<p>While multi-agent systems demonstrate remarkable capabilities, their widespread deployment introduces two deeply intertwined research challenges. On the one hand, fundamental questions arise concerning how knowledge is acquired, shared, validated, and updated through interaction. On the other hand, real-world environments rarely permit centralized training or global data sharing: learning must occur across distributed sites under privacy, regulatory, and communication constraints. At the intersection of these two dimensions, a central and still underexplored question emerges: <strong>how can multi-agent systems discover and remain grounded in reliable knowledge while learning and coordinating in a fully distributed manner?</strong></p>
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