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RoboPhD optimizes LLM agent evolution under tight evaluation budgets by replacing traditional validation sets with an Elo-based tournament on training data and allowing agents to evolve their own diagnostic instrumentation. The results are backed by solid empirical comparisons, outperforming GEPA and Autoresearch on 3 out of 4 benchmarks (ARC-AGI, Text2SQL, DocFinQA) under a strict 1,500 evaluation budget.
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2026-04-06 | University of Washington, NVIDIA, Rice University, University of Waterloo, Cisco Research, Independent Researcher | 2604.04335
GENSERVE optimizes the co-serving of text-to-image and text-to-video diffusion models on shared GPUs using a dynamic programming scheduler that jointly manages step-level preemption, dynamic batching and elastic sequence parallelism. The results are empirically backed, demonstrating up to a 44% improvement in SLO attainment over baselines like SRTF on an 8-GPU cluster. The most useful takeaway is their two-stage DP formulation: they first generate a small set of anchored candidate actions (hold, resume, scale SP) per request, then run a knapsack DP to maximize global SLOs in under 2ms.
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