1 citations · 1 across the 11 of their papers we have counts for
12 papers
One-Sided Verification Under Limited Expert Review
Fan Nie, Haotian Ye, Shurui Liu +3
AI has become increasingly helpful in scientific discovery, yet human expert review remains essential for those problems without reliable automated evaluation, posing a fundamental…
MatrAIx: Simulating the World with 8.3 Billion Persona Agents
Xiaomin Li, Yuexing Hao, Jianheng Hou +90
Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and inter…
Automated Benchmark Auditing for AI Agents and Large Language Models
Junlin Wang, Federico Bianchi, Shang Zhu +4
Modern AI benchmarks operate at a complexity that outpaces traditional verification methods. Tasks authored by domain experts often contain implicit assumptions, incomplete environ…
Peer-Predictive Self-Training for Language Model Reasoning
Shi Feng, Hanlin Zhang, Fan Nie +2
Mechanisms for continued self-improvement of language models without external supervision remain an open challenge. We propose Peer-Predictive Self-Training (PST), a label-free fin…
Physion-Eval: Evaluating Physical Realism in Generated Video via Human Reasoning
Qin Zhang, Peiyu Jing, Hong-Xing Yu +7
Video generation models are increasingly used as world simulators for storytelling, simulation, and embodied AI. As these models advance, a key question arises: do generated videos…
DSGym: A Holistic Framework for Evaluating and Training Data Science Agents
Fan Nie, Junlin Wang, Harper Hua +6
Data science agents promise to accelerate discovery and insight-generation by turning data into executable analyses and findings. Yet existing data science benchmarks fall short du…
When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought
Yiyang Zhou, Haoqin Tu, Zijun Wang +11
We propose MIRA, a new benchmark designed to evaluate models in scenarios where generating intermediate visual images is essential for successful reasoning. Unlike traditional CoT…
UQ: Assessing Language Models on Unsolved Questions
Fan Nie, Ken Ziyu Liu, Zihao Wang +11
Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usa…
EvoLM: In Search of Lost Language Model Training Dynamics
Zhenting Qi, Fan Nie, Alexandre Alahi +6
Modern language model (LM) training has been divided into multiple stages, making it difficult for downstream developers to evaluate the impact of design choices made at each stage…
Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors
Fan Nie, Lan Feng, Haotian Ye +5
Efficiently leveraging of the capabilities of contemporary large language models (LLMs) is increasingly challenging, particularly when direct fine-tuning is expensive and often imp…
TAROT: Targeted Data Selection via Optimal Transport
Lan Feng, Fan Nie, Yuejiang Liu +1
We propose TAROT, a targeted data selection framework grounded in optimal transport theory. Previous targeted data selection methods primarily rely on influence-based greedy heuris…
FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees
Fan Nie, Xiaotian Hou, Shuhang Lin +3
The propensity of Large Language Models (LLMs) to generate hallucinations and non-factual content undermines their reliability in high-stakes domains, where rigorous control over T…