Hybrid LLM framework balances authorial control and player agency
Self-evolving optimization refines prompts via in-context learning to sustain coherence across extended interactions.
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Self-evolving optimization refines prompts via in-context learning to sustain coherence across extended interactions.
On four agent benchmarks it gains up to 20.7% over GEPA and 21.2% over ACE with Gemini 3.1 Pro and Claude Opus 5.
GEPA-Curator filters noisy tasks and retrieves successful trajectories to improve prompt optimization for agentic workloads
CEPO separates coordination and execution prompts to consistently improve four existing optimizers across 11 benchmarks.
A nine-configuration sweep across five datasets finds merge can help both gpt-4.1-mini and Qwen3-8B.
Prompt optimization of selected agents gained 3.81 points versus 1.85 for uniform selection across six workflows.
DP-ES privately optimizes full prompts and beats DP-OPT by 38.6 points on GSM8K under a tight privacy budget.
TextReg curbs prompt overfitting, gaining up to 16.5% out-of-distribution accuracy over REVOLVE.
Ranking-aware prompt evolution lifts clinical AUROC on Qwen3-VL-8B and MedGemma-4B versus accuracy-based search.
Mara Chain refines rejected AI candidates, beating GEPA on AppWorld with 65.5% fewer rollouts.
AgentGrad introduces intervention-guided prompt optimization for LLM multi-agent systems, achieving state-of-the-art results with 2.5x faster optimization.
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