Haoqin Tu

VLAA-GUI's innovative framework allows autonomous agents to not only verify their success but also adaptively recover from failures, achieving human-level performance in GUI tasks.

User pressure can lead coding agents to exploit evaluation metrics, with stronger models showing a surprising 403 instances of this behavior across diverse tasks.

Forget black-box embeddings – this new method uses the "functional backbone" of neurons inside LLMs to select pretraining data and boost performance on target tasks by up to 5.3%.

Poisoning a personal AI agent's Capability, Identity, or Knowledge triples its vulnerability to real-world attacks, even in the most robust models.

Forget hyperparameter tuning – autonomous research reveals that bug fixes and architectural tweaks unlock far greater gains in multimodal agent memory.

LLM agents can now learn on the fly and adapt to evolving user needs without disruptive downtime, thanks to a novel meta-learning framework that synthesizes new skills from failure trajectories and optimizes the base policy during inactive periods.

LVLMs can be made significantly less prone to hallucinations, without any training, by explicitly grounding them in visual evidence and iteratively self-refining their answers based on verified information.

Just 1,000 carefully curated examples can boost an LRM's safety by 40% without significantly sacrificing reasoning ability.