4 papers
TEMPO: Scaling Test-time Training for Large Reasoning Models
Qingyang Zhang, Xinke Kong, Haitao Wu +7
Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the reach of offline training. De…
Computational Reasoning of Large Language Models
Haitao Wu, Zongbo Han, Joey Tianyi Zhou +2
With the rapid development and widespread application of Large Language Models (LLMs), multidimensional evaluation has become increasingly critical. However, current evaluations ar…
Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization
Qingyang Zhang, Haitao Wu, Changqing Zhang +2
Existing methods to enhance the reasoning capability of large language models predominantly rely on supervised fine-tuning (SFT) followed by reinforcement learning (RL) on reasonin…
Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior
Haitao Wu, Qing Li, Changqing Zhang +2
Can our brain signals faithfully reflect the original visual stimuli, even including high-frequency details? Although human perceptual and cognitive capacities enable us to process…