Learning GenAI via SOTA Papers - Explainer
Yun WuThis 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 open.spotify.com/show/7B2L4YDgRdi9LcsdFo9vP3
| Publishes | Daily | Episodes | 42 | Founded | 22 days ago |
|---|---|---|---|---|---|
| Number of Listeners | Category | Technology | |||
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Latest Episodes
Title: MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning
Source: arxiv.org/abs/2605.13037v1
Summary:
MAP proposes a paradigm shift for interactive agents by establishing environmental understanding through structured... more
Title: Harnessing Agentic Evolution
Source: arxiv.org/abs/2605.13821v1
Summary:
AEvo introduces a meta-editing framework that treats the evolution context as a process-level state, allowing agents to iteratively refine their own procedures.... more
Title: SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory
Source: arxiv.org/abs/2605.12061v1
Summary:
SAGE introduces a self-evolving graph-memory engine that couples a memory writer with a Graph Founda... more
Title: On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment
Source: arxiv.org/abs/2605.11882v1
Summary:
FATE establishes a foundational framework for on-policy self-evolution by transforming agentic failure traject... more
Title: Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems
Source: arxiv.org/abs/2605.12213v1
Summary:
This paper presents Goal-Mem, a framework that employs backward chaining and Natural Language Logic to cre... more
Title: The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions
Source: arxiv.org/abs/2605.10698v1
Summary:
This study formalizes the 'Bystander Effect' in multi-agent systems, identifying a... more
Title: PIVOT: Bridging Planning and Execution in LLM Agents via Trajectory Refinement
Source: arxiv.org/abs/2605.11225v1
Summary:
PIVOT introduces a novel self-supervised framework that treats agent trajectories as optimizable objects refin... more
Title: DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement LearningSource: arxiv.org/abs/2605.10488v1
Summary:
DeepRefine establishes a general reinforcement learning framework for the autonomous refinement of agent-compiled kn... more
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