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20242026
most citedA Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems

3 citations · 8 across the 16 of their papers we have counts for

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7 papers · 1 filter

cs.AI2025

LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild

Jiayu Wang, Yifei Ming, Riya Dulepet +7

Deep research -- producing comprehensive, citation-grounded reports by searching and synthesizing information from hundreds of live web sources -- marks an important frontier for a…

cs.AI2025

Helpful Agent Meets Deceptive Judge: Understanding Vulnerabilities in Agentic Workflows

Yifei Ming, Zixuan Ke, Xuan-Phi Nguyen +2

Agentic workflows -- where multiple large language model (LLM) instances interact to solve tasks -- are increasingly built on feedback mechanisms, where one model evaluates and cri…

cs.AI2025

Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning

Jiayu Wang, Yifei Ming, Zixuan Ke +4

Reinforcement learning (RL) has become the dominant paradigm for improving the performance of language models on complex reasoning tasks. Despite the substantial empirical gains de…

cs.CL2025

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…

cs.AI2025★ 3 cited

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.CL2025

NAACL2025 Tutorial: Adaptation of Large Language Models

Zixuan Ke, Yifei Ming, Shafiq Joty

This tutorial on adaptation of LLMs is designed to address the growing demand for models that go beyond the static capabilities of generic LLMs by providing an overview of dynamic,…

cs.CL2025

Demystifying Domain-adaptive Post-training for Financial LLMs

Zixuan Ke, Yifei Ming, Xuan-Phi Nguyen +2

Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance. However, significant challen…