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LLM-X: A Scalable Negotiation-Oriented Exchange for Communication Among Personal LLM Agents

arXiv:2605.1137668.5
Predicted impact top 49% in AI · last 90 daysOriginality Incremental advance
AI Analysis

For researchers building multi-agent LLM systems, this work provides a novel architecture for agent-to-agent coordination with policy enforcement, though the empirical results are preliminary and limited to small-scale experiments.

The paper introduces LLM-X, a scalable negotiation-oriented exchange for structured communication among personal LLM agents, and demonstrates its stability under sustained load with bounded latency drift across 5, 9, and 12 agents.

We propose a personal-LLM exchange (LLM-X), a scalable negotiation-oriented environment that enables direct, structured communication across populations of personal agents (LLMs), each representing an individual user. Unlike existing tool-centric protocols that focus on agent-API interaction, LLM-X introduces a message bus and routing substrate for LLM-to-LLM coordination with guarantees around schema validity and policy enforcement. We contribute: (1) an architecture for LLM-X comprising federated gateways, topic-based routing, and policy enforcement; (2) a typed message protocol supporting capability negotiation and contract-net-style coordination; and (3) the first empirical evaluation of LLM-based multi-agent negotiation at scale. Experiments span 5, 9, and 12 agents, under distinct negotiation policies (Low, Medium, High), and across both short-run (minutes) and long-run (2h, 12h) load conditions. Results highlight clear policy-performance trade-offs: stricter policies improve robustness and fairness but increase latencies and message volume. Extended runs confirm that LLM-X remains stable under sustained load, with bounded latency drift.

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