← Back to all news
arXiv Machine Learning By Nian Ran, Tianhe Jia, Zhengyang Mu, Yue Wang, Sebastian Brulin, Markus Olhofer, Mauricio A. Alvarez, Yang Liu, Richard Allmendinger

An important Bottleneck in LLM Optimizers: Principle-Level Local Optima

Read the original on arXiv Machine Learning →

The paper identifies a phenomenon called principle-level search collapse in feedback-driven large language model (LLM) optimizers, where the search narrows to a limited set of design principles despite ongoing candidate generation and minor improvements. Using a diagnostic benchmark across molecular optimization, network sentinel-node selection, and GPU-kernel optimization, the authors find that 78.3% of runs exhibit collapse, with 55.3% showing strong support. They evaluate common remedies such as diversity injection and reference-principle interventions, noting limited and inconsistent benefits, and emphasize the need for online collapse detection and improved mechanisms for generating and maintaining alternative principles.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
1d ago

Organon : From Evolutionary Search to Recursive Self-Improving Agents for Scientific Discovery

Organon is a recursively self‑improving agent designed for scientific discovery that autonomously revises its search strategies, population management, diagnostics, memory, and worker coordination during execution. By diagnosing and correcting its own adaptation procedures, the agent continuously refines the mechanisms guiding its future self‑improvement. Across 21 computational discovery tasks—including algorithm design, GPU kernel optimization, and systems optimization—Organon outperforms competing frameworks, achieving a 20.5% mean performance gain on ALE‑Bench Lite and up to 1.67× speedups on KernelBench.

By Cheikh Ahmed, Zhiyu Zhang, Kun Mao, Yong Zhang, Zirui Zhou
llmsagents
More like this →
arXiv AI
Sep 30

OptiCom : A Unified Framework for State-Conditioned Composition in LLM-Driven Optimization

OptiCom introduces a unified framework for state-conditioned composition in large language model (LLM)-driven optimization. It models LLM optimizers within a shared configuration space (artifact, query, operator, evaluation, memory, strategy) and uses an Optimization Controller to dynamically compose mechanisms while a Strategy Adapter refines long-term preferences. Experiments on 32 benchmark groups show OptiCom outperforms 14 configurations, achieving the top score in 23 groups.

By Chenxing Wei, Sichen Liu, Lizhao Liu, Ningyuan Sun, Chen Bingzhou, Ying He, Bo Jiang, Fei Yu, Yao Shu
llmsbenchmarks
More like this →
arXiv Machine Learning
Oct 15

PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization

PMO-Dock introduces a standardized benchmark for docking‑based molecular optimization, comprising 25 tasks that span hit generation, lead optimization, and a specificity task that rewards strong on‑target binding while penalizing off‑target interactions. The protocol separates model development from final evaluation, using validation tasks for hyperparameter tuning and held‑out test tasks evaluated under a strict oracle budget to mimic realistic drug‑discovery constraints. Four diverse methods—Saturn, GenMol, Genetic‑guided GFlowNet, and ChemLlama—were benchmarked, revealing no single method dominates across all tasks and highlighting limited hyperparameter transferability, which underscores the need for method‑dependent selection strategies.

By Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan, Tigran Fahradyan, Hrant Khachatrian
llmsdiffusionreinforcement-learningbenchmarks
More like this →