CLIC: Closed-Loop Information Control for Efficient and Robust Multi-Agent Systems

Multi-agent language model systems rely on communication to combine complementary evidence, but unrestricted message exchange increases token costs, repeats stale information, and allows faulty agents to spread misleading claims. The central challenge is to determine when agents should communicate and how strongly received messages should influence subsequent decisions while preserving independent reasoning. Existing communication-pruning, learned-topology, and robust-aggregation methods address parts of this challenge without jointly controlling information freshness, communication cost, and adversarial influence. We introduce CLIC, Closed-Loop Information Control, a training-free controller that regulates which messages enter each agent’s context and how much context they receive. CLIC represents proposals as compact semantic sketches and uses Bayesian surprise to transmit informative messages, with an age-based trigger preventing prolonged silence. Receivers evaluate message usefulness and apply geometric-median fusion to limit the influence of outlying proposals. A spectral controller then balances collective agreement, preservation of independent evidence, and recovery speed, translating communication weights into context-token budgets. Under stated assumptions, we establish epochwise convergence of the external semantic controller, bounded message-sending silence, and a conditional bound on Byzantine influence. Across reasoning, code generation, collaborative planning, and adversarial benchmarks, CLIC improves task performance while reducing total token usage by 39.9% relative to full-mesh communication in the primary five-agent setting. With 30% Byzantine agents, it achieves 63.7% robust accuracy versus 58.9% for the strongest baseline, SAC, and reduces PEAR attack success from 16.8% to 11.4%, without updating the underlying models.