Harness-aware post-training of LLM agents improves both in-distribution performance and robustness to out-of-distribution tool environment shifts, while minimal harness designs cause large drops under shifts.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Hierarchical prompt-domain control framework separates schema distillation from online semantic adaptation in agentic LLMs using an oracle loop, evaluated on a Multi-Fidelity Bayesian Optimization testbed.
citing papers explorer
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The Interplay of Harness Design and Post-Training in LLM Agents
Harness-aware post-training of LLM agents improves both in-distribution performance and robustness to out-of-distribution tool environment shifts, while minimal harness designs cause large drops under shifts.
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Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models
Hierarchical prompt-domain control framework separates schema distillation from online semantic adaptation in agentic LLMs using an oracle loop, evaluated on a Multi-Fidelity Bayesian Optimization testbed.