RI

#1Reinforced 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.

#2Advancing 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.

#3Code-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.

#4Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning

2026-03-05 | Argonne National Laboratory, Rice University |2603.05696

This paper applies LLM-guided evolutionary search (similar to FunSearch/AlphaEvolve) to discover novel regularization algorithms for ptychographic image reconstruction. The results are backed by solid empirical metrics, showing up to +0.26 SSIM improvements over unregularized baselines across multiple datasets. Second, the LLM autonomously discovered the benefit of embedding stateful optimizers (like Adam) and iterative sub-loops directly inside a single heuristic step.

#1SLO-Aware Compute Resource Allocation for Prefill-Decode Disaggregated LLM Inference

2026-03-05 | Kingsoft Cloud |2603.04716

This paper proposes a hybrid resource allocation method for disaggregated Prefill/Decode (P/D) inference, using M/M/1 queuing theory to model prefill throughput under TTFT constraints and empirical profiling for decode. The results are real and validated on NVIDIA H200 clusters running DeepSeek-V3.1.{eff} = TP{max} - \frac{L{in}}{TTFT - T{overhead}}$).

#2Serving 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.

#3PromptTuner: SLO-Aware Elastic System for LLM Prompt Tuning

2026-03-05 | Nanyang Technological University, Unaffiliated |2603.05087

PromptTuner is a cluster management system for LLM prompt tuning that combines a 'Prompt Bank' (retrieving similar past prompts to speed up convergence) with a hierarchical scheduler (warm/cold GPU pools) to meet latency SLOs. The authors demonstrate real-world efficacy on 32-96 GPU clusters, showing 4-8x reductions in SLO violations compared to INFless and ElasticFlow.

#4BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning

2026-03-05 | Fudan University, Shanghai Innovation Institute |2603.04918

BandPO replaces the standard static clipping in PPO/GRPO with dynamic bounds derived from projecting f-divergence trust regions, specifically addressing a bottleneck where allowable updates vanish for low-probability tokens. Empirical results are rigorous, showing consistent gains (2-10%) on math benchmarks and crucially, maintaining policy entropy where baselines collapse. The key takeaway is that standard clipping scales update margins linearly with probability, effectively freezing rare tokens; BandPO decouples this, allowing the model to actually reinforce novel, high-advantage tail strategies.