Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- You YangSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works12 from public data
- Go Wider Instead of Deeper112
This paper designs WideNet, a parameter-efficient framework that scales along model width by replacing feed-forward network with mixture-of-experts (MoE), and proposes to use individual layernorms to transform various semantic representations in a more parameter- efficient way.
- RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents101
Empirical evaluations demonstrate RAP's effectiveness, where it achieves SOTA performance in textual scenarios and notably enhances multimodal LLM agents' performance for embodied tasks, highlighting RAP's potential in advancing the functionality and applicability of LLM agents in complex, real-world applications.
- MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE15
Mixture-of-Novices-and-Experts (MoNE), a novel expert pruning method that replaces redundant experts with lightweight novices to achieve effective and robust model compression, is proposed.
- 5
- 5
- 3
- MoST: Mixing Speech and Text with Modality-Aware Mixture of Experts2
MoST is presented, a novel multimodal large language model that seamlessly integrates speech and text processing through the proposed Modality-Aware Mixture of Experts (MAMoE) architecture and represents the first fully open-source speech-text LLM built on a Mixture of Experts architecture.
- Memory Transfer Planning: LLM-driven Context-Aware Code Adaptation for Robot Manipulation2
Memory Transfer Planning is introduced, a framework that leverages successful control-code examples from different environments as procedural knowledge, using them as in-context guidance for LLM-driven planning for robust LLM-based planning across diverse robotic manipulation scenarios.
- EnvBridge: Bridging Diverse Environments with Cross-Environment Knowledge Transfer for Embodied AI2
The proposed EnvBridge approach alleviates environmental constraints, offering a more flexible and generalizable solution for robotic manipulation tasks, and demonstrates that LLM agents can successfully leverage diverse knowledge sources to solve complex tasks.
- DiffuSpeech: Silent Thought, Spoken Answer via Unified Speech-Text Diffusion1
This work introduces a paradigm where speech LLMs generate internal text reasoning alongside spoken responses, with thinking traces informing speech quality, and presents \method, the first diffusion-based speech-text language model supporting both understanding and generation.
- –
- AsyncLane: Decoupling Refinement from Advancement in Diffusion Language Model Decoding–
AsyncLane is a drop-in replacement for block-wise DLM samplers and requires no retraining, and experiments on mathematical reasoning and code generation show that AsyncLane consistently improves throughput while maintaining competitive quality.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it. You will be asked to sign in.