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arXiv:2603.09161v1 Announce Type: new Abstract: Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual …
arXiv:2603.09165v1 Announce Type: new Abstract: Accurate lithology identification from well logs is crucial for subsurface resource evaluation. Although Transformer-based models excel at sequence modeling, their …
arXiv:2603.09168v1 Announce Type: new Abstract: In this paper, we study the distributed experts problem, where $n$ experts are distributed across $s$ servers for $T$ timesteps. …
arXiv:2603.09184v1 Announce Type: new Abstract: Most multi-agent systems rely exclusively on autoregressive language models (ARMs) that are based on sequential generation. Although effective for fluent …
arXiv:2603.09195v1 Announce Type: new Abstract: Message Passing Graph Neural Networks (MP-GNNs) have garnered attention for addressing various industry challenges, such as user recommendation and fraud …
arXiv:2603.09201v1 Announce Type: new Abstract: We study a problem of signal separation: estimating a signal of interest (SOI) contaminated by an unknown non-Gaussian background/interference. Given …
arXiv:2603.09208v1 Announce Type: new Abstract: Provably efficient and robust equilibrium computation in general-sum Markov games remains a core challenge in multi-agent reinforcement learning. Nash equilibrium …
arXiv:2603.09221v1 Announce Type: new Abstract: Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting …
arXiv:2603.09253v1 Announce Type: new Abstract: We study efficient reasoning under tight compute. We ask how to make structured, correct decisions without increasing test time cost. …
arXiv:2603.09257v1 Announce Type: new Abstract: Many existing transductive bounds rely on classical complexity measures that are computationally intractable and often misaligned with empirical behavior. In …
arXiv:2603.09274v1 Announce Type: new Abstract: Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used …
arXiv:2603.09288v1 Announce Type: new Abstract: Aggregate outcome variables collected through surveys and administrative records are often subject to systematic measurement error. For instance, in disaster …