activity
20142026
most citedScheduled Sampling for Sequence Prediction with Recurrent Neural Networks

1.6k citations · 4.2k across the 75 of their papers we have counts for

collaborators

78 papers

cs.LG2026

SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign

Jiarui Lu, Yuyang Wang, Yizhe Zhang +4

Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-dimensional structure. Devel…

cs.CL2026

Scaling Properties of Continuous Diffusion Spoken Language Models

Jason Ramapuram, Eeshan Gunesh Dhekane, Amitis Shidani +6

Speech-only spoken language models (SLMs) lag behind text and text-speech models in performance, with recent discrete autoregressive (AR) SLMs indicating significant computational…

cs.LG2026

Path-Constrained Mixture-of-Experts

Zijin Gu, Tatiana Likhomanenko, Vimal Thilak +2

Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of \emph…

cs.CL2026

Embarrassingly Simple Self-Distillation Improves Code Generation

Ruixiang Zhang, Richard He Bai, Huangjie Zheng +3

Can a large language model (LLM) improve at code generation using only its own raw outputs, without a verifier, a teacher model, or reinforcement learning? We answer in the affirma…

cs.CL2025

CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning

Jie He, Richard He Bai, Sinead Williamson +3

Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval-generation optimizati…

cs.CL2025

Closing the Gap Between Text and Speech Understanding in LLMs

Santiago Cuervo, Skyler Seto, Maureen de Seyssel +5

Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counte…