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In the field of artificial intelligence, reward models are a critical component for aligning large language models (LLMs) with human feedback, but existing models face the issue of "reward hacking." These models often focus on superficial features, such as the length or format of responses, rather than identifying genuine quality metrics, such as factual accuracy and relevance. The root cause lies in standard training objectives failing to distinguish between spurious associations and true causal drivers present in the training data. This failure leads to fragile reward models (RMs), which generate misaligned policies.

Recently, the Qwen VLo multimodal large model was officially released, achieving significant advancements in image content understanding and generation, offering users a brand-new visual creation experience. According to the introduction, Qwen VLo has been comprehensively upgraded based on the advantages of the original Qwen-VL series models. The model not only can accurately understand the "world", but also can perform high-quality re-creation based on understanding, truly achieving the transition from perception to generation. Users can now access Qwen Chat (chat.qwen.ai)

Welcome to AIbase's [AI Daily Report]! Spend three minutes every day to learn about the latest AI news, helping you understand AI industry trends and innovative AI product applications. For more AI updates, visit: https://www.aibase.com/zh1. Tencent open-sources the lightweight Huyuan-A13B model, which can be deployed with just one mid-range GPU card. Tencent has released a new member of the Huyuan large model family, the Huyuan-A13B model, which uses a mixture of experts (MoE) architecture, with a total parameter scale of 80 billion and an activated parameter count of 13 billion, large

With the continuous progress of artificial intelligence technology, multimodal data processing has gradually become a popular topic. Recently, the globally renowned electrical appliances brand Panasonic launched its latest R&D multimodal large model — OmniFlow. This model can efficiently convert between multiple modalities such as text, image, and audio, achieving any-to-any generation tasks, providing users with a more flexible experience. The design concept of OmniFlow is based on modularity, allowing the various components of the model to be independently pre-trained. This approach not only improves training efficiency but also avoids...

Recently, an open-source RAG (Retrieval-Augmented Generation) engine called RAGFlow has attracted significant attention in the industry. This enterprise-level AI tool, based on deep document understanding, provides companies with a brand-new solution for processing complex documents and achieving precise question answering through its powerful multimodal data processing capabilities and efficient workflows. RAGFlow: The Pioneer of Deep Document Understanding RAGFlow is a fully open-source RAG engine that focuses on deep document understanding, aiming to help businesses and individuals extract valuable information from massive amounts of unstructured data.

The Meta AI research team has made another breakthrough in the artificial intelligence field, officially releasing the new video understanding model V-JEPA2 (Video Joint Embedding Predictive Architecture2) on June 11, 2025. Led by Meta's chief AI scientist Yann LeCun, this model has opened up new possibilities in video understanding and physical world modeling with its innovative self-supervised learning technology and zero-shot robot control capabilities.

In recent years, the field of artificial intelligence has undergone tremendous changes, especially with large language models (LLMs) making remarkable progress in multi-modal tasks. These models have demonstrated strong potential in their ability to understand and generate language, but most current multi-modal models still adopt auto-regressive (AR) architectures, which limit inference processes to be rather monotonous and lacking flexibility. To address this limitation, a research team from The University of Hong Kong and Huawei Noah's Ark Lab has proposed a brand new model – FUDOKI, aiming to break these constraints. The core innovation of FUDOKI is

In the recent Fiction.Live benchmark test, Gemini2.5Pro performed excellently in understanding and reproducing complex stories and backgrounds, leading ahead of OpenAI's o3 model. This test goes far beyond traditional "needle-in-a-haystack" tasks, focusing on a model's ability to handle deep semantics and context-dependent information within large contexts. According to the test data, when the context window length reaches 192,000 tokens (approximately 144,000 words), the performance of the o3 model drops sharply, while

Recently, the Beijing Academy of Artificial Intelligence (BAEI), in collaboration with Shanghai Jiao Tong University and other institutions, officially released a new generation of ultra-long video understanding model - Video-XL-2. The introduction of this model represents a significant breakthrough in the field of open-source ultra-long video understanding technology, injecting new vitality into the development of multimodal large models for understanding long video content. In terms of technical architecture, Video-XL-2 mainly consists of three core components: a visual encoder, the Dynamic Token Synthesis (DTS) module, and a large language model (LLM). The model adopts

Recently, Hume AI officially released its third-generation voice interaction model, EVI3. This new voice AI has drawn extensive attention in the industry due to its outstanding emotion understanding capabilities and personalized interaction experience. EVI3 can accurately identify emotions in users' voices and generate specific styles of sound and personality based on user preferences, marking a significant breakthrough in the field of emotional interaction and natural communication. Below is the latest information and in-depth analysis about EVI3 from AIbase. Experience it at: https://demo.

Recently, the Speech Team of Qwen Laboratory has made a milestone achievement in the field of spatial audio generation and launched the OmniAudio technology. This technology can directly generate FOA (First-order Ambisonics) audio from 360-degree videos, bringing new possibilities to virtual reality and immersive entertainment. Spatial audio, as a technology that simulates real auditory environments, can enhance immersive experiences. However, existing technologies are mostly based on fixed perspective videos and underutilize the spatial information of 360-degree panoramic videos. Traditional video-to-audio generation

Recently, Qwen released CoGenAV, innovating speech recognition technology with the concept of audio-visual synchronization. It effectively addresses the challenge of noise interference in speech recognition. Traditional speech recognition performs poorly in noisy environments, while CoGenAV takes a different approach by learning the temporal alignment relationships among audio-visual-text, building a more robust and generalizable speech representation framework, systematically improving tasks such as speech recognition (VSR/AVSR), speech reconstruction (AVSS/AVSE), and audio-visual synchronization (A').