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Language-model agents increasingly rely on harnesses that manage bounded context, persistent memory, tools, verification, and repeated execution, yet existing notions of model capability do not quantify the computational resources these mechanisms consume. We introduce the Language Model Agent Machine (LAM), a resource...
Evolutionary approaches to LLM-driven discovery often generate new programs from a small set of selected ancestors. This keeps contexts manageable but can omit useful evidence from other experiments, whereas including the full experimental history produces long, redundant contexts. We introduce a simple, single-agent d...
This paper proposes the Global-Local Adaptive Detector (GLAD), a Hierarchical Global-Local backbone that explicitly bridges the granularity gap by fusing global linguistic and acoustic features with fine-grained local signal details and introduces SaniBoost, a composite data augmentation strategy for robust signal stan...
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