Haochen Liu

HARP outperforms traditional CVE prioritization methods by dynamically adapting to implicit operational preferences, revealing the power of leveraging historical data without explicit prompts.

A unified framework reveals that the choice of tokenization and vocabulary topology can significantly influence the performance of discrete diffusion models, unlocking new avenues for optimization.

KAT-Coder-V2.5 outperforms existing models in agentic tool-use, showcasing a new paradigm for autonomous coding agents within executable environments.

Human-as-Humanoid achieves a staggering 4.8–7.2x increase in demonstration throughput, transforming how humanoid robots learn from human actions.

Post-training on synthesized safety-critical scenarios can dramatically enhance the reliability of autonomous driving systems, reducing failures in rare but critical events.

Discrete-WAM enables compositional causal reasoning in autonomous driving, outperforming traditional methods that struggle with complex state-action dynamics.

Even state-of-the-art vision-language models frequently lie and hallucinate when playing social deduction games, raising serious questions about their reliability in real-world applications requiring grounded reasoning.

Predicting driver behavior in response to traffic conditions is now possible with a new world model that causally links external context to internal driver states.

LLMs struggle with conflicting medical evidence, but a clever two-stage agentic approach can reconcile discordant signals while preserving patient privacy.