VisCoder2: Building Multi-Language Visualization Coding Agents Paper • 2510.23642 • Published 8 days ago • 20
BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions Paper • 2510.10666 • Published 20 days ago • 27
A Rigorous Benchmark with Multidimensional Evaluation for Deep Research Agents: From Answers to Reports Paper • 2510.02190 • Published 30 days ago • 18
EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing Paper • 2509.26346 • Published Sep 30 • 18
VideoScore2: Think before You Score in Generative Video Evaluation Paper • 2509.22799 • Published Sep 26 • 24
VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use Paper • 2509.01055 • Published Sep 1 • 72
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs Paper • 2410.15438 • Published Oct 20, 2024
VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search Paper • 2503.10582 • Published Mar 13 • 24
An Analysis of Decoding Methods for LLM-based Agents for Faithful Multi-Hop Question Answering Paper • 2503.23415 • Published Mar 30 • 1
Breaking the Batch Barrier (B3) of Contrastive Learning via Smart Batch Mining Paper • 2505.11293 • Published May 16
VideoEval-Pro: Robust and Realistic Long Video Understanding Evaluation Paper • 2505.14640 • Published May 20 • 16
Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem Paper • 2506.03295 • Published Jun 3 • 17
VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation Paper • 2506.03930 • Published Jun 4 • 26
BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent Paper • 2508.06600 • Published Aug 8 • 40
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models Paper • 2404.05904 • Published Apr 8, 2024 • 9
DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding Paper • 2002.12591 • Published Feb 28, 2020
Runtime error 34 34 OPEN-MOE-LLM-LEADERBOARD 🔥 Display and submit models for evaluation on an LLM leaderboard