1 citations · 1 across the 16 of their papers we have counts for
16 papers
AuRA: Internalizing Audio Understanding into LLMs as LoRA
Bo Cheng, Lei Shi, Zhanyu Ma +5
Recent efforts to extend large language models (LLMs) to speech inputs typically rely on cascaded ASR-LLM pipelines, end-to-end speech-language models, or bridge/distillation-based…
DuplexOmni: Real-Time Listening, Seeing, Thinking, and Speaking for Full-Duplex Interaction
Muye Huang, Lingling Zhang, Xingyu Yu +7
Human interaction is continuous, multimodal, and full-duplex by nature. Although recent omni models have made substantial progress in unified speech, vision, and text modeling, com…
Hidden States Know Where Reasoning Diverges: Credit Assignment via Span-Level Wasserstein Distance
Xinzhu Chen, Wei He, Huichuan Fan +7
Group Relative Policy Optimization (GRPO) performs coarse-grained credit assignment in reinforcement learning with verifiable rewards (RLVR) by assigning the same advantage to all…
MUSE: Multi-Domain Chinese User Simulation via Self-Evolving Profiles and Rubric-Guided Alignment
Zihao Liu, Hantao Zhou, Jiguo Li +5
User simulators are essential for the scalable training and evaluation of interactive AI systems. However, existing approaches often rely on shallow user profiling, struggle to mai…
Silence the Judge: Reinforcement Learning with Self-Verifier via Latent Geometric Clustering
Nonghai Zhang, Weitao Ma, Zhanyu Ma +5
Group Relative Policy Optimization (GRPO) significantly enhances the reasoning performance of Large Language Models (LLMs). However, this success heavily relies on expensive extern…
State Rank Dynamics in Linear Attention LLMs
Ao Sun, Hongtao Zhang, Heng Zhou +9
Linear Attention Large Language Models (LLMs) offer a compelling recurrent formulation that compresses context into a fixed-size state matrix, enabling constant-time inference. How…
From Absolute to Relative: Rethinking Reward Shaping in Group-Based Reinforcement Learning
Wenzhe Niu, Wei He, Zongxia Xie +10
Reinforcement learning has become a cornerstone for enhancing the reasoning capabilities of Large Language Models, where group-based approaches such as GRPO have emerged as efficie…
LTS-VoiceAgent: A Listen-Think-Speak Framework for Efficient Streaming Voice Interaction via Semantic Triggering and Incremental Reasoning
Wenhao Zou, Yuwei Miao, Zhanyu Ma +5
Real-time voice agents face a dilemma: end-to-end models often lack deep reasoning, while cascaded pipelines incur high latency by executing ASR, LLM reasoning, and TTS strictly in…
Long-term Task-oriented Agent: Proactive Long-term Intent Maintenance in Dynamic Environments
Qinglong Shi, Donghai Wang, Hantao Zhou +5
Current large language model agents predominantly operate under a reactive paradigm, responding only to immediate user queries within short-term sessions. This limitation hinders t…
Efficient Paths and Dense Rewards: Probabilistic Flow Reasoning for Large Language Models
Yan Liu, Feng Zhang, Zhanyu Ma +6
High-quality chain-of-thought has demonstrated strong potential for unlocking the reasoning capabilities of large language models. However, current paradigms typically treat the re…
UserLM-R1: Modeling Human Reasoning in User Language Models with Multi-Reward Reinforcement Learning
Feng Zhang, Shijia Li, Chunmao Zhang +7
User simulators serve as the critical interactive environment for agent post-training, and an ideal user simulator generalizes across domains and proactively engages in negotiation…
Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management
Weitao Ma, Xiaocheng Feng, Lei Huang +7
Effective memory management is essential for large language model agents to navigate long-horizon tasks. Recent research has explored using Reinforcement Learning to develop specia…
GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR
Jiaying Zhang, Lei Shi, Jiguo Li +4
Reinforcement Learning with Verifiable Rewards (RLVR) is a key paradigm for improving large-scale reasoning models. Unlike supervised fine-tuning (SFT), RLVR exhibits distinct opti…
VoiceAgentEval: A Dual-Dimensional Benchmark for Expert-Level Intelligent Voice-Agent Evaluation of Xbench's Professional-Aligned Series
Pengyu Xu, Shijia Li, Ao Sun +15
We propose OutboundEval, a comprehensive benchmark for evaluating large language models (LLMs) in expert-level intelligent outbound calling scenarios. Unlike existing methods that…
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments
Yile Liang, Jiuxia Zhao, Donghui Li +5
The recent past has witnessed a notable surge in on-demand food delivery (OFD) services, offering delivery fulfillment within dozens of minutes after an order is placed. In OFD, po…
A Framework for Multi-stage Bonus Allocation in meal delivery Platform
Zhuolin Wu, Li Wang, Fangsheng Huang +8
Online meal delivery is undergoing explosive growth, as this service is becoming increasingly popular. A meal delivery platform aims to provide excellent and stable services for cu…