1 citations · 1 across the 10 of their papers we have counts for
12 papers
OPFL: Optimistic Verification of Federated Learning via Empirical Boundary
Hongxu Su, Jianzhu Yao, Xuechao Wang +1
Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into local training makes it difficu…
OVIG: Optimistic Verification of AI Training Integrity via Gradient Signals
Hongxu Su, Jianzhu Yao, Huan Zhang +2
The rapid growth of AI has increased the demand for domain-specific models. Post-training of open-source models offers a more economical way to meet this growing demand, but the co…
Crypto x AI, AI x Crypto: A Survey
Sarah Allen, Pranay Anchuri, James Austgen +22
The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exactly has been done, what the opportuni…
MINDGAMES: A Live Arena for Evaluating Social and Strategic Reasoning in Multi-Agent LLMs
Kevin Wang, Anna Thöni, Benjamin Kempinski +50
Large language models (LLMs) are increasingly deployed as interactive agents, yet their capacity for social and strategic reasoning over extended interaction remains poorly underst…
MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games
Yunfei Xie, Kevin Wang, Bobby Cheng +9
Multi-turn, multi-agent LLM game evaluations often exhibit substantial run-to-run variance. In long-horizon interactions, small early deviations compound across turns and are ampli…
TabularMath: Evaluating Computational Extrapolation in Tabular Learning via Program-Verified Synthesis
Zerui Cheng, Jiashuo Liu, Jianzhu Yao +3
Standard tabular benchmarks mainly focus on the evaluation of a model's capability to interpolate values inside a data manifold, where models good at performing local statistical s…
VeRA: Renewing Reasoning Benchmarks with Executable Specifications
Zerui Cheng, Jiashuo Liu, Chunjie Wu +5
Reasoning benchmarks need renewal along two axes: freshness and headroom. VeRA makes both executable and auditable by turning each item into a task family: a natural-language templ…
FrontierCS: Evolving Challenges for Evolving Intelligence
Qiuyang Mang, Wenhao Chai, Zhifei Li +48
We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competiti…
TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks
Jianzhu Yao, Hongxu Su, Taobo Liao +4
Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little about what actually ran or whether…
AutoCode: LLMs as Problem Setters for Competitive Programming
Shang Zhou, Zihan Zheng, Kaiyuan Liu +18
Writing competitive programming problems is exacting. Authors must: set constraints, input distributions, and edge cases that rule out shortcuts; target specific algorithms (e.g.,…
LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?
Zihan Zheng, Zerui Cheng, Zeyu Shen +16
Recent reports claim that large language models (LLMs) now outperform elite humans in competitive programming. Drawing on knowledge from a group of medalists in international algor…
SPIN-Bench: How Well Do LLMs Plan Strategically and Reason Socially?
Jianzhu Yao, Kevin Wang, Ryan Hsieh +5
Reasoning and strategic behavior in social interactions is a hallmark of intelligence. This form of reasoning is significantly more sophisticated than isolated planning or reasonin…