From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation

A significant shift in the artificial intelligence stock trade is prompting investors in Asia to seek new equity opportunities, as technological advancements and concerns about market bubbles reshape the investment landscape. This trend reflects a growing interest in identifying potential winners in the AI sector amidst fluctuating market conditions.

ByteDance and DeepSeek, two prominent players in China's artificial intelligence sector, are pursuing markedly different strategies, highlighting the divergent paths within the industry. While ByteDance focuses on leveraging AI for content creation and user engagement, DeepSeek is emphasizing open-source AI models, such as its recent release that rivals GPT-5.

The paper introduces MathBode, a diagnostic tool designed to assess mathematical reasoning in large language models (LLMs) by analyzing their frequency response to parametric problems. It focuses on metrics like gain and phase to reveal systematic behaviors that traditional accuracy measures may overlook.

MagicView has been introduced as a lightweight adaptation framework that enhances existing generative models by enabling multi-view consistent identity customization through 3D priors-guided in-context learning. This innovation addresses the limitations of current methods that struggle with viewpoint control and identity consistency across different scenes.

ExPairT-LLM has been introduced as an exact learning algorithm for code selection, addressing the challenges in code generation by large language models (LLMs). It utilizes pairwise membership and equivalence queries to enhance the accuracy of selecting the correct program from multiple outputs generated by LLMs, significantly improving success rates compared to existing algorithms.

A new paper on arXiv presents datasets aimed at improving the understanding of idiomatic and figurative language in Natural Language Processing (NLP). These datasets are designed to assist large language models (LLMs) in better interpreting informal language, which has become increasingly prevalent in social media and everyday communication.

A novel self-supervised symbolic auto-encoder has been introduced, enabling symbolic computer vision to interpret diagrams through structured representations and logical rules. This approach contrasts with traditional pixel-based visual models by parsing diagrams into geometric primitives, enhancing machine vision's interpretability.

FloodDiffusion has been introduced as a novel framework for text-driven, streaming human motion generation, capable of producing seamless motion sequences in real-time based on time-varying text prompts. This approach improves upon existing methods by employing a tailored diffusion forcing framework that addresses the limitations of traditional models, ensuring better alignment with real motion distributions.