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Current autonomous agents excel at practical problem-solving but often lack true methodological innovation, revealing critical gaps in their development as independent researchers.
Harness-assisted data synthesis boosts LLM forecasting performance by reducing temporal leakage and increasing sampling efficiency, leading to superior predictive capabilities.
ToFu achieves superior token efficiency and cost-effectiveness in agentic coding, all while empowering researchers with a transparent, modifiable framework.
