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Current research agents still struggle with retrieval robustness and hallucination control, even when evaluated in a static, verifiable research environment.
Failure-driven post-training, combined with a meticulously curated 10M token STEM dataset, unlocks a 4.68% performance boost in LLM reasoning, proving that strategic data synthesis around model weaknesses is a powerful path to improvement.