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technology
Hosted by Yun Wu · technology · EN-US · 259 episodes
This short video set 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. This is complementary to https://open.spotify.com/show/7B2L4YDgRdi9LcsdFo9vP3
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Signup to Generate a PitchLearning GenAI via SOTA Papers - Explainer is a technology podcast hosted by Yun Wu, with 259 episodes on record and a Required Pod Score of 80.
Yun Wu hosts Learning GenAI via SOTA Papers - Explainer, a technology show with 259 episodes published.
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Episode #461
Title: AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMsSource: http://arxiv.org/abs/2608.26004v1 Summary: This paper introduces a novel speculative decoding technique optimized for agentic LLMs. It represents a significant efficiency breakthrough by intelligently managing context to speed up LLM inference, which is crucial for the iterative reasoning processes of AI agents.
Episode #460
Title: On-policy Distillation with Verifiable Reward Source: http://arxiv.org/abs/2608.24696v1Summary: This work presents a crucial advance in AI training by combining on-policy distillation with verifiable reward mechanisms. It offers a significant efficiency breakthrough for training generative models and agents while also addressing the foundational challenge of ensuring reliable and aligned AI behavior through robust and transparent reward signals.
Episode #459
Title: Joint Optimization of Tool Creation and Use for Large Language Model AgentsSource: http://arxiv.org/abs/2608.24571v1 Summary:This paper introduces a foundational framework for LLM agents to not only utilize existing tools but also autonomously create new ones, then optimize both their creation and use. This represents a significant breakthrough in agentic reasoning, enabling more autonomous, adaptive, and capable AI agents by extending their problem-solving repertoire beyond predefined functions.
Episode #458
Title: TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM Agents Source: http://arxiv.org/abs/2608.22793v1 Summary: This research introduces a novel agentic reasoning framework centered on a 'Self-Evolving Skill Bank,' which allows LLM agents to autonomously acquire, refine, and manage their capabilities. This directly addresses foundational challenges in Agentic AI by enabling consistent performance, continuous learning, and intelligent adaptation to operational limits.
Episode #457
Title: Object-Uni: A Unified Model for Object-Centric Spatial Understanding and Controllable Generation Source: http://arxiv.org/abs/2608.22757v1 Summary:This paper proposes a unified, object-centric model for spatial understanding and controllable generation, representing a new architectural primitive for GenAI. By enabling models to intrinsically reason about and manipulate individual objects, it offers a significant breakthrough for highly controllable and semantically grounded generative AI.
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