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The paper presents a physics-inspired approach to pruning LLM blocks by modeling block removal as a constrained binary optimization problem mapped to an Ising glass, achieving significant compression gains without benchmarking each configuration.
Schema is a harness that enables frontier AI models to achieve 99% on the ARC-AGI-3 benchmark by having them write executable programs to model game environments, test predictions, and plan.
This paper introduces a physics-inspired framework for structural attribution in cyber-physical IoT systems, using an undirected energy-based representation to provide dependency-aware explanations without requiring a directed causal graph. Experiments on an industrial IoT testbed demonstrate higher attribution accuracy, robustness, and scalability compared to existing graph-based methods.
This paper proposes NeTMY, an amortization-free coordinate neural field for inverse problems in NV-center quantum sensing, using a corrected forward model and sparse reconstruction losses to overcome center-collapse pathologies.