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technology
Hosted by Yun Wu · technology · EN-US · 470 episodes
This podcast is focusing on sharing the papers on GenAI related topic, especially the SOTA (State of the Art) papers that are the foundations of GenAI work. It shows how these researches paved the way to the GenAI tools that we are using every day such as ChatGPT, Gemini, Claude Code etc.
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Signup to Generate a PitchLearning GenAI via SOTA Papers is a technology podcast hosted by Yun Wu, with 470 episodes on record and a Required Pod Score of 80.
Yun Wu hosts Learning GenAI via SOTA Papers, a technology show with 470 episodes published.
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Episode #470
Title: Higher-Dimensional Rotary Position Embedding Source: http://arxiv.org/abs/2608.29715v1 Summary: This paper introduces an extension to Rotary Position Embedding (RoPE), a critical architectural primitive in Transformer models. This advancement can significantly enhance GenAI by improving how models process and understand complex, multi-dimensional data, leading to more capable and efficient models.
Episode #469
Title: AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM ReasoningSource: http://arxiv.org/abs/2608.29988v1 Summary: This research proposes a novel framework for adaptively controlling stochasticity and computational resources during LLM reasoning. This represents a significant breakthrough in agentic reasoning, enabling more efficient, targeted, and adaptable decision-making for LLM-based agents and advanced GenAI systems.
Episode #468
Title: Toward Latent Language Model Skills Steering and Optimization: An Empirical Study Source: http://arxiv.org/abs/2608.29459v1Summary: This research explores methods for steering and optimizing latent language model skills, offering a deeper level of control over LLM capabilities. This represents a significant reasoning breakthrough for GenAI by enabling more efficient and targeted utilization of generative models' intrinsic intelligence.
Episode #467
Title: LiteSearch-VL: Small Multimodal Search Agents via Trajectory Distillation and Synthetic Step-DPO Source: http://arxiv.org/abs/2608.29357v1 Summary: This paper introduces novel techniques like trajectory distillation and synthetic Step-DPO to develop small, efficient multimodal search agents. Such advancements offer a significant efficiency and scaling breakthrough for Agentic AI, making sophisticated agents more practical and widely deployable.
Episode #466
Title: Omni-Interactive Universal EmbedderSource: http://arxiv.org/abs/2608.27044v1 Summary: This paper proposes a "universal embedder" capable of processing and unifying diverse, interactive data modalities into a singular representation. Such a primitive could fundamentally change how GenAI and Agentic AI systems perceive and represent the complex, multimodal world, enabling more versatile and capable models.
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