  5. 2026The Internet of Agentic AI: Communication, Coordination, and Collective Intelligence at Scale
  6. 2026Proxifield: Decentralized Multi-Agent Communication through Semantic Proximity
  7. 2026Managing Context and Communication in Distributed Agentic UAV Swarms
  8. 2026Distributed General-Purpose Agent Networks: Architecture, Key Mechanisms, and Prototypes