Papers

• UI-TARS: Pioneering Automated GUI Interaction with Native Agents (2025)
  Yujia Qin et al.
  5.13
• GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models (2025)
  GLM-4. 5 Team et al.
  5.10
• OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale (2026)
  Jingze Shi et al.
  3.23
• Union of Experts: Adapting Hierarchical Routing to Equivalently Decomposed Transformer (2025)
  Yujiao Yang et al.
  2.87
• Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation (2025)
  Emil Njor et al.
  2.65
• Multimodal Continuous Reasoning via Asymmetric Mutual Variational Learning (2026)
  Shijie Li et al.
  2.00
• Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model (2026)
  Xinyin Ma et al.
  2.00
• RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation (2026)
  Haoyu Zhao et al.
  2.00
• Linear Attention Architectures: Mechanisms, Trade-offs, and Cross-Layer Routing (2026)
  Tommaso Cerruti et al.
  2.00
• Xmodel-2.5: 1.3B Data-Efficient Reasoning SLM (2025)
  Yang Liu et al.
  1.56
• Benchmarking Optimizers for Large Language Model Pretraining (2025)
  Andrei Semenov et al.
  1.44
• CMoE: Fast Carving of Mixture-of-Experts for Efficient LLM Inference (2025)
  Zehua Pei et al.
  1.28
• A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone (2025)
  Jitai Hao et al.
  1.28
• Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents (2025)
  Yueqi Song et al.
  1.28
• Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization (2025)
  Taishi Nakamura et al.
  1.06
• Joey NMT: A Minimalist NMT Toolkit for Novices (2019)
  Julia Kreutzer et al.
  —
• Knowledge Graph Embedding with Atrous Convolution and Residual Learning (2020)
  Feiliang Ren et al.
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• MetaMIML: Meta Multi-Instance Multi-Label Learning (2021)
  Yuanlin Yang et al.
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• Multi-Head Deep Metric Learning Using Global and Local Representations (2021)
  Mohammad K. Ebrahimpour et al.
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• Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning (2022)
  Haokun Liu et al.
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• Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts (2024)
  Tong Zhu et al.
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• Approximating Two-Layer Feedforward Networks for Efficient Transformers (2023)
  Róbert Csordás et al.
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• Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning (2024)
  Yifeng Ding et al.
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• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (2024)
  Jun Shern Chan et al.
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