Jun Chen

ASDchat, a multimodal large language model designed for evidence-based ASD screening, which takes video, audio, and dialogue as input, provides a feasible path for large-scale, evidence-based early ASD screening in clinical practice.

StepAudio 3 Realtime, an audio-language foundation model organized around a continuous listen-converse-think-act loop, achieves dialogue and reasoning performance comparable to dedicated reasoning models while speaking in real time, and resolves the tension between deep deliberation and latency via Think-While-Speaking.

This study introduces StepAudio 3 Gen, a general-purpose audio generation model that supports zero-shot text-to-speech (TTS), voice design, vocal generation, sound effects, music, vibe speech, and mixtures of multiple audio types within a unified framework.

EvoGS achieves superior dynamic view synthesis by modeling Gaussian deformations as a temporal evolution process, overcoming the limitations of traditional independent estimation methods.

Diff-Symbo achieves unprecedented quality and diversity in text-controlled music generation, outperforming leading models by leveraging a novel latent diffusion framework.

RLVR, the dominant training paradigm for audio language models, may be turning them into unfeeling "answering machines" that excel on benchmarks but fail the vibe check.

Ditch repeated Riccati equation solves: DeepONets learn a fast, generalizable LQR solution operator, slashing computation time while maintaining provable stability guarantees.

UniBlendNet achieves state-of-the-art ambient lighting normalization by adaptively correcting for spatially-varying illumination, leading to visually superior and more stable image restoration.

LLMs can now generate STEM animations that not only improve student learning outcomes but also boost engagement and reduce cognitive load compared to traditional methods.