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Learning GenAI via SOTA Papers
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education

Learning GenAI via SOTA Papers

Hosted by Yun Wu · 🇺🇸 US · EN · 208 episodes

Where this show ranks

Episodes
208
Last ep.
6 days ago
Avg length
22m
Booking Probability™
44
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Listen Score
32
Niche reach.
Virality (30d)
54
Steady cadence.

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About this podcast

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.

education

About the host

Yun Wu hosts Learning GenAI via SOTA Papers, a education show with 208 episodes published.

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Recent episodes

Our AI reads these to draft pitches

EP221: ScrapMem Mimics Human Memory Through Forgetting

Jun 1, 202622mEp. 221S1

Title: ScrapMem: A Bio-inspired Framework for On-device Personalized Agent Memory via Optical Forgetting Source: http://arxiv.org/abs/2605.03804v1 Summary: ScrapMem introduces a novel on-device memory architecture for ag

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EP220: How PARSE Makes AI Four Times Faster

Jun 1, 202624mEp. 220S1

Title: Parallel Prefix Verification for Speculative Generation Source: http://arxiv.org/abs/2605.04263v1 Summary: This paper introduces PARSE, a novel speculative generation primitive that enables semantic-level verifica

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EP219: OpenSeeker V2 Shatters The AI Compute Myth

May 31, 202620mEp. 219S1

Title: OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories Source: http://arxiv.org/abs/2605.04036v1 Summary: This paper establishes a high-efficiency paradigm for trainin

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EP218: JoyAI-Image Solves AI 3D Geometry Errors

May 31, 202621mEp. 218S1

Title: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation Source: http://arxiv.org/abs/2605.04128v1 Summary: JoyAI-Image establishes a new foundational architecture for multimodal agents by t

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EP217: Why forced compliance triggers metacognitive collapse

May 30, 202622mEp. 217S1

Title: The Compliance Trap: How Structural Constraints Degrade Frontier AI Metacognition Under Adversarial Pressure Source: http://arxiv.org/abs/2605.02398v1 Summary: This work identifies the 'Compliance Trap,' a fundame

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EP216: Shadow memory stops long horizon AI heists

May 30, 202623mEp. 216S1

Title: MAGE: Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory Source: http://arxiv.org/abs/2605.03228v1 Summary: MAGE introduces the 'shadow memory' abstraction, a novel defensive framework that mai

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EP215: Finding specialized AI agents in milliseconds

May 29, 202622mEp. 215S1

Title: GRAIL: A Deep-Granularity Hybrid Resonance Framework for Real-Time Agent Discovery via SLM-Enhanced Indexing Source: http://arxiv.org/abs/2605.02489v1 Summary: GRAIL introduces a novel SLM-enhanced indexing and re

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EP214: ARISE Maps Data Flow For AI Agents

May 29, 202620mEp. 214S1

Title: ARISE: A Repository-level Graph Representation and Toolset for Agentic Fault Localization and Program Repair Source: http://arxiv.org/abs/2605.03117v1 Summary: ARISE introduces a multi-granularity program graph an

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EP213: Why AI agents fail at negotiation

May 28, 202618mEp. 213S1

Title: Talk is Cheap, Communication is Hard: Dynamic Grounding Failures and Repair in Multi-Agent Negotiation Source: http://arxiv.org/abs/2605.01750v1 Summary: This research identifies 'dynamic grounding' as a foundatio

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EP212: Sheaf Geometry Fixes Robot Logic

May 28, 202623mEp. 212S1

Title: Sheaf-Theoretic Planning: A Categorical Foundation for Resilient Multi-Agent Autonomous Systems Source: http://arxiv.org/abs/2605.01879v1 Summary: This paper introduces Sheaf-Theoretic Planning (STP) as a transfor

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EP211: SciResearcher turns AI into a scientific detective

May 27, 202620mEp. 211S1

Title: SciResearcher: Scaling Deep Research Agents for Frontier Scientific Reasoning Source: http://arxiv.org/abs/2605.01489v1 Summary: Introduces a novel paradigm for automated data construction to scale agentic reasoni

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EP210: AI that rewrites its own logic

May 27, 202624mEp. 210S1

Title: Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks Source: http://arxiv.org/abs/2605.01293v1 Summary: Proposes a neuro-symbolic framework that lifts i

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EP209: Fixing AI agent memory with SAGA

May 26, 202623mEp. 209S1

Title: SAGA: Workflow-Atomic Scheduling for AI Agent Inference on GPU Clusters Source: http://arxiv.org/abs/2605.00528v1 Summary: SAGA represents a foundational breakthrough in agentic AI systems by transitioning from re

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EP208: Bayesian Orchestration for Overconfident AI Agents

May 26, 202624mEp. 208S1

Title: Position: agentic AI orchestration should be Bayes-consistent Source: http://arxiv.org/abs/2605.00742v1 Summary: This position paper establishes a novel theoretical foundation for agentic AI by arguing that the or

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EP207: Robots learn the math of anticipation

May 25, 202622mEp. 207S1

Title: PRTS: A Primitive Reasoning and Tasking System via Contrastive Representations Source: http://arxiv.org/abs/2604.27472v1 Summary: PRTS establishes a new foundation model paradigm by reformulating VLA pretraining a

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EP206: ObjectGraph replaces Markdown for AI agents

May 25, 202621mEp. 206S1

Title: ObjectGraph: From Document Injection to Knowledge Traversal -- A Native File Format for the Agentic Era Source: http://arxiv.org/abs/2604.27820v1 Summary: ObjectGraph introduces a new native file format that recon

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EP205: Qiushi AI Discovers Optical Computing Hardware

May 24, 202619mEp. 205S1

Title: End-to-end autonomous scientific discovery on a real optical platform Source: http://arxiv.org/abs/2604.27092v1 Summary: This work marks a milestone by demonstrating the first AI agentic system to autonomously ide

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EP204: Solving the AI compositionality crisis

May 24, 202623mEp. 204S1

Title: AGEL-Comp: A Neuro-Symbolic Framework for Compositional Generalization in Interactive Agents Source: http://arxiv.org/abs/2604.26522v1 Summary: This framework introduces a principled neuro-symbolic architecture th

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EP203: How AI Agents Trade Real Money

May 23, 202621mEp. 203S1

Title: Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital Source: http://arxiv.org/abs/2604.26091v1 Summary: This paper identifies the "operating layer"—comprising prompt compilation, typed con

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EP202: Why ADEMA AI Never Loses The Plot

May 23, 202620mEp. 202S1

Title: ADEMA: A Knowledge-State Orchestration Architecture for Long-Horizon Knowledge Synthesis with LLMAgents Source: http://arxiv.org/abs/2604.25849v1 Summary: ADEMA establishes a foundational architecture for long-hor

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Audience demographics

Age
25-54
Consumer type
Lifelong learners

Topics covered

education

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Who is the host of Learning GenAI via SOTA Papers?

Learning GenAI via SOTA Papers is hosted by Yun Wu. The show is categorised under education and has published 208 episodes.

How many episodes does Learning GenAI via SOTA Papers have?

Learning GenAI via SOTA Papers has published 208 episodes.

What topics does Learning GenAI via SOTA Papers cover?

Learning GenAI via SOTA Papers regularly covers education. It sits in the education category.

Is it hard to get booked on Learning GenAI via SOTA Papers?

Learning GenAI via SOTA Papers is accessible for guests with genuine education expertise. A personalised, episode-aware pitch will still outperform a generic one every time.

Is Learning GenAI via SOTA Papers currently accepting guest pitches?

Learning GenAI via SOTA Papers hasn't explicitly signalled guest openness in recent episodes. That doesn't rule out pitching. your hook just needs to be especially compelling and relevant to their recent content.

How long are Learning GenAI via SOTA Papers episodes?

Episodes of Learning GenAI via SOTA Papers average 22 minutes. a focused format where a clear narrative arc and tight preparation matter most.

What guest credentials does Learning GenAI via SOTA Papers typically look for?

Our data rates Learning GenAI via SOTA Papers's guest bar at 80/100 (Premium tier). Established thought leaders with verified media credentials. Sign in to PitchCentric to see how your own Pod Score compares against this show.

Methodology. Booking Probability™ blends Listen Score, 30-day Virality, open-to-guests detection, and Apple ratings. Data refreshed every 60 minutes. Listen Score and Booking Probability are calculated by PitchCentric. Last enriched 12 days ago.

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