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Qwen3 is an AI model family developed by Alibaba, released as a set of large language models for natural-language tasks.
…Built on top of Qwen3-VL-Instruct-2B/4B/8B, P2R consistently improves performance across model scales. In particular, P2R-4B achieves 93.2% on V-Star, 81.9% on HR-Bench…
…curation approach called Local Modality Substitution improves cross-modal representation alignment and reasoning performance. Generated by Qwen/Qwen2.5-Coder-32B-Instruct Vision-Language Models (VLMs) have achieved substantial progress across a…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks. However, most existing MLLMs rely on autoregressive generation, which limits their…
…Empirically, TRACE achieves competitive performance and efficiency gains on typical agentic benchmarks, e.g., improving Qwen3-14B Multi-Hop QA average accuracy by 2.8 points over competitive baselines at equal sampling…
…gradient conflicts and merges results via weighted averaging, achieving performance comparable to centralized methods with reduced communication overhead. Generated by Qwen/Qwen2.5-Coder-32B-Instruct Instruction tuning aligns large language models…
…Generated by Qwen/Qwen2.5-Coder-32B-Instruct Mastering terminal environments requires language agents capable of multi-step planning , feedback-grounded execution , and dynamic state adaptation . However, training such agents is currently…
…in persuasive conversation despite generating coherent arguments, with user-specific profiles significantly improving performance. Generated by Qwen/Qwen2.5-Coder-32B-Instruct Personalization is a crucial capability of modern language agents . However…
…is introduced that combines high-performance detection with native explainability by showing specific textual indicators that help users make informed judgments about authorship. Generated by Qwen/Qwen2.5-Coder-32B-Instruct Research…
…Fine-tuning VITA-1.5 , Qwen2.5-Omni-7B and Qwen3-Omni-30B on OmniVideo-100K yields performance gains of up to 20.59% on OmniVideo-Test , demonstrating strong generalization (up to…
…Youngjoon Jeong , , , , Abstract PoLAR introduces a geometrically structured latent action representation in hyperbolic space that separates transition extent from transition mode, improving robotic policy learning performance. Generated by Qwen/Qwen2.5-Coder…