Hanghang Tong

PILL achieves up to 6.0 BLEU-2 improvement on text infilling while running 1.82x faster than the best existing method, revolutionizing efficiency in diffusion language models.

Achieving high-quality time series forecasting with just a few examples, MetaCaster revolutionizes the way lightweight forecasters are trained in data-scarce environments.

AFANet achieves high accuracy in agent failure attribution with a fraction of the computational resources required by traditional LLM-based methods.

Bridging the gap between inference and adaptation in VLMs could lead to significant performance boosts by ensuring robust pseudo-labels that accurately reflect sample-level relationships.

Long-horizon LLM agents can achieve 96.9% task success by learning to adapt their external execution support through trainable harness policies.

Generating synthetic power-grid scenarios that are both operationally feasible and statistically accurate could revolutionize planning and resilience assessments in power systems.

RECONTEXT boosts long-context reasoning in LLMs by effectively reusing evidence from the input, leading to superior performance without the need for retraining.

A unified model that seamlessly integrates text and graph learning outperforms traditional methods by up to 3.9 points on benchmark tasks.

G2Rec captures user interest prototypes more accurately than existing methods, enabling generative recommendation systems to operate without ground-truth user interests.

Looping language models isn't just for single agents anymore: Recursive Multi-Agent Systems (RecursiveMAS) show that agent collaboration itself can be scaled through recursion, yielding faster and more efficient problem-solving.

Current multimodal LLMs still struggle to integrate information and reason critically when assessed on real scientific papers, despite progress on isolated tasks.

Diffusion language models can achieve faster decoding and better accuracy by learning directly from the token reveal order suggested by a lightweight autoregressive teacher, without expensive distillation.