How Much Vision Does Multimodal Reasoning Need? Vision-Stripping for Multimodal Benchmarks
Vision-stripping for multimodal benchmarks and multimodal reasoning.
Research Portfolio
Research on reliable AI agents, multimodal reasoning, and the social and ethical behavior of large language models.
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Latest Research
Vision-stripping for multimodal benchmarks and multimodal reasoning.
A study of compressed moral composition in frontier LLMs.
A framework for diagnosing and repairing computer-use agent failures with a CUA-specific error taxonomy, benchmark, and tool-augmented debugger.
A systematic study of how bias can be inherited through LLM-generated synthetic data and how mitigation strategies behave across tasks.
Research in 2025
Agentic information flow for unlocking multimodal reasoning in text-only LLMs.
A study of LLM agent failures and how agents can learn from failed trajectories.
Research in 2024