3 articles tagged with #optimization-framework. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose OFMU, a bi-level optimization framework designed to enable large language models to selectively unlearn specific data without full retraining, addressing privacy and regulatory compliance needs. The method balances forgetting targeted information while maintaining model performance through hierarchical optimization with theoretical convergence guarantees.
Researchers have developed a KPI-driven framework to assess resilience in urban transit disruption response, tested on Paris's RER B line. The model combines optimization algorithms with agent-based simulation to evaluate multiple dimensions including service continuity, cost, emissions, and equity, finding that coordinated multimodal strategies outperform single-mode alternatives.
Researchers introduce Generate-Select-Refine (GSR), a Bayesian optimization framework that dynamically discovers and refines tasks during scientific workflows rather than optimizing fixed objectives. The approach demonstrates superior performance across product development, chemical synthesis, algorithm analysis, and patent repurposing compared to existing LLM-based optimizers.
