RI

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

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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

M=8 P=6 I=9

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. THE SINGLE MOST USEFUL TAKEAWAY: We can run our evolutionary search (e.g., AlgoEvo) offline to collect (baseheuristic, 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. This is highly relevant to our work as it offers a completely new, training-free mechanism to solve the sample efficiency and scalability bottlenecks in LLM evolutionary search.

#2Reinforced Generation of Combinatorial Structures: Ramsey Numbers

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

M=8 P=3 I=9

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. The single most useful takeaway for us is their meta-algorithm's scoring function: instead of only rewarding valid states, they evaluate a larger, infeasible 'prospect' state and provide a dense, continuous reward based on its violation count relative to a random baseline. We should immediately steal this 'prospect evaluation' trick for AlgoEvo to smooth the reward landscape when evolving heuristics for highly constrained OR problems like VRP, where finding strictly feasible intermediate solutions is a bottleneck.

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

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

M=9 P=8 I=9

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. This matters immensely for our AlgoEvo and MASPRM projects; we should immediately test pausing our VRP/scheduling environments mid-execution to feed intermediate state metrics to a coordinator LLM, which could drastically reduce the number of LLM samples we need to find optimal heuristics.

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

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2026-03-10 | Google DeepMind | 2603.10098

M=8 P=7 I=8

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. The single most useful takeaway for us is their 'context abstraction' technique—using an LLM to summarize opponent code into natural language to bypass context window limits during evolutionary search. This is highly relevant for our AlgoEvo project; we should immediately discuss implementing their two-level loop (outer meta-game equilibrium, inner AlphaEvolve refinement) and context abstraction for our multi-agent evolutionary search.

#1Serving Compound Inference Systems on Datacenter GPUs

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

M=7 P=8 I=7

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. The single most useful takeaway for us is their specific MILP formulation, which successfully linearizes the complexities of task-graph multiplicative factors and spatial GPU segments into a tractable objective for latency and accuracy SLOs. This is highly relevant for our GPUSched project; we should extract their MILP constraints for spatial partitioning and task-graph budgeting to adapt for our own LLM inference scheduling and multi-agent resource allocation models.