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.
Learning GenAI via SOTA Papers
Claim This Podcastby Yun Wu
353 episodes
Updated Daily
Accepts GuestsHas Sponsors
Podcast Overview
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.
Language
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Publishing Since
2/22/2026
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Recent Episodes
August 7, 2026
EP354: How AI Agents Code Their Own Habits
<p>Title: From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents</p><p>Source: http://arxiv.org/abs/2607.07321v1</p><p>Summary:</p><p>This work introduces a novel framework for self-evolving agents by enabling them to autonomously synthesize low-level, atomic actions into reusable, higher-order Standard Operating Procedures (SOPs). This dynamic tool-optimization loop represents a major paradigm shift in reducing reasoning overhead and improving long-horizon task execution for autonomous systems.</p>
August 7, 2026
EP353: How IGRPO stops AI search distractions
<p>Title: Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents</p><p>Source: http://arxiv.org/abs/2607.06223v1</p><p>Summary:</p><p>This paper introduces a novel policy optimization framework (IGRPO) that dynamically allocates rollout budgets based on node-level information gain during tree-structured exploration. By unifying adaptive search-tree exploration with a principled reinforcement learning target, it provides a foundational methodology for scaling and training multi-turn reasoning agents.</p>
August 6, 2026
EP352: Hidden states predict AI agent failure
<p>Title: Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade</p><p>Source: http://arxiv.org/abs/2607.06503v1</p><p>Summary:</p><p>This paper introduces a novel framework that uses internal activation probes to detect and early-abort doomed agent trajectories, saving up to 47% of inference compute. This represents a significant efficiency breakthrough for agentic loops, addressing the critical challenge of compute wastage in multi-step agent environments.</p>
353 total episodes available
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