RI

#1CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges

2026-03-12 | Tsinghua University, Peking University, Southern University of Science and Technology, University of Bristol, The Hong Kong University of Science and Technology (Guangzhou), Xi’an Jiaotong University |2603.11863

Wang et al. introduce CreativeBench to evaluate LLM code generation creativity and propose EvoRePE, a representation engineering technique that extracts a 'creativity vector' from AlphaEvolve search trajectories to steer model activations at inference time. The results are backed by solid empirical evaluations, showing that injecting this vector improves novelty and correctness even without running the full evolutionary search.heuristic, evolvedheuristic) pairs, compute the PCA of their hidden state differences and inject this vector during standard inference to force the model into an exploratory mode.

#2Reinforced Generation of Combinatorial Structures: Ramsey Numbers

2026-03-11 | Google DeepMind, Google, University of California, Berkeley |2603.09172

Nagda et al. (DeepMind) apply the AlphaEvolve framework to discover novel stochastic search algorithms that improve lower bounds for five classical Ramsey numbers and match SoTA on 23 others. The results are mathematically verified and represent genuine SoTA advances in extremal combinatorics, proving the framework's capability to generate highly specialized, non-trivial heuristics.

#3Advancing Automated Algorithm Design via Evolutionary Stagewise Design with LLMs

2026-03-09 | Nanjing University, Huawei Noah’s Ark Lab |2603.07970

EvoStage enhances LLM-based automated algorithm design by decomposing the generation process into sequential stages, using a multi-agent system (coordinator and coders) to iteratively refine code based on real-time intermediate execution feedback. The results are highly credible and backed by strong empirical numbers; it achieves state-of-the-art HPWL on 16 chip placement benchmarks and beats AlphaEvolve/EoH on Bayesian Optimization tasks using an incredibly small budget of just 9 to 25 evaluations. The single most useful takeaway is the shift from black-box end-to-end evaluation to stagewise intermediate feedback, where a coordinator agent reflects on mid-execution metrics to guide the next stage of heuristic design.

#4Code-Space Response Oracles: Generating Interpretable Multi-Agent Policies with Large Language Models

2026-03-10 | Google DeepMind |2603.10098

This paper replaces the deep RL oracle in Policy-Space Response Oracles (PSRO) with an LLM that generates interpretable Python code policies, using AlphaEvolve to iteratively refine the code against opponent meta-strategies. The results are backed by solid empirical metrics, showing that the AlphaEvolve variant achieves competitive exploitability and higher population returns than RL baselines (IMPALA) and CFR+ on Repeated Rock-Paper-Scissors and Leduc Poker.

#1Serving Compound Inference Systems on Datacenter GPUs

2026-03-09 | University of Illinois Urbana-Champaign |2603.08797

JIGSAWSERVE uses a Mixed Integer Linear Programming (MILP) formulation to jointly optimize model variant selection (accuracy scaling) and fine-grained GPU spatial partitioning (MIG/MPS) for serving compound inference DAGs. The results are strongly backed by empirical numbers on real hardware (H100s), demonstrating an 11.3x capacity improvement over the closest prior work (Loki) while maintaining under 0.6% SLO violations.