Epistemic Uncertainty for Test-Time Discovery

arXiv:2605.11328v1 Announce Type: cross Abstract: Automated scientific discovery using large language models relies on identifying genuinely novel solutions. Standard reinforcement learning penalizes high-variance mutations, which

Interpretability Can Be Actionable

arXiv:2605.11161v1 Announce Type: cross Abstract: Interpretability aims to explain the behavior of deep neural networks. Despite rapid growth, there is mounting concern that much of

Trade-offs in Decentralized Agentic AI Discovery Across the Compute Continuum

arXiv:2605.11839v1 Announce Type: cross
Abstract: Agentic systems deployed across the compute continuum need discovery mechanisms that remain effective across cloud, edge, and intermittently connected domains. In some emerging agentic architectures, decentralized discovery is already an active design direction, placing DHT-based lookup on the path toward agent directories. This paper studies the trade-offs among major structured-overlay families for agent discovery, comparing Chord, Pastry, and Kademlia as candidate indexing substrates within a shared control-plane framework. Using a benchmark subset centered on a 4096-node stationary comparison and a representative 4096-node churn benchmark, the paper characterizes how discovery reliability, startup behavior, and control-plane overhead vary across these overlays. The goal is to clarify the operating points they expose for agent discovery across edge-to-cloud environments.

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