collaborators

17 papers

cs.AI2026

Privileged Likelihood Is Not Automatically Value: Three Checks for Token Credit in On-Policy Self-Distillation

Xuan-Phi Nguyen, Shrey Pandit, Yiran Zhao +3

Outcome verifiers score completed reasoning traces but do not assign credit to intermediate tokens. Privileged self-distillation attempts to fill this gap by rescoring a model's ow…

cs.DC2026

Mixture-of-Parallelisms: Towards Memory-Efficient Training Stack for Mixture-of-Experts Models

Xuan-Phi Nguyen, Shrey Pandit, Yiran Zhao +3

This paper showcases a memory-efficient training stack for Mixture-of-Experts (MoE) models. It is a training paradigm that combines and specializes various existing and novel paral…

cs.AI2026

MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks

Zixuan Ke, Yifei Ming, Austin Xu +7

While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliver. Such shortcomings stem from…

cs.SE2026

SweRank: Software Issue Localization with Code Ranking

Revanth Gangi Reddy, Tarun Suresh, JaeHyeok Doo +7

Software issue localization, the task of identifying the precise code locations (files, classes, or functions) relevant to a natural language issue description (e.g., bug report, f…

cs.AI2026

A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems

Zixuan Ke, Fangkai Jiao, Yifei Ming +9

Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making. With the rapid advancement of large language models (LLMs), reaso…

cs.CL2026

MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision

Zixuan Ke, Austin Xu, Yifei Ming +4

Multi-agent systems (MAS) leveraging the impressive capabilities of Large Language Models (LLMs) hold significant potential for tackling complex tasks. However, most current MAS de…