Topic
scheduling
Optimal Scheduling in QA Forums Could Boost Knowledge Worker Efficiency, New Research Shows
Researchers Negi, Rohit, Yilmaz, and Mustafa present a model for optimal scheduling in question-answering forums where knowledge workers answer requests. They calculate system capacity and design schedulers to keep the system stable, also exploring how collaboration can increase capacity.
New Fluid-Guided Algorithm Optimizes LLM Inference Scheduling Under Memory Constraints
A new paper from researchers including David Simchi-Levi introduces a fluid-guided online scheduling approach for LLM inference that addresses memory constraints from Key-Value cache growth. The WAIT and Nested WAIT algorithms approximate an optimal fluid benchmark, reducing latency in overloaded regimes according to simulations on Llama-2-7B with A100 GPUs.
Surprise-Guided MergeSort Reduces Human Ranking Costs by Using AI to Prioritise Comparisons
Researchers propose Surprise-Guided MergeSort (SGS), a framework that combines a Vision-Language Model with MergeSort to schedule pairwise comparisons for subjective ranking tasks. SGS routes only ambiguous comparisons to humans, achieving Kendall's τ×100 improvements of +6 to +12 over Active Elo under the same budget.