1 citations · 1 across the 15 of their papers we have counts for
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CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation
Yaoning Yu, Kai-Min Chang, Ye Yu +3
Online credit card discussions provide a natural setting for studying how consumers communicate about financial products. Simulating these discussions requires more than just gener…
KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling
Peng Kuang, Haibo Jin, Xiaoyu Han +5
Process Reward Models (PRMs) have been proven to be highly effective in guiding test-time scaling (TTS) methods, which significantly boost the capabilities of LLM-based multi-agent…
Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
Yonggan Fu, Lexington Whalen, Abhinav Garg +23
We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR…
Closing the Loop on Latent Reasoning via Test-Time Reconstruction
Xiaopeng Yuan, Haibo Jin, Ye Yu +4
Recent work moves intermediate reasoning from natural-language traces into latent or cache-level representations to reduce token overhead and avoid a discrete communication bottlen…
ThreadBench: Benchmarking Realism in Agentic Simulation of Real-world Discussions
Yaoning Yu, Ye Yu, Haojing Luo +4
LLM agents are increasingly used to simulate real-world interactions, but it remains unclear whether simulated behaviors preserve the content patterns and interaction dynamics of r…
Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation
Ye Yu, Xiaopeng Yuan, Haibo Jin +3
Recent advances in LLM agents enable systems that autonomously refine workflows, accumulate reusable skills, self-train their underlying models, and maintain persistent memory. How…
Learning to Communicate: Toward End-to-End Optimization of Multi-Agent Language Systems
Ye Yu, Heming Liu, Haibo Jin +3
Multi-agent systems built on large language models have shown strong performance on complex reasoning tasks, yet most work focuses on agent roles and orchestration while treating i…
Cachemir: Fully Homomorphic Encrypted Inference of Generative Large Language Model with KV Cache
Ye Yu, Yifan Zhou, Yi Chen +3
Generative large language models (LLMs) have revolutionized multiple domains. Modern LLMs predominantly rely on an autoregressive decoding strategy, which generates output tokens s…
Agent Primitives: Reusable Latent Building Blocks for Multi-Agent Systems
Haibo Jin, Peng Kuang, Ye Yu +2
While existing multi-agent systems (MAS) can handle complex problems by enabling collaboration among multiple agents, they are often highly task-specific, relying on manually craft…
Now You Hear Me: Audio Narrative Attacks Against Large Audio-Language Models
Ye Yu, Haibo Jin, Yaoning Yu +2
Large audio-language models increasingly operate on raw speech inputs, enabling more seamless integration across domains such as voice assistants, education, and clinical triage. T…