Yi Ma

Judgment models can solve complex problems when the answer is directly evaluable from the input, but fail catastrophically when simulation is required, even if they possess the underlying knowledge.

Robots can master delicate tool manipulation directly from unpaired human videos, achieving a 73% performance leap over SOTA by anchoring control in object-centric 3D pose priors rather than raw end-to-end pixels.

This paper proposes Generalized Agent Iteration (GAI), a formal framework that describes iterative policy improvement and RSI as two cases of a single learning paradigm that rests on the classical account, makes existing systems comparable, and provides a principled basis for analyzing and designing new ones.

WarpSAC redefines off-policy RL by tailoring stabilizers to data availability, achieving up to 96.4% success rates in challenging environments.

Robots can mimic human actions but fail to grasp the underlying intent, with performance collapsing when faced with novel tasks that require true understanding.

MERaLiON-GR outperforms existing models in gender recognition across multiple Southeast Asian languages, showcasing the power of specialized speech models.

Training updates that improve performance in LLMs can actually degrade inference quality—unless you use the new Monotonic Inference Policy Update framework.

Achieving state-of-the-art performance with just 8 billion parameters, Embodied-R1.5 redefines the capabilities of embodied models in complex physical tasks.

Forget task-specific fine-tuning – teaching VLMs basic geometry yields a +29% boost on spatial reasoning benchmarks.

LLMs, like humans, exhibit a "frequency bias," performing better when prompted and fine-tuned with more common textual expressions.