Liang Wang

A simple logistic regression can match the performance of advanced models in evaluating emotion descriptions, raising questions about the validity of current multimodal benchmarks.

FlowWAM achieves a remarkable 92.94% success rate in manipulation tasks by harnessing optical flow as a video-native action representation.

Climb-settle cadence can eliminate overshoot errors in quadruped stair navigation, outperforming traditional methods even at lower loop rates.

Transforming image quality assessment from a single score to a nuanced diagnosis of multiple quality issues could revolutionize smartphone ISP tuning.

DrivingDepth achieves state-of-the-art depth estimation by leveraging sparse LiDAR to fine-tune pixel-wise scale without sacrificing geometric coherence.

Arko-T achieves superior performance in text-to-structured 3D generation while being ten times more cost-effective than leading models.

E-TTS achieves up to a 33.14% performance boost in robotic manipulation by leveraging historical context and iterative refinement, redefining how we approach test-time scaling.

Structured supervision can boost VLA model performance by over 50% in complex robotic tasks, transforming how we approach fine-tuning in manipulation.

Achieving comparable performance to full-precision models, BITEMBED slashes storage costs and enhances embedding efficiency with extreme low-bit quantization.

Robots can now navigate complex environments without continuous goal updates, relying solely on their internal spatial memory.

CRANE achieves a remarkable 96.9% Grounded Success in knowledge editing for reasoning MLLMs, overcoming traditional failure modes that plague existing methods.

Personalizing LLMs through a sociologically grounded framework reveals the hierarchical nature of user behavior, leading to significant performance gains across tasks.

EAPO enables agents to learn when to forgo tool use, achieving a remarkable 10.45% performance boost while slashing tool calls by over 18%.

Achieve state-of-the-art results in agentic knowledge base question answering by distilling gold-action policies into on-policy student rollouts, bridging the gap between sparse rewards and weakly supervised intermediate actions.