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Wei Ping

48 papers hereh-index 203.3k citations39 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author29
  • last author14

Across the 46 of 48 papers where every author was matched, so the position is known.

fields
  • cs.CL26
  • cs.SD9
  • cs.LG6
  • cs.CV5
  • eess.AS1
  • stat.ML1
same name
  • Wei Ping — 16 papers, h 29
  • Wei Ping — 1 paper, h 7
  • Wei Ping — 1 paper
  • Wei Ping — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142026
most citedDeep Voice 2: Multi-Speaker Neural Text-to-Speech

212 citations · 660 across the 46 of their papers we have counts for

collaborators
Showing 2026 · cs.CLShow all

4 papers · 2 filters

cs.CL2026

Unified Audio Intelligence Without Regressing on Text Intelligence

Zhifeng Kong, Sang-gil Lee, Jaehyeon Kim +17

Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-te…

cs.CL2026

Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation

Zhuolin Yang, Zihan Liu, Yang Chen +14

We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size,…

cs.CL2026

MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos

Arushi Goel, Sreyan Ghosh, Vatsal Agarwal +16

Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However, their ability to jointly reason over…

cs.CL2026

On Data Engineering for Scaling LLM Terminal Capabilities

Renjie Pi, Grace Lam, Mohammad Shoeybi +3

Despite rapid recent progress in the terminal capabilities of large language models, the training data strategies behind state-of-the-art terminal agents remain largely undisclosed…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.