RI

#1optimize_anything: A Universal API for Optimizing any Text Parameter

2026-05-19 | UC Berkeley, MIT |2605.19633

Agrawal et al. introduce optimize_anything, a unified LLM-based evolutionary search API that optimizes arbitrary text artifacts (code, prompts, agent architectures) using Pareto-based selection and diagnostic side information. Results are highly rigorous and backed by numbers, including a controlled ablation showing it beats OpenEvolve on circle packing in 3x fewer evaluations and achieving SOTA on ARC-AGI (89.5%), cloud scheduling and CUDA kernel generation. The most actionable takeaway is the combination of multi-task search with a shared Pareto frontier, allowing optimization patterns discovered in one task to transfer to related tasks, alongside the formalization of Side Information (SI) as a first-class 'text gradient' to drive targeted mutations rather than blind evolution.

#2Property-Guided LLM Program Synthesis for Planning

2026-05-18 | University of Oxford, Linköping University, Federal University of Rio Grande do Sul |2605.16142

This paper replaces scalar fitness scores in LLM heuristic synthesis with a counterexample-guided repair loop that checks formal properties and feeds specific failure states back to the LLM. The results are backed by strong empirical evidence on 10 planning domains, showing a 7.4x reduction in LLM generation cost, a 1000x reduction in evaluation compute and higher task coverage compared to the previous sample-and-select state of the art. The key insight is that using verifiable properties to generate concrete, localized counterexamples (e.g., 'at state X, your heuristic gave value Y, but successors gave Z') is vastly more sample-efficient than end-to-end scalar rewards. This is highly relevant for LLM evolutionary search and automated algorithm design; the community should investigate defining checkable properties for routing or scheduling heuristics to replace or augment standard scalar fitness evaluations, drastically reducing LLM sample complexity.

#3MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning

2026-05-14 | Amazon AGI, UCSD, Oregon State University, Pennsylvania State University, AG2AI, Inc. |2605.14212

MetaAgent-X is an end-to-end reinforcement learning framework that jointly optimizes a meta-agent (Designer) that generates multi-agent workflows and the downstream agents (Executors) that run them. The results are backed by strong empirical evidence, showing up to 21.7% improvement over baselines on math and code benchmarks using Qwen3-8B. The key insight is the Executor-Designer Hierarchical Rollout combined with stagewise co-evolution: by sampling M designs and executing each N times, the system isolates the designer's reward from the executor's reward, while alternating training phases stabilizes the non-stationary RL environment.

#4Adapting AlphaEvolve to Optimize Fully Homomorphic Encryption on TPUs

2026-05-14 | Google DeepMind, Google, Georgia Institute of Technology |2605.14718

This paper applies the AlphaEvolve framework to automatically optimize Fully Homomorphic Encryption (FHE) kernels for TPU execution, using LLM-driven code generation guided by hardware-in-the-loop latency feedback and strict correctness checks. The results are real and backed by physical hardware deployments, achieving up to a 2.5x speedup over human-engineered state-of-the-art baselines on TPUv5e chips. The key insight is that optimizing isolated operations often fails to yield end-to-end speedups; instead, co-evolving multiple components and scoring them against complete system execution, while feeding back low-level execution traces (e.g., Xprof) to the LLM, is necessary to discover non-obvious compiler and scheduling optimizations.

#1PALS: Power-Aware LLM Serving for Mixture-of-Experts Models

2026-05-20 | Harvard University, Boston University |2605.21427

PALS introduces a power-aware runtime for LLM serving that jointly optimizes GPU power caps and batch sizes using a closed-loop controller. Backed by hardware measurements on multi-GPU setups, it achieves up to 26.3% energy efficiency improvements and a 4x-7x reduction in QoS violations. The key insight is that for communication-bound MoE models, increasing power beyond a specific threshold degrades efficiency by accelerating communication overheads rather than useful computation.

#2Frontier: Towards Comprehensive and Accurate LLM Inference Simulation

2026-05-20 | The Chinese University of Hong Kong, Anuttacon, StepFun |2605.21312

Frontier is a discrete-event simulator for modern LLM inference serving that accurately models disaggregated architectures, complex parallelism and stateful reasoning workloads. Backed by extensive physical H800 GPU profiling, it reduces end-to-end latency prediction error from over 45% in existing simulators to under 7% by replacing average-case analytical proxies with hardware-aware predictors. The key insight is that coarse analytical models for KV-cache and operator runtimes distort SLA predictions and can reverse optimization conclusions; accurate evaluation requires modeling the closed-loop dynamics of memory state and batch composition.

#3PlexRL: Cluster-Level Orchestration of Serviceized LLM Execution for RLVR

2026-05-20 | National University of Singapore, Nanyang Technological University, Beihang University, Shanghai Qiji Zhifeng Co., Ltd., Infrawaves, Shanghai Innovation Institute |2605.20863

PlexRL is a cluster-level runtime that multiplexes LLM execution across multiple Reinforcement Learning with Verifiable Rewards (RLVR) jobs to reclaim idle GPU capacity caused by long-tailed rollouts and phase alternation. The results are backed by strong empirical numbers on a 2048-GPU cluster, demonstrating up to a 37.58% reduction in GPU-hour costs for 7B to 235B models compared to asynchronous split deployments. The key insight is that decoupling algorithm control from model execution allows the system to treat rollout and training as shared cluster services, enabling spatio-temporal packing that interleaves jobs to hide the massive latency of long-tailed generation and tool-use stalls.

#4TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload

2026-05-19 | Rice University, University of Central Florida, Mobi.AI |2605.20179

TIDE optimizes inference for Mixture-of-Experts diffusion LLMs by formulating the GPU-CPU expert offloading schedule as a mathematical programming problem based on the temporal stability of expert activations. The results are backed by empirical hardware profiling, achieving up to 1.5x throughput improvements on LLaDA2.0 models using a single GPU-CPU setup. The key insight is that the temporal locality of expert routing in diffusion models allows for interval-based expert refreshing, where the optimal interval balancing I/O overhead and CPU compute can be solved analytically via mathematical programming.