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Adaptive-GEPA system optimizes LLM prompts for diverse requests

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Adaptive-GEPA, a novel system designed to optimize language model prompts for heterogeneous requests. Unlike previous methods that either optimize a single program for all requests or pre-define specialists, Adaptive-GEPA learns to both route requests and solve them using a library of specialized programs. This approach, demonstrated with the Qwen3-8B model, significantly improved performance on a mixed set of task families, achieving a higher family-mean test score compared to existing methods like GEPA and GRPO. AI

IMPACT Introduces a more efficient method for handling diverse LLM requests, potentially improving agent performance and adaptability.

RANK_REASON The cluster describes a new research paper detailing a novel AI system and its performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Adaptive-GEPA system optimizes LLM prompts for diverse requests

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The cluster describes a new research paper detailing a novel AI system and its performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tianyu Chen, Yasi Zhang, Ruiyi Wang, Xinran Zhao, Taoran Li, Mingyuan Zhou ·

    Adaptive-GEPA: Make Your Harness Fit Heterogeneous Requests

    arXiv:2609.38762v1 Announce Type: cross Abstract: Reflective optimizers such as GEPA improve language model prompts from execution traces and evaluator feedback; full-program extensions can also rewrite tools and control flow. In practice, a user hands the same endpoint heterogen…

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