Our peer-reviewed publications, open-source contributions, and technical reports driving the field forward.
Architecture innovation, efficient training at scale, long-context modeling, and multi-modal integration for next-generation LLMs.
Constitutional AI, RLHF, interpretability, red-teaming, and developing robust frameworks to keep AI systems aligned with human intent.
Object detection, scene understanding, generative imaging, video comprehension, and multi-modal visual reasoning systems.
Multi-agent systems, reward modeling, sim-to-real transfer, and training agents for complex real-world decision making.
Multilingual understanding, semantic parsing, dialogue systems, and information extraction across 100+ languages.
Planning, tool use, world models, and building AI agents capable of sustained reasoning and action in open-ended environments.
A novel sparse attention mechanism enabling 128K token context windows with 3× lower memory overhead. Achieves new SOTA on SCROLLS, BookSum, and our internal LongEval benchmark.
A framework where language models evaluate and improve their own outputs through constitutionally grounded reward signals. Reduces harmful outputs by 94% on HarmBench while maintaining helpfulness scores.
Hierarchical feature pyramid network that generalizes to unseen categories without fine-tuning. Outperforms GLIP and OWL-ViT on LVIS and Objects365 zero-shot benchmarks.
Demonstrating that RL agents develop structured communication protocols when incentivized to cooperate, with implications for scalable multi-agent AI systems.
A novel distillation pipeline that compresses 70B parameter models to 3B with less than 5% quality degradation, enabling powerful on-device inference.
An automated red-teaming framework that generates diverse adversarial probes and systematically evaluates model robustness across safety-critical domains.
Achieving state-of-the-art temporal coherence in AI-generated video through a novel latent diffusion architecture with temporal attention layers.
Production-ready constitutional alignment toolkit with plug-and-play safety layers for any LLM deployment.
Efficient sparse attention implementation for PyTorch enabling 128K+ token context windows with minimal memory overhead.
Automated adversarial evaluation suite for LLMs with 1000+ curated attack vectors across safety-critical domains.
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