RI

#1How to Guide LLM Generation: Dual-Surrogate Guided Search for Automated Heuristic Design

2026-07-15 | The Hong Kong Polytechnic University, Nankai University, South China Agricultural University |2607.13911

Wang et al. introduce Dual-Surrogate Guided Search (DGS) to improve sample efficiency in LLM-based automated heuristic design by learning which parent heuristics and mutation operators to select before querying the LLM. The results are backed by solid empirical evidence, showing superior performance over strong baselines like FunSearch and EoH across five combinatorial optimization tasks (including TSP and CVRP) within a strict 200-query budget. The key insight is the use of a transition surrogate that predicts the latent distribution of an ungenerated child heuristic based on the parent and operator, coupled with a utility surrogate to estimate its performance.

#2Reward-Free Evolving Agents via Pairwise Validator

2026-07-15 | Accenture |2607.14408

Liu et al. replace the expensive scalar task reward in self-evolving agentic loops with a training-free pairwise LLM validator that compares parent and child candidates. The results are backed by rigorous empirical evaluations across prompt and code evolution engines, including ShinkaEvolve, demonstrating that this reward-free approach matches or exceeds full-reward baselines. The key insight is the 'Soft Elo' mechanism, which uses pairwise LLM verdicts to update candidate ratings for parent selection, entirely eliminating the need for scalar validation rewards.

#3GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization

2026-07-11 | Georgia Institute of Technology, Shanghai Academy of AI for Science |2607.10127

Chen et al. introduce Graph-Augmented Evolution (GAE), which enhances LLM-guided evolutionary search by integrating a GNN-based RL meta-controller for mutation selection and online GRPO fine-tuning for the LLM operator. The results are rigorously backed by empirical data, showing that GAE significantly outperforms static baselines like OpenEvolve and PACEvolve on symbolic regression benchmarks. The key insight is that replacing structurally blind, prompt-driven mutations with a learned policy (Discrete SAC) and continuously updating the LLM weights via group-relative rewards drastically improves sample efficiency and search trajectory.

#1General Non-Clairvoyant KV-Cache Scheduling via Regime-Aware Routing

2026-07-10 | Hong Kong University of Science and Technology, Shanghai Jiao Tong University |2607.09248

Feng et al. introduce a non-clairvoyant scheduling algorithm for batched LLM inference that minimizes total completion time under hard KV-cache constraints. The authors mathematically prove the first O(1)-competitive theoretical guarantee against an optimal clairvoyant scheduler for arbitrary prompt and response lengths. The key insight is that a single global priority rule cannot simultaneously maintain high memory utilization and area-consistent completion order; instead, requests must be dynamically routed into three geometric regimes (large, prompt-heavy and response-heavy).