7 papers · 1 filter
Workspace-Bench 1.0: Benchmarking AI Agents on Workspace Tasks with Large-Scale File Dependencies
Zirui Tang, Xuanhe Zhou, Yumou Liu +19
Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a worker's workspace, enabling t…
Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation
Xue Liu, Xin Ma, Yuxin Ma +36
As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficiency in complex, open-ended tasks c…
VeRA: Verified Reasoning Data Augmentation at Scale
Zerui Cheng, Jiashuo Liu, Chunjie Wu +4
The main issue with most evaluation schemes today is their "static" nature: the same problems are reused repeatedly, allowing for memorization, format exploitation, and eventual sa…
FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains
Jiashuo Liu, Siyuan Chen, Zaiyuan Wang +38
Building upon FutureX, which established a live benchmark for general-purpose future prediction, this report introduces FutureX-Pro, including FutureX-Finance, FutureX-Retail, Futu…
LPFQA: A Long-Tail Professional Forum-based Benchmark for LLM Evaluation
Liya Zhu, Peizhuang Cong, Jingzhe Ding +17
Large Language Models (LLMs) perform well on standard reasoning and question-answering benchmarks, yet such evaluations often fail to capture their ability to handle long-tail, exp…
RLoop: An Self-Improving Framework for Reinforcement Learning with Iterative Policy Initialization
Zeng Zhiyuan, Jiashuo Liu, Zhangyue Yin +3
While Reinforcement Learning for Verifiable Rewards (RLVR) is powerful for training large reasoning models, its training dynamics harbor a critical challenge: RL overfitting, where…