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New APEx framework enhances AI research agents with adaptive skill distillation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced APEx, a novel framework designed to enhance deep research agents that use large language models with external tools. APEx organizes interaction history into instance-level memories and category-level procedural skills, which are then optimized through a three-stage training process. This approach allows for reward-guided skill distillation and enables agents to self-improve at test time without ground truth, by adapting online through skill-guided reinforcement learning. Experiments show APEx significantly outperforms existing methods, including GPT-5.4, on various benchmarks. AI

IMPACT This framework could lead to more capable AI agents for complex research tasks, improving efficiency and accuracy in scientific discovery.

RANK_REASON The cluster contains an academic paper detailing a new AI framework and its experimental results. [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 →

New APEx framework enhances AI research agents with adaptive skill distillation

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The cluster contains an academic paper detailing a new AI framework and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jie Ding, Rui Sun, Xinyuan Zhang, Zeyu Zhang, Xin Liu ·

    APEx: Distillation of Agent Procedural Experience for Adaptive Deep Research Question Answering

    arXiv:2609.02253v1 Announce Type: new Abstract: Deep research agents augment large language models with external tools to answer complex, long-horizon questions through multi-turn reasoning. Learning from prior experience is crucial for continual improvement, yet existing methods…

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