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#1Search Hardness-Aware LLM-Based Problem Formulation for Expensive Simulation-Driven Design

2026-07-23 | Xidian University, Victoria University of Wellington |2607.21220

This paper proposes an LLM-based evolutionary framework to automatically generate and refine optimization problem formulations (objectives and constraints) for expensive simulation-driven design. The authors empirically demonstrate on a hydrology task and five antenna design benchmarks that their evolved formulations require significantly fewer expensive simulator evaluations to reach feasible designs compared to expert-designed or zero-shot LLM formulations. The key insight is using initial random simulation data to identify 'hard but promising' (rare and non-dominated) states and then using a candidate formulation's ability to prioritize these anchor states as the fitness signal for the evolutionary search. This is highly relevant for research in LLM evolutionary search and automated OR modeling, as it provides a concrete method for constructing proxy rewards that improve sample efficiency when evaluating generated code is computationally expensive.

#1SpecAHD: Localize to Specialize for Automated Heuristic Design in Large-Scale Routing Problems

2026-07-26 | Guangdong University of Technology |2607.23676

SpecAHD introduces a bilevel LLM evolutionary search framework that co-evolves an upper-level program to expose local repair regions and a lower-level repertoire of specialized constructive heuristics to repair them. The results are rigorously backed by extensive empirical evidence, showing cost reductions of up to 57.7% against state-of-the-art AHD baselines (like EoH and ReEvo) on large-scale routing benchmarks including CVRP and VRPTW up to 10,000 nodes. The key insight is the 'Repair Response Archive'—a memory mechanism that logs heuristic performance on specific local structures during training, allowing a k-NN router to dynamically select the best specialized heuristic for new regions at inference time.

#2Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

2026-07-24 | University of Illinois Urbana-Champaign |2607.21971

Wu et al. introduce METAEVOLVE, a framework that uses reinforcement learning (GRPO) to explicitly train LLMs to perform iterative self-evolution and refinement. The results are rigorously backed by execution-based metrics, showing a 24% absolute improvement over AlphaEvolve on out-of-distribution coding tasks and a 46% relative speedup on open-ended algorithm optimization benchmarks. The key insight is that instead of relying on zero-shot prompting in an evolutionary loop, researchers can synthesize evolution trajectories (current code, fitness, history) from static datasets and use execution feedback as a verifiable reward to fine-tune the mutator LLM.

#3NeurGO: Learning to Generate Elite Candidates for Meta-Black-Box Expensive Optimization

2026-07-26 | China University of Geosciences |2607.23408

NeurGO is a generative meta-black-box optimization framework that uses a Transformer encoder to capture population search trends and an MLP decoder to directly synthesize a small set of elite candidates for expensive optimization tasks. The method is backed by strong empirical results on CEC 2008 and COCO BBOB benchmarks (D=10), demonstrating faster convergence and better final performance than Bayesian Optimization and other MetaBBO baselines under a strict 300-evaluation budget. The key insight is that instead of generating raw candidate coordinates, the decoder outputs contribution weights used to recombine the existing population's representations, ensuring generated candidates remain anchored to known promising regions and avoiding out-of-distribution proposals. This architectural design is highly relevant for continuous latent-space optimization and could be directly adapted to improve sample efficiency when optimizing continuous representations of algorithms or heuristics.

#1TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters

2026-07-24 | Imperial College London, Peking University, Microsoft Research, University of Edinburgh, Shanghai Jiao Tong University, Tile-AI |2607.22432

TileSight is a first-principles, tile-centric analytical performance model that predicts GPU kernel and end-to-end LLM serving latency by simulating intra-tile resource usage, inter-tile cache reuse and cross-device communication. The results are rigorously backed by hardware measurements, achieving 12.35% MAPE on single-GPU kernels and 13.52% wMAPE on end-to-end vLLM serving across diverse architectures (A100 to B6000), outperforming learned predictors. The key insight is that lifting performance modeling to the tile abstraction (rather than thread or cache-line level) enables fast, schedule-sensitive and deterministic latency predictions without requiring per-architecture ML training. This is highly relevant for research in LLM serving scheduling and GPU resource allocation, as TileSight can serve as a highly accurate, white-box cost estimator for optimization formulations.