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Indirect PINNs offer a more accurate and stable approach to PDE-constrained optimal control by explicitly enforcing optimality conditions, outperforming direct PINN methods.
Even state-of-the-art LLMs struggle to adapt to mid-task changes in long-horizon web navigation, highlighting a critical gap in their ability to handle realistic user interactions.
Current research agent benchmarks miss critical flaws, as MiroEval reveals that process quality is a reliable predictor of research outcome, and multimodal tasks expose weaknesses invisible to output-level metrics.
LLM agents can achieve 3x faster web search and higher accuracy by dynamically routing between multiple context management strategies.
