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The paper investigates scaling laws for hypernetwork-based knowledge injection into LLMs, finding predictive power law scaling and reliable out-of-distribution generalization, establishing hypernetworks as a scalable alternative to LoRA and full fine-tuning.
This paper presents SALSA, a single-pass autoregressive LLM structured classification method for detecting machine-generated code, achieving strong OOD generalization at SemEval-2026 Task 13.
This paper introduces SCOPE-Bench, a benchmark for evaluating molecular out-of-distribution generalization, and POMA, a framework using reinforcement learning to select source domains for domain adaptation, achieving significant error reductions on 3D molecular models.
This paper investigates the generalization behavior of Fourier Neural Operators and Deep Operator Networks under distribution shifts in a variable-coefficient wave equation, revealing that FNO struggles with high-frequency inputs while DeepONet shows milder degradation.