Life-Harness evolves reusable runtime interventions from training failures to improve frozen LLM agents by 88.5% on average across 126 settings in seven deterministic environments while transferring across 18 model backbones.
International Conference on Learning Representations , volume=
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An LLM agent converts user prompts into optimization-model patches and selects primal-based re-optimization methods from a toolbox to produce feasible solutions for dynamic supply-chain and exam-scheduling problems.
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Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents
Life-Harness evolves reusable runtime interventions from training failures to improve frozen LLM agents by 88.5% on average across 126 settings in seven deterministic environments while transferring across 18 model backbones.
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Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
An LLM agent converts user prompts into optimization-model patches and selects primal-based re-optimization methods from a toolbox to produce feasible solutions for dynamic supply-chain and exam-scheduling problems.