Trending Research
Agent-as-a-Judge: Evaluate Agents with Agents
To address this, we introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems.
Qwen2 Technical Report
qwenlm/qwen1.5
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This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #3 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation
To effectively learn the HealthGPT, we devise a comprehensive medical domain-specific comprehension and generation dataset called VL-Health.
An Empirical Study of Qwen3 Quantization
efficient-ml/qwen3-quantization
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The Qwen series has emerged as a leading family of open-source Large Language Models (LLMs), demonstrating remarkable capabilities in natural language understanding tasks.
LARGE: Legal Retrieval Augmented Generation Evaluation Tool
Recently, building retrieval-augmented generation (RAG) systems to enhance the capability of large language models (LLMs) has become a common practice.
Erasing Without Remembering: Safeguarding Knowledge Forgetting in Large Language Models
In this paper, we explore machine unlearning from a novel dimension, by studying how to safeguard model unlearning in large language models (LLMs).
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
This system includes two foundation components: a large-scale shape generation model -- Hunyuan3D-DiT, and a large-scale texture synthesis model -- Hunyuan3D-Paint.
ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
volcengine/verl
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While reasoning models (e. g., DeepSeek R1) trained with reinforcement learning (RL), excel in textual reasoning, they struggle in scenarios requiring structured problem-solving, such as geometric reasoning, concise computation, or complex equation solving-areas where computational tools like code interpreters (CI) demonstrate distinct advantages.
What Kind of Visual Tokens Do We Need? Training-free Visual Token Pruning for Multi-modal Large Language Models from the Perspective of Graph
jytmelon/g-prune
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Recent Multimodal Large Language Models(MLLMs) often use a large number of visual tokens to compensate their visual shortcoming, leading to excessive computation and obvious visual redundancy.
Registration of 3D Point Sets Using Exponential-based Similarity Matrix
However, state-of-the-art registration techniques often struggle when large rotational differences exist between point sets or when the data is significantly corrupted by sensor noise.
