12 papers
Few-Step Diffusion Language Models via Trajectory Self-Distillation
Tunyu Zhang, Xinxi Zhang, Ligong Han +9
Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding. However, realizing this poten…
Reward Modeling for Multi-Agent Orchestration
King Yeung Tsang, Zihao Zhao, Vishal Venkataramani +5
Multi-Agent Systems (MAS) built on Large Language Models (LLMs) require effective orchestration to coordinate specialized agents, yet training such orchestrators is hindered by lim…
iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis
Yang Song, Yixuan Zhang, Lingfa Meng +5
Parameter-efficient adaptation has made LLMs practical for domain prediction, but standard LoRA still relies on a static low-rank update and does not expose the latent interactions…
TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning
Tunyu Zhang, Haizhou Shi, Yibin Wang +9
While Large Language Models (LLMs) have demonstrated impressive capabilities, their output quality remains inconsistent across various application scenarios, making it difficult to…
Dist2ill: Distributional Distillation for One-Pass Uncertainty Estimation in Large Language Models
Yicong Zhao, King Yeung Tsang, Harshil Vejendla +9
Large Language Models (LLMs) often exhibit misalignment between the quality of their generated responses and the confidence estimates they assign to them. Bayesian treatments, such…
MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems
Vishal Venkataramani, Haizhou Shi, Zixuan Ke +6
Multi-Agent Systems (MAS) built on Large Language Models (LLMs) often exhibit high variance in their reasoning trajectories. Process verification, which evaluates intermediate step…