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20242026
most citedBASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data

23 citations · 33 across the 29 of their papers we have counts for

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Showing 2026Show all

12 papers · 1 filter

cs.AI2026

HiGenTrans: Hierarchical LLM Knowledge Transfer for Generative Recommendation

Zhuohang Jiang, Yuxin Chen, Haohao Qu +13

Generative recommendation promises to simplify conventional multi-stage recommender systems by generating item Semantic IDs (SIDs) autoregressively. However, two obstacles limit it…

cs.AI2026

Diagnosing with Insights: Structured Analysis of Agent Failures via Behavioral Abstractions

Jiayi Bi, Yanjie Gao, Yuanmin Xie +4

With the proliferation of LLM agents, the ability to understand and diagnose failures in agents is essential to achieving superior effectiveness and trustworthiness. As agent failu…

cs.AI2026

Why Does Feedback-Augmented Self-Distillation Fail to Improve Retrieval-Interleaved Search Agents?

Fan Yang, Rui Meng, Yuxin Wen

On-policy self-distillation (OPSD) offers a promising approach for training large language models without relying on a separate teacher model. However, its effectiveness on complex…

cs.CV2026

TraceVision: Trajectory-Aware Vision-Language Model for Human-Like Spatial Understanding

Fan Yang, Shurong Zheng, Hongyin Zhao +5

Recent Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities in image understanding and natural language generation. However, current approaches focus predominan…

cs.CV2026

CREM: Compression-Driven Representation Enhancement for Multimodal Retrieval and Comprehension

Lihao Liu, Yan Wang, Biao Yang +10

Multimodal Large Language Models (MLLMs) have shown remarkable success in comprehension tasks such as visual description and visual question answering. However, their direct applic…

cs.CL2026

ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders

Ofer Meshi, Krisztian Balog, Sally Goldman +5

The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…

cs.CV2026

UniRef-Image-Edit: Towards Scalable and Consistent Multi-Reference Image Editing

Hongyang Wei, Bin Wen, Yancheng Long +22

We present UniRef-Image-Edit, a high-performance multi-modal generation system that unifies single-image editing and multi-image composition within a single framework. Existing dif…

cs.IR2026

Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoning

Huishi Luo, Shuokai Li, Hanchen Yang +10

Awakening dormant users, who remain engaged but exhibit low conversion, is a pivotal driver for incremental GMV growth in large-scale e-commerce platforms. However, existing approa…

cs.LG2026

Multi-Head Attention as a Source of Catastrophic Forgetting in MoE Transformers

Anrui Chen, Ruijun Huang, Xin Zhang +15

Mixture-of-Experts (MoE) architectures are often considered a natural fit for continual learning because sparse routing should localize updates and reduce interference, yet MoE Tra…

cs.LG2026

SD-MoE: Spectral Decomposition for Effective Expert Specialization

Ruijun Huang, Fang Dong, Xin Zhang +16

Mixture-of-Experts (MoE) architectures scale Large Language Models via expert specialization induced by conditional computation. In practice, however, expert specialization often f…

cs.AI2026

Neuro-Symbolic Verification on Instruction Following of LLMs

Yiming Su, Kunzhao Xu, Yanjie Gao +4

A fundamental problem of applying Large Language Models (LLMs) to important applications is that LLMs do not always follow instructions, and violations are often hard to observe or…

cs.AI2026

Owen-Shapley Policy Optimization: A Principled RL Algorithm for Generative Search LLMs

Abhijnan Nath, Alireza Bagheri Garakani, Tianchen Zhou +3

Large language models are increasingly trained via reinforcement learning for personalized recommendation tasks, but standard methods like GRPO rely on sparse, sequence-level rewar…