Jun Zhou

Multi-format training can drastically enhance language model consistency across different answer formats, with just 30% of training data needing augmentation to achieve significant gains.

SearchSwarm reveals that effective delegation in LLMs can significantly boost performance on long-horizon tasks, achieving state-of-the-art results in complex research scenarios.

Current vision-language models falter in ultra-resolution reasoning, with errors primarily stemming from evidence grounding and local perception.

Code agents are only eliminating 50% of code smells, revealing critical gaps in their understanding of maintainability and cross-file dependencies.

Bridging the perception-reasoning gap in visual planning, MGSD boosts model performance by over 19% while relying solely on visual inference during deployment.

Current AI agents falter in autonomous development, revealing critical gaps in robustness and alignment as they struggle against human-engineered solutions.

Forget memorizing manipulation artifacts: comparing against a reference library of authentic examples lets you spot forgeries and adapt to new domains without retraining.

Ditch SwiGLU's quadratic instability: PowLU offers a rational power function that stabilizes LLM pre-training without sacrificing performance.